Ovarian Cancer: An Integrated Review

Affiliations.

  • 1 Carolina's Medical Center, Charlotte, NC. Electronic address: [email protected].
  • 2 Assistant Vice President Patient Care Services, Carolina's Medical Center, Rock Hill, SC.
  • 3 Interim Dean, Harris College of Nursing & Health Sciences, Associate Dean for Nursing & Professor, Texas Christian University, Ft Worth, TX.
  • PMID: 30867104
  • DOI: 10.1016/j.soncn.2019.02.001

Objective: To provide an overview of the risk factors, modifiable and non-modifiable, for ovarian cancer as well as prevention, diagnostic, treatment, and long-term survivorship concerns. This article will also examine current and future clinical trials surrounding ovarian cancer.

Data sources: A review of articles dated 2006-2018 from CINAHL, UpToDate, and National Comprehensive Cancer Network guidelines.

Conclusion: There is no screening test for ovarian cancer and with diagnosis often in the late stages, recurrence is high in this population. Early identification can range from knowing the vague symptoms associated with the cancer to prophylactic surgical removal of at-risk tissue. Standard treatment for ovarian cancer is surgery followed by combination chemotherapy. Although advances are being made, ovarian cancer remains the most fatal female gynecologic cancer.

Implications for nursing practice: Becoming familiar with and educating women about risk factors and the elusive symptoms of ovarian cancer can increase patient autonomy and advocacy, as well as potentially improve patient outcomes for those affected by ovarian cancer.

Keywords: BRCA; gynecologic; oncology; ovarian cancer; prevention; risk factors.

Copyright © 2019 Elsevier Inc. All rights reserved.

Publication types

  • Ovarian Neoplasms / diagnosis
  • Ovarian Neoplasms / epidemiology*
  • Ovarian Neoplasms / prevention & control
  • Ovarian Neoplasms / therapy
  • Risk Factors

Advances in Ovarian Cancer Research

Image from a mouse model of ovarian cancer in color-enhanced 3D detail.

An ovarian tumor grown in a mouse using human cells. Special techniques were used to create the high-resolution, 3-D view of the cancer’s cell structure and inner workings.

Ovarian cancers include cancers that begin in the epithelial cells that line the fallopian tubes or peritoneum as well as the ovaries , and they are collectively called epithelial ovarian cancers . Other types of ovarian cancer arise in other cells, including germ cell tumors , which start in the cells that make eggs, and stromal cell tumors , which start in supporting tissues. 

NCI-funded researchers are working to advance our understanding of how to prevent, detect early, and treat ovarian cancer.

This page highlights some of what’s new in the latest research in ovarian cancer, including clinical advances that may soon translate into improved care, NCI-supported programs that are fueling progress, and research findings from recent studies.

Prevention of Ovarian Cancer

Women who carry certain mutations in the BRCA1 or BRCA2 genes are at increased risk of developing ovarian cancer. Scientists are looking at ways to reduce the risk in women with these mutations. Surgery to remove the ovaries and fallopian tubes in these women is the recommended method to reduce their risk of getting ovarian cancer. However, removing the ovaries results in immediate menopause, which may cause other health problems. 

Research has shown that the most common type of ovarian cancer begins in the fallopian tubes , not in the ovaries. This discovery has led doctors to reconsider ways of preventing ovarian cancer.

  • Removing fallopian tubes only. An ongoing NCI-sponsored clinical trial is testing whether removing the fallopian tubes but delaying removal of the ovaries will be as effective to reduce the risk of ovarian cancer in women with BRCA1 mutations as removing both the ovaries and fallopian tubes at the same time. This would allow women to maintain premenopausal levels of hormones produced by the ovaries and delay many of the complications associated with menopause.
  • Removal of fallopian tubes in people seeking to prevent pregnancy. The discovery that epithelial ovarian cancers most often start in the fallopian tubes has also led to changes in the way some gynecologists approach surgery to prevent pregnancy. Women seeking tubal ligation to prevent pregnancy (often called having your tubes tied) may be offered the option of having their tubes removed instead. Doing so might reduce the possibility of ovarian cancer in the future. 
  • Testing relatives for gene mutations. NCI is funding efforts to test the relatives of women who have been diagnosed with ovarian cancer in the past.  Researchers are locating women diagnosed with ovarian cancer with the hope to test them and/or their family members for ovarian cancer-related gene mutations, so that family members who learn they carry a mutation may take steps to reduce their risk. The overall goal is not only to prevent ovarian cancer, but also to find the best ways to communicate sensitive genetic information to ovarian cancer patients and their family members.

Ovarian Cancer Treatment

Surgery and chemotherapy are the main treatments for ovarian cancer. The location and type of cells where the cancer begins, and whether the cancer is high-grade or low-grade , may influence the success of treatment. Surgery can cure most people with early-stage ovarian cancer that has not spread beyond the ovaries. For advanced ovarian cancer, the goal of surgery is to remove as much of the cancer as possible, called surgical debulking . 

Platinum-based chemotherapy drugs, such as cisplatin or carboplatin (Paraplatin) , often given in combination with other drugs, are usually effective in treating epithelial ovarian cancer at any stage. However, in most people with advanced ovarian cancer, the cancer usually comes back. Treating the cancer again with platinum drugs may work, but eventually the tumors become resistant to the drugs.

Targeted Therapy

Targeted therap y uses drugs or other agents to attack specific types of cancer cells. PARP inhibitors are a type of targeted therapy that can stop a cancer cell from repairing its damaged DNA, causing the cell to die. Cancers in people who have certain mutations in the BRCA genes are considered particularly susceptible to PARP inhibitors. That’s because BRCA genes are involved in the repair of some types of DNA damage, so cancers with BRCA gene alterations already have defects in DNA repair.

The use of PARP inhibitors has transformed treatment for people with advanced epithelial ovarian cancer and harmful mutations in a BRCA gene. Since the 2014 approval of olaparib (Lynparza) , the first PARP inhibitor, the number of PARP inhibitors has grown and their uses for people with ovarian cancer have expanded. For example, researchers are testing PARP inhibitors as maintenance therapy to prevent cancer from coming back or growing.

Clinical trials have shown that using PARP inhibitors as long-term therapy in women with advanced epithelial ovarian cancer delayed progression of the cancer. 

Treatment after Cancer Progression

Typically, chemotherapy and targeted therapies are stopped once ovarian cancer begins to come back. But clinical trials for patients previously treated with the drug bevacizumab (Avastin) have found that resuming a treatment regimen with bevacizumab and a platinum-based chemotherapy even after the cancer started to grow again slowed the growth of platinum-sensitive disease . And in women who no longer benefited from platinum-based chemotherapy, non–platinum-based chemotherapy combined with bevacizumab kept the cancer in check longer than chemotherapy alone.  

Researchers are also testing an experimental drug called adavosertib in women with relapsed or treatment-resistant ovarian cancer. Adavosertib blocks a protein in cells called Wee1 that helps regulate how cells grow and divide. In a clinical trial, combining adavosertib and gemcitabine improved how long women with recurrent or treatment-resistant epithelial ovarian cancer lived before their cancer got worse. 

Targeted therapies may also be helpful for people with low-grade ovarian cancer. A trial of the drug trametinib in women with low-grade serous ovarian cancer that had come back showed that it delayed the cancer’s growth compared with treating the cancer with chemotherapy again.

Secondary Surgery

Several clinical trials have studied the use of secondary surgery for women with advanced epithelial ovarian cancer that has come back after being in remission, or to remove more tumor after their initial surgery. 

  • An NCI-funded phase 3 clinical trial found that secondary surgery followed by chemotherapy did not increase overall survival compared with chemotherapy alone. 
  • A trial done in China that tested secondary surgery followed by chemotherapy, however, did show improvements in how long women with recurrent epithelial ovarian cancer lived without their cancer growing .
  • In a third trial, conducted in Europe, women who underwent secondary surgery followed by chemotherapy lived an average of nearly 8 months longer than women who only received chemotherapy.
  • In the Chinese and European trials, and in an analysis of 64 clinical trials and other studies , the benefits of secondary surgery were observed only in women who had all of their visible cancer removed.

Hyperthermic Intraperitoneal (HIPEC) Chemotherapy

Doctors have used chemotherapy injected into the peritoneal cavity to treat ovarian cancer for decades. Now, researchers are studying the usefulness of infusing heated drugs directly into the peritoneal cavity in a procedure called HIPEC (hyperthermic intraperitoneal chemotherapy). HIPEC treatment involves washing the abdominal cavity with heated high-dose chemotherapy immediately after surgery to help kill any remaining cancer.

A large clinical trial found that people with stage 3 ovarian cancer treated with HIPEC during surgery lived almost a year longer than those who received only intravenous chemotherapy after surgery. Studies are underway to confirm this finding.

NCI-Supported Research Programs

Many NCI-funded researchers at the National Institutes of Health campus, and across the United States and the world, are seeking ways to address ovarian cancer more effectively. Some research is basic, exploring questions as diverse as the biological underpinnings of ovarian cancer and the social factors that affect cancer risk. And some is more clinical, seeking to translate this basic information into improving patient outcomes.

The Women’s Malignancies Branch in NCI’s Center for Cancer Research conducts basic and clinical research in breast and gynecologic cancers, including early-phase clinical trials at the NIH Clinical Center in Bethesda, Maryland. 

The Ovarian Specialized Programs of Research Excellence (SPOREs) promote collaborative translational cancer research. This group works to improve prevention and treatment approaches, along with molecular diagnostics , in the clinical setting to help people with ovarian cancer.

The Ovarian Cancer Cohort Consortium , part of the NCI Cohort Consortium, is an international consortium of more than 20 cohort studies that follow people with ovarian cancer to improve understanding of ovarian cancer risk, early detection, tumor differences, and prognosis. 

NCI’s clinical trials programs, the National Clinical Trials Network , Experimental Therapeutics Clinical Trials Network , and NCI Community Oncology Research Program , all conduct or sponsor clinical studies of ovarian cancer.

Clinical Trials for Ovarian Cancer

NCI funds and oversees both early- and late-phase clinical trials to develop new treatments and improve patient care. Trials are available for the treatment of ovarian cancer.

Ovarian Cancer Research Results

The following are some of our latest news articles on ovarian cancer research:

Implanted “Drug Factories” Deliver Cancer Treatment Directly to Tumors

Trametinib Is a New Treatment Option for Rare Form of Ovarian Cancer

When Ovarian Cancer Returns, Surgery May Be a Good Choice for Selected Patients

How Does Ovarian Cancer Form? A New Study Points to MicroRNA

Ovarian Cancer Studies Aim to Reduce Racial Disparities, Improve Outcomes

Surgery for Recurrent Ovarian Cancer Does Not Improve Survival

View the full list of Ovarian Cancer Research Results and Study Updates .

  • Open access
  • Published: 03 June 2022

Evolutionary perspectives, heterogeneity and ovarian cancer: a complicated tale from past to present

  • Patriciu Achimas-Cadariu 1 , 2   na1 ,
  • Paul Kubelac 2 , 3   na1 ,
  • Alexandru Irimie 1 , 2 ,
  • Ioana Berindan-Neagoe 4 , 5 , 6 &
  • Frank Rühli 7  

Journal of Ovarian Research volume  15 , Article number:  67 ( 2022 ) Cite this article

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Ovarian cancer is composed of a complex system of cells best described by features such as clonal evolution, spatial and temporal genetic heterogeneity, and development of drug resistance, thus making it the most lethal gynecologic cancer. Seminal work on cancer as an evolutionary process has a long history; however, recent cost-effective large-scale molecular profiling has started to provide novel insights coupled with the development of mathematical algorithms. In the current review, we have systematically searched for articles that focused on the clonal evolution of ovarian cancer to offer the whole landscape of research that has been done and highlight future research avenues given its characteristic features and connections to evolutionary biology.

Introduction

Worldwide, each year more than 300.000 new cases of ovarian cancer are diagnosed and 185.000 patients succumb to their disease [ 1 ], without any major improvement in the long-term overall survival over the past three decades, despite improved disease control rates measured as 5-year overall survival [ 2 ].

As Theodosius Dobzhansky said in a seminal paper in 1973 that “Nothing in biology makes sense except in the light of evolution” [ 3 ], Darwinian principles applied in cancer science have brought much to our current understanding of this disease, and ovarian cancer makes no exception [ 4 , 5 ]. The high incidence of ovarian cancer can also be attributed to an evolutionary mismatch to our rapid social evolution. The rising incidence in industrialized societies can be partly explained by reproductive patterns such as increased total number of ovulations, increased age at first birth, fewer pregnancies [ 6 , 7 ], and a prolonged estrogen exposure [ 8 ] with partial attenuation through the introduction of oral contraceptives but predicted increases for the following years [ 9 ]. Interestingly, the high prevalence of founder BRCA1/2 mutation carriers can be explained by their increased lifetime reproductive success in natural fertility conditions that also masked their detrimental oncogenic potential for cancers of the reproductive tract [ 10 , 11 ].

Within its natural history, ovarian cancer is generally a disease that remains localized to the peritoneal cavity throughout its course, with occasional distant metastases. With vague and nonspecific signs and symptoms, the initial diagnosis is usually delayed until the occurrence of extensive intra-abdominal spread through the contiguous peritoneal surfaces, ascites fluid, and rich lymphatics. Death usually occurs through progressive inanition and gastrointestinal tract obstruction that cannot be corrected through surgery due to extensive carcinomatosis [ 7 ].

Ovarian cancer should be regarded as not one but many diseases. Several histological subtypes have been described, with high-grade serous carcinoma as the most commonly diagnosed. However, its exact point of origin is still a matter of ongoing debate [ 12 ], and in-depth transcriptional analysis by The Cancer Genome Atlas project has defined four different transcriptional subtypes [ 13 ]. Still, the established standard strategy for treating advanced ovarian cancer has been maximum cytoreductive surgery and platinum based chemotherapy followed by surveillance for potential recurrence [ 14 ]. Complete debulking to no residual (0 mm vs 1–10 mm) was associated with improved overall survival and also impacted outcomes after the occurrence of relapsed disease, probably through the physical depleting of the reservoir of chemotherapy resistant clones. Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is an option for treating patients with advanced bulky disease where upfront primary debulking surgery (PDS) is not technically feasible [ 15 ]. There is still doubt if the survival advantage of complete debulking is the same whether through PDS or IDS. Two randomized trials have shown similar survival rates for PDS and IDS, but recent evidence suggests that IDS correlates with a higher risk of developing platinum resistance [ 16 ]. This is most likely explained through the exposure of a high tumor volume with multiple tumor subclones to the stringent selection pressure of chemotherapy with subsequent expansion of resistant clones [ 17 , 18 ]. The incorporation of antiangiogenic agents to standard therapy has brought only minor increments in PFS, while the addition of PARP inhibitors (PARPi) as maintenance therapy in BRCA mutated patients has significantly prolonged PFS with OS results still not mature [ 19 , 20 ].

Despite high initial response rates, all too often relapse occurs, and subsequent treatment strategies maximize quality and length of life but are less likely to be curative. Rechallenge with platinum-based chemotherapy depends on the platinum free interval while surgery is limited to a subset of patients where OS results are still pending [ 21 ]. If not present from the first relapse, after several lines of treatment platinum resistant disease develops and represents a daunting clinical entity with limited therapeutic options and an overall survival of under 12 months [ 22 ]. Interestingly, about 15% of patients survive more than ten years however survivors of advanced stage disease represent a heterogeneous group that we have not yet determined or understood what makes them long-term survivors with more research needed for an understanding of this particular group [ 23 ].

Many of the clinical aspects previously presented depict evolutionary concepts such as spatial heterogeneity, temporal heterogeneity, and system induced selection pressure. Our current understanding of cancer has recently seen an exponential growth with the continuous technological development that offered the necessary tools to more precisely infer tumor cell dynamics. Hence, in the current review, we have systematically searched for articles that focused on the clonal evolution of ovarian cancer in an effort to offer the full landscape of research that has been done and highlight future research avenues given its characteristic features and connections to evolutionary biology. In the context that ‘Evolution has no eyes to the future’ [ 24 ] perfectly applies to the interaction between tumour and host microenvironment, we envision that using evolutionary principles we could be able to understand better the processes that drive tumor heterogeneity and select anticipative therapeutic strategies for improving patients’ outcomes.

The present systematic review was written in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols statement. This review was also registered at PROSPERO under registration number CRD42018105413.

A comprehensive search of English written articles was performed on Web of Science – Science Citation Index Expanded, PubMed, EMBASE with no date restriction until July 2018. Secondary references were identified through screening of the reference lists of relevant studies. The following headings were used in the search strategy, including closely related words: genetic heterogeneity, clonal evolution, biological evolution, ovarian cancer. The detailed search strategy is presented in Table 1 . After retrieving all articles generated by the search strategy and excluding duplicates, titles and abstracts were evaluated for eligibility. Included studies were restricted to human tissue, pathologically confirmed as epithelial ovarian cancer, and had a minimum of two paired samples per case. Subsequently, full text articles were retrieved and assessed for eligibility using the same search criteria, detailed in Tables 2 and 3 . 

Early inferences of tumor heterogeneity

More than six decades ago, clinicians were asking to some extent, the same clinical questions as we do today but to a greater depth regarding ovarian cancer: “Do the cells of the metastasis or the recurrence behave as did the primary? Does the apparent acceleration in the downhill course of the patient depend upon an increase in the intrinsic malignancy of the tumor?”. The authors analyzed a number 550 samples from different areas of 36 patients and 12 temporally paired cases were evaluated by the authors in light microscopy, concluding that in most cases, the tumor structure remained unchanged [ 25 ].

Cytogenetic studies demonstrated that chromosomal abnormalities precede histologic changes. There was evidence for the same stem lines with identical chromosomal changes in bilateral cystadenocarcinomas, but without the possibility of drawing a conclusion towards a common ancestor hypothesis or a parallel malignant process in both ovaries, although the authors favored the latter given the similar pattern seen in bilateral cystadenomas [ 26 ]. Another cytogenetic study on 34 samples from 15 patients identified identical karyotypes in primary and metastatic samples from the same patient, without any evidence towards an increase in cytogenetic diversity during tumor progression [ 27 ]. Through the technique of inferred clonal cytogenetic evolution, a study conducted on three spatially separated samples of ovarian carcinoma from the same patient demonstrated the clonal evolution in ovarian cancer by mapping the frequency of occurrence of 18 different chromosomal breakpoints [ 28 ]. Performing repetitive karyotyping of malignant effusions during disease progression or after treatment administration in 9 patients evidenced aneuploidy, karyotyping diversity, and double minute chromosomes but in paired samples reported there were identical chromosomal alterations [ 29 ].

The use of restriction fragment length polymorphism probing in 7 patients demonstrated the coexistence of malignant cell clones, and the deletion of chromosome sequence 11p13-11p15.5 was considered a late event in disease progression [ 30 ]. Similar results were subsequently obtained in a larger series and with the addition of high-resolution comparative genomic hybridization (CGH), showing that metastases to the contralateral ovary had occurred as a late event in the clonal evolution [ 31 ].

A proof of principle study using PCR-based loss of heterozygosity (LOH) detection on flow sorted tumor cells demonstrated the feasibility of this method to confirm the monoclonal origin of different tumor cell populations and may be helpful in reconstructing the clonal evolution in solid tumors [ 32 ]. The evaluation of 10 microsatellites through PCR on 9 cases with primary tumors and paired metastases found an identical LOH spectrum in 4 cases, while in 5 cases the LOH patterns were different in the primary tumor and the metastatic nodes [ 33 ]. A study conducted on 8 cases with 21 samples showed that in 4 cases, the number of chromosomal aberrations in the metastatic site was lower than in the corresponding primary tumor site, in contradiction with the expected evolutionary finding [ 34 ]. Fishman et al. used comparative genomic hybridization to analyze the chromosomal profile of seven primary high grade serous ovarian cancer tumors and their paired metastases. A wide range of genetic alterations were present in the primary tumors however in 6 out of 7 metastatic lesions there were fewer genetic alterations or normal genomes, suggestive in the author’s opinion that this might reflect not ordinary metastases migrated from the primary tumor but developed independently as de novo carcinogenesis [ 35 ].

Molecular inferences of temporal heterogeneity

One of the first studies that analyzed in three cell line series the genetic changes associated with the transition from platinum sensitive to platinum resistant disease suggested they were not linearly related, and that platinum resistant disease emerges through the outgrowth of a pre-existing platinum resistant subclone under the selective pressure of treatment. Vast differences between sensitive and resistant clones were confirmed through multicolor fluorescence in situ hybridization and array CGH, with a higher genomic complexity at presentation than at relapse. A similar analysis of 6 paired tissue samples taken before and after three cycles of neoadjuvant chemotherapy revealed very few differences. The lack of differences after neoadjuvant chemotherapy could be attributed to a short exposure to treatment, survival of sensitive clones due to environmental reasons, or to the presence of a dominant clone at presentation [ 36 ]. Next generation sequencing of two of the above samples identified besides loss of homologous recombination (HR), that the tandem duplicator mutator phenotype is an ongoing mutator phenotype that arose early before lineage divergence. Its persistence may be responsible for the continuous evolution and might represent a novel, unknown deficit in DNA repair different from HR, with an estimated frequency of 12.8% [ 37 ]. Performing whole exome sequencing on ascites derived tumor cells at three time points found that besides TP53 mutations that were present at all time points, 89% of mutations found in recurrent tumors were also present at the beginning. This is concordant with previous reports that recurrent disease arises from the selective pressure of chemotherapy on pre-existent clones, even after two lines of chemotherapy [ 38 ]. A similar report underscored the situation in which the primary tumor is composed of mutationally heterogeneous clones, some of which give rise to the recurrences, with 41% shared somatic variants between 1 primary and 2 recurrent samples [ 39 ]. An extensive study that analyzed 31 paired primary and recurrent samples found extreme variability in heterogeneity within tumor pairs, likely caused by branched evolution in the primary tumor of a platinum resistant subclone that causes subsequent relapse. An average of 47 non-synonymous confirmed somatic mutations per tumor pair (range 5–147) were observed, with TP53 as the most frequently observed in 78% of cases, but few other genes were recurrently mutated. Out of the 1074 mutations, 58% were shared, whereas 15% (range 0–42%) and 27% (range 0–100%) were unique for the primary or recurrent samples. Similarly, 41% of the genome was affected in both primary and recurrent samples by copy number alterations. None of the clinical variables correlated with tumor heterogeneity. Interestingly, platinum sensitive tumors maintained HR deficiency when converting to a platinum resistant phenotype, suggesting that PARPi could be useful in this clinical situation, although they are currently approved only for platinum sensitive disease [ 40 ].

Molecular inferences of spatial heterogeneity

One of the first studies that conducted a comprehensive evaluation of intra-tumor heterogeneity included 110 samples from 16 patients with advanced high grade serous ovarian cancer. Screening for genetic alterations was done using microsatellite analysis and single nucleotide polymorphism (SNP) analysis, with maximum parsimony tree analysis used to infer the clonal relationships. Both approaches reached the same conclusions that there is extensive intratumor heterogeneity between all regions of the same patient despite their similar morphological appearance. By reconstructing their evolutionary history a monoclonal origin was suggested with no evidence of two or more ancestral lines. Common alterations included deletions on chromosomes 13 and 17, where BRCA1/2 and p53 genes are also located [ 41 ]. Employing similar methods, a subsequent study was conducted by the same group and focused on the relationship between primary and metastatic lesions. The authors found no cases in which the genetic profiles of all the metastases of a patient were the same, and there were no significant differences in the level of genetic heterogeneity between metastatic samples and primary tumors. The data presented support a model with a common clonal origin that becomes polyclonal from which clones with different genetic backgrounds have the potential to metastasize during the early and late stages of genetic divergence [ 42 ].

An in-depth approach that evaluated the genomic diversity at nucleotide, copy number, and gene expression scales in 31 samples from 6 patients revealed individualized extensive intratumor heterogeneity. A range of 31–137 unique mutations/case was present with 51.5% (range, 10.2–91.4%) mutations present in all samples of a case. Except case 1, all other harbored a p53 mutation present in all samples, making it the most stable genomic feature. In one case, the fallopian tube lesion was a metastatic implant, whereas in another case, it harbored two dominant clones that gave rise to two histologically distinct populations that had a common ancestor, indicating the early occurrence of polyclonal subpopulations, thus complicating even more the evolutionary origin of ovarian cancer in the fallopian tubes. Two paired temporal samples with almost identical genomic mutations characterized a case with extended survivorship. Analysis of plasma cell free circulating tumor DNA detected a range of 1–12 mutations from the ancestral clone, illustrating a rather narrow and heterogeneous phenomenon of tumor DNA shedding across cases [ 43 ]. A study that analyzed a higher number of 11 spatially separated samples from an advanced stage high grade serous ovarian cancer reported a lower rate of 6% for shared somatic mutations in all samples, and there was an early divergence of two primary clusters with one of them leading to the formation of a metastatic cluster with little accumulation of somatic mutations [ 44 ].

Serous tubal intraepithelial carcinomas (STIC) possess most of the genomic aberrations of other intraperitoneal metastases and only in 4 out of 8 cases they represent the evolutionary precursor lesions, while other STIC lesions might actually represent metastases of other anatomic sites with patients specific mutational signature characterizing high grade serous ovarian cancer (HG-SOC) as a heterogeneous disease without a specific mutational signature except patient specific ubiquitous TP53 mutations [ 45 ]. Phylogenetic analysis of bilateral ovarian cancer samples demonstrated a common ancestry, and early disemination, with marked intra- and inter-tumor heterogeneity, as previously presented [ 46 ]. Another study that reconstructed the evolutionary history from the RNA of 4 patients from 9 spatially separated samples for each case reached similar conclusions with early branching of peritoneal metastases, and the presence of multiple subclones at each tumor implant [ 47 ].

Tumor heterogeneity has been less frequently described in low grade SOC, however, on a study on 11 cases, 1 in 5 (20%) patients with RAS/RAF pathway mutations exhibited spatial and temporal heterogeneity, despite not receiving targeted treatment against the mutation [ 48 ].

An in depth study using the MEDICC phylogenetic algorithm demonstrated that high intra-tumour heterogeneity measured through a clonal expansion index was associated with longer survival, supporting the hypotheses that clonal expansion is a surrogate for genetic diversity that favors the development of treatment resistant clones. Evolutionary clades in the patient specific trees often agreed with the anatomical sites where the sample was taken, supporting the physical shedding from the invasive lesions in the fallopian tube. In 8 out of 9 evaluable cases, cells retained their metastatic potential, and a model of metastasis to metastasis spread was supported with significant branching of tree topologies. Investigating whether evolutionary change occurs at a constant rate, the study found that 2 out of 14 patients had significant non-clock-like evolutionary trajectories with potentially unknown mutator phenotypes. Neoadjuvant therapy induced only minor genomic changes compared to the overall changes, with an average of 46 new events. Phylogenetic reconstruction of relapsed samples in 2 cases demonstrated their early divergence from the common ancestor. In one case NF1 deletion, while present in the dominant population at relapse, was already present at diagnosis in a minor proportion with subsequent clonal expansion [ 49 ].

A study that performed clonal population profiling of spatially distinct intraperitoneal clones (68 tumor samples from 7 patients) through whole-genome and single-nucleus sequencing identified evolutionary features such as mutation loss, convergent evolution and time dependent mutational signatures. Interestingly, metastatic sites were composed of clonally pure or highly related clones with at least one tumor site in each patient containing multiple subclones. In 5 cases, intraperitoneal spread was monoclonal and unidirectional, while two cases exhibited polyclonal spread and reseeding underscoring two different migratory patterns [ 50 ]. The same group of authors recently showed that among the reasons for non-random distribution of malignant clones into the peritoneal cavity are the immune related cells of the tissue microenvironment that seem to have a role in shaping the evolutionary history of cancer cells. The authors defined three patterns of tumor infiltrating lymphocytes (TILs), reflecting their density and distribution within the tumor microenvironment, with ES-TIL being the most immunogenic population (substantial epithelial and stromal TILs) in comparison with S-TIL (stromal TILs) and N-TIL (sparse TILs). Within the same patients extensive spatial variation was observed, with 17 out of 31 patients harboring more than one pattern of TILs. Using four different measures for assessing sample clone complexity it was evident that samples with ES-TIL elicit immune editing of subclonal populations through T Cell tumor clone tracking with subsequent expansion of tumor cell populations that harbor neoantigen loss and/or human leukocyte antigen LOH. However, multi-site TIL diversity also implies that immune deficient sites might represent cradles of clonal diversity for subsequent disease relapse. Another important aspect is that specific classes of genomic aberrations such as fold-back inversions that are present in a significant proportion of cases lead to poor immunogenic responses whereas homologous recombination deficient tumors are associated with upregulated imune pathways. Overall, patient specific spatial diversity of the tumor microenvironment significantly influences the intraperitoneal dissemination, offering a new perspective on HG-SOC clonal evolution [ 51 ].

The utility of using cell free DNA to monitor treatment induced genomic changes was assesed on 20 patients with paired pre/post NACT tumor and plasma samples through targeted next generation sequencing (NGS) and found that it was minimal and larger studies are needed to determine the role of cell free DNA in the management of HGSOC [ 52 ]. Given that multiregion sampling is not always feasible, a study on 4 patients evaluated if the genomic information extracted from ascitic cells can accurately reflect the tumor burden. The ascitic cells genomes included 84–100% of the common mutations and a considerable fraction (22.9–75.8%) of shared mutations that were present in at least two distinct samples, thus offering a large view of the mutational lanscape of advanced ovarian cancer. Inferring the phylogenies of ascitic cells in relation with spatially separated tissue samples demonstrated an early evolutionary divergence and polyseeding [ 53 ].

Conclusions

Therapeutic strategies should be based on accurate knowledge of a tumor’s trajectory. It is obvious from the first published report that there were many questions regarding the heterogeneous clinical course of ovarian cancer however the lack of accurate tools to infer on its evolutionary history could not be surmounted even by a large number of evaluated samples, and no conclusions could be drawn except that in light microscopy in most cases there were no changes in tumor morphology [ 25 ].

In the following three decades, chromosomal banding techniques used in the study of spatially separated samples increased the analysis resolution. Similar complex chromosomal changes were observed in tumor samples, and there were no firm conclusions towards clonal heterogeneity [ 26 ]. It was suggested this was the result of a late metastatic process without any evolution after the emergence of the metastatic subclone, but the alternate hypotheses of an identical clonal evolution in both the primary and the metastatic lesions could not be excluded. Another proposed concept as a possible explanation for the identical chromosomal lesions seen in bilateral carcinomas was that of clonal dominance, the overgrowth of the primary tumor by cells that have a growth advantage [ 27 ]. In a proof of principle study, a diagram of the inferred cytogenetic changes of three spatially separated samples created a branching pattern for the clonal evolution of ovarian cancer [ 28 ]. This was in accordance with the general hypothetical model of clonal evolution presented by Nowel [ 54 ] and represented a new method that could be applied in the study of similar tumors from different patients or from sequential samples. Due to lack of genetic resolution, a study performing Giemsa banding chromosomal analysis of treatment or progression induced chromosomal changes reported the same clonal chromosomal aberrations [ 29 ].

Further studies that used more accurate techniques such as restriction fragment length polymorphism probing or high-resolution CGH identified the coexistence of malignant cell clones however the development of metastasis was considered a late event in evolution [ 30 , 31 ]. After the introduction of PCR based LOH in ovarian cancer [ 32 ], a study based on a larger number of cases discovered a different spectrum of genetic alterations in metastases and confirmed the dissemination of only certain subclones [ 33 ], thus offering more precise interpretations of tumor evolution than previously studied based on chromosomal information [ 34 , 35 ].

The advent of high throughput technologies demonstrated the existence of a common ancestor and revealed the scale of intratumor heterogeneity [ 41 ]. Analyzing the relationships between different metastatic samples of the same patient, there were no cases in which all metastatic samples of a patient were identical. It also became evident from the emerging data that it was in support of a model of clonal origin that soon after becomes polyclonal with different clones acquiring metastatic potential during early and late stages of genetic divergence [ 42 , 43 , 44 , 45 , 47 ].

Extensive analysis of paired samples from diagnosis and recurrent disease showed that platinum resistant disease emerges from a minor pre-existent population through the selection pressure of chemotherapy with huge variability between the primary and recurrent disease [ 36 , 38 , 39 , 40 , 43 , 46 , 47 ], but a short administration of neoadjuvant chemotherapy didn`t seem to inflict significant genomic damage [ 36 , 49 ]. Analysis of cell free DNA has been already tested in following the clonal dynamics of colorectal cancer patients [ 55 ]. Cells in the ascites fluid have been proven to reflect most the common somatic mutations of a patient as a potential future surrogate for monitoring the genomic burden of disease while circulating cell free tumor DNA has prooved non informative so far, owing to its small amount and presence of diluting nonneoplastic DNA [ 52 , 53 ].

Subsequent analysis also showed that the presence of a tandem duplicator phenotype besides the well known homologous recombination deficiency as mechanisms that drive mutagenesis in a significant proportion of patients [ 37 ], suggesting that except TP53 other known actionable driver mutations are still elusive [ 38 , 43 , 44 ], contrary to the distinct entity of low grade serous ovarian cancer where cases with somatic mutations generally show stability across samples and time [ 48 ].

Previous observations that a stable genomic structure is associated with a longer overall survival [ 43 ] were confirmed through the phylogenetic quantification of heterogeneity that significantly predicted overall survival based on a clonal expansion index, in support of the hypotheses that high genetic diversity favors the development of treatment resistant disease [ 49 ].

Recent research has highlighted that most intraperitoneal mixtures are comprised in general of an oligoclonal population and at least one polyclonal site exists in every patient. Also, two non-random trajectories have been described, the first monoclonal and unidirectional and the second polyclonal with reseeding [ 50 ]. Theese patterns of spread seem to result from the spatial heterogeneity of the immune microenvironment that can actively shape the evolutionary history of cancer cells, with other clinical relevant interactions between mutator phenotypes and immune responses [ 51 ].

Cancer heterogeneity and cancer evolution represent a major challenge in front of effective therapy. A model of clonal evolution in ovarian cancer based upon some of the most important issues presented in this article is depicted in Fig.  1 . Many of the published research on heterogeneity in ovarian cancer has been reffering to the genetic component, however heterogeneity in cancer is a more broader phenomenom that can potentially impact any of the aproximately ten hallmarks of cancer[ 56 ]. In ovarian cancer, heterogeneity beyond the genetic component can impact tumor cell subpopulations on cancer hallmarks such as sustained proliferative signaling, activation of the angiogenic switch, genomic instability, and evading immune destruction. In an effort to address this issues, several trials focused on specific tyrosine kinase inhibitors with some of them demonstrating activity against VEGFR [ 57 ]. Antiangiogenic drugs have been studied extensively as an addition to the chemotherapy backbone [ 58 ], but a clear benefit was seen only in a high risk patient population [ 59 ], however novel combinations are under way in order to augment their therapeutic potential in combination with immunotherapy [ 60 ] or PARPi [ 61 ]. In addition, the combination of PARPi with immunotherapy could be synergistic and is under evaluations in recent clinical trials [ 62 ]. Hence, future prospects should incorporate all aspects of cancer heterogeneity together with host and tumor microenvironment related factors.

figure 1

Concept of clonal progression in cancer. Primary ancestral clone (P) has divergent evolution with early (M1) and late (M2, M4) acquisition of metastatic potential and re-seeding of metastases (M3). A high immune infiltrated microenvironment shapes clonal evolution. Pre-existent platinum resistant clones drive tumor relapse

Evolutionary computational methods in addition to the biomedical, genetic and clinical evidence we had so far can generate evidence based treatment strategies that can be further validated. A framework of tumor dynamics in ovarian cancer predicted the superiority of primary debulking surgery in a low volume disease setting [ 63 ], while other analyses focused on optimizing the sequence of chemotherapy in relation to immunotherapy [ 64 ] or targeting VEGF-mediated angiogenesis [ 65 ], approaches that can help us better understand the development of treatment resistance and design more efficient clinical trials. Characterization of growth and dissemination kinetics could also influence treatment strategies [ 66 ], while individual patient quantification of the clonal expansion index provides prognostic information that could further influence treatment intensity [ 49 ].

Methods such as high throughput single cell sequencing have recently offered the chance to study intratumor heterogeneity from the perspective of rare subclones [ 67 ], and together with novel evolutionary computational methods [ 68 ] they offer us the tools to have a real and acurate understanding of disease progression and optimal treatment strategies.

Availability of data and materials

The data analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Comparative genomic hybridization

High grade serous ovarian cancer

Homologous recombination

Interval debulking surgery

Loss of heterozygosity

Neoadjuvant chemotherapy

Next generation sequencing

PARP inhibitors

Primary debulking surgery

Single nucleotide polymorphism

Serous tubal intraepithelial carcinomas

Tumor infiltrating lymphocytes

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This study was supported by the Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca (Internal research grant number 3066/29/01.02.2018). Universitatea de Medicina si Farmacie "Iuliu Hatieganu",3066/29/01.02.2018,Paul Kubelac

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Patriciu Achimas-Cadariu and Paul Kubelac contributed equally to this work.

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Department of Surgery, The Oncology Institute ‘Prof. Dr. Ion Chiricuta’, 34-36 Republicii street, 400015 , Cluj-Napoca, Romania

Patriciu Achimas-Cadariu & Alexandru Irimie

Department of Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania

Patriciu Achimas-Cadariu, Paul Kubelac & Alexandru Irimie

Department of Medical Oncology, The Oncology Institute ‘Prof. Dr. Ion Chiricuta’, Cluj-Napoca, Romania

Paul Kubelac

Research Centre for Functional Genomics, Biomedicine and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania

Ioana Berindan-Neagoe

Research Center for Advanced Medicine Medfuture, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania

Department of Functional Genomics and Experimental Pathology, The Oncology Institute ‘Prof. Dr. Ion Chiricuta’, Cluj-Napoca, Romania

Institute of Evolutionary Medicine, Zurich, Switzerland

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Achimas-Cadariu, P., Kubelac, P., Irimie, A. et al. Evolutionary perspectives, heterogeneity and ovarian cancer: a complicated tale from past to present. J Ovarian Res 15 , 67 (2022). https://doi.org/10.1186/s13048-022-01004-1

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Ovarian Cancer: Prevention, Detection and Treatment of the Disease and Its Recurrence. Molecular Mechanisms and Personalized Medicine Meeting Report

Francesmary modugno.

1 Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine

2 Department of Epidemiology, University of Pittsburgh School of Public Health

3 Women’s Cancer Research Center, Magee-Womens Research Institute and University of Pittsburgh Cancer Institute

Robert P. Edwards

To review the current understanding of the underlying molecular, biologic and genetic mechanisms involved in ovarian cancer development and how these mechanisms can be targets for prevention, detection and treatment of the disease and its recurrence.

In May 2012, we convened a meeting of researchers, clinicians and consumer advocates to review the state of current knowledge on molecular mechanisms and identify fruitful areas for further investigations.

The meeting consisted of seven scientific sessions, ranging from Epidemiology, Early Detection, and Biology to Therapeutics and Quality of Life. Sessions consisted of talks and panel discussions by international leaders in ovarian cancer research. A special career-development session by the CDMRP Department of Defense Ovarian Cancer Academy as well as an oral abstract and poster session showcased promising new research by junior scientists.

Conclusions

Technological advances in the last decade have increased our knowledge of the molecular mechanisms involved in a host of biological activities related to ovarian cancer. Understanding the role these mechanisms play in cancer initiation and progression will help lead to the development of prevention and treatment modalities that can be personalized to each patient, thereby helping to overcome this highly-fatal malignancy.

Introduction

Ovarian cancer is the sixth most common cancer worldwide among women in developed countries and the most lethal of all gynecologic malignancies.( 1 ) Currently, most women have advanced stage disease at the time of diagnosis. Despite aggressive surgery and chemotherapy, the prognosis for these women is poor, with a 5-year survival rate of less than 30%. This poor outcome is due in part to the lack of effective prevention and early detection strategies: when diagnosed at an early stage, the survival rate is approximately 85–90%. Thus, prevention and early detection are key to overcoming this disease. With the exception of oral contraceptives, there are no successful chemopreventive agents available. Bilateral oophorectomy has also been shown to reduce disease incidence, but the procedure has several drawbacks in terms of women’s health.( 2 ) Existing screening techniques (CA125, transvaginal ultrasound) have not been demonstrated to reduce morbidity or mortality. Thus, better prevention, detection and screening methods are urgently needed. As well, because of the virulent and usually fatal nature of the disease, most women with ovarian cancer live with fear of recurrence, which happens in about 85% of cases. Current treatments offer little hope and survival has remained virtually unchanged for almost three decades. New methods to prevent, detect and treat recurrence are urgently needed.

With advances in molecular biology and the emergence of new technologies, scientists are gathering remarkable knowledge about the genetic and biologic basis of ovarian cancer carcinogenesis. Such knowledge opens the door to new strategies for prevention, early detection and treatment of the disease. Importantly, it allows for the development of “personalized medicine,” wherein prevention, detection and treatment modalities are aimed at the specific molecular mechanisms of an individual tumor and its microenvironment, as well as at the specific genetic and biologic profile of the host. Science stands on the precipice of a new era for making profound progress in ovarian cancer research.

To facilitate this progress, we convened a scientific symposium on ovarian cancer. The meeting was held May 10–11, 2012 in Pittsburgh, Pennsylvania. The meeting brought together over 300 researchers, scientists, clinicians, policy makers and advocates for an intensive two-day discussion of molecular mechanisms and personalized medicine in the prevention, detection and treatment of ovarian cancer and its recurrence.

This article summarizes the highlights of the main presentations. Also included are abstracts chosen by the program committee as among the top submissions, as well as abstracts of the presentations by members of the Department of Defense Ovarian Cancer Academy. Readers are referred to the conference website in order to view videos of the complete talks and interactive panel discussions that were part of each session ( www.upci.upmc.edu/ovarian ).

Julene Fabrizio Keynote Lecture: 40 Years of Ovarian Cancer Research

The meeting opened with a keynote address by Nelly Auersperg, MD, PhD who provided a look at the progress made in ovarian cancer biology during the last 40 years. The ovarian cancer cell of origin remains an ongoing debate. Early studies implicated the ovarian surface epithelium (OSE) as the origin of high-grade serous ovarian carcinomas (HGSOCs) based on the observation of early transformed cells within ovarian epithelial inclusion cysts ( 3 ) This view was held until quite recently when it was observed that lesions resembling HGSOC were found in the oviductal fimbriae of BRCA1 carriers suggesting that some HGSOCs arise in the fallopian tubes.( 4 )

Despite evidence for both theories, none of the evidence supports either theory 100%. Thus it is possible that a subset of HGSOCs arises from the OSE while another subset has a tubal origin. This raises the question: how do two epithelia from different organs with very different structure and function give rise to identical carcinomas? Emerging molecular evidence suggests that the OSE and distal fimbrial epithelium are a continuous, incompletely determined zone of epithelial transition from one epithelial type to another.( 5 ) Such transitional epithelia in other parts of the body, such as the squamo-columnar junction of the cervix, are known to be prone to neoplastic transformation. This theory provides a partial explanation for why after prophylactic salpingo-oopohorectomies the remaining portion of the fallopian tube does not represent a cancer risk and why salpingectomy alone may not provide adequate protection against ovarian cancer development. Future work will provide greater insight into this theory and how it can help explain ovarian carcinogenesis.

Epidemiologic and Genetic Factors

C. Leigh Pearce, PhD presented work on endometriosis and ovarian cancer. Women with endometriosis are at an increased risk of ovarian cancer. Despite the prevalence of endometriosis (about 10% in the general population ( 6 )), only a small percentage of women with the condition develop ovarian cancer. Identifying these high-risk women remains elusive. Moreover, the association between endometriosis-associated disease and well-known ovarian cancer protective factors, such as OC use and parity, remains unclear. The relationship between ovarian cancer risk and endometriosis-related factors, such as anatomical location, type and timing of treatments, and symptom and treatment response, are not also known. Clarifying these factors will help establish risk estimates based on an individual’s profile and will help tailor prevention interventions. The Ovarian Cancer Association Consortium (OCAC) confirmed that the endometriosis-ovarian cancer link is limited to the invasive clear cell and endometrioid subtypes.( 7 ) No association was found for borderline tumors, suggesting that contrary to current theories, borderline clear cell and endometrioid tumors may not be the pre-cursors of their invasive counterparts. Identifying the pre-cursor lesion for endometriosis-associated ovarian cancer as well as factors associated with progression from pre-malignant to malignant disease will support developing targeted prevention therapies.

Ellen L. Goode, PhD, MPH contended that ovarian cancer has a genetic component beyond the rare, high-risk, low prevalence (<1%) mutations in genes such as BRCA1/2 . Combinations of common genetic variants with minor allele frequencies (MAFs) greater than 5% and which confer modest risk likely account for the remaining heritability. Candidate gene studies have identified many potential susceptibility variants. However, data have been inconsistent due to small sample sizes and heterogeneity across study populations. OCAC provides a large sample size, pooling of data and examining of between-study heterogeneity in order to address these limitations. OCAC has confirmed some variants associated with ovarian cancer while refuting others. Using GWAS, 6 novel susceptibility loci (in 2q31, 3q25, 8q24, 9p22,17q21, 19p13) were identified.( 8 , 9 ) Each of these variants is common (frequency >8%) and confers only a modest change in risk (< 20%); however, their functions are mostly unknown. Further work in understanding how they impact ovarian cancer and how they are affected by host factors is needed. Further research is also needed to understand the relationship between these loci, specific disease subtypes and other disease phenotypes such as survival. This will help evaluate the clinical utility of the markers as well as uncover novel prevention and treatment targets.

Kirsten Moysich, PhD examined the role of immunosuppression pathways in ovarian cancer and explored the hypothesis that a strong immunosuppressive genotypic and phenotypic profile is associated with ovarian carcinogenesis. Specifically, her work is investigating the roles of immunosuppressive regulatory T cells (Tregs) and myeloid derived suppressor cells (MDSCs) in ovarian cancer. Ovarian cancers appear to have much higher levels of Tregs and MDSCs compared to benign tumors. Work clarifying the meaning of immunosuppressive cells in ovarian cancer development and outcome is needed. In addition to the phenotypic associations, functional SNPs in genes involved in the Treg and MDSC pathways were also associated with ovarian cancer risk and poorer survival in initial studies. When these same candidate genes were examined among 17,421 cases and 25,878 controls in OCAC, only a handful of associations were found and the effect sizes were of questionable relevance (less than 5–10%). However, when examining associations by histologic subtypes, several associations of modest size were found. The associations differed among the histologic types in both magnitude and direction. This raises interesting questions about the functionality of SNPs, the subsequent phenotype and the relationship to specific histology.

Prevention, Early Detection and Biomarkers

Robert C. Bast, Jr, MD discussed biomarkers for detecting and treating incident and recurrent ovarian cancer. Identifying sensitive (>75%) and specific (>99.6%) screening modalities are key to early detection. CA125 is currently the standard marker; however, it is only 99% specific. UKCTOCS showed improved detection when using ultrasound (US) after assessing CA125 levels relative to a woman’s baseline value rather than a “standard” clinical value: early stage detection was doubled (48%) and the number of operations per cancer case detected was remarkably low (4 versus 36 for US alone). ( 10 ) Despite these promising findings, 20% of ovarian cancers will not be detected with this two-tier approach because they fail to express CA125. Moreover, cancers originating in the fallopian tube cannot be visualized on ultrasound prior to metastasis. Additional biomarkers are needed. Promising directions include panels of serum-based tumor markers and autoantibodies. To date neither has demonstrated the requisite sensitivity and specificity to be clinically useful. Better imaging techniques able to detect cancers at even smaller volumes are also needed. Finally, work is needed to understand how to combine both serum marker and visualization approaches in a way that will produce a cost-effective screening tool that can easily be adapted to the clinic.

Kristin Zorn, MD presented data on ovarian cancer development in “high-risk” women, defined as women who possess a germ-line mutation that confers an increased risk of ovarian cancer. In addition to garnering insight into how to best care for these women, studying the high-risk population helps understand the pathogenesis of sporadic disease. The discovery of incident fallopian tube cancers among women undergoing risk-reducing bilateral salpingo-oophorectomy (RRBSO) raises the intriguing notion that the fallopian tube and not the ovary is the origin for some HGSOCs. Further laboratory data support this hypothesis and even suggest that tubal intraepithelial carcinomas (TICs) may serve as the precursor lesion for HGSOC: both lesions exhibit cytological atypicia, high proliferative indices, and the presence of p53. ( 11 ) The presence of TICs in women diagnosed with HGSOC further supports this hypothesis. ( 12 ) However, the data are not consistent. For example, high rates of p53 foci are found in the tubal epithelium of normal-risk women. Thus, other molecular alterations must be necessary for the progression to malignant disease. Elucidating these alterations as well as developing techniques to more carefully profile specimens from high-risk women represent areas for future research. ( 13 )

Marian Mourits, MD, PhD talked about the impact of the new fallopian tube hypothesis on preventive strategies in high-risk woman. Screening is ineffective for detecting ovarian cancer at an early stage and has not been shown useful in managing high-risk women. RRBSO is currently the only preventive option available. However, the procedure has many side effects that may negatively impact a woman’s health and quality of life, including bone and cardiovascular health as well as sexual functioning.( 2 ) Thus, care must be taken to manage postmenopausal symptoms arising from a RRBSO. In light of the tubal origin of the disease, another option is for high-risk women to have a “risk-reducing” salpingectomy. However, no studies have been conducted to assess the effectiveness of this strategy. As well, while data support a tubal origin of the disease especially in murine models, ( 14 ) the evidence in humans is far from conclusive. Many questions remain, such as how cancerous or even pre-cancerous cells from the fallopian tubes migrate to the ovary and become HGSOC, what is the precursor to TICs, what is necessary for a TIC to convert to HGSOC, and through what mechanism does ovulation suppression affect the tubal epithelium?

Ovarian Cancer Biology – Mechanisms and Targets

Setsuko K. Chambers, MD presented data showing that micro RNAs (miRNAs) interact with messenger RNAs (mRNAs) to influence ovarian cancer etiology. RNA binding proteins control translational regulation of mRNA through a binding element typically located in the 3’ untranslated region (3’UTR). mRNA binding proteins can have both oncogenic and tumor suppressor roles. Emerging data also show that miRNAs are important mRNA regulatory components through both translational control as well as modulation of mRNA decay. miRNAs are frequently dysregulated in cancers, have both oncogenic and tumor suppressive roles, and are involved in ovarian proliferation, invasion and metastasis. Interaction between RNA binding proteins and miRNAs is another mechanism whereby each influences disease. For example, CSF-1, which plays a key role in ovarian cancer etiology,( 15 ) is regulated by miR130a and miR301a. Both miRNAs are dependent on the RNA binding protein nucleolin for gene expression of CSF-1 and as well as for tumor cell motility. These data suggest that regulators of the 3’UTR can control gene expression and tumor behavior. Whether these mechanisms can be exploited for therapeutic intervention warrants further investigation.

There is limited understanding of the role of nuclear receptors (NRs) in HGSOC. Steffi Oesterreich, PhD presented in silico analyses of the Cancer Genome Atlas (TCGA) Project data sets, which identified members of the NR4A family of orphan receptors as potential drivers of a subset of HGSOC. The relevance of this finding is unclear and further studies of the mechanics and function of these receptors are needed. The association between hormonal exposures and HGSOC suggests that hormones play a role in the etiology of the diseases and, thus, endocrine therapies may be fruitful at least for some subset of cancers. However, little is known about the role of steroid hormone receptors such as the estrogen receptor (ER) in HGSOC. Preliminary studies in ovarian cancer cell lines indicate that ER expression is not a reliable biomarker of estrogen response and better predictive markers are needed.

Melanie Flint, PhD discussed the role of stress on cancer initiation and progression with a focus on the adaptive immune system. Stress triggers a complex response mechanism that affects various systems, including the immunes system. In vitro , release of stress hormones induces T cell activation and migration, while decreasing cell proliferation. ( 16 ) The mechanism underlying these changes may be through rearrangement of the actin cytoskeleton. In a transgenic mouse model of ovarian cancer, chronic stress decreases CD3+ T cell activation and results in earlier onset of tumors. However, the tumors appear to be more confined to the ovary compared cancers induced in unstressed mice. This suggests that stress hormones, in addition to affecting the immune system, may also directly interact with cancer cells thereby impacting proliferation. Elucidating the molecular mechanisms underlying these findings will be important in understanding the impact of chronic and acute stress on cancer initiation and progression.

Plenary Talk - Karen A. Johnson Memorial: Creating Effective Patient-Physician

Partnerships, Martha E. Gaines, JD. Dr. Gaines, an 18 year ovarian cancer survivor, founded the Center for Patient Partnerships to address the need to enhance the many partnerships that result from a diagnosis of ovarian cancer. Central to successful treatment for ovarian cancer is the physician-patient partnership. Choosing a healing path requires many elements, including understanding the roles and goals of both the physician and patient, clear and honest communication and a balance of power. Only through an effective patient-clinician partnership will a woman be truly empowered to face the fight of her life.

Novel Approaches to Symptom Assessment and Management

Dana Bovbjerg, PhD discussed biobehavioral models for symptom management. Managing symptoms of both the disease and treatment present significant challenges for women and clinicians. In addition to medical complications, behavioral comorbidities, such as depression, fatigue, disrupted sleep and cognitive dysfunction, often initiate with diagnosis and treatment, and may continue into the survivorship period. Post-operative pain is a significant symptom which often influences other comorbidities throughout treatment and beyond. However, there are great inter-individual differences in pain perception even in healthy adults and the determinants of pain remain poorly understood. Recent data suggest sleep disruption may be one such determinant. In women undergoing breast conserving surgery, lower sleep efficiency (i.e., frequently disrupted sleep the night before surgery) has been associated with greater pain severity and interference with daily activities in the week following surgery. ( 17 ) Similar results were found in women undergoing more invasive gynecologic surgeries. These data illustrate the intricate relationships among psychological, behavioral and physical factors. However, the biologic mechanisms underlying these relationships remain unclear. Recent work has focused on neuroendocrine-immune factors, which jointly influence central nervous system functions. Understanding these mechanisms will help identify at-risk women who can then be targeted for therapeutic interventions.

Sandra Mitchell, PhD, CRNP discussed using patient-reported outcomes (PROs) to measure therapeutic response and toxicities in clinical trials. PROs provide important insight into treatment efficacy, but to date have been underutilized. Many issues are involved in selecting an appropriate PRO instrument, including understanding the purpose of the measurement, assessing the correspondence between the instrument domain and scientific questions, and having clear scoring guidelines. Practical aspects must be considered, too, such as respondent burden, which can negatively impact results. The ability of current PRO systems to detect cancer treatment effects and their sensitivity to organ-specific issues remain unknown. Many challenges remain in incorporating PRO into gynecologic cancer trials, including measuring the value of the PRO instrument in the clinical trial setting, understanding when and how to incorporate a PRO system into trials, how to correlate PRO with diverse clinical and biomedical endpoints, how to handle missing data, developing methodologies to define responders and analyze results over time, understanding the clinical significance of changes in PRO over time, and how to report results in conjunction with other scientific findings. ( 18 )

Heidi Donovan, PhD, RN presented data on multi-symptom management. Women with ovarian cancer report 10–14 concurrent symptoms during treatment. Identifying and prioritizing symptoms as well as providing clinicians and women with both medical and self-care strategies remain areas for further research. Effective symptom management poses many challenges because some symptoms, such as fatigue, have no efficacious medical treatment, while the treatment of some symptoms often causes or exacerbates others. Symptom management is an ongoing part of care for women with ovarian cancer and may continue after active treatment ends. Thus, symptom management requires effective patient-clinician communication as well as substantial self-management on the part of women. Many trials exist to investigate symptom management, from those investigating a single method to address a single symptom to those investigating multiple methods to address multiple symptoms. Translating the findings on symptom management from the research setting to the clinic is an area for future investigation. The substantial self-management component for symptom management raises questions about its consequences on women with advanced disease. Identifying ways to support women in symptom management as well as to overcome barriers to self-management represent fruitful research directions.

Diane C. Bodurka, MD discussed cancer survivorship.( 19 ) The number of cancer survivors, especially long term survivors, continues to grow. Many factors influence the health care and other needs of this growing population, including disease site and treatment, age at and time since diagnosis, comorbidities, lifestyle and behavioral factors and social support. Health issues affecting survivors, both pre-diagnosis issues and those resulting from treatment, are also not well understood. Such factors include fatigue, sexual dysfunction, sleep disturbance, neurological issues, urinary complaints and bowel complaints. Different treatments and combination of treatments impart different risk for these health effects and are experienced at all points on the survivorship spectrum. Interventions to mitigate these treatment-related effects throughout the entire survivorship period are needed. As well, educating both patients and primary care clinicians on managing short- and long-term survivor care is an important but currently unmet need.

New Directions in Therapeutics

Cancer stem cells are self-renewing cells capable of initiating tumorigenesis, recurrence and metastasis. Patricia K. Donahoe, MD proposed that at diagnosis, ovarian cancers have both stem and non-stem cell populations which must be differentially treated in order to ensure both cell populations are effectively targeted. An ovarian cancer stem cell-enriched population marked by three markers conserved across primary cancers and normal Fallopian tube fimbria (CD44, CD24 and Epcam) and by negative selection for Ecadherin has been identified. ( 20 ) These cells comprise less than 1% of cancer cells, have increased colony formation and shorter tumor-free intervals in vivo . Moreover, they are resistant to but stimulated by standard chemotherapeutic agents. They are inhibited by Mullerian inhibiting substance (MIS). These data support the use of combination of markers to develop targeted “tumor stem cell therapies” individualized to the specific tumor-initiating population identified in a lesion. Further work is needed to identify and understand mechanistic differences between different putative stem cell populations that could serve as therapeutic targets. Understanding the mechanisms underlying stem cell self-renewal or differentiation can also shed light on how these cells contribute to chemoresistence and whether modulation of these mechanisms can impact patient outcome.

Kunle Odunsi, MD, PhD discussed vaccine development. An effective immunotherapy will generate a robust, clonal expansion of T-cells that can differentiate into both effector cells with capacity to kill tumor targets and memory cells with capacity for recall response. Identifying targets for immune recognition is the first step in vaccine development. NY-ESO-1, a tumor-specific antigen, is one such target. Vaccination with NY-ESO-1 epitope induces integrated humoral CD4+ and CD8+ T cell response with the capacity to recognize tumor targets. ( 21 ) However, as the time from vaccination increases, functional immune response decreases. Thus, while the vaccine generates substantial effector T-cells, it does not generate a high frequency of memory T-cells. Understanding the mechanisms underlying this phenomenon as well as identifying agents that can influence the type of T-cells generated are critical in designing vaccines with durable protection. mTOR blockade may be one possibility. In In vitro and animal studies, mTOR blockade influences T-cell differentiation towards memory cells, suggesting that including an agent that blunts mTOR may improve vaccine efficacy. However, even when an effective long-lived functional T-cell response is generated, most subjects will relapse. Improved understanding of how tumors escape immune attack is needed.

Mechanisms underlying immune system escape and exploiting those mechanisms to enhance vaccine efficacy was addressed by Pawel Kalinksi, MD, PhD . One way tumors escape the immune system is by creating a highly immunosuppressive environment through both the production of MDSCs and the suppression of type-1 immune effector cells. Local production of prostaglandin E 2 (PGE2) and COX2 appear to play a role. A COX2-PGE2 positive feedback loop controls CXCR4/CXCL12-guided accumulation of MDSCs as well as induction and stability of the immunosuppressive MDSC phenotype and function. PGE2 and COX2 also suppress induction and function of type-1 effector cells (Teff) and selectively inhibit production of Teff-attracting chemokines. Disrupting this feedback loop by suppressing COX2 restores local immunosurveillance in vitro . These findings have important implications for vaccine development. Cancer vaccine adjuvants can amplify PGE2-driven suppressive events when used alone. However, when combined with COX2 inhibitors, they induce Teff-attracting chemokines and also suppress MDSC and Treg attracting chemokines in tumor but not marginal tissue ( 22 ). This suggests that conditioning the tumor microenvironment by disrupting the PGE2-COX2 feedback loop prior to vaccination may enhance vaccine efficacy. Studies to test that hypothesis are needed.

William Zamboni, PhD presented data on the translational development of nanoparticles for drug delivery. Relative to non-encapsulated drugs, nanoparticle encapsulated drugs can have prolonged circulation and may selectively accumulate in different organs and blood components. They may also have different cellular distributions. Moreover, the size and surface properties of the encapsulating nanomaterial (“carrier”) may lead to greater accumulation in tumors as a result of the enhanced permeability and retention (EPR) effect. Together these data suggest that nanoparticle encapsulation can alter the pharmacokinetics and distribution of a drug in ways that can improve drug efficacy while reducing toxicity. ( 23 ) However, recent studies suggest nanoparticle encapsulation results in greater pharmacokinetic variability – the individual rate of clearance of an encapsulated drug is highly variable between patients. Some agents can have 10 to 100 times the variability of their non-encapsulated counterparts. The mechanisms underlying this variability are poorly understood, although emerging data suggest that it may be due to the ability of cells to recognize the carrier and activate the drug, coupled with the effect of drug activation on those cells. A greater understanding of these mechanisms and their effects on toxicity and efficacy is needed.

David G. Huntsman, MD discussed how current genomic work can lead to new opportunities for cancer control. Advances in cellular and molecular biology confirm that “ovarian cancer” is not a single disease but rather a group of molecularly- and etiologically-distinct diseases that share an anatomical location. Prevention and treatment efforts must therefore exploit subtype-specific precursor lesions and tumor biology. Conducting subtype-specific clinical trials is challenging because of the sparse number of cases for subtypes of an already rare cancer. One possibility is to group cancers not by their site of origin and clinical presentation but by their molecular characteristics. Thus, clear cell and endometrioid ovarian cancers could be grouped with a subset of uterine cancers with which they share similar molecular profiles. However, successfully targeting a tumor-specific mutation in one cancer does not necessarily mean the same approach will be effective in another cancer expressing the same mutation: BRAF inhibition is highly effective in treating melanoma but shows limited response in colon cancers harboring the same oncogenic lesion. ( 24 ) Understanding other factors that influence targeted therapeutics within a specific molecular context will be key to implementing this new paradigm in clinical trials.

Michael V. Seiden, MD, PhD presented data on the success of several therapies targeted at molecular alterations in other cancers. In almost all cases, these therapies targeted an activated oncogene (eg, EGFR, HER-2). Yet when these molecularly-targeted therapies have been tested in ovarian cancer, they have failed to show significant efficacy in progression-free survival or in clinical response. Data from TCGA provide insight: within HGSOC there is a great deal of genomic instability/variability with no single oncogene or group of oncogenes to target. ( 25 ) However, most HGSOC have lost at least three tumor suppressor genes (p53, OPCML, BRCA1/2) early in carcinogenesis. Mutated tumor suppressor genes cannot be directly targeted for reversal of function and the large number of pathways each affects makes targeting all subsequently activated genes prohibitive. Together, these observations raise the question of whether personalized, targeted, molecular therapies can really be developed in ovarian cancer and if so, what resources will be needed to accomplish that goal. Therapies targeting the tumor microenvironment and stem cells may prove more effective for ovarian cancer.

Technological advances in the last decade have increased our knowledge of the molecular mechanisms involved in a host of biological activities related to normal ovarian function as well as to ovarian cancer development. The advantages of applying molecular approaches and supporting technologies to ovarian cancer prevention and early detection are many. The most effective way to prevent any disease is to understand its underlying cause and change the conditions that permit it to occur. Identifying the precise molecular and biologic steps that characterize pre-malignant change will provide the foundation for the search to find agents that reverse these changes or block the steps critical to the full development of cancer. Similar steps can also be used to detect and prevent disease recurrence. Moreover, a precise characterization and understanding of the mechanisms involved in cancer initiation and progression will help lead to the development of prevention and treatment modalities that can be personalized to each patient, thereby helping to overcome this highly-fatal malignancy.

Acknowledgements

We thank the Symposium speakers for their thought-provoking presentations and engaging, interactive discussions: Nelly Auersperg, MD, PhD, University of British Columbia. Robert C. Bast, Jr, MD, University of Texas M. D. Anderson Cancer Center. Martina Bazzaro, PhD, University of Minnesota. Diane C. Bodurka, MD, University of Texas M. D. Anderson Cancer Center.Dana Howard Bovbjerg, PhD, University of Pittsburgh Cancer Institute. Setsuko K. Chambers, MD, University of Arizona Cancer Center. Jeremy Chien, PhD, Mayo Clinic. Patricia K. Donahoe, MD, Massachusetts General Hospital. Heidi S. Donovan, PhD, RN, University of Pittsburgh School of Nursing. Melanie S. Flint, MSc, PhD, University of Pittsburgh Cancer Institute. Martha E. Gaines, JD, University of Wisconsin Law School. Ellen L. Goode, PhD, MPH, Mayo Clinic College of Medicine. David G. Huntsman, MD, University of British Columbia. Pawel Kalinski, MD, PhD, University of Pittsburgh School of Medicine. Panagiotis Konstantinopoulos, MD, PhD, Harvard Medical School. Charles Landen, MD, University of Alabama School of Medicine. Sandra A. Mitchell, PhD, CRNP, National Cancer Institute. Marian J. Mourits, MD, PhD, University of Groningen. Kirsten B. Moysich, PhD, MS, State University of New York at Buffalo. Kunle Odunsi, MD, PhD, Roswell Park Cancer Institute. Steffi Oesterreich, PhD, Magee-Womens Research Institute. C. Leigh Pearce, PhD, University of Southern California. Michael V. Seiden, MD, PhD, Fox Chase Cancer Center. Kathryn Terry, ScD, Harvard Medical School. Anda Vlad, MD, PhD, University of Pittsburgh School of Medicine. William Zamboni, PharmD, PhD, UNC Lineberger Comprehensive Cancer Center. Rugang Zhang, PhD, Fox Chase Cancer Center. And Kristin K. Zorn, MD, University of Pittsburgh School of Medicine.

We Judy Koryak, Caitlin Antonacci and Jeffrey Eppinger for their assistance with the program and manuscript.

Support : Support for the Ovarian Cancer Symposium was provided by National Cancer Institute Division of Cancer Prevention (R13-1165638). Grant support was provided by educational grants from Abbot and Genetech. Additional support was provided by Magee-Womens Hospital, the National Ovarian Cancer Coalition, Dr. and Mrs. Joseph L. Kelley, the Fabrizio Family, the Morris and Carolyn Barkon Lectureship in Gynecologic Oncology Survivorship and the Jewish Healthcare Foundation.

Appendix 1: Ovarian Cancer Academy Abstracts

The Ovarian Cancer Academy, a virtual career development and research training platform, convened for its first inaugural meeting on May 9, 2012. These highly committed early-career investigators, their mentors, and the Academy Dean have been busy collaborating and networking as they work toward eliminating ovarian cancer and becoming the next generation of leaders in ovarian cancer research. Cumulatively, they have published more than 65 ovarian cancer research articles, presented at national conferences, filed patents, mentored trainees, and served on editorial boards as well as peer review panels. The DOD OCRP is proud of their accomplishments as they continue to make strides in their fields.

Martina Bazzaro, University of Minnesota, is targeting ubiquitin-mediated protein degradation pathways for ovarian cancer treatment. She is focused on diagnostic and prognostic markers, targeted therapy, and the inhibitors’ potential mechanisms of anti-cancer activity. Dr. Bazzaro has found deubiquitinating enzymes (DUBs) involved in ovarian cancer cells, and she is testing probes to specifically target those DUBs and cells that overexpress them. Targeting the DUBs may lead to a new treatment for ovarian cancer in the future.

Jeremy Chien, University of Kansas, is investigating integrative functional genomics and proteomics to uncover mechanisms of chemotherapy resistance in ovarian cancer. Using the TCGA (The Cancer Genome Atlas) data, he has found a list of genes that could be used to predict prognosis of ovarian cancer. Dr. Chien observed a difference between tumors that express high and low levels of the gene FBN1, and while it may have no role in primary resistance, it may have a role in platinum-sensitive recurrence and acquired resistance. He also proposed a new model of chemotherapy resistance that builds on other existing models. Results from Dr. Chien’s work may be able to help improve patients’ responses to chemotherapy.

Panagiotis Konstantinopoulos, Harvard Medical School, is working toward developing a biomarker for “BRCAness,” a phenotype that is characterized by responsiveness to platinum and PARP (poly ADP ribose polymerase) inhibitors and improved survival in patients. He is investigating the association of the BRCAness gene expression profile with clinical outcome and molecular aberrations underlying defective homologous recombination in high-grade, advanced stage EOCs in the TCGA dataset. Dr. Konstantinopoulos found that the BRCAness profile is associated with certain defects (e.g., events involving BRCA 1/2 genes and deletions of homozygous PTEN) in homologous recombination DNA repair. Additionally, he found that patients with BRCA-like tumors had improved overall survival when compared to patients with non-BRCA-like tumors. Efforts are under way to improve the predictiveness of the BRCAness profile.

Charles Landen, University of Alabama at Birmingham, is looking at the emerging aspect of cancer stem cells (i.e., tumor-initiating cells). His research is supportive of the hypothesis that subpopulations within heterogeneous ovarian tumors contribute to chemoresistance. Dr. Landen has shown that markers in ovarian cancer cells, “side population,” CD44, CD133, and aldehyde dehydrogenase (ALDH1), have cancer stem cell-like properties. Additionally, results from an analysis of primary and recurrent tumors showed chemoresistant tumors were enriched in CD133 and ALDH1-positive cells although they were not the only ones in the chemoresistant population. His findings could contribute important targets for chemotherapy and overcoming chemoresistance in addition to helping build better models to understand tumor heterogeneity.

Kathryn Terry, Harvard Medical School, is focused on understanding risk factors by etiologic pathway. Based on measurements from pathology reports, she and her colleagues have classified more than 1,700 cases from the New England Case Control Study and the Nurses’ Health Study into dominant tumors (likely of ovarian origin) defined as those restricted to one side or with one side that is two times greater than the other and nondominant (likely tubal origin). They have found that dominant tumors are more strongly associated with multiparity, tubal ligation, and endometriosis, whereas nondominant tumors are more strongly associated with a family history of ovarian cancer and genetic variation in a telomere-associated protein, TERT. Results from Dr. Terry’s work provide a better understanding of ovarian cancer risk factors, which is important for prevention.

Anda Vlad, University of Pittsburgh School of Medicine and Magee-Womens Research Institute, is working on preclinical modeling of ovarian cancer for vaccine development—she is using genetically engineered mice with conditional mutations in the Kras and/or Pten pathways for the preclinical modeling of gynecologic malignancies. She is focusing on the well-defined tumor antigen, MUC1, as an oncoprotein/vaccine candidate because it is overexpressed in more than 80% of EOCs. Dr. Vlad has found that MUC1 expression in her transgenic mice mirrors human MUC1 tissue distribution. She is testing MUC1 as a vaccine candidate in the transgenic mice and demonstrated that the vaccine significantly prolonged survival in vaccinated mice with ovarian tumors as well as restored immune surveillance. Additional results from her research indicate that MUC1 influences ovarian cancer pathogenesis and will be a valuable target for immune therapy.

Rugang Zhang, Wistar Institute, is exploring how canonical Wnt signaling activated by loss of Wnt5a contributes to EOC development through overcoming senescence, a state of irreversible cell growth arrest. He has found that Wnt5a is expressed at lower levels in primary EOCs, and the loss of Wnt5a correlates with a high cell proliferation index. It was a poor prognosis biomarker in EOC as well. Dr. Zhang observed that Wnt5a suppressed the human EOC cell growth in vitro and in an orthotopic mouse model. Reconstituting Wnt5a induced senescence in EOC cells too. Based on his results, he concluded that targeting Wnt signaling is a novel strategy for causing EOC cells to undergo senescence, which may be a possible mechanism for ovarian cancer therapeutics.

Appendix 2: Selected Abstracts

Molecular targeted ultrasound imaging of αvβ3-integrins expressing microvessels in association with anti-NMP antibodies detects ovarian cancer at early stage

Animesh Barua 1 , Qureshi T 1 , Bitterman P 1 , Bahr JM 2 , Sanjib Basu 3 , Rotmensch J 4 and Abramowicz JA 4

1 Departments of Pharmacology, Pathology and Obstetrics &amp; Gynecology, Rush University, Chicago, IL, 2 Laboratory of Animal Sciences, University of Illinois at Urbana-Champaign (UIUC), Urbana-Champaign, IL, 3 Department of Preventive Medicine, Rush University, Chicago, IL, 4 Department of Obstetrics &amp; Gynecology, Rush University, Chicago, IL

Background: Changes in nuclear morphology including nuclear matrix proteins (NMPs) followed by tumor associated neoangiogenesis (TAN) are the two earlier events in malignant transformation and progression of ovarian cancer (OVCA). Anti-NMP antibodies are produced in response to NMPs shed during malignant transformation. Expression of avb3-integrins by TAN vessels is one of the features of ovarian TAN. Anti-NMP antibodies and ovarian TAN may represent markers of early stage OVCA. Identification of patients at early stage OVCA is very difficult and laying hens have been shown to develop spontaneous OVCA with histopathology similar to humans. The goal of this study was to examine the feasibility of αvβ3-integrin targeted transvaginal ultrasound (TVUS) imaging and anti-NMP antibodies in detecting OVCA at early stage.

Methods: Hens with (n= 50) or without (n= 20) serum anti-NMP antibodies and without any TVUS detectable ovarian abnormality were selected for prospective monitoring by avb3-integrins targeted TVUS at 15 weeks interval up to 45 weeks. Hens were scanned before, during and after injection of targeted microbubbles at each interval. Pre- and post-injection images archived and analyzed off-line. Hens were euthanized at diagnosis for OVCA or at the end of the 45 weeks. Gross diagnosis was recorded at euthanasia. Serum and tissues were processed for histology, immunohistochemistry (SMA and αvβ3 integrins) and immunoblotting.

Results: Within 30–45 weeks of monitoring, 7 hens with serum anti-NMP antibodies developed OVCA spontaneously. αvβ3-integrins targeted microbubles enhanced the visualization of ovarian tumors remarkably. Targeted microbubbles bounded areas appeared as a ring on the ovarian surfaces of 5 hens on TVUS and suspected for OVCA. Tumors were confirmed in all hens predicted to have OVCA by targeted TVUS at euthanasia. In 4 hens, tumors were limited to the ovaries (early stage) and in one hen the tumor metastasized to abdominal cavity. Targeted TVUS imaging could not detect two hens with microscopic lesions without any solid tumor mass. Thus 7 of 50 hens had OVCA and targeted microbubbles detected approximately 72% (5 of 7 hens). Serum reacted against NMP antigens of various molecular wt from malignant ovaries in immunoblotting. The frequency of TAN vessels (SMA and αvβ3-integrins expressing) were significantly higher in OVCA hens than normal hens (P&lt;0.05).

Conclusions: Targeted imaging enhanced TVUS detection of early stage OVCA. Anti-NMP antibodies together with targeted imaging may constitute an early detection test for OVCA. The results may form the foundation for a clinical study.

Support: Department of Defense (#09-3303), Sramek Foundation

Ovarian Cancer Survivors’ Experiences of Self-Advocacy: A Focus Group Study

Teresa Hagan, BSN, RN, BA; Judy Knapp, PhD, LCSW, Jun Guo, MS; Susan Hughes, MSN, RN; Mary Roberge, BSN, RN; Sara Klein, MEd, BSN; Sandra Ward, PhD, RN, FAAN; Mary Beth Happ, PhD, RN; Heidi Donovan, PhD, RN

Background: Women with ovarian cancer experience a multitude of co-occurring, severe, and distressing symptoms that directly impact their quality of life. Optimal symptom management depends on patients’ ability to negotiate the health care system, communicate with health care providers, and manage complex medical and self-care strategies. The concept of self-advocacy is frequently promoted within cancer survivorship research and policy as a key factor ensuring patient participation and engagement in their care. However, a well-defined understanding of this phenomenon has yet to be established. The purpose of this qualitative, descriptive study is to explore the concept of self-advocacy from the lived experience of ovarian cancer survivors.

Methods: Participants were recruited through the WRITE Symptoms Study (GOG-0259) and Pittsburgh’s National Ovarian Cancer Coalition (NOCC). Five focus group sessions comprised of 2 to 4 women with ovarian cancer were conducted (n=14). Verbatim transcriptions were systematically analyzed by 2 trained, independent researchers using a constant comparison method with axial coding. Each focus group was analyzed separately and then successively integrated together in order to uncover pervasive and meaningful themes and sub-themes. All participants were given the opportunity to validate the findings.

Results: Common themes, sub-themes, and exemplar quotations of self-advocacy and symptom management will be presented followed by a complete and succinct description of the phenomenon. Major themes include “learning how to live with my symptoms”, “knowing how to manage my symptoms, and “overcoming obstacles”. Complexities of self-advocating include “fighting against death while maintaining power”, “leveraging personal strengths and working with others”, and “trusting and being frustrated with your healthcare team”. Self-advocacy was defined by these women as a strong will that activates survivors to stand up for themselves and address personally-meaningful obstacles to symptom management.

Conclusions: Exploring and describing the process of self-advocacy among cancer patients can provide needed insight into how patients engage in the symptom management process and participate in their healthcare in personally meaningful ways. A deepened understanding of self-advocacy can lead to improved clinical support for and research approaches to improving patient-centered care in addition to providing potential interventions for women with ovarian cancer.

Sex-steroid hormones and epithelial ovarian cancer: a nested case-control study.

Annekatrin Lukanova 1 , Schock H 1 , Zeleniuch-Jacquotte A 2,3 , Lakso HA 4 , Hallmans G 5 , Pukkala E 6,7 , Lehtinen M 7 , Toniolo P 3,8,9 , Grankvist K 4 , Lundin E 4,5 , Surcel HM 10

1 Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany, 2 Institute of Environmental Medicine, New York University School of Medicine, NY, 3 NYU Cancer Institute, New York, NY, 4 Department of Medical Biosciences, University of Umea, Umea, Sweden, 5 Department of Public Health and Clinical Medicine, Nutritional Research, University of Umea, Umea, Sweden, 6 Finnish Cancer Registry, Institute for Statistical and Epidemiological Cancer Research, Helsinki, Finland, 7 University of Tampere, Tampere, Finland, 8 Department of Obstetrics and Gynecology, New York University School of Medicine, NY, 9 Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland, 10 National Institute for Health and Welfare, Oulu, Finland

Background: The association of most established risk factors for ovarian cancer could be mediated by alterations in sex-steroid hormones concentrations. However, the results from the few epidemiological studies directly relating pre-diagnostic sex-steroids levels to risk of ovarian cancer so far have been unremarkable.

Methods: A case-control study was nested in the Finnish Maternity Cohort, which collects first trimester serum samples from all pregnant women in the country since 1983. For the current analyses, 257 invasive (114 serous, 79 mucinous, 42 endometrioid and clear cell) and 184 borderline (91 serous and 92 mucinous) epithelial ovarian cancers diagnosed by the end of 2007 among cohort members after recruitment were selected. Eligible samples were those from the last pregnancy preceding cancer diagnosis that led to the delivery of a singleton offspring. Three controls individually matching each case for age, parity and date at sample donation were selected. Progesterone, 17 hydroxyprogesterone (17-OHP), androgens (testosterone and androstenedione) and estradiol were measured by high-performance liquid chromatography tandem mass spectrometry. Odds ratios (OR) and 95% confidence intervals for doubling of hormone concentrations were estimated by conditional logistic regression.

Results: Doubling of androgen concentrations were associated with about 30% greater risk of ovarian cancer [OR 1.34 (1.13–1.58), p&lt;0.0007 for testosterone and 1.36 (1.14–1.63), p=0.0006 for androstenedione], with similar risk estimates in analyses stratified by tumor invasiveness. There was no association of ovarian cancer (overall, invasive or borderline) with circulating concentrations of progesterone, 17-OHP and estradiol. However, these seemingly consistent overall associations differed substantially by tumor histology and invasiveness. For example, the associations of androgens were evident in all subgroups but serous invasive tumors (risk estimates of similar magnitude, but not all statistically significant), progesterone was significantly positively associated with invasive mucinous tumors only [OR 1.88 (1.03–3.44)] and the strongest association of estrogens was observed with endometrioid and clear cell tumors [OR 1.52 (0.91–2.54), p=0.11]. Switching perspective, the most consistent picture was observed for endometrioid and clear cell tumors with an indication for an inverse association with progesterone and 17-OHP and a direct one with androgens and estradiol. However the number of endometrioid and clear cell tumors was small and risk estimates reached borderline significance at most.

Conclusions: The study provides further evidence that sex-steroid hormones are involved in ovarian cancer pathogenesis and that the associations may differ by histological subtype and invasiveness of the tumors. An extension of the study is on-going.

Potential effect of the Risk of Ovarian Cancer Algorithm (ROCA) on the mortality outcome of the Prostate, Lung, Colorectal and Ovarian (PLCO) Trial.

Paul Pinsky 1 , Zhu C 1 , Berg C 1 , Black A 2 , Partridge E 3 , Skates S 4

1 Division of Cancer Prevention, NCI, 2 Division of Cancer Epidemiology and Prevention, 3 Univ of Alabama, Birmingham, 4 Massachusetts General Hospital

Background: Recently, the ovarian component of The Prostate, Lung, Colorectal and Ovarian (PLCO) Trial reported no mortality benefit of annual screening with CA-125 and trans-vaginal ultrasound (TVU) versus usual care. Currently ongoing is the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), a three-armed trial where one arm utilizes the Risk of Ovarian Cancer Algorithm (ROCA). In contrast to PLCO, which defined a positive CA-125 test based on the current CA-125 level only, ROCA considers serial CA-125 levels in assigning ovarian cancer risk probabilities of low, intermediate or high. The unfavorable stage distribution in PLCO of CA-125 detected cancers (85% stage III–IV) gives rise to the speculation that the CA-125 cutoff (35 IU/ml) is too high and catches cancers too late. A serial CA-125 algorithm may be able to catch cancers sooner, but without engendering too high a false positive rate, by considering CA-125 levels over time. A high false positive rate is problematic due to the frequent subsequent use of oophorectomy.

Methods: We investigate whether use of ROCA in PLCO could have potentially favorably affected the trial’s outcome. Specifically, we utilized observed PLCO CA-125 values to calculate a ROCA score at each screen and analyzed how many women would have had their tumor detected earlier (or later) using ROCA than they did under the standard PLCO protocol (CA-125 ≥ 35 U/ml and/or positive TVU). Under a “best-case” scenario, any women dying of ovarian cancer who would have had her cancer detected earlier with ROCA is considered “saved” under ROCA.

Results: Updated PLCO data shows 132 ovarian cancer deaths in the screened versus 119 in the control arm (RR=1.11). Of the 132, 81 were “in play” to be detectable by ROCA, as defined by diagnosis within 3 years of a CA-125 screen. For ROCA cutoffs that classified 14% of all PLCO screens as intermediate and 3% as high risk, 25 of the above 81 women would have had their cancer detected earlier with ROCA screening. This gives, under a best-case” assumption, 107 screened arm ovarian cancer deaths and a RR of 0.90 (95% CI: 0.69–1.17).

Conclusion: Having utilized ROCA in PLCO likely would not have resulted in a significant mortality reduction for the screened arm, although it may have prevented some ovarian cancer deaths in that arm.

Body Mass Index and Risk of Epithelial Ovarian Cancer: The HOPE Study

Francesmary Modugno 1,2,3 , Wei-Hsuan Lo-Ciganic 3 , Clare Bunker 3 , Joseph L. Kelley 2 , Robert B. Ness 4 , Kirsten Moysich 5 , Robert P. Edwards 1,2

1 Womens Cancer Research Program, Magee-Womens Research Institute and University of Pittsburgh Cancer Institute

2 Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine

3 Department of Epidemiology, University of Pittsburgh Graduate School of Public Health

4 University of Texas School of Public Health

5 Roswell Park Cancer Institute

Objective: Studies examining the association between body mass index (BMI) and risk of epithelial ovarian, fallopian tube and peritoneal cancer (EOC) have been inconsistent. Notably, these studies use recent weight and height in examining the BMI-ovarian cancer link. A potential explanation for the lack of association is that BMI in early adult life may play a role. As well, hormonal exposures throughout the life course, such as use of oral contraceptives (OCs), parity, breast feeding and use of hormone replacement therapy (HRT) may influence the BMI-EOC relationship. The purpose of this study is to examine the relationship between BMI and risk of EOC at multiple ages and in light of life course hormonal exposures.

Methods: Self-reported height and weight at ages 18 and at 9 months before interview were used to assess BMI in 902 incident EOC cases and 1802 community controls participating in the Hormones and Ovarian cancer PrEdiction (HOPE) Study, a population-based, case-control study of epithelial ovarian, peritoneal and fallopian tube cancers undertaken in the contiguous regions of western Pennsylvania, eastern Ohio and western New York between 2003 and 2008. Multivariable, unconditional logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) adjusting for age, race, education, oral contraceptive use, parity, breast feeding, tubal ligation, hysterectomy, talc use and family history of breast or ovarian cancer. Cross-product terms were included in the regression models to assess differences in any observed relationships based on ever use of OCs, ever use of HRT, ever breast fed and ever parous (independent analyses).

Results: BMI at age 18 was significantly associated with EOC (adjusted- OR=1.06; 95% CI=1.01–1.12). This relationship remained unchanged when adjusting for current BMI. In contrast, current BMI was not associated with EOC (adjusted-OR=1.00, 95%CI=0.98–1.01). Stratified analyses showed a significant interaction with HRT use. BMI at age 18 was significantly associated with EOC in never (OR=1.08; 95%CI=1.01–1.15) but not ever (OR=0.95; 95%CI=0.83–1.08) users of HRT (p for interaction < 0.008). No other hormonal-BMI interactions were found.

Conclusions: Early adult life BMI is significantly associated with ovarian cancer risk and this association is modified by HRT use. Given the relationship between increasing BMI and increasing levels of circulating endogenous estrogens, which is altered by HRT use, our findings suggest that endogenous estrogen exposure may play a role in EOC risk. Moreover, because early-life BMI is a modifiable factor and in light of the growing epidemic of childhood obesity, these data suggest that interventions to reduce obesity in young girls may prove fruitful in reducing their risk of EOC.

Monoclonal antibody-based immunotherapy of ovarian cancer: targeting of differentiated and cancer initiating cells with the B7-H3-specific mAb 376.96 and sunitinib

Donald Buchsbaum 7 , Fauci JM 1 , Londoño-Joshi A 2 , Sellers J 3 , Zinn KR 4 , Azure T 5 , Straughn Jr JM 6 , Wang X 8 , Yu L 8 , Sabbatino F 9 , Wang YY 8 , Ferrone S 9

1 University of Alabama at Birmingham, Department of Obstetrics and Gynecology, Birmingham, AL, 2 Department of Pathology, 3 Department of Radiation Oncology, 4 Department of Radiology, 5 Department of Radiology, 6 Department of Obstetrics and Gynecology, 7 Department of Radiation Oncology, 8 University of Pittsburgh, Department of Immunology, Pittsburgh, PA, 9 Department of Surgery

Background : The high rate of relapse following surgical debulking and adjuvant chemotherapy in advanced ovarian cancer likely reflects the chemoresistance of cancer initiating cells (CICs) which play a crucial role in disease recurrence. This possibility has prompted us to develop therapy to target not only differentiated ovarian cancer cells, but also CICs. To this end, we combine the monoclonal antibody (mAb) 376.96, which recognizes a B7-H3 epitope with selective expression on malignant cells including ovarian carcinoma cells, with the tyrosine kinase inhibitor sunitinib. We show that this combination targets not only differentiated ovarian cancer cells but also CICs. In addition we show that the mAb 376.96 is amenable to an intraperitoneal delivery method.

Methods : Eight ovarian cancer cell lines including 2 chemoresistant cell lines A2780.cp20 and SKOV3ip2.TR were stained with mAb376.96 and analyzed by flow cytometry to establish expression. In vitro studies to assess the effect of mAb376.96 ± chemotherapy ± Sunitinib were performed on chemosensitive (SKOV3.ip1) and chemoresistant (SKOV3ip2.TR and A2780.cp20) cell lines. The effect of mAb376.96 on CICs was evaluated via analysis of aldehyde dehydrogenase (ALDHbright) activity using an ALDEFLUOR kit. Cells isolated from an ovarian cancer patient were IP injected into immunodeficient e mice and radiolabeled mAb376.96 (99mTc-376.96) was injected IP and localization of antibody was assessed after 24 hours.

Results : The B7-H3 epitope recognized by mAb 376.96 is expressed by both chemosensitive and chemoresistant ovarian cancer cell lines. In vitro treatment of A2780.cp20 and SKOV3ip2.TR cells with single agent mAb376.96 - revealed cell growth inhibition of 30% and 45%, respectively. Combination treatment of SKOV3.ip1 cells with Sunitinib - and mAb376.96 - resulted in 28% cell growth inhibition vs. 10% inhibition with Sunitinib alone. Analysis of CICs revealed that treatment with Sunitinib - and mAb376.96 -reduced the proportion of CICs by one-third as compared with untreated cells. In vivo studies showed that 24 hours following IP dosing, 99mTc-376.96 showed higher uptake in tumors grown in mice (n=3) following IP injection of human ovarian cancer pleural fluid, as compared to 99mTc-labeled isotype control mAb (3 tumors had values of 15.1, 24.1 and 11.8% ID/g, vs. 4.3±1.4% ID/g in isotype control). Additionally, 99mTc-376.96 was retained in the tumors in peritoneal cavity.

Conclusion : In vitro studies using mAb376.96 showed an inhibitory effect against chemosensitive and chemoresistant ovarian cancer cells alone and in combination with Sunitinib. Importantly, the combination treatment inhibits CICs. Intraperitoneal 212Pb-376.96 is a feasible method for locoregional treatment delivery, which is of particular interest in the treatment of ovarian cancer.

Meso-TR3: A Novel TRAIL-Based Targeted Therapeutic in Ovarian Cancer

Gunjal Garg 1 , Hawkins WG 2 , Powell MA 1 , Mutch DG 1 , Gibbs J 3 , Goedegebuure P 3 , Spitzer D 3

1 Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, 2 Department of Surgery, Washington University School of Medicine, St. Louis, MO, 3 Department of Surgery, Washington University School of Medicine, St. Louis, MO

Background : Chemoresistance limits the utility of cytotoxic agents in the treatment of ovarian cancer, as more than half of ovarian tumors eventually acquire inactivating p53 mutations. TNF-related apoptosis- inducing ligand (TRAIL) is a member of the tumor necrosis factor (TNF) superfamily. It kills cancer cells via the extrinsinc death pathway independent of p53, thus offering a complementary approach to conventional cancer therapy. We have developed a novel TRAIL form, designated TR3, which represents a fusion protein of three consecutive TRAIL ectodomains that is generically extensible with stoichiometric control and improved stability. It has been shown that tethering TRAIL (TR3) to the surface of cancer cells enhances cell killing. Ovarian cancer cells have been described to express MUC16, which shows high-affinity interaction with mesothelin protein. Therefore, we reasoned that modifying the TR3 drug platform by attaching the mesothelin protein sequence to the N-terminus of TR3 (generating Meso-TR3) would enhance target cell killing compared to its non-targeted TR3 counterpart.

Methods: Human embryonic kidney cells [HEK293T] were used for the production of soluble mesothelin, Meso-TR3, and TR3. Fluorescence activated cell sorter (FACS) analysis was used to determine the expression of MUC16 on different cancer cell lines (OVCAR-3, Jurkat-H, and HeLa) and the binding of mesothelin to MUC16-positive cells. The killing activity of Meso-TR3 and TR3 was compared using a luminescence-based cell viability assay (Cell-TiterGlo).

Results: While MUC16 was strongly expressed in OVCAR-3 cells (100% positive), it was completely absent in Jurkat cells (our reporter cells to determine antigen-independent killing activities of Meso-TR3 and TR3). Using soluble mesothelin alone, we were able to demonstrate strong binding to MUC16 on OVCAR-3 cells. Furthermore, at concentrations where Meso-TR3 and TR3 killed the same number of MUC16-deficient Jurkat reporter cells, significantly greater killing was seen with Meso-TR3 in the MUC16-positive OVCAR-3 cells. Finally, to obtain evidence for the targeted killing capacity of Meso-TR3, we challenged HeLa cells (a native mix of MUC16+ [80%] and MUC16- cells [20%]), where treatment with Meso-TR3 resulted in a selective reduction of the MUC16+ population to 54% (33% reduction), whereas TR3 alone did not change this ratio.

Conclusions : Meso-TR3, the novel tumor-targeted TRAIL, enhances TRAIL-mediated killing in MUC16 positive ovarian cancer via selective drug delivery to the tumor marker. The ability to target TRAIL to a cell surface protein via a native ligand/receptor interaction presents a unique opportunity to create a cancer selective drug with fewer off-site toxicities and enhanced killing capacities.

Enhancement of the COX2-PGE2 axis by activated lymphocytes promotes the activity of myeloid-derived suppressor cells in ovarian cancer patients

Jeffrey Wong 1 , Obermajer N 1 , Muthuswamy R 1 , Odunsi K 2 , Edwards RP 3–5 , Kalinski P 1,5

1 Department of Surgery, University of Pittsburgh, Pittsburgh, PA, 2 Department of Gynecologic Oncology and Immunology, Roswell Park Cancer Institute, Buffalo, NY, 3 Magee-Womens Research Institute Ovarian Cancer Center of Excellence, Pittsburgh, 4 Peritoneal/Ovarian Cancer Specialty Care Center, Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 5 University of Pittsburgh Cancer Institute, Pittsburgh, PAUniversity of Pittsburgh School of Medicine

Background: Myeloid-derived suppressor cells (MDSCs) are crucial contributors to tumor environment-associated immune suppression, representing a key mechanism for tumor progression and a significant barrier to effective immunotherapy.

Methods: We demonstrate that the development, accumulation, and persistent suppressive functions of CD11b+CD14+CD33+CD34+CXCR4+ MDSCs in the tumor ascites of ovarian cancer patients critically depend on the positive feedback between the tumor-associated inflammatory mediator prostaglandin E2 (PGE2) and cyclooxygenase 2 (COX2), the key regulator of PGE2 synthesis. We further demonstrate that activated T cells and NK cells enhance MDSC activity within the ovarian cancer environment through IFNγ-dependent activation of the COX2-PGE2 axis. Inhibition of the COX2-PGE2 axis was capable of reversing MDSC-associated immune suppression and the hyper-activation of MDSCs by activated T cells and NK cells.

Results: These data reveal the central importance of COX2-PGE2 feedback in supporting MDSCs within the human ovarian cancer environment, and provide strong rationale for the therapeutic targeting of PGE2 signaling in promoting spontaneous and therapy-induced immune responses in ovarian cancer patients.

A genetically engineered mouse model for high grade serous “ovarian” carcinoma arising in the fallopian tube.

Ruth Perets 1 , Katherine W. Muto 2 , Jonathan G. Bijron 3 , Kenneth T. Chin 2 , Barish B. Poole 2 , Christopher P. Crum 3 , Daniela M. Dinulescu 2 *, Ronny Drapkin1, 3 *

1 Department of Medical Oncology, Center for Molecular Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA.USA, 2 Eugene Braunwald Research Center, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA, 3 Department of Pathology, Division of Women’s and Perinatal Pathology, Brigham & Women's Hospital, Boston, USA

* These authors contributed equally to this work

Background : Ovarian cancer is the most lethal gynecologic malignancy because the vast majority of cases are detected in late stage, a finding that has thwarted attempts to understand the pathogenesis and cell-of-origin of this disease. The traditional view of epithelial ovarian pathogenesis asserts that all tumor subtypes share a common origin in the ovarian surface epithelium (OSE). There is robust data to support the OSE as the site of origin for many ovarian tumors, including low-grade carcinomas and borderline tumors.

However, the pathogenesis of high-grade serous ovarian carcinoma, the most common type of ovarian cancer, continued to defy explanation by the OSE model. More recent studies suggested that the fallopian tube epithelium, rather than the OSE, may be the site-of-origin for a majority of pelvic serous carcinomas (PSC, defined as ovarian, peritoneal and tubal high grade serous carcinomas).

Methods: We show here that the fallopian tube epithelium can be site of origin for PSC by genetically engineering a mouse model that specifically targets the fallopian tube secretory cell with defined genetic alterations that are characteristic of human PSC. These mice developed tubal intraepithelial serous carcinomas, a precursor to PSC, that are morphologically and immunophenotypically similar to the lesions described in human patients.

Results: Furthermore, these intraepithelial lesions progress to widespread peritoneal disease that recapitulates the presentation of high-grade PSC in women. The tumors express common serous markers such as P53, PAX8 and WT1. Tumor bearing mice show high levels of the best characterized serum marker of ovarian cancer, CA-125.

Taken together our model is the first genetically engineered mouse model that truly recapitulates human serous carcinoma by means of pathogenesis, clinical characteristics, immunophenotype and serum biomarkers. Our model serves as proof-of-concept that the fallopian tube epithelium can be site-of-origin to PSC.

An optimized primary ovarian cancer xenograft model mimics patient tumor biology and heterogeneity .

Zachary Dobbin 1 , Katre AA 1 , Ziebarth A 1 , Shah MM 1 , Steg AD 1 , Alvarez RD 1 , Conner MG 2 , Landen CN 1

1 Department of Obstetrics and Gynecology, Medical Scientist Training Program, University of Alabama at Birmingham, AL, 2 Department of Pathology, University of Alabama at Birmingham, Birmingham, AL

Background: Current xenograft and transgenic models of ovarian cancer are predominantly homogeneous and inadequately predict response to therapy in clinical trials. Use of patient tumors may represent a better model for tumor biology and offer potential to test personalized medicine approaches, but poor take rates and questions of recapitulation of patient tumors have limited this approach. We have developed a protocol for improved feasibility of such a model and examined its similarity to the patient tumor.

Methods: Under IRB and IACUC approval, 23 metastatic (omental) ovarian cancer samples were collected at the time of tumor reductive surgery. Samples were implanted either subcutaneously (SQ), intraperitoneally (IP), in the mammary fat pad (MFP), or in the subrenal capsule (SRC) and monitored for tumor development. Cohorts from eight xenolines were treated with combined carboplatin and paclitaxel chemotherapy or vehicle, and response to therapy was compared between xenografts and patients. Expression of tumor-initiating cell (TIC) markers ALDH1, CD133, and CD44 was assessed by immunohistochemistry in tumors from patients and treated and untreated xenografts.

Results: At least one of the SQ implanted tumors developed in 91.3% of xenografts, significantly higher than in the MFP (63.6%), IP (23.5%), or SRC (8%). Xenografts were similar in expression of putative TIC’s compared to patient tumors (ALDH: 17% vs 19%, CD44: 2.4% vs 5%, CD133 10% vs 3%, p&gt;0.05). The patients and the xenografts also have similar responses to chemotherapy in that xenografts from patients with a partial response to therapy responded more slowly than those xenografts from patients achieving a complete response (45 vs 21 days, p=.004). Interestingly, xenografts that were treated with chemotherapy were more densely composed of TICs, with ALDH1 increasing to 36.1% from 16.2% (p=0.002) and CD133 increasing to 33.8% from 16.2% (p=0.026).

Conclusions: Xenoline development can be achieved at a high rate when tumors collected from metastatic sites are implanted SQ. These xenografts are similar to patient tumors with regard to chemotherapy response and TIC expression profiles. Our model may represent a more accurate model for in vivo pre-clinical studies as compared to current models. As this xenograft model is developed directly from patient samples and the treated xenografts become enriched in chemoresistant cells, this model represents a novel mechanism to test patient-specific therapies.

Wild type TRP53 (53) promotes ovarian cancer cell survival

Lisa Mullany 1 , Zhilin Liu 1 , Kwong-Kwok Wong 2 , Erin R. King 2 , JoAnne S. Richards 1

1 Department of Molecular and Cellular Biology, Baylor College of Medicine, 2 The University of Texas MD Anderson Cancer Center, Houston, TX2.

Introduction: Ovarian cancers have been divided into two categories – low-grade type I and high-grade type II. One distinguishing and relevant feature of type I and type II ovarian cancer is the expression of the tumor repressor protein 53 (Trp53; or p53): almost all high-grade serous adenocarcinomas (96%) have Trp53 mutations whereas low-grade tumors express elevated levels of wild type Trp53. We have recently shown that Pten/Kras (Ptenfl/fl;KrasG12D fl/fl;Amhr2-Cre) mice exhibit many features similar to human low-grade invasive serous ovarian carcinomas, including elevated levels of wild type Trp53. To investigate the functions of TRP53 in the mutant mouse OSE cells, Trp53 was conditionally deleted in Pten/Kras mice.

Methods: Purified OSE cells were isolated from mutant and control mouse ovaries and RNA was prepared for gene profiling and Q-PCR analyses. Purified mutant and control cells were also grown in culture and matrigel to characterize the transformed phenotype of the TRP53+ (Pten/Kras (Trp53+)) and TRP53- (Pten/Kras(Trp53-)) OSE cells.

Results: In the TRP35 +cells, wild type TRP53 controls or enhances the expression of genes regulating proliferation, DNA repair and mitotic activity and markedly decreases genes with tumor suppressor functions. Rather than activating cell cycle arrest or apoptosis, wild type TRP53 in the Pten/Kras(Trp53+) OSE cells promotes the formation of papillary-like structures, cell migration, adhesion and invasion. By contrast, cells lacking Trp53 exhibit a less aggressive phenotype and gene expression profiles more like control OSE cells. Thus, we have unveiled: a novel role for wild-type TRP53 as a major promoter of ovarian cancer cell survival, differentiation and migration. These results provide a new paradigm: wild type TRP53 at low levels of activity does not preferentially induce apoptotic or senescent related genes in the Pten/Kras(Trp53+) cells. To test this paradigm, purified TPR53+ positive and TRP53- negative OSE cells were exposed to the p53 activator nutlin-3a. In TRP53+ OSE cells, nutlin-3a stimulated TRP53 activity as indicated by the rapid induction of cell cycle arrest and apoptotic genes.

Summary/Conclusion: In the Pten/Kras mutant mouse OSE cells and likely in human low grade ovarian cancer cells,TRP53 controls global, molecular changes that are dependent not only on the level of wild type TRP53 expression but also on its activation status. Low levels of TRP53 activity promote tumor survival and growth, whereas higher TRP53 activity induces cell cycle arrest and apoptosis. Thus, nutlin-3a provides a promising therapeutic for managing type I ovarian cancer and other cancers where wild-type TRP53 is expressed.

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Ovarian cancer articles within British Journal of Cancer

Article 14 December 2023 | Open Access

Discordance between GCIG CA-125 progression and RECIST progression in the CALYPSO trial of patients with platinum-sensitive recurrent ovarian cancer

  • Danka Sinikovic Zebic
  • , Angelina Tjokrowidjaja
  •  &  Chee Khoon Lee

Compartment-specific multiomic profiling identifies SRC and GNAS as candidate drivers of epithelial-to-mesenchymal transition in ovarian carcinosarcoma

  • C. Simon Herrington
  • , Ailsa J. Oswald
  •  &  Robert L. Hollis

Article | 21 October 2023

Menopausal hormone therapy use and risk of ovarian cancer by race: the ovarian cancer in women of African ancestry consortium

  • Jessica L. Petrick
  • , Charlotte E. Joslin
  •  &  Lynn Rosenberg

Article | 11 October 2023

Frequency of peripheral PD-1 + regulatory T cells is associated with treatment responses to PARP inhibitor maintenance in patients with epithelial ovarian cancer

  • Junsik Park
  • , Jung Chul Kim
  •  &  Jung-Yun Lee

Article 18 August 2023 | Open Access

Ascitic autotaxin as a potential prognostic, diagnostic, and therapeutic target for epithelial ovarian cancer

  • Jung-A Choi
  • , Hyosun Kim
  •  &  Jae-Hoon Kim

Article | 03 August 2023

Association of inflammation-related exposures and ovarian cancer survival in a multi-site cohort study of Black women

  • Courtney E. Johnson
  • , Anthony J. Alberg
  •  &  Joellen M. Schildkraut

Article 20 July 2023 | Open Access

Pamiparib in combination with tislelizumab in patients with advanced solid tumours: results from the dose-expansion stage of a multicentre, open-label, phase I trial

  • Michael Friedlander
  • , Linda Mileshkin
  •  &  Alexander Spira

Article 29 April 2023 | Open Access

In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer

  • Marcos Quintela
  • , David W. James
  •  &  Lewis W. Francis

Article | 06 April 2023

Impact of obesity on chemotherapy dosing of carboplatin and survival of women with ovarian cancer

  • Alexandra L. Martin
  • , Christelle M. Colin-Leitzinger
  •  &  Lauren C. Peres

Article | 30 March 2023

APOBEC3B stratifies ovarian clear cell carcinoma with distinct immunophenotype and prognosis

  • Xiaoran Long
  • , Huaiwu Lu
  •  &  Xia Yin

Article 21 February 2023 | Open Access

The DNA damage response in advanced ovarian cancer: functional analysis combined with machine learning identifies signatures that correlate with chemotherapy sensitivity and patient outcome

  • Thomas D. J. Walker
  • , Zahra F. Faraahi
  •  &  Richard J. Edmondson

Article 09 February 2023 | Open Access

INOVATYON/ ENGOT-ov5 study: Randomized phase III international study comparing trabectedin/pegylated liposomal doxorubicin (PLD) followed by platinum at progression vs carboplatin/PLD in patients with recurrent ovarian cancer progressing within 6-12 months after last platinum line

  • , A. Gadducci
  •  &  Alexander Reinthaller

Article | 02 January 2023

Prognostic relevance of HRDness gene expression signature in ovarian high-grade serous carcinoma; JGOG3025-TR2 study

  • Shiro Takamatsu
  • , Kosuke Yoshihara
  •  &  Noriomi Matsumura

Article | 22 December 2022

Risk of ovarian cancer in women who give birth after assisted reproductive technology (ART)—a registry-based Nordic cohort study with follow-up from first pregnancy

  • Marie Søfteland Sandvei
  • , Anja Pinborg
  •  &  Signe Opdahl

Article | 08 December 2022

Efficacy and safety of rucaparib treatment in patients with BRCA-mutated, relapsed ovarian cancer: final results from Study 10

  • Rebecca S. Kristeleit
  • , Yvette Drew
  •  &  Ronnie Shapira-Frommer

Article 07 December 2022 | Open Access

Drug response profiles in patient-derived cancer cells across histological subtypes of ovarian cancer: real-time therapy tailoring for a patient with low-grade serous carcinoma

  • Astrid Murumägi
  • , Daniela Ungureanu
  •  &  Olli Kallioniemi

Article 19 November 2022 | Open Access

Folate receptor alpha in ovarian cancer tissue and patient serum is associated with disease burden and treatment outcomes

  • Heather J. Bax
  • , Jitesh Chauhan
  •  &  Debra H. Josephs

Article 02 November 2022 | Open Access

Increased FOXJ1 protein expression is associated with improved overall survival in high-grade serous ovarian carcinoma: an Ovarian Tumor Tissue Analysis Consortium Study

  • Ashley Weir
  • , Eun-Young Kang
  •  &  Susan J. Ramus

Review Article | 06 September 2022

Dynamic host immunity and PD-L1/PD-1 blockade efficacy: developments after “IFN-γ from lymphocytes induces PD-L1 expression and promotes progression of ovarian cancer”

  • Kaoru Abiko
  • , Junzo Hamanishi
  •  &  Masaki Mandai

Article | 22 July 2022

Clinical characteristics and molecular aspects of low-grade serous ovarian and peritoneal cancer: a multicenter, observational, retrospective analysis of MITO Group (MITO 22)

  • Lucia Musacchio
  • , Daniela Califano
  •  &  Sandro Pignata

Article 01 July 2022 | Open Access

Fibroblast growth factor signalling influences homologous recombination-mediated DNA damage repair to promote drug resistance in ovarian cancer

  • Hugh A. Nicholson
  • , Lynne Sawers
  •  &  Gillian Smith

Article | 27 June 2022

Pre-diagnosis and post-diagnosis dietary patterns and survival in women with ovarian cancer

  • Naoko Sasamoto
  • , Tianyi Wang
  •  &  Holly R. Harris

Article | 24 June 2022

Macrophage-derived CCL23 upregulates expression of T-cell exhaustion markers in ovarian cancer

  • Kalika Kamat
  • , Venkatesh Krishnan
  •  &  Oliver Dorigo

Article 17 June 2022 | Open Access

Ovarian carcinosarcoma is a distinct form of ovarian cancer with poorer survival compared to tubo-ovarian high-grade serous carcinoma

  • Robert L. Hollis
  •  &  C. Simon Herrington

Article | 16 June 2022

Impact of germline mutations in cancer-predisposing genes on long-term survival in patients with epithelial ovarian cancer

  • Joanne Kotsopoulos
  • , Neda Zamani
  •  &  Steven A. Narod

Article 30 April 2022 | Open Access

BRCA mutations lead to XIAP overexpression and sensitise ovarian cancer to inhibitor of apoptosis (IAP) family inhibitors

  • Mattia Cremona
  • , Cassandra J. Vandenberg
  •  &  Bryan T. Hennessy

Review Article | 05 April 2022

The STING pathway: Therapeutic vulnerabilities in ovarian cancer

  • Noor Shakfa
  • , Deyang Li
  •  &  Madhuri Koti

Article 31 March 2022 | Open Access

Low probability of disease cure in advanced ovarian carcinomas before the PARP inhibitor era

  • , Lilian Van Wagensveld
  •  &  Olivier Colomban

Article 21 March 2022 | Open Access

CXCL9 inhibits tumour growth and drives anti-PD-L1 therapy in ovarian cancer

  • Stefanie Seitz
  • , Tobias F. Dreyer
  •  &  Holger Bronger

Article 18 January 2022 | Open Access

Integrative genomic and transcriptomic analysis reveals immune subtypes and prognostic markers in ovarian clear cell carcinoma

  •  &  Huijuan Yang

Article 14 January 2022 | Open Access

Prospective evaluation of 92 serum protein biomarkers for early detection of ovarian cancer

  • Trasias Mukama
  • , Renée Turzanski Fortner
  •  &  Rudolf Kaaks

Article 18 November 2021 | Open Access

Phase 1A/1B dose-escalation and -expansion study to evaluate the safety, pharmacokinetics, food effects and antitumor activity of pamiparib in advanced solid tumours

  • Jason D. Lickliter
  • , Mark Voskoboynik
  •  &  Michael Millward

Article | 03 November 2021

Basal expression of RAD51 foci predicts olaparib response in patient-derived ovarian cancer xenografts

  • F. Guffanti
  • , M F Alvisi
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Research Article

Familial risks of ovarian cancer by age at diagnosis, proband type and histology

Roles Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

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Roles Formal analysis, Methodology, Software, Validation, Writing – review & editing

Roles Validation, Writing – review & editing

Affiliations Cancer Gene Therapy Group, Faculty of Medicine, University of Helsinki, Helsinki, Finland, Department of Obstetrics and Gynecology, Helsinki University Hospital, Helsinki, Finland

Affiliations Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, Center for Primary Health Care Research, Lund University, Malmö, Sweden

Roles Data curation, Funding acquisition, Investigation, Resources, Visualization, Writing – review & editing

Affiliation Center for Primary Health Care Research, Lund University, Malmö, Sweden

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

  • Guoqiao Zheng, 
  • Hongyao Yu, 
  • Anna Kanerva, 
  • Asta Försti, 
  • Kristina Sundquist, 
  • Kari Hemminki

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  • Published: October 3, 2018
  • https://doi.org/10.1371/journal.pone.0205000
  • Reader Comments

26 Oct 2018: Zheng G, Yu H, Kanerva A, Försti A, Sundquist K, et al. (2018) Correction: Familial risks of ovarian cancer by age at diagnosis, proband type and histology. PLOS ONE 13(10): e0206721. https://doi.org/10.1371/journal.pone.0206721 View correction

Fig 1

Ovarian cancer is a heterogeneous disease. Data regarding familial risks for specific proband, age at diagnosis and histology are limited. Such data can assist genetic counseling and help elucidate etiologic differences among various histologic types of ovarian malignancies. By using the Swedish Family-Cancer Database, we calculated relative risks (RRs) for detailed family histories using a two-way comparison, which implied e.g. estimation of RRs for overall ovarian cancer when family history was histology-specific ovarian cancer, and conversely, RRs for histology-specific ovarian cancer when family history was overall ovarian cancer. In families of only mother, only sisters or both mother and sisters diagnosed with ovarian cancer, cancer risks for ovary were 2.40, 2.59 and 10.40, respectively; and were higher for cases diagnosed before the age of 50 years. All histological types showed a familial risk in two-way analyses, except mucinous and sex cord-stromal tumors. RRs for concordant histology were found for serous (2.47), endometrioid (3.59) and mucinous ovarian cancers (6.91). Concordant familial risks were highest for mucinous cancer; for others, some discordant associations, such as endometrioid-undifferentiated (9.27) and serous-undifferentiated (4.80), showed the highest RRs. Familial risks are high for early-onset patients and for those with multiple affected relatives. Sharing of different histological types of ovarian cancer is likely an indication of the complexity of the underlying mechanisms.

Citation: Zheng G, Yu H, Kanerva A, Försti A, Sundquist K, Hemminki K (2018) Familial risks of ovarian cancer by age at diagnosis, proband type and histology. PLoS ONE 13(10): e0205000. https://doi.org/10.1371/journal.pone.0205000

Editor: Mohammad R. Akbari, University of Toronto, CANADA

Received: January 24, 2018; Accepted: September 18, 2018; Published: October 3, 2018

Copyright: © 2018 Zheng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The use of these data is governed by an agreement with the Swedish National Board of Health and Welfare with Jan Sundquist, which does not allow redistribution of original data. Anyone who is interested in the dataset should contact the Swedish National Board of Health and Welfare and apply for the access to the dataset ( https://www.socialstyrelsen.se/statistics ). If anyone gets the approval, they can access to the database in the same manner as the authors.

Funding: This work was supported by the German Cancer Aid; and the Swedish Research Council for Health, Working Life and Welfare (in Swedish: FORTE; Reg. no. 2013-1836), and FORTE (Reg. no. 2014-0804) and the Swedish Research Council (2012-2378 and 2014-10134); and ALF funding from Region Skåne. This research was also supported by the China Scholarship Council (201606100057 to GZ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Ovarian cancer is the seventh most common cancer and the eighth leading cause of cancer-related deaths in women worldwide [ 1 ]. The incidence is highest in Eastern and Northern Europe. In Sweden, the incidence has been declining during the last decade [ 2 ]. Ovarian cancer is a heterogeneous disease; the most common types are epithelial ovarian cancers and they have been divided into two groups (type I and II) based on distinctive clinicopathologic and molecular genetic features [ 3 ]. Type I group includes low-grade serous, low-grade endometrioid, clear cell and mucinous carcinomas, which are indolent and have a good prognosis. While in type II group, tumors are more aggressive and are composed of high-grade serous carcinoma, high- grade endometrioid carcinoma, carcinosarcomas and undifferentiated carcinomas. Non-epithelial ovarian malignancies are far less common and contain sex cord-stromal malignancy; the latter includes thecoma, which is the most prevalent non-epithelial ovarian cancer.

Ovarian cancer risk increases with aging and peaks between the ages of 50 and 80 years [ 4 ]. In the general Swedish population, the lifetime risk of ovarian cancer is 1% [ 2 ]. Reproductive and menstrual factors are strongly influential regarding ovarian cancer. Factors that can decrease the total number of ovulatory cycles such as pregnancy, breastfeeding and use of oral contraceptives reduce the risk for ovarian cancer. Factors that prolong exposure to ovulation, such as low parity, early menarche and late menopause increase the risk for ovarian cancer [ 4 ]. Oral contraceptive use is a confirmed protective factor for ovarian cancer and the widespread use of it during recent decades is considered to be one of the main reasons for the decreasing incidence of ovarian cancer [ 5 ]. However, an increased risk for mucinous ovarian cancer was observed in individuals using oral contraceptives [ 6 – 8 ]. The protective effect of high parity has also been confirmed and it is most strongly associated with endometrioid and clear cell types [ 7 ]. Other non-reproductive factors, which can influence the risk of ovarian cancer, include smoking and body size (height or body mass index) [ 9 , 10 ].

Family history is a strong risk factor for ovarian cancer, and the relative risk is estimated to be 2.0 to 4.0 for those that have a first-degree relative affected by the disease [ 11 – 15 ]. The siblings’ risks by age difference are similar, which suggests that the familial clustering of ovarian cancer is mainly heritable [ 16 ]. Familial risk is associated with mutations in BRCA1 and BRCA2 genes, which conferred respectively 59% and 16.5% risk of developing ovarian cancer by the age of 70 in the Epidemiological Study of BRCA1 and BRCA2 mutation carriers (EMBRACE) in the UK [ 17 ]. Ovarian cancer is also a manifestation in hereditary nonpolyposis colorectal cancer (HNPCC) syndrome which is caused by mutations in mismatch repair (MMR) genes [ 18 ]. The related lifetime risk of developing epithelial ovarian cancer is around 12% [ 19 ]. Mutations in other genes such as BRIP1 , RAD51C and RAD51D also contribute to hereditary ovarian cancer [ 20 ]. Each histological type of ovarian cancer harbors distinct mutations. Germline alterations of BRCA1 and BRCA2 were reported to be associated with high-grade serous histology [ 21 , 22 ], and families with HNPCC syndrome present a tendency towards endometrioid and clear cell types [ 19 , 23 , 24 ].

Based on the above, it can be hypothesized that histology-specific etiology may exist in ovarian cancers. There are limited data regarding familial risk for specific histologic types of ovarian malignancy; such data may help elucidate etiologic differences among the various histologic types of ovarian malignancy and assist clinical genetic counseling. In this study, we use the recent national Swedish Family-Cancer Database, which included 16.1 million individuals, to estimate the familial risks of ovarian cancer by age at diagnosis, proband type (mother or sisters) and histology.

The Swedish Family-Cancer Database (FCD) includes all people born in Sweden since 1932 (offspring generation) together with their biological parents (parental generation) [ 25 ]. The latest version of FCD contains 16.1 million individuals among which nearly 2.0 million were cancer patients recorded to the end of 2015. The 3-digital codes of the 7th revision of the International Classification of Diseases (ICD-7) were used to identify ovarian cancers. Histological types of ovarian cancers were classified according to Systemized Nomenclature of Medicine (SNOMED) codes since 1993. The follow-up for cancer in offspring generation (8.8 million individuals) commenced from the beginning of 1958 (for histological analysis it was started in 1993), the birth year, or the immigration year, whichever came latest. The follow-up was terminated when a person was diagnosed with cancer, emigrated or died, or at the end of 2015, whichever came first.

In this study, all the incident cases of ovarian cancers reported between 1958 and 2015 were analyzed. The world standard population was used to calculate age-standardized incidence [ 26 ]. For familial risk analysis by proband type, first-degree relatives (including parents and/or siblings), who were affected by ovarian cancers, were considered as family history. However, in the present study only mother-sister, sister-sister and mother-two sisters family history were taken into consideration. Familial risk for individuals diagnosed < 50 years and ≥ 50 years old were estimated separately. A two-way comparison was employed to estimate relative risks (RRs) for overall (histology-specific) ovarian cancer when family history was histology-specific ovarian cancer, and conversely, RRs for histology-specific ovarian cancer when family history was overall (histology-specific) ovarian cancer. The reference group was individuals without a family history of ovarian cancer, i.e. unaffected relatives.

Poisson regression model was employed to estimate RRs and corresponding confidence intervals (CIs) for 5%, 1% and 0.1% significance levels. Trend tests were performed by modeling the three proband types (only mother, only sisters and mother and sisters) as a continuous covariate. Potential confounders, including age group, calendar period, residential area and socioeconomic status as well as parity were added to the model as covariates. SAS version 9.4 was used to perform the statistical analysis.

The study was approved by the Ethical Committee of Lund University.

Results and discussion

A total of 46,015 ovarian cancer cases were found in our database and of these 11,301 cases were in the offspring generation diagnosed at age 0–83 years, for which RRs were calculated. The age-standardized incidence per 100 000 person-years ( Fig 1 ) in the six periods were the following: 12.6 (1958–1970), 14.7 (1971–1980), 13.4 (1981–1990), 11.2 (1991–2000), 8.9 (2001–2010) and 7.3 (2011–2015). Since the 1970s, the incidence of invasive ovarian cancer has declined in Sweden for a number of reasons, mainly due to the widespread use of oral contraceptives [ 27 ]. As for histological type, apart from sex cord-stromal ovarian cancer, incidence of all the other types has decreased. In 2011–2015, the highest incidence was noted for serous (4.23) and endometriod (0.65) types. It has been reported that oral contraceptive use may increase the risk of mucinous ovarian cancer, as opposed to other histologies [ 6 ]. Yet the incidence of mucinous ovarian cancer still decreased during 1993–2015, which suggests complex influence of many factors.

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Since the record of SNOMED was started in 1993, the periods for subtypes only included 1993–2000, 2001–2010 and 2011–2015.

https://doi.org/10.1371/journal.pone.0205000.g001

The median ages at diagnosis for all ovarian cancers were 63 for the period 1958–2015 and 65 for the period 1993–2015. Ages at diagnosis and proportion of different histological types are displayed in Table 1 . Sex cord-stromal type had the lowest age at diagnosis and median ages at diagnosis of other types were all over 60. Age-specific incidence data during 1993 to 2015 are shown in S1 Fig . The maximal incidences for overall and the most common histology-specific ovarian cancers were in the group 70–74 years, which is similar to a Danish report of the period 1978–2002 [ 28 ], but relatively younger than the report from the USA in 2011 and older than the report from South Korea during 1999–2012 [ 29 , 30 ].

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https://doi.org/10.1371/journal.pone.0205000.t001

Serous ovarian cancer accounted for the 45.50% of all the ovarian malignancies, followed by endometrioid type (10.82%). The two proportions are slightly different from the results of the ovarian cancer patients diagnosed during 1993–1990, showing 38.4% for serous and 12.4% for endometrioid types [ 31 ]. The changes may be due to the influence of the use of molecular markers for subtypes to regroup some high-grade endometrioid cancer with high-grade serous cancer [ 32 ]. Alteration is also observed for the mucinous type, decreasing from 9.7% to 7.8% [ 31 ], probably resulted from the application of immunohistochemical staining that can distinguish the primary mucinous ovarian cancer from the metastatic gastrointestinal malignancies [ 32 ].

The total number of familial cases was 807 among daughters and mothers; among them a total of 487 were daughters. Thus 4.3% (487/11,301) of invasive ovarian cancer cases were familial in Sweden. The overall familial risk of ovarian cancer was 2.51 (2.29–2.75, P <0.001). Table 2 shows that in families of only mother, only sister and both mother and sisters diagnosed with ovarian cancer, familial risks were 2.40, 2.59 and 10.40, respectively, and all of them were significant at the 0.001 level. Risk was higher with a sororal family history, compared to maternal family history, implying recessive inheritance or shared environmental factors among sisters. When considering the cases diagnosed before the age of 50 years the risks increased up to 2.74, 3.86 and 16.05, respectively ( P <0.001 for all). While for cases over 50 years old, the respective familial risks were lower, 2.22, 2.12 and 8.33, but they were still highly significant ( P <0.001). Notably, the risks for mother and sister history were equal in the older age group, in contrast to the younger counterpart, which indicates that the excess sororal risk only influenced early onset cases with possible interactions with sex-related hormone levels.

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https://doi.org/10.1371/journal.pone.0205000.t002

Familial risk was very high when both the mother and the sister were diagnosed with ovarian cancer, which may be related to high penetrant dominant effects. For the high-risk group of RR 10.40, histology was available only for ten patients; five were serous, two were non-specified adenocarcinomas and the remaining three were diverse histologies (clear cell, endometrioid and undifferentiated). Five of eight specific histologies were serous which may suggest an association with BRCA1/2 [ 21 , 22 ]. In a previous UK study, it was estimated that BRCA1 and BRCA2 mutations account for about 24% of the familial risk of epithelial ovarian cancer among first-degree relatives, and in the remaining cases familial relative risk was estimated at 2.24 [ 15 ]. That study reported a higher familial risk for serous than non-serous cases, most likely associated with BRCA mutations; in the present analyses no marked differences were noted. An unknown factor in population-based studies on ovarian cancer without data on mutation status is the lack of information on ovariectomies. Removal of ovaries in mutation carriers would obviously suppress familial risk for serous cancers.

Familial associations of ovarian cancer with histology- specific ovarian cancers are displayed in Table 3 . Overall ovarian cancer risk was associated with family history of ovarian cancers of undifferentiated (4.79, P <0.001) > endometrioid (3.81, P <0.001) > sex cord-stromal (2.72) > mucinous (2.21, P <0.01) > clear cell (2.16) and > serous (2.15, P <0.001) type. On the other hand, i.e. for specific histological types of ovarian cancer when probands had any ovarian cancer, increased risks were found in all but mucinous and sex cord-stromal types. The order of RRs was undifferentiated (5.45, P <0.001) > serous (2.96, P <0.001) > endometrioid (2.81, P < 0.001) and > clear cell (1.67). In comparison, the international Ovarian Cancer Cohort Consortium found the overall familial risk was 1.48 in the combined cohort covering 5,584 invasive ovarian cancer patients and only serous ovarian cancer was observed to be associated with the family history of 1.61 [ 7 ]. The differences between the present familial risks (2.51, excess familial risk 1.51) and those reported by the Ovarian Cancer Cohort Consortium (1.48, excess risk 0.48) are vast. The authors of the international consortium did not compare their risk estimates to the reported values from the literature and we can only speculate that family history was underreported in that study [ 11 – 15 ]. Self-reported family histories tend to be unreliable and for ovarian cancer the positive predictive value of correct reporting was only 69% in a published pooled analysis [ 33 ]. Among epithelial ovarian cancers, serous, endometrioid and clear cell tumors are proposed to display Muellerian phenotype according to their origins [ 3 ]. Therefore, we did the similar analysis as Table 3 in S1 Table by only including undifferentiated, clear cell, endometrioid and serous types in overall ovarian cancers. However, no interesting results were found.

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https://doi.org/10.1371/journal.pone.0205000.t003

Table 4 shows familial associations among different histological types of ovarian cancers in cases and probands and those associations are summarized in S2 Fig . Risk of undifferentiated ovarian cancer increased when a first-degree relative was diagnosed with clear cell (15.44, P <0.01), serous (6.01, P <0.001) and mucinous (9.23) ovarian cancer. Endometrioid ovarian cancer risk was associated with family history of the concordant histological type of ovarian cancer (3.59); increased risk of this histological type was also observed in family of patients affected by undifferentiated (9.27, P <0.01) and serous (2.26) ovarian cancer. With the exception of clear cell type, serous ovarian cancer risk was associated with family history of all the other histological types. Mucinous ovarian cancer risk was associated with the concordant histological type (6.91, P <0.001) and undifferentiated type (7.08). Risk of sex cord-stromal type increased in families of clear cell ovarian cancer patients (9.70). A striking finding was that concordant familial risks were highest only for mucinous cancer, for all others some discordant associations showed the highest RRs. For example, the endometrioid-undifferentiated RR was 9.27 and the serous-undifferentiated RR was 4.80. This suggests that histology in familial ovarian cancer is not specific, and if genes or polygenes contribute to familial clustering they may not define histology or that they are influenced by hormonal and environmental factors to a variable degree.

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https://doi.org/10.1371/journal.pone.0205000.t004

The present study is by far the largest family study of its kind in the world published and one of the few studies reporting familial risks by specific histology in cases and probands. The main limitation of this study is that the cases with identifiable histology were diagnosed only after 1993 since the application of ICD-O/2 in the cancer registry. This affects familial risk estimates because 22 years of follow-up is short for intergenerational studies considering risks of both the parental and offspring generations. Furthermore, histological classification has not been updated to meet the current guidelines. For example, serous histology is now considered to be either low-grade or high-grade with different prognoses and molecular events/etiologies. Moreover, compared to low-grade serous ovarian cancer, high-grade serous, endometrioid, clear cell, mucinous types are considered to evolve from different pathways and originate outside of ovary: high-grade serous type may evolve from fallopian tube while endometroid and clear cell may originate from endometrial tissue passing through the fallopian tube resulting in endometriosis [ 3 ]. Insufficient clinico-behavioral information, such as smoking, is also a caveat in the analysis since they can be construed as potential confounders. However, as we adjusted the data for socioeconomic factors, this reduces greatly the possible confounding by smoking [ 34 ].

Conclusions

In summary, by using the latest Swedish Family-Cancer Database, we found that each histological type of ovarian cancer was associated with at least two other histological types and was associated with the overall ovarian cancer, suggesting that the causes for familial clustering do not define a specific histology. Sharing of different histological types of ovarian cancer is likely an indication of the complexity of the underlying mechanisms. Our results provide useful information for genetic counseling; familial risks are high, particularly, for early-onset patients and for those with multiple affected relatives.

Supporting information

S1 fig. incidence by age group for overall and for different histological types of ovarian cancer in the period 1993–2015..

https://doi.org/10.1371/journal.pone.0205000.s001

S2 Fig. Familial associations between different histological types of ovarian cancer.

Risk of histology with grey background was significant within concordant histology of ovarian cancer. Risk of the two histologies between full line was significant in the two-way comparison. Risk of the two histologies between imaginary line was significant in one way and the histology the arrow points to is from offspring.

https://doi.org/10.1371/journal.pone.0205000.s002

S1 Table. Familial associations of overall ovarian cancer with histology-specific ovarian cancer by including undifferentiated, clear cell, endometrioid and serous types in overall ovarian cancers.

https://doi.org/10.1371/journal.pone.0205000.s003

Acknowledgments

We thank Patrick Reilly for language editing.

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Materials and methods, authors' disclosures, authors' contributions, acknowledgments, cell state of origin impacts development of distinct endometriosis-related ovarian carcinoma histotypes.

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Current address for H. Fan: Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas.

I. Beddows and H. Fan contributed equally as the co-first authors to this article.

Cancer Res 2024;84:26–38

  • Funder(s):  National Institutes of Health (NIH)
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Ian Beddows , Huihui Fan , Karolin Heinze , Benjamin K. Johnson , Anna Leonova , Janine Senz , Svetlana Djirackor , Kathleen R. Cho , Celeste Leigh Pearce , David G. Huntsman , Michael S. Anglesio , Hui Shen; Cell State of Origin Impacts Development of Distinct Endometriosis-Related Ovarian Carcinoma Histotypes. Cancer Res 1 January 2024; 84 (1): 26–38. https://doi.org/10.1158/0008-5472.CAN-23-1362

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Clear cell ovarian carcinoma (CCOC) and endometrioid ovarian carcinoma (ENOC) are ovarian carcinoma histotypes, which are both thought to arise from ectopic endometrial (or endometrial-like) cells through an endometriosis intermediate. How the same cell type of origin gives rise to two morphologically and biologically different histotypes has been perplexing, particularly given that recurrent genetic mutations are common to both and present in nonmalignant precursors. We used RNA transcription analysis to show that the expression profiles of CCOC and ENOC resemble those of normal endometrium at secretory and proliferative phases of the menstrual cycle, respectively. DNA methylation at the promoter of the estrogen receptor (ER) gene ( ESR1 ) was enriched in CCOC, which could potentially lock the cells in the secretory state. Compared with normal secretory-type endometrium, CCOC was further defined by increased expression of cysteine and glutathione synthesis pathway genes and downregulation of the iron antiporter, suggesting iron addiction and highlighting ferroptosis as a potential therapeutic target. Overall, these findings suggest that while CCOC and ENOC arise from the same cell type, these histotypes likely originate from different cell states. This “cell state of origin” model may help to explain the presence of histologic and molecular cancer subtypes arising in other organs.

Two cancer histotypes diverge from a common cell of origin epigenetically locked in different cell states, highlighting the importance of considering cell state to better understand the cell of origin of cancer.

Cell-of-origin and genetic mutations are often considered the most important determinants in the initiation and shaping of the final molecular and phenotypic landscape of cancer cells ( 1, 2 ). This framework, however, is insufficient to explain the two ovarian carcinoma histotypes, which share the same cell of origin and common genetic mutations, yet present striking differences in cellular phenotype and clinical behavior.

Epithelial ovarian cancer, or ovarian carcinoma, has historically been treated as one disease entity. In recent years, it has become clear that different histotypes of ovarian carcinoma have distinct etiologies as well as genetic and epigenetic underpinnings of the disease ( 3–6 ). High-grade serous ovarian carcinoma (HGSOC) is the most common histotype (∼75%), and one of the first cancers to be comprehensively characterized by The Cancer Genome Atlas (TCGA) Project. Other histotypes are not included in TCGA and remain poorly understood. Clear cell ovarian carcinoma (CCOC) accounts for approximately 5% to 12% of ovarian carcinomas cases. It is generally unresponsive to chemotherapy and has a worse prognosis than HGSOC when discovered at late stages ( 7 ). Notably, CCOC is more common in East Asian women ( 8 ), and accounts for as much as 30% of ovarian carcinoma in the Japanese population. Endometrioid ovarian carcinoma (ENOC) accounts for an additional 5% to 10% of epithelial ovarian carcinomas. Other histotypes, such as mucinous ovarian carcinoma (MOC) and low-grade serous carcinoma (LGSOC) are less common and comprise approximately 3% and 5% to 8% of all ovarian carcinomas, respectively ( 9 ). Despite similarity in nomenclature, LGSOCs exhibit distinct clinical behavior and molecular profiles compared with HGSOC tumors. Primary MOCs, which develop from benign and borderline tumors at the ovary ( 10 ), are often confused with metastases originating from mucus-secreting cells that line the gastrointestinal tract, endocervix, and other organ sites ( 9 ).

The origin of ovarian cancer has been the subject of intense debate for over two decades ( 3 ). It was only recently that most researchers agreed on a unique feature of ovarian carcinoma: most ovarian carcinoma histotypes arise from cells that are not native to the ovary ( 11 ). HGSOC likely originates from the fallopian tube epithelium (FTE; ref. 12 ), whereas ENOC and CCOC are thought to arise from ovarian endometriotic cysts, particularly atypical endometriosis ( 13 ). Endometriosis is a chronic disease that affects approximately 10% of uterus-bearing individuals of reproductive age in the United States ( 14–16 ), characterized by the presence of endometrium-like tissue outside of the uterine cavity, elevated systemic inflammation, and a diversity of clinical symptoms. Endometriotic tissue thickens and bleeds in response to changes in hormone levels during the menstrual cycle, similar to the eutopic endometrium.

The two endometriosis-associated histotypes display distinct cellular phenotypes and clinical behaviors, particularly with CCOC being chemoresistant and ENOC associated with better prognosis ( 7 ). Interestingly, ENOC and CCOC have very similar mutation profiles ( 17, 18 ). In contrast to the observation of near-universal, early-occurring TP53 mutations in HGSOC, these two histotypes instead share frequent somatic mutations affecting PIK3CA and the chromatin regulator ARID1A ( 17, 18 ). Importantly, these mutations are also frequently present in nonmalignant endometriotic lesions ( 19–22 ), suggesting that they may not be directly responsible for malignant transformation, let alone for histotype divergence. Previous studies on epigenomic and transcriptomic profiles of FTE, including ENOC and CCOC, focused primarily on clustering and molecular subtype identification, prognostic markers, or comparison to HGSOC ( 23–25 ). Little work has been done to understand the divergence between ENOC and CCOC, with only a single study suggesting that CCOC arises from a particular cell type located in the endometrium ( 26 ), a theory that has garnered some controversy ( 27 ).

These histotypes show important differences in gene expression, which have provided some insight into their biology. The most well-known difference between histotypes is the universal overexpression of hepatocyte nuclear factor-1β ( HNF1B ) in clear cell tumors ( 28, 29 ). Germline variants of HNF1B are associated with susceptibility to ovarian cancer histotypes ( 5 ), with opposing effects for CCOC and ENOC, the protective allele for ENOC is risk-conferring for CCOC, and vice versa. The “clear cell” phenotype observed in CCOC is associated with the accumulation of intracellular glycogen. HNF1B regulates multiple genes in the glycolytic and glucose metabolism pathways and is linked to increased glucose uptake and lactate secretion ( 30 ). However, there remains a lack of understanding of this apparent miswiring in CCOC.

The poor prognosis and lack of effective treatment options for advanced stage CCOC make it a research priority ( 31, 32 ). With RNA transcription and DNA methylation analysis of CCOC and ENOC tumors, we observe that they transcriptionally resemble two menstrual cycle states of normal endometrium. We propose that while these histotypes originate from the same cell type , they arise from different cell state s (here defined as transitory transcriptional program in the same cell type). Furthermore, the histotypes appear to be epigenetically locked into the different menstrual cycle states through epigenetic control of estrogen signaling. In addition, by comparing different histotypes to their corresponding cell states of origin, we dissected cancer-specific pathways and processes that may offer therapeutic opportunities for these histotypes, particularly CCOC.

Patient samples and preparation

Ovarian carcinoma samples were selected from the UBC/VGH Gynecological Tissue Bank. Patients were recruited with written informed consent for prospective molecular analysis. Representative formalin-fixed paraffin-embedded (FFPE) slides from each case were subjected to a centralized pathology review to confirm the histotype. Frozen tissue specimens were also reviewed to ensure minimal cellularity for analysis. DNA and/or total RNA were extracted from frozen tissue sections (10–40 10 μm sections depending on tissue face size) using Qiagen QIAamp DNA or RNA Blood and Tissue Kits (Qiagen), following the manufacturer's protocols.

For normal-appearing uterine tissue, FFPE biopsies from women between the ages of 21 to 44, which had undergone biopsy for abnormal uterine bleeding were categorized into menstrual cycle phase (proliferative or secretory) by histomorphology ( 33 ). We excluded specimens with any visible pathology, coexisting malignancy, or known somatic alterations ( 33 ).

DNA methylation profiling

Three major histotypes of epithelial ovarian cancer were examined using the Infinium HumanMethylation450 BeadChip (HM450 array), including 60 HGSOC, 48 CCOC, and 19 ENOC samples. Bisulfite conversion was performed on 1 μg of genomic DNA from each sample using the EZ-96 DNA Methylation Kit (Zymo Research) according to the manufacturer's instructions. We assessed the amount of bisulfite-converted DNA and completeness of bisulfite conversion using a panel of MethyLight-based quality control (QC) reactions, as described previously ( 34 ). Bisulfite-converted DNA was whole-genome amplified and enzymatically fragmented prior to hybridization to the arrays. These samples were processed in the same facility using the same protocol as TCGA samples.

Transcriptome profiling

cDNA libraries for 28 CCOC and 29 ENOC were prepared using a strand‐specific RNA‐Seq Sample Preparation Kit (stranded, polyA+) from Illumina. Data were generated from paired-end sequencing at Canada's Michael Smith Genome Sciences Centre on Illumina platforms: HiSeq 2500 using V3 or V4 chemistry and paired‐end 125 base reads targeting 200 million paired-end reads per sample.

DNA methylation data processing and sample quality control

Raw IDAT files were processed using the R package SeSAMe ( 35 ) with background correction, nonlinear dye bias correction, and nondetection masking (any data point not significantly different from the background was replaced with NA). Probes with design issues were masked ( 35 ). DNA methylation β values, ranging from 0 to 1 (with “0” indicating fully unmethylated and “1” fully methylated), were calculated as the quantitative percentage of methylated signals over both methylated and unmethylated signals.

SNP probes (‘rs’ probes) were used to examine potential sample swaps that could occur in genomic studies. No swaps were identified. DNA methylation β values for three MIR141/200C promoter probes (“cg12161331,” “cg18185189,” “cg19794481”) were examined to track mesenchymal content within each sample. MIR141/200C is considered a master regulator of epithelial/mesenchymal phenotype transition, and this process is controlled by its promoter methylation state ( 36 ). Methylation levels at these three probes were highly correlated with the mesenchymal content in the flow sorting results ( 37 ). Tumor samples with a mesenchymal content of >65% were removed, together with one CCOC sample with an ambiguous pathology report. In total, 54 HGSOC, 41 CCOC, and 18 ENOC were included for further methylation analysis.

Transcriptome data processing and sample quality control

In-house generated CCOC and ENOC RNA-seq data were combined with publicly available paired-end RNA-seq data for 84 HGSOC tumor samples, 20 normal endometrium, and 3 normal epithelial brushings of the fallopian tube. Each raw sequencing file (FASTQ format) was aligned to the human reference genome (GRCh37) using STAR version 2.7 ( 38 ) with default settings. Estimation of gene-level abundance was performed using RSEM version 1.3.1 ( 39 ). Raw read count output from RSEM was further batch-corrected using the R package sva (function ComBat_seq ) and normalized using Reads Per Kilobase of transcript per million mapped reads (RPKM) with library size scale factors estimated using R package edgeR. Log2-transformed RPKM values were used to visualize downstream expression. Uniform Manifold Approximation and Projection (UMAP) was performed using the R package uwot. Two CCOC and two ENOC samples were removed because of clear grouping with different histotypes in the UMAP analysis and ambiguous pathology reports. Furthermore, three CCOC samples were excluded because of their high mesenchymal content, as assessed using the HM450 array. In total, 84 HGSOC, 23 CCOC, 27 ENOC, and all normal samples were included in further tumor transcriptomic analysis.

Validation of RNA-seq data for expression of key genes through menstrual cycle

Affymetrix Human Genome U133 Plus 2.0 Array data from GSE4888 ( 40 ) were downloaded in MINiML format. The data represented the output of the Gene Chip Operating System version 1.1 using Affymetrix default analysis settings and global scaling as the normalization method. Array probes corresponding to HNF1B and ESR1 were obtained from the hgu133plus2.db Bioconductor R package, specifically the ProbeAnnDbBimap class object, hgu133plus2ALIAS2PROBE. Patients with no menstrual phase information were excluded ( n = 6). Next, to account for probe intensity differences, z scores were calculated for each probe ( ESR1 , n = 9; HNF1B , n = 2) using the remaining 22 samples. The z -scores were plotted using phase grouping.

DNA methylation analyses

Unsupervised hierarchical clustering was performed on the top variable CpG probes ( N = 10,000, filtered by SDs) across all ovarian tumor samples measured on the HM450 array using the R function hclust . Differentially methylated regions (DMR) were calculated using the R package DMRcate ( 41 ), based on its default FDR cutoff of 0.05 and MIR200c methylation was used as a covariate to adjust for purity. The β cut-off was set at 0.2. The significant DMRs were divided into hypermethylated in CCOC and hypermethylated in ENOC whereafter a locus overlap analysis (LOLA) for enrichment of genomic features (LOLA) was done using the LOLA core database with the “ucsc_features” and “encode_tfbs” collections. The userUniverse parameter was specified as 200 bp, centered on all 450k array probes. ESR1 promoter methylation was visualized using the VisualizeGene function from SeSAMe. Silencing events for MLH1 were called using its corresponding promoter probe cg00893636 at a beta cut-off of 0.1. Probes hypermethylated in CCOC relative to ENOC and overlapping with the ERalpha tfbs dataset were extracted and plotted as a heatmap ordered by average methylation in the normal endometrium.

Transcriptomic analyses

Differential gene expression analysis was performed using the R package edgeR v3.34.1 ( 42 ), with batch-corrected read counts as input. Genes with less than three read counts in more than 80% of samples were filtered out prior to differential analysis to minimize multiple testing on minimally expressed features. Differentially expressed genes were defined on the basis of an FDR cutoff of 0.05, and an absolute fold change greater than 2. The top-ranked differentially expressed genes (DEG) were ordered on the basis of their fold changes after satisfying an FDR significance level of 0.05. Pathway enrichment analysis and visualization were performed using the R package clusterProfiler v4.0.5, with biological process ontology terms or Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets ( 43 ). Upset plots were generated using UpSetR package v1.4.0. Enrichment networks were visualized using the cnetplot function in the R package enrichplot v1.12.3.

DNA methylation datasets for normal endometrium and fallopian tube samples

Additional DNA methylation data for normal endometrial and fallopian tube samples were downloaded from TCGA ( 44 ). Data on additional FTE samples were obtained from GSE65820 ( 45 ), and GSE81224 ( 46 ). Early- and mid-secretory methylation endometrial samples were obtained from the GSE90060 dataset ( 47 ).

IHC for HNF1B was performed on a Leica Bond platform using anti-HNF1B primary antibody (HPA002083 rabbit polyclonal; Millipore Sigma) at 1:200 using ER1 antigen retrieval (Leica) followed by polymer detection ( 29, 48, 49 ). Staining was interpreted using established standards ( 29, 48–50 ), where nuclear staining was visible and scored as negative/absent (complete absence or focal in <50% of epithelial cells) or positive/present (diffuse staining of >50% of epithelial cells). A positive control of ovarian clear cell carcinoma was used in each staining batch ( 29, 50 ).

Data availability

Transcriptome and methylation array data generated in this study were deposited in the NCBI Gene Expression Omnibus Series GSE226872 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE226872 ). All other raw data generated in this study are available upon request from the corresponding author. Publicly available data generated by others were used by the authors. Raw RNA-seq datasets of normal endometrium samples and normal epithelial brushings of the fallopian tube were downloaded from GSE132711 ( 51 ) and GSE114493 ( 52 ), respectively. Sequencing data for primary tumor tissues from HGSOC were downloaded from GSE102073 ( 52 ). Functional gene sets were downloaded from the Molecular Signatures Database (v7.2). Expression data shown in Fig. 3A were from GSE4888. The images presented in Fig. 3D were cropped from these images retrieved from the Human Protein Atlas with the links below, as orthogonal datasets to validate our discovery with two different antibodies: (1) Antibody 1 (CAB068192): 33-year-old female (Patient ID 2941) and (2) Antibody 2 (HPA002083): 27-year-old female (Patient ID 2004; https://www.proteinatlas.org/ENSG00000275410-HNF1B/tissue/endometrium#img ).

Tumor sample quality control, purity, and composition

In bulk tumor-based studies, tumor purity and composition have a substantial impact on molecular readout due to mixed signals from tumor and stromal cells. To exclude any potential impact of tumor purity on our analysis and to capture the tumor microenvironment as an important feature of the tumors, we first used multiple orthogonal deconvolution methods to estimate tumor purity and stromal composition, including canonical marker genes for multiple cell types ( Fig. 1 ). These included two DNA methylation-based methods and one RNA-seq–based method.

Figure 1. Quality control of samples used in this study. A–C, Cellular composition and tumor purity estimation from DNA methylation with three orthogonal methods. A, Estimate of immune cell fraction with leukocyte-specific DNA methylation signature for the OV histotypes (see Materias and Methods). Y-axis indicates the estimated leukocyte proportion of each sample, with samples divided by subtype on the x-axis. Significance level: *, P ≤ 0.05; **, P ≤ 0.01. Horizontal lines denote the first quantile, median, and third quantile. B, Estimate of ovarian stroma-like component fraction (y-axis), with tissue-specific methylation signature (see Materials and Methods). C, Total mesenchymal fraction estimated by DNA methylation β values at the polycistronic MIR141/200C promoter (y-axis) is highly correlated with the sum (x-axis) of leukocyte fraction (A) and stroma fraction (B). D, RNA-seq shows histotype and cellular composition differences by expression of known marker genes for various cell populations. Color represents the Z-score of counts per million for marker genes (rows) across samples (columns). Samples are clustered within each histotype and genes are clustered within each marker category indicated on the left of the heatmap. Canonical histotype markers are plotted as controls (top). E, DNA methylation–based leukocyte estimates from A (y-axis) correlate to PTPRC mRNA expression for samples, with matching RNA-seq and methylation data. F, As in E, but with ovarian stroma estimate from DNA methylation (y-axis) and the RNA-seq stroma markers (x-axis).

Quality control of samples used in this study. A–C, Cellular composition and tumor purity estimation from DNA methylation with three orthogonal methods. A, Estimate of immune cell fraction with leukocyte-specific DNA methylation signature for the OV histotypes (see Materias and Methods). Y -axis indicates the estimated leukocyte proportion of each sample, with samples divided by subtype on the x -axis. Significance level: *, P ≤ 0.05; **, P ≤ 0.01. Horizontal lines denote the first quantile, median, and third quantile. B, Estimate of ovarian stroma-like component fraction ( y -axis), with tissue-specific methylation signature (see Materials and Methods). C, Total mesenchymal fraction estimated by DNA methylation β values at the polycistronic MIR141/200C promoter ( y -axis) is highly correlated with the sum ( x -axis) of leukocyte fraction ( A ) and stroma fraction ( B ). D, RNA-seq shows histotype and cellular composition differences by expression of known marker genes for various cell populations. Color represents the Z-score of counts per million for marker genes (rows) across samples (columns). Samples are clustered within each histotype and genes are clustered within each marker category indicated on the left of the heatmap. Canonical histotype markers are plotted as controls (top). E, DNA methylation–based leukocyte estimates from A ( y -axis) correlate to PTPRC mRNA expression for samples, with matching RNA-seq and methylation data. F, As in E , but with ovarian stroma estimate from DNA methylation ( y -axis) and the RNA-seq stroma markers ( x -axis).

The first method estimated the composition of immune cells and ovarian stroma in tumors with a cell type-specific methylation signature, as described previously ( 53, 54 ). The endometriosis-derived histotypes, CCOC and ENOC, had comparable levels of immune cell infiltration, both significantly lower than that of HGSOC (Wilcox tests, P < 0.05, Fig. 1A ). CCOC was characterized by extensive stromal content, particularly when compared with ENOC (Wilcox test P = 0.007, Fig. 1B ). Interestingly, ARID1A mutants had significantly lower tumor purity in CCOC (Wilcox test, P = 0.029; Supplementary Fig. S1A) but not in ENOC. The overall immune cell fraction did not differ according to ARID1A status (Supplementary Fig. S1B), whereas samples with mutations tended to have lower stroma content in both CCOC and ENOC (Supplementary Fig. S1C).

The second method used the DNA methylation fraction (measured by the beta value) at the polycistronic MIR141/200C promoter region. This promoter region is fully unmethylated in epithelial/carcinoma cells and fully methylated in mesenchymal stromal cells. Thus, the methylation level can be used as a direct surrogate for mesenchymal contamination in tumors of epithelial origin ( 37 ). This fraction should include two major nontumor components from Method 1. Indeed, the sum of leukocyte and stroma contents from Method 1 showed a high correlation with the total mesenchymal content assessed by Method 2 (Spearman ρ = 0.93, P < 2.2e−16, Fig. 1C ), validating each other.

Next, we visualized the mRNA levels of key marker genes in the known cell populations ( Fig. 1D ). Canonical histotype markers were plotted as controls (top). Although these gene products are typically used as protein markers, the mRNA level often correlates with the final protein level and should show cell-type specificity. As expected, the mRNA levels of these marker genes showed clear segregation according to histotype. This validated the pathology-reviewed histotype labels of each sample. Consistent with the DNA methylation-based estimates, CCOC and ENOC were depleted in T cells compared with HGSOC (Supplementary Fig. S1D). There were no observed differences between subtypes for expression of CD4 + , CD8 + or monocyte/macrophase markers (Supplementary Figs. S1E–S1G). In contrast, the stromal content in CCOC observed in DNAm-based estimates appeared to be attributable to endothelial cells. DNA methylation-based estimates again correlated well with the expression levels of marker genes ( Fig. 1E and F ).

On the basis of these analyses, we excluded three samples profiled using DNA methylation arrays with low purity (less than 35% tumor) from subsequent analyses (Supplementary Table S1).

Histotype differences reflect menstrual cycle differences

UMAP showed that tumors largely clustered by annotated histologies with a few exceptions (Supplementary Fig. S2). A sample with both gene expression and DNA methylation data were annotated as ENOC, but consistently showed clustering with CCOC for both data types (Supplementary Fig. S2). Pathology report review revealed documentation ambiguity in this sample and in three others; these samples were excluded from subsequent analyses (Supplementary Table S2).

To provide a reference point for the ovarian cancer datasets, we included transcriptomic data from two normal tissue types: FTE and endometrium (Supplementary Table S2). When the UMAP included these additional normal samples, the FTE samples clustered with HGSOC ( Fig. 2A ); this is expected, as FTE has been suggested as the cell of origin for HGSOC. The normal endometrium, however, unexpectedly split into two clusters, with one group of endometrial samples clustering with CCOC and the other cluster with ENOC ( Fig. 2A ). Examination of all covariates associated with normal endometrium revealed that this split correlated with the menstrual cycle phase of the endometrium: the mid-secretory endometrium (secE) clustered with CCOC, whereas the proliferative endometrium (proE) clustered with ENOC.

Figure 2. CCOC and ENOC resemble different phases of normal cyclic endometrium based on gene expression profile. A, RNA-seq UMAP shows tumors clustering by histotype and with putative corresponding normal cell(s) of origin. Each dot represents a sample, with colors indicating sample type. B, Globally, CCOC–ENOC expression differences for all genes (log2-fold change, x-axis) are positively correlated with fold change between secretory and proliferative endometrium (y-axis). Top 20 DEGs by P value for secE versus proE are labeled. C, Significant DEGs between CCOC and ENOC also separate endometrium of different phases (FDR <0.05 and absolute fold change >2). Each row represents a DEG and each column a sample. Both rows and columns are clustered by Euclidian distance after first grouping by fold change sign (rows) as well as into CCOC, ENOC, and endometrium (columns). Note that secE and proE separate perfectly based on CCOC versus ENOC DEGs. Gene expression (Log2 RPKM) is row normalized into Z-scores, capped at ±2. The 25 most up- and downregulated genes by P value are labeled. D, Gene-concept network plot showing enriched molecular pathways for genes upregulated in both CCOC relative to ENOC and secE relative to proE. Numbered nodes represent pathways, with DEGs in that pathway connected to the corresponding node. The size of each node is scaled on the basis of the number of overlapped DEGs in that pathway. E, As in D, but for pathways enriched in genes overexpressed in ENOC relative to CCOC and proE relative to secE. F, CCOC and ENOC share key metabolic pathways with secE and proE, respectively. Color of the heatmap represents Z-score as in C for genes (rows) from four pathways significantly overrepresented in DEGs between secretory and proliferative endometrium.

CCOC and ENOC resemble different phases of normal cyclic endometrium based on gene expression profile. A, RNA-seq UMAP shows tumors clustering by histotype and with putative corresponding normal cell(s) of origin. Each dot represents a sample, with colors indicating sample type. B, Globally, CCOC–ENOC expression differences for all genes (log 2 -fold change, x -axis) are positively correlated with fold change between secretory and proliferative endometrium ( y -axis). Top 20 DEGs by P value for secE versus proE are labeled. C, Significant DEGs between CCOC and ENOC also separate endometrium of different phases (FDR <0.05 and absolute fold change >2). Each row represents a DEG and each column a sample. Both rows and columns are clustered by Euclidian distance after first grouping by fold change sign (rows) as well as into CCOC, ENOC, and endometrium (columns). Note that secE and proE separate perfectly based on CCOC versus ENOC DEGs. Gene expression (Log 2 RPKM) is row normalized into Z -scores, capped at ±2. The 25 most up- and downregulated genes by P value are labeled. D, Gene-concept network plot showing enriched molecular pathways for genes upregulated in both CCOC relative to ENOC and secE relative to proE. Numbered nodes represent pathways, with DEGs in that pathway connected to the corresponding node. The size of each node is scaled on the basis of the number of overlapped DEGs in that pathway. E, As in D , but for pathways enriched in genes overexpressed in ENOC relative to CCOC and proE relative to secE. F, CCOC and ENOC share key metabolic pathways with secE and proE, respectively. Color of the heatmap represents Z -score as in C for genes (rows) from four pathways significantly overrepresented in DEGs between secretory and proliferative endometrium.

Globally, CCOC–ENOC differences correlated with normal secE-proE differences (Spearman ρ = 0.31, P < 2.2e-16; Fig. 2B ). Next, we identified DEGs between CCOC and ENOC, with absolute fold changes greater than 2 ( N = 2,873; FDR < 0.05; Supplementary Table S3). When clustered on these genes, normal endometrium samples again clustered by menstrual cycle phase, with expression patterns in proE reflecting ENOC and secE reflecting CCOC ( Fig. 2C ). This included differential expression of estrogen receptor 1 ( ESR1 ), progesterone receptor ( PGR ), and HNF1B .

Next, we identified genes upregulated in both CCOC relative to ENOC (Supplementary Table S3) and secE relative to proE (Supplementary Table S4) and tested them for enrichment of biological pathways using the MSigDB C2 collections ( Fig. 2D ). CCOC and secE shared upregulation of several hallmark pathways, such as epithelial to mesenchymal transition ( Fig. 2D ; node #14), hypoxia (node #11), inflammatory response (node #7), and extracellular matrix organization (node #3). ENOC- and proE-shared upregulated genes were involved in early and late estrogen responses ( Fig. 2E ). Expression across key pathways characteristic of CCOC/ENOC differences, such as hypoxia, glucan, phospholipid, and xenobiotic metabolism, also showed similar parallelism between the cancer and normal tissue subgroups ( Fig. 2F ), emphasizing that these known histotype differences can be at least partially explained by cell states corresponding to menstrual cycle phases and are not necessarily cancer-specific.

Validation of HNF1B expression in normal endometrium

Expression of HNF1B has been deemed as a central feature of CCOC cancer cells, but our data suggest that it is not cancer-specific, but rather tied to specific menstrual phases of normal endometrium. To validate this, we used a public microarray-based normal endometrium RNA expression dataset ( 40 ) with annotated menstrual cycle phase information. In this orthogonal external dataset, HNF1B showed menstrual cycle variation, with the highest HNF1B expression level in the mid-secretory phase ( Fig. 3A ). ESR1 is well established to have prominent expression in the glandular epithelium in the proliferative and early secretory phases ( 55, 56 ) and is included as a control. We validated HNF1B protein expression by IHC in the normal human endometrium, independently dated during pathology review ( Fig. 3B ; ref. 33 ). This confirmed strong mid-secretory expression, consistent with the mRNA results. Specifically, HNF1B protein expression was low in the proliferative and early secretory phases but became positive in the mid- and late-secretory phases ( Fig. 3C ; P = 0.002, Fisher exact test between proliferative and mid/late secretory). Finally, images from the human protein atlas of the two available HNF1B antibodies validated HNF1B expression in the normal secretory endometrium (proteinatlas.org; Fig. 3D ; ref. 57 ).

Figure 3. Validation of HNF1B expression in normal mid-secretory endometrium. A, Expression z-scores averaged from all probes for ESR1 and HNF1B on an external microarray-based dataset (GSE4888), validating expression of HNF1B in mid-to-late secretory phase. B, IHC staining of endometrium at various menstrual cycle phases for HNF1B. Control specimens used are tissues from endometrioid endometrial carcinoma (negative) and clear cell carcinoma of the ovary (positive) that have been previously described as being negative or positive for HNF1B (respectively). These were rerun in the same experimental lot as endometrium samples. Note that stromal cells further serve as internal negative control in the CCOC control specimen as staining is restricted to nuclei of tumor epithelium. C, Tabulation of HNF1B IHC results from a panel of 12 normal endometrium samples shows segregation by menstrual cycle phase. D, HNF1B IHC results from Human Protein Atlas, with two different antibodies, for two normal endometrium samples at the secretory phase showing strong staining for HNF1B.

Validation of HNF1B expression in normal mid-secretory endometrium. A, Expression z -scores averaged from all probes for ESR1 and HNF1B on an external microarray-based dataset (GSE4888), validating expression of HNF1B in mid-to-late secretory phase. B, IHC staining of endometrium at various menstrual cycle phases for HNF1B. Control specimens used are tissues from endometrioid endometrial carcinoma (negative) and clear cell carcinoma of the ovary (positive) that have been previously described as being negative or positive for HNF1B (respectively). These were rerun in the same experimental lot as endometrium samples. Note that stromal cells further serve as internal negative control in the CCOC control specimen as staining is restricted to nuclei of tumor epithelium. C, Tabulation of HNF1B IHC results from a panel of 12 normal endometrium samples shows segregation by menstrual cycle phase. D, HNF1B IHC results from Human Protein Atlas, with two different antibodies, for two normal endometrium samples at the secretory phase showing strong staining for HNF1B.

Epigenetic mechanisms lock in cellular states

Transcriptional state per se is not heritable through cell division. Genetic or epigenetic alterations are required to propagate transcriptional states from parent to daughter cells during tumor cell proliferation. Therefore, to understand how initial cell states can be maintained during tumor initiation and progression, we examined DNA methylation in different histotypes. We identified 1,339 DMRs between CCOC and ENOC; 1,018 were hypermethylated in CCOC, and 321 were hypermethylated in ENOC (FDR <0.05; Supplementary Table S5).

With these DMRs, we used LOLA ( 58 ) to test for the enrichment of transcription factor binding sites (TFBS) separately in CCOC hypermethylated DMRs or ENOC hypermethylated DMRs (Supplementary Table S6). Sites of higher methylation in CCOC than in ENOC were enriched for binding sites of estrogen receptor (ER; ref. Fig. 4A ) and other TF related to ER signaling, such as FOXA1/2 and GATA3 ( 59 ).

Figure 4. Epigenetic differences between CCOC and ENOC reveal how cell state differences are propagated through mitosis. A, −Log10 (P value) for TFBS enrichment for probes hypermethylated in ENOC compared with CCOC (y-axis) and CCOC compared with ENOC (x-axis). Each dot represents a TFBS region set. Labels are shown for region sets related to chromatin architecture (enriched for hypermethylation in ENOC) and those related to estrogen signaling (enriched for hypermethylation in CCOC). B, Heatmap of probes (rows) overlapping ERα TFBSs sorted by average methylation in endometrium. Boxplots show the methylation distribution for these probes for samples (columns). CCOC shows gain of methylation at ERα binding sites compared with normal endometrium. C, In addition, the promoter of ERα’s encoding gene, ESR1, gains methylation in CCOC at most probes around the transcription start site; ESR1 transcription start site is unmethylated in normal endometrium. D, A model for ESR1 promoter methylation in normal endometrium and tumors. In normal endometrium, regardless of the phase, the ESR1 promoter is unmethylated, which allows for cyclic modulation by transcription factors through normal monthly cycling. In the cell of origin of CCOC, which is likely secretory endometrium-like, ESR1 is not expressed, and DNA methylation can accumulate stochastically and then become clonally expanded.

Epigenetic differences between CCOC and ENOC reveal how cell state differences are propagated through mitosis. A, −Log 10 ( P value) for TFBS enrichment for probes hypermethylated in ENOC compared with CCOC ( y -axis) and CCOC compared with ENOC ( x -axis). Each dot represents a TFBS region set. Labels are shown for region sets related to chromatin architecture (enriched for hypermethylation in ENOC) and those related to estrogen signaling (enriched for hypermethylation in CCOC). B, Heatmap of probes (rows) overlapping ERα TFBSs sorted by average methylation in endometrium. Boxplots show the methylation distribution for these probes for samples (columns). CCOC shows gain of methylation at ERα binding sites compared with normal endometrium. C, In addition, the promoter of ERα’s encoding gene, ESR1 , gains methylation in CCOC at most probes around the transcription start site; ESR1 transcription start site is unmethylated in normal endometrium. D, A model for ESR1 promoter methylation in normal endometrium and tumors. In normal endometrium, regardless of the phase, the ESR1 promoter is unmethylated, which allows for cyclic modulation by transcription factors through normal monthly cycling. In the cell of origin of CCOC, which is likely secretory endometrium-like, ESR1 is not expressed, and DNA methylation can accumulate stochastically and then become clonally expanded.

Indeed, ENOC had much lower methylation across regions identifiable as ER-binding sites ( Fig. 4B ), consistent with the overactivation of ER signaling in this histotype. In contrast, CCOC contained the majority of methylated ER-binding sites. In addition, the estrogen receptor ( ESR1 ) gene was methylated across the CCOC samples ( Fig. 4C ). This promoter hypermethylation appeared to be cancer-specific, as the region was uniformly unmethylated in the normal endometrium of all phases, despite the modulation in transcription level through the menstrual cycle (some methylation in tumor-adjacent normal is presumably due to tumor contamination or field effects). This lack of methylation in the normal endometrium likely provides a permissive state that allows for maximum flexibility during normal cycling ( Fig. 4D , top). Active expression repels the DNA methyltransferase (DNMT) machinery ( 60 ), whereas periods of low or no expression may allow for aberrant accumulation of methylation at the ESR1 promoter in CCOC or its progenitors. In this model ( Fig. 4D , bottom), DNA methylation at both the ESR1 promoter likely inhibited the response to estrogen signaling in CCOC precursors and restricted cells to a secretory-like state. Extensive DNA methylation at ER binding sites suggests inactive ER-associated regulatory elements in CCOCs.

Transcriptomic comparison with corresponding normal tissue types for cancer-specific changes

To isolate cancer-specific transcriptional changes, we compared each cancer type to the corresponding normal tissue and cell state that it most resembled: CCOC versus secE, and ENOC versus proE (Supplementary Tables S7 and S8). In this analysis, Hepatitis A virus receptor/kidney injury molecule 1 ( HAVCR1 ) was the most overexpressed protein-coding gene in CCOC ( Fig. 5A ). HAVCR1 was not expressed in the normal endometrium in either phase, and its expression was limited or absent in ENOC ( Fig. 5B ). Likewise, the promoter region for HAVCR1 was unmethylated in CCOC compared with that in ENOC and other normal tissue types in the female reproductive tract ( Fig. 5C ), and the expression level was inversely correlated with DNA methylation levels ( Fig. 5D ). Residual methylation in CCOC appears to be attributable to the presence of noncarcinoma cells in the bulk tumor (as measured by MIR200C promoter methylation level in Fig. 5C ) and increased as tumor purity decreased, suggesting consistent clonal loss of methylation of HAVCR1 across CCOC. Other top upregulated transcripts included LINC01671 (AP001626.1), RBBP8 N-terminal like ( RBBP8NL ), and laminin subunit alpha 1 ( LAMA1 ), among many others. FGF receptor 3 ( FGFR3 ) was also consistently upregulated in CCOC compared with secE, consistent with the dense stroma observed in CCOC.

Figure 5. Comparison with matched normal yields cancer-specific alterations. A, Volcano plot showing gene expression alterations between CCOC and secE. X-axis is log2-fold change and y-axis is negative log10-transformed FDR. Top ranked DEGs are labeled in black (upregulated in CCOC relative to secE) and blue (downregulated). B, HAVCR1 is expressed only in CCOC samples; lines indicate first quartile, median, and third quartile. C, HAVCR1 shows demethylation in three probes near the transcription start site in CCOC samples only. D, For samples with matched expression and methylation data, HAVCR1 expression corresponds to methylation at the transcription start site as illustrated by the probe cg07320595. E, As in A, but for ENOC vs. proE. F, Expression by sample group boxplots for PTHLH showing that this gene is downregulated in ENOC relative to proE but upregulated in CCOC relative to secE. These demonstrate that using a matched normal is critical because the merged signals from whole endometrium without regard to phase would obscure the change in ENOC relative to its putative cell state of origin. G, Methylation at the PTHLH gene shows some hypermethylation at the transcription start site in ENOC, but this does not fully explain the expression. H, As in D but for PTHLH. Athough expression is significantly associated with methylation at the transcription start site probe cg08533745, it does not seem to fully explain suppressed expression of PTHLH in ENOC samples.

Comparison with matched normal yields cancer-specific alterations. A, Volcano plot showing gene expression alterations between CCOC and secE. X -axis is log 2 -fold change and y -axis is negative log 10 -transformed FDR. Top ranked DEGs are labeled in black (upregulated in CCOC relative to secE) and blue (downregulated). B, HAVCR1 is expressed only in CCOC samples; lines indicate first quartile, median, and third quartile. C, HAVCR1 shows demethylation in three probes near the transcription start site in CCOC samples only . D, For samples with matched expression and methylation data, HAVCR1 expression corresponds to methylation at the transcription start site as illustrated by the probe cg07320595. E, As in A , but for ENOC vs. proE. F, Expression by sample group boxplots for PTHLH showing that this gene is downregulated in ENOC relative to proE but upregulated in CCOC relative to secE. These demonstrate that using a matched normal is critical because the merged signals from whole endometrium without regard to phase would obscure the change in ENOC relative to its putative cell state of origin. G, Methylation at the PTHLH gene shows some hypermethylation at the transcription start site in ENOC, but this does not fully explain the expression. H, As in D but for PTHLH . Athough expression is significantly associated with methylation at the transcription start site probe cg08533745, it does not seem to fully explain suppressed expression of PTHLH in ENOC samples.

Top downregulated genes in CCOC compared with secE include T Cell Receptor Delta Constant ( TRDC ), a surface marker for γδ T cells; chemokine (C motif) ligand 2 ( CXL2 ), a chemokine expressed in activated cells; granzyme A ( GZMA ) and granzyme B ( GZMB ), characteristic genes of cytotoxic cells. We also examined the expression levels of key marker genes for immune cells and related cell types in CCOC, ENOC, and normal endometrium (Supplementary Fig. S3). The microenvironment of CCOC was similar to that of secE, both featuring an abundance of endothelial cells. However, consistent with the granzyme results, killer cell immunoglobulin-like receptor (KIR) genes ( KIR2DL4 , KIR2DL1 , KIR2DS4 , etc.) were significantly lower in CCOC than in secE (Supplementary Fig. S3). It appears that although CCOC largely resembles secE in terms of cell composition, its microenvironment is characterized by a lack of activated cytotoxic cells.

For the ENOC-proE comparison ( Fig. 5E ), the top up genes were enriched for solute carriers (SLC), including SLC6A14 , which is responsible for non-polar amino acids, and SLC6A20, a proline transporter. Potassium channel genes (KCN) were also visibly enriched for the top expressed genes and resulted in a single biological process term enriched for ENOC-proE DEGs: chronic inflammatory response ( P = 2.7e−5), and nine molecular function terms were enriched (FDR <0.05), including RAGE receptor binding, carboxylic acid transporter activity, Toll-like receptor binding, and long-chain fatty acid binding. The most downregulated gene in ENOC compared with proE was parathyroid hormone-like hormone ( PTHLH ), which encodes a parathyroid hormone-related protein (PTHrP). Interestingly, PTHLH was upregulated in CCOC ( Fig. 5F ). This downregulation of ENOC appeared to be mediated by DNA hypermethylation ( Fig. 5G and H ). Related to this, the receptor of PTHrPs, PTH1R was also 10 times lower in ENOC than in CCOC (Supplementary Table S3).

Notably, only a very small subset of genes was consistently up- or downregulated (39 and 8 genes, respectively) in both histotypes compared with their normal counterparts (Supplementary Fig. S4). Most of these common genes appeared to be associated with cell type differences in the microenvironment (e.g., GZMA ), instead of tumor cell-specific changes. Taken together, the oncogenic pathways are likely divergent in these two histotypes.

Pathway enrichment for CCOC–ENOC contrasted with secE–proE comparisons

We reasoned that dissecting the differences between CCOC and ENOC into those shared with the normal secE–proE difference, and those that were unique to CCOC and ENOC, will help better delineate key molecular drivers of tumorigenesis for both histotypes. Genes upregulated in CCOC relative to ENOC (fold change >2, FDR <0.05) were three times as likely to overlap with those upregulated in secE, compared with those upregulated in proE. Similarly, genes upregulated in ENOC were three times more likely to overlap with those upregulated in proE ( Fig. 6A ).

Figure 6. CCOC–ENOC differences beyond proEM–secEM differences highlight alterations in cysteine/methionine biogenesis and iron metabolism. A, Overlap of genes upregulated in ENOC (relative to CCOC), CCOC (relative to ENOC), proEM (relative to secEM), and secEM (relative to proEM) showing the shared gene sets used for enrichment testing. B, CTH and CBS are two highly differentially expressed genes between ENOC and CCOC, whereas not different between two phases of normal endometrium. C, Distribution of fold changes for genes in the four significantly enriched KEGG pathways upregulated in CCOC. D, Cysteine synthesis pathway with the fold change between CCOC and ENOC indicated for each expressed gene. E, Heatmap of iron and ferroptosis-related genes showing Z-score and absolute log10 (P value) for CCOC vs. ENOC and secEM vs. proEM, with direction indicated as a barplot on the right of the heatmap.

CCOC–ENOC differences beyond proEM–secEM differences highlight alterations in cysteine/methionine biogenesis and iron metabolism. A, Overlap of genes upregulated in ENOC (relative to CCOC), CCOC (relative to ENOC), proEM (relative to secEM), and secEM (relative to proEM) showing the shared gene sets used for enrichment testing. B, CTH and CBS are two highly differentially expressed genes between ENOC and CCOC, whereas not different between two phases of normal endometrium. C, Distribution of fold changes for genes in the four significantly enriched KEGG pathways upregulated in CCOC. D, Cysteine synthesis pathway with the fold change between CCOC and ENOC indicated for each expressed gene. E, Heatmap of iron and ferroptosis-related genes showing Z -score and absolute log 10 ( P value) for CCOC vs. ENOC and secEM vs. proEM, with direction indicated as a barplot on the right of the heatmap.

In addition to those shared with secE, 967 genes were upregulated exclusively in CCOC. These genes were enriched for only two biological process terms: homocysteine metabolism and regulation of anatomic structure. In particular, cystathionine gamma-lyase ( CTH ) was upregulated 3.9 times (FDR = 5.1e−12) and cystathionine β-synthase ( CBS ) was upregulated 2.2 times ( Fig. 6B ; FDR = 0.0045) compared with ENOC (Supplementary Table S3). Both CTH and CBS are key genes involved in cysteine synthesis via homocysteine transulfuration. Interestingly, in the CCOC–ENOC comparison without considering how secE and proE compared, cysteine and methionine metabolism was the top enriched KEGG pathway ( Fig. 6C ; Supplementary Table S9). Given these two results indicating the importance of cysteine synthesis, we explored the expression behavior of all genes involved in cysteine/glutathione metabolism and homeostasis ( Fig. 6D ). Strikingly, 22 of 24 genes in these pathways exhibited a significant difference between CCOC and ENOC (Supplementary Table S10), with many genes mirroring the difference between secE and proE (Supplementary Fig. S5). The cysteine transporter SLC3A1 (rBAT) was also nine times upregulated in CCOC compared with ENOC (FDR = 1.8E−16; Supplementary Fig. S5). The increased cysteine influx and biogenesis of cysteine converged, highlighting the importance of cysteine in CCOC. Interestingly, the other cysteine transporter, SLC7A11 (xCT), was downregulated by over two-fold in CCOC (FC = 0.41, P = 9E−7). This downregulation was significant, considering that SLC7A11 expression was 4-fold higher in secE than in proE cells. Unlike rBAT, xCT takes up cysteine via a 1:1 exchange of glutamate, which might not be favored by cells if glutamate is needed.

In addition, γ-glutamyl transpeptidase (GGT) cleaves extracellular glutathione such that cells have access to more cysteine, and the expression level of GGT is directly related to cisplatin treatment in prostate cancer ( 61 ). Interestingly, GGT1 and GGT2 were upregulated in CCOC and secE, whereas GGT6 was significantly downregulated (Supplementary Table S10). The glycine importer SLC6A17 was also highly upregulated in a few CCOC samples, whereas the glutathione exporter CFTR was downregulated (Supplementary Table S10), likely indicating a dependence on increased intracellular glutathione levels. Indeed, GLRX (Glutaredoxin) was the 11th most differentially expressed gene between CCOC and ENOC (FDR = 1.5E−33; fold change = 5.5; Supplementary Table S3).

Closely related to this observation, CCOC and ENOC also exhibited striking differences in iron storage and transportation ( Fig. 6E ). The iron antiporter ferroportin (SLC40A1) was 5-fold downregulated ( P < 0.0001) in CCOC, despite secE expressing far more SLC40A1 than proE. This apparent “switch” highlights the importance of shutting down iron outflow for CCOC. In addition, lactoferrin ( LTF ) was 14-fold downregulated in CCOC compared with ENOC. Transferrin ( TF ) was 1.8-fold downregulated (unadjusted P = 0.04), and ferritin light chain ( FTL ) was 1.65-fold higher (unadjusted P = 0.003) in CCOC. These suggest iron addiction in CCOC. Human glutathione peroxidase 4 ( GPX4 ), which prevents cells from entering ferroptosis with iron-induced ROS, was highly expressed in CCOC and secE together with the closely related GPX3 , consistent with a high-iron state in CCOC.

The study of cells-of-origin is an important aspect of cancer research ( 2 ). Traditionally, cell type has been the focus of research for the cell-of-origin for a particular cancer type. It is believed that cells-of-origin and genetic mutations jointly shape the characteristics of the cancer cells ( 1 ). However, in the case of ENOC and CCOC, which share the same cell-of-origin and have overlapping mutational profiles, how they yield phenotypically different cancer entities is an intriguing question.

Our results suggest that the same cell type in different cell states—endometrial or endometriotic progenitor/stem cells in proliferative and mid-to-late secretory phases—are likely associated with different transformation paths towards ENOC and CCOC, respectively. This offers a potential explanation for the presence of molecular and/or histologic subtypes of cancers arising in many different organs ( 1 ), in which the progenitor cell may have been “locked” at different cellular states that the particular cell lineage can adopt. These cells share the same functional type, and similar epigenetic profiles, but upon response to external signals—such as hormones—can adopt a different transcriptional state, reversible upon signal withdrawal. Deposition of epigenetic marks, such as DNA methylation, can be influenced by the current transcriptional state. Active transcription repels the DNA methylation machinery, whereas the latter can deposit the methyl mark to promoters of genes that are not expressed ( 60 ), subsequently “locks in” the unexpressed state, providing mitotically heritable variation for selection during clonal evolution. In the case of CCOC, the lack of ESR1 transcription in the secretory state permits stochastic deposition of DNA methylation at this promoter. This DNA methylation gain persists through mitotic division and prevents transcriptional changes in response to estrogen. Frequent clonal loss of the chromatin remodeler ARID1A in CCOC/ENOC may also reflect an oncogenic advantage for the cell-of-origin to not respond to such extrinsic signals and somehow stay “locked in” to existing cell states ( 62 ). We discuss this model in a cancer type that arise from the female reproductive tract, which exhibits exceptional plasticity, with both monthly modulation and remodeling during pregnancy. Nonetheless, cells in other tissues can also adopt various states under normal conditions, and subtypes arising in these other organ sites can be explained by a difference in initial cell state upon transformation, which is subsequently maintained through mitotic divisions by epigenetic mechanisms. For example, breast carcinoma histotypes may arise through a similar mechanism.

The cell state difference associated with the menstrual cycle offers a plausible explanation for the many known differences between CCOC and ENOC. The most well-known characteristics of CCOC that differentiate it from ENOC are (i) hobnail appearance, (ii) glycogen-filled cytoplasm, (iii) HNF1B expression, and (iv) resistance to chemotherapy. Accordingly, (i) the hobnail appearance is often seen as part of the Arias-Stella reaction in secretory (and gestational) endometrium ( 63 ). (ii) Intracellular glycogen concentration is also known to be low in proliferative endometrium and increases by over 10-fold by the early secretory phase ( 64 ). (iii) Our study validated the CCOC diagnostic biomarker HNF1B expression, both RNA and protein, in the mid-secretory endometrium and showed it to be absent in the proliferative endometrium. (iv) Resistance to chemotherapy may be explained, in part, by upregulated xenobiotic metabolism in the CCOC and secretory endometrium. On the other hand, the similarity between ENOC and proliferative endometrium also makes immediate sense—“endometrioid” literally means endometrium-like, and cancer represents a heightened proliferative state. Consistent with the biological similarities between ENOC and proE, progesterone treatment, which induces exit from the proliferation phase, can reduce the survival of primary cultures of endometrioid ovarian cancer ( 65 ). Hormone receptor positivity ( 66 ) and hormone responsiveness are well recognized in ENOC, and targeting is not uncommon. Nonetheless, 20% of ENOC are ER-negative ( 66 ), likely representing a further change from this base state.

The separation of these normal cell features from cancer-specific alterations is helpful to better define how cancer develops and point to true cancer-specific changes. HNF1B has been recognized as the most important CCOC marker, but here we show it is also expressed in the normal secretory endometrium. Instead, our analyses highlighted clonal loss of HAVCR1 promoter DNA methylation as a potential driver. Germline alterations in HAVCR1 are common in early-onset clear cell renal cell carcinoma (ccRCC), and elevated expression promotes angiogenesis via IL6 ( 67, 68 ). The IL6/STAT3/HIF1A axis has been identified as a key pathway in CCOC ( 69 ). In our data, IL6 RNA expression was 14.7 times higher on average in CCOC than in ENOC (Supplementary Table S3). The mechanism of HAVCR1 overexpression in ccRCC has been elusive, with only gene amplification examined and excluded ( 67 ). Our results suggest that DNA demethylation is a potential mechanism for the high expression observed in CCOC, and could also be responsible for HAVCR1 overexpression observed in other clear cell tumors. In addition, PTHLH was the top downregulated gene for ENOC. PTHLH was identified in a study of humoral hypercalcemia of malignancy (HHM), a paraneoplastic syndrome in which elevated levels of PTHrP lead to increased osteoclastic bone resorption and serum calcium levels. High expression of PTHLH in CCOC has been reported previously ( 70 ) and implicated in other cancer types ( 71 ). However, its promoter hypermethylation and associated transcriptional downregulation have not yet been reported, as in the case of ENOC. Parathyroid hormone 1 receptor ( PTH1R ) was also highly downregulated in ENOC. These suggest that HHM may be a CCOC-specific phenomenon, and the contrast between CCOC and ENOC with regard to this pathway warrants further exploration.

Our analysis also highlights a key therapeutic vulnerability of CCOC. On the basis of transcriptional analysis, this histotype demonstrates an apparent dependence on cysteine and iron. Endometriotic cysts contain chocolate-colored fluids from the menstruation-like blood. ENOC and CCOC appear to adopt different approaches to address the abundance of iron in the microenvironment. Although ENOC appears to keep iron out of the cells, likely with E2-driven intracellular iron efflux ( 72 ), we hypothesize that CCOC accumulates iron and likely relies on cysteine to counteract the high intracellular iron, based on gene expression profiles. Consistent with this hypothesis, a recent study showed that a subset of stromal cells with elevated expression of iron export proteins donate iron to the associated CCOCs ( 73 ). Increased iron content in cancer cells is associated with resistance to chemotherapy, a known feature of CCOC, and creates an attractive therapeutic target. A recent study ( 74 ) screened four clear cell ovarian cancer cell lines and showed that cysteine inhibition leads to ferroptosis in these cells. Another recent study ( 75 ) showed that cysteine deprivation resulted in cell death via oxidative stress and iron-sulfur cluster biogenesis deficits. Both these studies were done in cell lines only, but validated the key pathways discovered in our analysis based on primary human tumors. Our analysis provides transcriptomic explanations for these experimental results, and further underscores the importance of cysteine and iron metabolism in targeting CCOC, for which better therapeutic options are sorely needed. This dependency on cysteine and ferroptosis appears to be central to clear cell carcinomas across tissue types ( 76 ).

Finally, CCOC and ENOC both resembled their corresponding normal tissues (secE and proE, respectively) not only in transcriptional states, but also in cellular composition of the microenvironment. For example, we showed CCOC and secE were both rich in endothelial cells, compared with ENOC and proE. CCOC and secE only appeared to differ in terms of cellular composition was the lack of activated cytotoxic cells, such as NK cells (Supplementary Fig. S3). The mechanism for NK inactivation in these tumors may be worth exploring. Other aspects of the tumor microenvironment and how they might affect the fate choice are also interesting questions. In particular, stroma is a potent regulator of the epithelial state, particularly in the female reproductive tract. The type and amount of stroma associated with endometriotic cells in establishing the lesion may affect the fate of epithelial cells. Because ENOC and CCOC display important differences in genes involved in iron homeostasis, the abundance of iron in the lesion may also contribute to fate decisions. The type of endometriosis (e.g., deep infiltrating, endometrioma, superficial; ref. 77 ) might also affect the histotype choice. Although not examined in the current study, these are interesting leads for further studies.

K. Heinze reports grants from Deutsche Forschungsgesellschaft during the conduct of the study. A. Leonova reports personal fees from Aima Laboratories outside the submitted work. C.L. Pearce reports grants from NIH and DoD during the conduct of the study and personal fees from Ovarian Cancer Research Alliance outside the submitted work. M.S. Anglesio reports grants from Michael Smith Health Research BC and NIH during the conduct of the study. H. Shen reports other support from FOXO Biotechnologies and AnchorDx outside the submitted work. No disclosures were reported by the other authors.

I. Beddows: Data curation, formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. H. Fan: Data curation, formal analysis, validation, investigation, visualization, methodology, writing–original draft. K. Heinze: Validation, methodology, writing–original draft, writing–review and editing. B.K. Johnson: Methodology, writing–original draft. A. Leonova: Formal analysis, validation, investigation, writing–original draft. J. Senz: Validation, investigation, methodology, writing–original draft. S. Djirackor: Investigation, writing– draft. K.R. Cho: Investigation, writing–original draft, writing–review and editing. C.L. Pearce: Investigation, writing–original draft, writing–review and editing. D.G. Huntsman: Supervision, investigation, writing–original draft. M.S. Anglesio: Conceptualization, resources, data curation, supervision, funding acquisition, investigation, methodology, writing–original draft, writing–review and editing. H. Shen: Conceptualization, resources, data curation, supervision, funding acquisition, investigation, methodology, writing–original draft, project administration, writing–review and editing.

This research was supported by an NCI grant (R37CA230748) to H. Shen. K. Heinze was funded through a research scholarship by the Deutsche Forschungsgesellschaft (HE 8699/1–1).

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).

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  • Introduction
  • Conclusions
  • Article Information

Information on procedure type was obtained from the Canadian Institute for Health Information Discharge Abstract database for inpatient procedures and the Same-Day Surgery database for outpatient procedures.

Ovarian cancer includes invasive epithelial ovarian, fallopian tube, and primary peritoneal cancer. Log-rank P  = .31.

eMethods. Study Design, Population, and Data; Construction of Cohort; Covariates; Matching; Outcomes; and Statistical Analysis

eTable 1. Data Sources and Detailed Variable Definitions

eTable 2. Inclusions and Exclusions

eTable 3. Descriptive Characteristics of Matched Participants Included in Analysis of Salpingectomy Without Hysterectomy vs No Surgical Procedure

eTable 4. Hazard Ratios and 95% CIs of Ovarian Cancer by Analytic Model

eTable 5. Hazard Ratios and 95% CIs of Ovarian Cancer by Analytic Model, With Additional Censoring

eTable 6. Hazard Ratios and 95% CIs of Ovarian Cancer Among Women With a Bilateral Salpingectomy by Analytic Model

eFigure. Cumulative Incidence of Ovarian Cancer in Patients With Tubal Ligation vs No Surgical Procedure

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Giannakeas V , Murji A , Lipscombe LL , Narod SA , Kotsopoulos J. Salpingectomy and the Risk of Ovarian Cancer in Ontario. JAMA Netw Open. 2023;6(8):e2327198. doi:10.1001/jamanetworkopen.2023.27198

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Salpingectomy and the Risk of Ovarian Cancer in Ontario

  • 1 Women’s College Research Institute, Women’s College Hospital, Toronto, Ontario, Canada
  • 2 Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
  • 3 ICES, Toronto, Ontario, Canada
  • 4 Department of Obstetrics and Gynecology, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
  • 5 Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada

Question   Is salpingectomy associated with a lower risk of developing ovarian cancer?

Findings   In this cohort study of 131 516 women in Ontario, there were 31 incident ovarian cancers among 32 879 women (0.09%) who had a salpingectomy compared with 117 incident ovarian cancers among 98 637 women (0.12%) who did not have a pelvic procedure.

Meaning   These findings suggest no association between salpingectomy and the risk of ovarian cancer among women in the general population; given the rarity of this disease, additional follow-up is needed to reevaluate the potential association in an aging cohort.

Importance   A body of pathological and clinical evidence supports the position that the fallopian tube is the site of origin for a large proportion of high-grade serous ovarian cancers. Consequently, salpingectomy is now considered for permanent contraception (in lieu of tubal ligation) or ovarian cancer prevention (performed opportunistically at the time of surgical procedures for benign gynecologic conditions).

Objective   To evaluate the association between salpingectomy and the risk of invasive epithelial ovarian, fallopian tube, and peritoneal cancer.

Design, Setting, and Participants   This population-based retrospective cohort study included all women aged 18 to 80 years who were eligible for health care services in Ontario, Canada. Participants were identified using administrative health databases from Ontario between January 1, 1992, and December 31, 2019. A total of 131 516 women were included in the primary (matched) analysis. Women were followed up until December 31, 2021.

Exposures   Salpingectomy (with and without hysterectomy) vs no pelvic procedure (control condition) among women in the general population.

Main Outcomes and Measures   Women with a unilateral or bilateral salpingectomy in Ontario between April 1, 1992, and December 31, 2019, were matched 1:3 to women with no pelvic procedure from the general population. Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) and 95% CIs for ovarian, fallopian tube, and peritoneal cancer combined.

Results   Among 131 516 women (mean [SD] age, 42.2 [7.6] years), 32 879 underwent a unilateral or bilateral salpingectomy, and 98 637 did not undergo a pelvic procedure. After a mean (range) follow-up of 7.4 (0-29.2) years in the salpingectomy group and 7.5 (0-29.2) years in the nonsurgical control group, there were 31 incident cancers (0.09%) and 117 incident cancers (0.12%), respectively (HR, 0.82; 95% CI, 0.55-1.21). The HR for cancer incidence was 0.87 (95% CI, 0.53-1.44) when comparing those with salpingectomy vs those with hysterectomy alone.

Conclusions and Relevance   In this cohort study, no association was found between salpingectomy and the risk of ovarian cancer; however, this observation was based on few incident cases and a relatively short follow-up time. Studies with additional years of follow-up are necessary to define the true level of potential risk reduction with salpingectomy, although longer follow-up will also be a challenge unless collaborative efforts that pool data are undertaken.

Epithelial ovarian cancer is the fifth leading cause of cancer death among women in Canada, with a 5-year survival rate of 45%. 1 High-grade serous cancer is the most common subtype, typically presenting at an advanced stage; thus, the case fatality rate is high. 2 There has been little progress in screening for early detection; apart from oral contraceptives, few factors have been reported to reduce risk or increase survival. 3 Primary prevention with surgical procedures is only indicated for women at high risk of developing ovarian cancer. 4 Although this disease is relatively rare, interventions that may lower the risk of developing ovarian cancer are necessary to reduce incidence and death.

Given the compelling molecular and pathological evidence supporting the fallopian tube as the site of origin for high-grade serous cancers, there has been a shift in the gynecologic community to replace tubal ligation with salpingectomy (removal of both fallopian tubes) for permanent contraception. Multiple organizations have recommended opportunistic bilateral salpingectomy at the time of surgical procedures for benign conditions (most commonly hysterectomy) for primary prevention of ovarian cancer. 5 - 9 A few observational studies included in a meta-analysis 10 have reported on the association between salpingectomy and ovarian cancer and have collectively suggested a 49% to 65% reduction in risk. Although limited, previous studies 11 , 12 have reported no association of salpingectomy with ovarian function or morbidity. Findings regarding whether salpingectomy impacts mortality will not be available for several years given that salpingectomy in lieu of tubal ligation (or otherwise) was introduced into clinical practice guidelines around 2015. 13

We conducted a population-based cohort study using health care administrative databases to report patterns of salpingectomy and evaluate the association between salpingectomy and the risk of invasive epithelial ovarian, fallopian tube, and peritoneal cancer (hereinafter, ovarian cancer). We compared cancer incidence among women who underwent salpingectomy (with and without hysterectomy) and women who did not undergo salpingectomy, and we compared the clinical characteristics of the cases diagnosed among women with and without a salpingectomy.

This retrospective population-based matched cohort study used health care administrative databases in Ontario, Canada, which has a population of 14.5 million residents eligible for health care services under the province’s universal single-payer health care coverage. Data sets were linked using distinct encoded identifiers and analyzed at ICES, an independent nonprofit research institute whose legal status under Ontario’s health information privacy law allows it to collect and analyze health care and demographic data, without consent, for health system evaluation and improvement. The data sources and variable codes included in this analysis are shown in eTable 1 in Supplement 1 . This study was approved by the research ethics board of Women’s College Hospital. Given the study’s retrospective nature, the board waived the requirement for informed consent. This study was performed in accordance with the Declaration of Helsinki. 14 The study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) 15 and the Reporting of Studies Conducted Using Observational Routinely Collected Health Data ( RECORD ) 16 reporting guidelines for cohort studies.

The inclusion cohort consisted of all women aged 18 to 80 years who were eligible for health care services in Ontario. Participants were identified using administrative health databases from Ontario between January 1, 1992, and December 31, 2019. Participants were followed up until December 31, 2021. A total of 131 516 women were included in the primary (matched) analysis.

Women were observed from the date of cohort entry to the first salpingectomy and/or hysterectomy. The date of the first operation was considered the reference date. The index date was defined as 180 days after the first operation to account for lead time that may have existed because of the operation and to avoid inclusion of occult cases as events. Participants were assigned to 1 of 3 mutually exclusive groups (salpingectomy only, salpingectomy with hysterectomy, or hysterectomy only) based on surgical procedures that occurred in the 180-day period after the first operation. Participants were excluded if, on the index date, they were ineligible for the Ontario Health Insurance Plan at any point in the previous 2.5 years or had a history of any of the following before the index date: any cancer diagnosis, precancerous ovarian condition, ovarian cysts, radical gynecologic operation, or previous oophorectomy (eTable 2 in Supplement 1 ).

We similarly created a tubal ligation cohort; women in this cohort were observed from the date of cohort entry to the first tubal ligation that occurred within the accrual period. The index date was defined as 180 days after tubal ligation. Women were excluded based on the same criteria listed in the previous paragraph or if they had a history of hysterectomy or salpingectomy.

To identify a cohort of women that could serve as a nonsurgical control group, we randomly assigned index dates to all participants in the inclusion cohort. Index dates were assigned based on the distribution of index dates among all eligible women in the salpingectomy group. Women were excluded if they had a history of salpingectomy or hysterectomy before their index date. Women in the surgical cohorts were eligible to serve in the nonsurgical control cohort if their randomly assigned index date preceded their surgical date.

We collected information on a series of variables that describe demographic information, health services use, reproductive history, comorbidities, and indications for surgery. Demographic variables included neighborhood income quintile, residence location, and years eligible for provincial health coverage. Health services use included history of core primary care visits, specialist visits, inpatient hospitalizations, and emergency department visits. Patient comorbidities were assessed using the ACG System, version 10.0 (The Johns Hopkins University), to capture aggregate diagnosis groups based on health services use in the 2 years before a participant’s reference date. 6

We created 3 models using propensity score methods to compare the various groups. Model 1 compared the salpingectomy (with and without hysterectomy) group with the nonsurgical control cohort. Patients who underwent a unilateral or bilateral salpingectomy in Ontario between April 1, 1992, and December 31, 2019, were matched 1:3 to women who did not undergo a gynecologic procedure. Participants were matched on year of index date, age at index date (plus or minus 2 years), parity, history of tubal ligation, and propensity score.

Model 2 compared the salpingectomy (with and without hysterectomy) group with the hysterectomy only group. This model evaluated the difference in association between salpingectomy and a surgical comparator. All patients who underwent a salpingectomy were matched 1:1 to patients who underwent a hysterectomy alone.

Model 3 compared the tubal ligation group with the nonsurgical control cohort. Participants who underwent tubal ligation were matched 1:3 to women who did not undergo a gynecologic procedure. Participants who underwent tubal ligation were matched using the same methods and variables as those used in model 1; however, participants in the matched nonsurgical control cohort could not have a history of tubal ligation.

The primary outcome was incident invasive ovarian cancer (including epithelial ovarian, fallopian tube, or peritoneal cancer) documented in the Ontario Cancer Registry during the follow-up period (diagnosis and procedure codes are shown in eTable 1 in Supplement 1 ). Matched participants were followed up from their index date to the first of a primary outcome event, death, end of eligibility for the Ontario Health Insurance Plan, oophorectomy, or December 31, 2021.

Tracer events are outcomes expected to have no association with the exposure variable. An association with these outcomes may suggest the presence of residual confounding or bias. We selected 2 cancer-related tracer outcomes, incident breast cancer and incident lung cancer, that we suspected would not be associated with salpingectomy. We used the Ontario Cancer Registry to capture these incident cancers as tracer events. Participants were followed up using the same approach as that used for the primary outcomes.

In a sensitivity analysis, we censored women who underwent a gynecologic procedure of interest in the follow-up period that may have biased the effect estimates. Specifically, no participants in the nonsurgical control cohort and the tubal ligation group were censored if they underwent a salpingectomy or hysterectomy in the follow-up period, while women who had undergone a salpingectomy without a hysterectomy were censored if they had a hysterectomy in the follow-up period. We also limited the cohort to patients who had only bilateral salpingectomy by excluding those with a unilateral salpingectomy and those for whom laterality was unknown.

Baseline descriptive characteristics of the surgical and nonsurgical cohorts were compared using standardized differences. Analysis of variance and Kruskal-Wallis tests were used to compare continuous variables, and the χ 2 test was used to compare categorical variables, across 2 or more groups. A standardized difference of less than 0.10 was used to determine comparability between the groups for each covariate of interest. 17 Kaplan-Meier analysis was used to estimate the cumulative incidence of cancer among matched participants. Crude incident rates of cancer were calculated for each group by dividing the number of outcome events by the total number of person-years in the follow-up period. Cox proportional hazards regression models were used to estimate the adjusted hazard ratios (HRs) and 95% CIs for each exposure group. The threshold for statistical significance was 2-tailed P  = .05. All statistical analyses were performed using SAS software, version 9.4 (SAS Institute Inc). A more detailed description of the methods is available in the eMethods in Supplement 1 . We performed a post hoc sample size calculation to estimate the number of ovarian cancer events needed to detect an HR of 0.80 for our primary analysis at 80% power and α = .05.

Among 131 516 women included in the analyses, the mean (SD) age was 42.2 (7.6) years. Of those, 32 879 received a unilateral or bilateral salpingectomy (with and without hysterectomy), and 98 637 did not receive a surgical procedure.

To evaluate patterns in procedures from 2003 to 2021, we combined all women with a salpingectomy (irrespective of hysterectomy status and location of procedure). We found a decrease in tubal ligation starting in 2004 (from 16 811 procedures in 2004 to 6311 in 2019) and an increase in salpingectomy predominantly after 2010 (from 975 procedures in 2010 to 8060 in 2019) ( Figure 1 ).

A total of 13 451 women in the salpingectomy only group were compared with 20 842 women in the salpingectomy with hysterectomy group. Significant differences in baseline patient characteristics were observed between the 2 exposed groups ( Table 1 ). For example, patients in the salpingectomy only vs salpingectomy with hysterectomy group were younger (mean [SD] age, 38.7 [7.9] years vs 44.2 [6.6] years; P  < .001) and underwent fewer bilateral procedures (8739 [65.0%] vs 17 717 [85.0%]; P  < .001). There were 16 cases (0.12%) of ovarian cancer in the salpingectomy only group vs 15 cases (0.07 %) in the salpingectomy with hysterectomy group ( P  = .16). After matching, participants in the surgical groups were similar to those in the nonsurgical control cohort with respect to demographic characteristics, health services use, and comorbidities (eg, eTable 3 in Supplement 1 ).

The risk of composite ovarian cancer according to analytic model is shown in Table 2 . There were 31 incident cancers (0.09%) in the salpingectomy (with and without hysterectomy) group and 117 (0.12%) in the nonsurgical control cohort. A nonsignificant 18% reduction in risk was observed for the salpingectomy (with and without hysterectomy) group (n = 32 879) compared with the nonsurgical control cohort (n = 98 637; HR, 0.82; 95% CI, 0.55-1.21; P  = .31; mean [range] follow-up, 7.4 [0-29.2] years vs 7.5 [0-29.2] years). A nonsignificant 13% reduction in risk was observed for the salpingectomy (with and without hysterectomy) group (n = 21 724) vs the hysterectomy-only group (n = 21 724; HR, 0.87; 95% CI, 0.53-1.44; P  = .59; mean [range] follow-up, 9.0 [0-29.2] years vs 9.2 [0-29.2] years). There was a 23% significant decrease in the risk of cancer among women who had a tubal ligation (n = 141 698) compared with women who did not have a pelvic procedure (n = 425 094; HR, 0.77; 95% CI, 0.64-0.93; P  = .006; mean [range] follow-up, 12.5 [0-29.2] years vs 12.6 [0-29.3] years). The cumulative incidence of cancer for women in the salpingectomy (with and without hysterectomy) group compared with the nonsurgical control cohort is shown in Figure 2 . The cumulative incidence of cancer for women in the tubal ligation group compared with the nonsurgical control cohort is shown in the eFigure in Supplement 1 .

In the sensitivity analysis limited to invasive epithelial ovarian cancer only (excluding fallopian tube and peritoneal cancer), findings did not change substantially among those in the salpingectomy (with and without hysterectomy) group vs the nonsurgical control cohort (HR, 0.81; 95% CI, 0.54-1.22; P  = .32) (eTable 4 in Supplement 1 ). Results of the sensitivity analysis with additional censoring for a gynecologic procedure in the follow-up period also revealed no association with cancer risk (HR, 0.84; 95% CI, 0.56-1.24; P  = .38) (eTable 5 in Supplement 1 ).

In a post hoc analysis, we excluded matched pairs with a unilateral salpingectomy or unknown laterality. Among women with a bilateral salpingectomy (n = 25 409), there were 11 cases (0.04%) of cancer diagnosed, with a mean follow-up of 5.2 years (range, 0-19.2 years). Among women in the nonsurgical control cohort (n = 76 227), there were 60 cases (0.08%) of cancer diagnosed, with a mean follow-up of 5.2 years (range, 0-20.0 years). The HR for bilateral salpingectomy compared with no surgical procedure was 0.55 (95% CI, 0.29-1.05; P  = .07), representing a 45% decrease in risk; however, this reduction was not statistically significant (eTable 6 in Supplement 1 ).

Among cancer cases in the nonsurgical control cohort (n = 110), 45 (40.91%) were serous and 65 (59.09%) were nonserous or missing compared with 15 (51.72%) serous and 14 (48.28%) nonserous or missing in the salpingectomy (with and without hysterectomy) group (n = 29) (eTable 4 in Supplement 1 ). We could not report on stage of disease among the cases given the high rates of missingness for this variable.

In this large population-based cohort study with 7 years of follow-up, salpingectomy did not confer a statistically significant protective benefit for ovarian cancer compared with no surgical procedure. Our point estimate revealed an 18% nonsignificant reduction in risk in the salpingectomy (with and without hysterectomy) group (HR, 0.82) and a similar level of nonsignificant risk reduction with the inclusion of a hysterectomy comparator group (HR, 0.87). When limited to patients with a documented bilateral salpingectomy, the protective benefit was greater (HR, 0.55) despite a follow-up period of only 5.2 years, although the result was not significant. Tubal ligation, which is a bilateral procedure, was associated with a significant 23% reduction in risk (HR, 0.77); however, the follow-up period among women who received tubal ligation was substantially longer (mean, 12.5 years), and the number of women in the tubal ligation group was considerably larger (141 698 women vs 25 409 in the bilateral salpingectomy group).

Even with inclusion of a large population, the analysis remained underpowered to detect a statistically significant difference given the rarity of our end point of interest and the relatively short follow-up period in the salpingectomy group. In a post hoc sample size calculation, we estimated that 636 events (exposed and unexposed groups) were needed to detect an HR of 0.80 for our primary analysis (ie, model 1) at 80% power and α = .05. The time required for the data to mature to detect a difference is lengthy, and this longer follow-up period will also be a challenge unless collaborative efforts that combine data from large-scale observational studies are made.

The shift to salpingectomy is a recent clinical phenomenon ( Figure 1 ). Clinical practice guidelines are now recommending salpingectomy instead of tubal ligation (or at the time of another surgical procedure) based on the potential to prevent a subset of cancers originating in the fallopian tubes rather than the ovaries. The substantial (albeit nonsignificant) 45% reduction in risk in the analysis restricted to women with a known bilateral procedure is notable. The safety and acceptability of this procedure has been established, 12 , 18 - 21 and, although the implications for ovarian function are less defined, studies 11 , 22 , 23 have found no association with ovarian reserve or indicators of menopausal onset. Furthermore, Naumann et al 24 recently estimated that universal opportunistic salpingectomy may prevent deaths from ovarian cancer and reduce health care costs. Thus, a clear translational aspect of the experimental evidence is to offer bilateral salpingectomy to women at average population risk with the aim of preventing the most aggressive form of this rare but fatal disease. 25 , 26

To our knowledge, only 4 other studies 27 - 30 have also evaluated the association of salpingectomy with ovarian cancer risk. The first was a case-control study of 194 women with serous ovarian or primary peritoneal cancer and 388 women without either diagnosis. 27 Lessard-Anderson et al 27 reported a nonsignificant 64% decrease in risk among women who underwent excisional tubal sterilization compared with women who did not undergo sterilization and women who underwent nonexcisional tubal sterilization (odds ratio [OR], 0.36; 95% CI, 0.13-1.02). There was no association between any tubal sterilization procedure and risk of cancer (OR, 0.59; 95% CI, 0.29-1.17). 27 The researchers included complete salpingectomy, distal fimbriectomy, and partial salpingectomy in the exposed group; we included all procedure codes associated with a potential salpingectomy.

Madsen et al 28 used a Danish nationwide registry to evaluate the association of tubal ligation and salpingectomy with cancer risk (13 241 women in the epithelial ovarian cancer group and 194 689 in the control group). They found a significant 42% decrease in risk with bilateral salpingectomy vs no salpingectomy (OR, 0.58; 95% CI, 0.36-0.95) but no association with unilateral salpingectomy (OR, 0.90; 95% CI, 0.72-1.12) 28 ; however, these estimates were based on 17 women who underwent bilateral salpingectomy and 89 women who underwent unilateral salpingectomy, and the control group included women who underwent hysterectomy and/or tubal ligation.

In a study from Sweden that used an analytic approach similar to ours, Falconer et al 29 reported a significant 65% decrease in the risk of ovarian or fallopian tube cancer with bilateral salpingectomy for benign conditions compared with an unexposed population. There were 7 cases of ovarian cancer among the 3051 women (0.23%) who underwent bilateral salpingectomy vs 30 682 cases among the 5 449 119 women (0.56%) in the unexposed group (HR, 0.35; 95% CI, 0.17-0.73). 29 Unilateral salpingectomy was also associated with a significant (albeit less substantial) reduction in risk (HR, 0.71; 95% CI, 0.56-0.91). 29 Several reasons may explain the differences in association found in the study by Falconer et al 29 vs our study, including (1) longer follow-up period (mean, 18.0 years in the salpingectomy group and 23.1 years in the unexposed group 29 vs 7.4 years and 7.5 years in our study), (2) younger age at cohort entry (eg, salpingectomy group: mean, 35.7 years 29 vs 42.0 years in our study), and (3) higher ovarian cancer incidence rates in both the exposed and unexposed groups (0.23% and 0.56%, respectively, 29 vs 0.09% and 0.12% in our study).

Hanley et al 30 recently reported expected vs observed rates of ovarian cancer among women who underwent opportunistic salpingectomy in British Columbia between 2008 and 2017. The exposed group only included women who had fallopian tubes removed for the purpose of sterilization or at the time of hysterectomy. Using age-adjusted rates of ovarian cancer from the control group, they reported no cases of serous ovarian cancer vs 5.27 expected cases (and ≤5 cases of total epithelial ovarian cancers vs 8.68 expected cases) in the salpingectomy group. 30 This number of cases was substantially lower than expected. Hanley et al 30 concluded that opportunistic salpingectomy was a beneficial primary prevention strategy at the population level; however, future studies with more follow-up time were needed to provide more robust and definitive conclusions.

This study has several strengths. A key strength was the use of validated administrative databases in a large population, reflecting the landscape in a province with universal health care. The ability to link to provincial registries ensured complete data on both exposures and outcomes and avoided the impact of recall bias that can occur with the use of self-reported data. Our strict matching criteria ensured similarities across the comparison groups. Furthermore, our consistent findings in the sensitivity analyses with additional censoring suggests a robust statistical approach. Although we did not directly assess the impact of opportunistic salpingectomy, our exclusion and censoring criteria ensured that women undergoing surgical procedures for potential tumor, precancerous conditions, or ovarian cysts were excluded.

The study also has limitations. We did not have detailed information on various risk factors for ovarian cancer (eg, hormone use, family history of disease, or germline variant); however, there is no reason to expect differences in these potential confounders and type of procedure. Lack of indication for a surgical procedure may have resulted in selection bias, with women who had higher baseline risk more likely to have undergone a salpingectomy; however, this higher likelihood of undergoing salpingectomy would have attenuated any benefits for our outcome of interest. The analyses of cancer risk were not sufficiently powered, particularly to evaluate heterogeneity by site of origin or histological subtype given the rarity of this disease and short follow-up time.

This cohort study found an increase in the rates of salpingectomy over time, with a corresponding decrease in the rates of tubal ligation, among women in Ontario. Although the primary analysis was not sufficiently powered, the level of risk reduction with salpingectomy was similar to that observed with tubal ligation. This finding suggests that if removal of healthy fallopian tubes truly reduces the risk of ovarian cancer, future studies (with additional years of follow-up) should reveal a significant and clinically meaningful decrease in cases. However, the current study found no significant decrease in ovarian cancer rates in Ontario despite the increase in salpingectomy between 2003 and 2021. Given the rarity of this disease, additional follow-up is needed to reevaluate the potential association in an aging cohort. The increasing uptake of salpingectomy may offer an opportunity to prevent a proportion of cancers putatively arising from the fallopian tube and impact the mortality rates associated with a disease with a poor outcome.

Accepted for Publication: June 23, 2023.

Published: August 11, 2023. doi:10.1001/jamanetworkopen.2023.27198

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Giannakeas V et al. JAMA Network Open .

Corresponding Author: Joanne Kotsopoulos, PhD, Women’s College Research Institute, Women’s College Hospital, 76 Grenville St, Sixth Floor, Toronto, ON M5S 1B2, Canada ( [email protected] ).

Author Contributions: Dr Kotsopoulos had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Giannakeas, Murji, Kotsopoulos.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Giannakeas, Murji, Narod, Kotsopoulos.

Critical review of the manuscript for important intellectual content: Murji.

Statistical analysis: Giannakeas.

Obtained funding: Giannakeas, Kotsopoulos.

Administrative, technical, or material support: Giannakeas, Murji.

Supervision: Lipscombe, Narod, Kotsopoulos.

Conflict of Interest Disclosures: Dr Lipscombe reported receiving personal fees from the Novo Nordisk Network for Healthy Populations at the University of Toronto outside the submitted work. Dr Narod reported receiving a Tier I Canada Research Chair outside the submitted work. Dr Kotsopoulos reported receiving a Tier II Canada Research Chair outside the submitted work. No other disclosures were reported.

Funding/Support: This study was supported by grant 165983 from the Canadian Institutes of Health Research and the Peter Gilgan Center for Women’s Cancers at Women’s College Hospital in partnership with the Canadian Cancer Society. This study was also supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health and the Ministry of Long-Term Care. Parts of this material are based on data and information compiled and provided by Cancer Care Ontario and the Canadian Institute for Health Information.

Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The analyses, conclusions, opinions, and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred.

Data Sharing Statement: See Supplement 2 .

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  • Published: 02 January 2024

Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer

  • Zhihao Zhao 1   na1 ,
  • Qilin Wang 1   na1 ,
  • Fang Zhao 3 ,
  • Junnan Ma 1 ,
  • Xue Sui 1 ,
  • Hyok Chol Choe 1 , 4 ,
  • Peng Chen 1 ,
  • Xue Gao 2 &
  • Lin Zhang 1  

BMC Genomics volume  25 , Article number:  1 ( 2024 ) Cite this article

Metrics details

There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC.

Multiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses were conducted to construct a TF-mRNA-miRNA coregulatory network based on identified core targets. Furthermore, immunohistochemistry staining (IHC) and real-time quantitative PCR (RT-qPCR) were employed for the validation of the expression and biological functions of core proteins, including HSPAA1, HSPA8, SOD1, and transcription factors SREBF2 and GTAT2, in ovarian tumors.

The immune cell infiltration analysis based on mRNA expression data for both DM and OC, as well as analysis using single-cell datasets, reveals significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Simultaneously, IHC and RT-qPCR reveal differential expression of core targets in ovarian tumors after the onset of diabetes. RT-qPCR further revealed that SREBF2 and GATA2 may influence the expression of core proteins, including HSP90AA1, HSPA8, and SOD1.

This study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.

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Introduction

OC is as the primary contributor to mortality among malignant tumors affecting the female reproductive system, leading to a global toll of 207,252 deaths [ 1 ]. Conventional therapy, including cytoreductive surgery and chemotherapy, has a 90% effectiveness rate when the cancer is diagnosed at an early stageand confined to one or both ovaries. However, the majority of ovarian cancer cases are diagnosed at stage III or IV, when the cancer has metastasized, and the 5-year survival rate for these patients is 30% [ 2 ]. Recently, the tumor microenvironment and tumor immunology of OC have become research hotspots [ 3 ]. Factors such as tumor-related inflammation [ 4 ], angiogenesis [ 5 ], and immune evasion [ 6 ] play critical roles in the development and progression of OC. Peripheral blood mononuclear cells (PBMCs) have been widely utilized in DM research [ 7 ]. As a population of immune cells in peripheral blood, PBMCs consist of various cell types, including lymphocytes, monocytes, and natural killer cells [ 8 ]. Comprehensive analysis of PBMCs from diabetic patients enables exploration of key features such as immune-metabolic dysregulation, inflammatory responses [ 9 ], and cellular functional changes associated with diabetes [ 10 ]. Given the current fatality rate of OC and the prevalence of diabetes, researchers and clinicians are increasingly interested in investigating whether the presence of DM in OC patients contributes to further disease exacerbation and poorer outcomes for both OC and DM.

Among all cancer patients, 8–18% have diabetes [ 11 ]. Particularly, the risk of ovarian cancer is significantly increased in women with diabetes [ 12 ], and patients with epithelial ovarian cancer and concomitant diabetes exhibit a much lower overall survival rate compared to non-diabetic patients [ 13 ]. Insulin can induce apoptosis in ovarian cancer cells through cell cycle regulation and influence inflammation and immune response [ 14 ]. Moreover, extensive research suggests that anti-diabetic medications like metformin can significantly inhibit the occurrence and development of ovarian cancer [ 15 ]. Simultaneously, potential hyperglycemia has the potency to promote ovarian cancer formation. Elevated glucose levels accelerate the growth of ovarian tumors in a glucose concentration-dependent manner and significantly shorten overall survival [ 16 ]. Ovarian cancer cells also exhibit high glycolytic activity, and normal circulating glucose concentrations may not meet the energy demands of the tumor, potentially acting as a limiting factor in cancer cell metabolism. Elevated blood glucose in diabetic patients may fulfill these energy demands, thus promoting cancer progression. There is evidence to suggest that elevated insulin-like growth factor-1 (IGF-1) levels in diabetic patients lead to an increase in cytokine and estrogen levels, an imbalance in adipokines, and hyperinsulinemia, thereby increasing the risk of ovarian cancer and impacting patients’ survival [ 17 , 18 ]. Furthermore, various proteins involved in glucose metabolism, participating in glucose metabolism in diabetic patients, have been identified as potential therapeutic targets for OC treatment. Chronic hyperglycemia may lead to alterations in the ovarian cancer microenvironment, including increased angiogenesis and tumor immune evasion, promoting the progression of ovarian cancer. Therefore, it is essential to further investigate potential biomarkers associated with OC and reveal the possible mechanisms and common therapeutic targets in monocyte cells between OC and DM.

The advent of single-cell RNA sequencing (scRNA-seq) technology and its associated data analysis methods has presented an unprecedented opportunity to unravel the molecular characteristics of diverse immune cell populations within the tumor microenvironment (TME) [ 19 ]. Previous studies have revealed that exploring gene expression signatures based on molecular characteristics of immune cells derived from scRNA-seq data can be a powerful approach to predicting the prognosis and response to immunotherapy in cancer patients [ 20 , 21 ]. In this study, we analyzed scRNA-seq data (GSE184880 and GSE165816) from OC and DM patients, revealing significant differential expression in monocytes between the two disease models, with a larger proportion of monocytes in PBMCs from diabetic patients. Additionally, we downloaded mRNA data for OC and DM from the GEO database (GSE40595 and GSE29142) and performed immune infiltration analysis, showing shared immune cell infiltrations between OC and diabetic samples, including plasma cells, follicular helper T cells, monocytes, resting mast cells, and neutrophils. Further investigation involved screening 119 common differentially expressed genes related to monocytes from OC and DM scRNA-seq data, visualizing their expression levels in a heatmap. By constructing a PPI network and analyzing the connections between these targets, we identified 10 hub genes based on their topological importance. Subsequently, enrichment analysis using KEGG and GO was conducted to elucidate the biological functions associated with these central genes. After analyzing the 10 core genes, we compared their differential expression in 426 ovarian cancer tissues and 88 normal tissues from the TCGA database. Additionally, survival analysis was performed on 146 ovarian cancer patients from the TCGA database for these 10 core genes. Pearson correlation analysis was conducted on several hub genes from GSE40595 and GSE29142. Finally, gene regulatory network analysis was performed to identify key TFs and miRNAs enriched in the hub genes. Validation of core genes and TFs was conducted through immunohistochemical staining and RT-qPCR. In summary, our study provides new insights that can help understand the potential mechanisms and common therapeutic targets between OC and DM (Fig.  1 ).

figure 1

Flow chart of study design. PPI, protein‒protein interaction; TF, transcription factor; miRNA, microRNA; RT-qPCR, real time quantitative polymerase chains reaction

Materials and methods

Data capture from single-cell data.

Data for OC and DM were obtained from the Gene Expression Omnibus (GEO) database, a repository of scRNA-seq data maintained by the National Center for Biotechnology Information (NCBI) ( https://www.ncbi.nlm.nih.gov/geo/ ). The scRNA-seq dataset GSE184880 [ 22 ], which consisted of 7 untreated ovarian cancer patients with early or advanced tumor stages and 5 age-matched nonmalignant ovarian samples, was selected based on the GPL24676 Illumina NovaSeq 6000 human genome microarray study. For diabetic scRNA-seq, the dataset GSE165816 [ 23 ] was downloaded from the GEO platform. The GPL24676 protocol was employed for single-cell RNA-seq analysis of foot and forearm skin samples, as well as PBMC samples, from a cohort of 10 nondiabetic individuals and 17 individuals with diabetes.

Dimensionality reduction analysis and cell subpopulation identification of single-cell data

To ensure the accuracy of cell subpopulations, we utilized the Seurat R tool for data analysis. Normal and diseased tissues were merged into a single Seurat object. We applied the “LogNormalize” method to normalize the Seurat object, with the scale factor set to 10,000. Single-cell data were filtered based on the following criteria: Cells expressing fewer than 200 genes and fewer than three genes were excluded. Cells expressing 200 to 8000 genes and less than 20% mitochondrial genes were retained. P -values were calculated using the default Wilcoxon Rank Sum test through the FindMarkers function. Differentially expressed genes were determined by Bonferroni and adjusted p-values were computed using the Benjamini-Hochberg (BH) method, with a threshold set at or below 0.05. Subsequently, highly variable genes were identified through data normalization using log normalization and the FindVariableFeatures tool. Principal component analysis (PCA) was applied to reduce dimensionality after scaling the data using the ScaleData tool. The cells were clustered by employing the FindNeighbors and FindClusters functions (with a resolution set to 1.2) to identify cellular subgroups. Subsequently, different cell clusters were determined and annotated based on the composition pattern of marker genes using the single R package. Finally, manual validation and correction were performed using the CellMarker database.

Integration of microarray data

Human mRNA expression data from ovarian cancer (GSE40595) and diabetes (GSE29142) studies were obtained from the GEO database. GSE40595 [ 24 ] utilized the Affymetrix Human Genome U133 Plus 2.0 Array platform to analyze gene expression profiles of 77 samples, including stromal and epithelial components from 63 ovarian cancer patients, as well as gene expression profiles of 14 normal ovarian stromal and epithelial samples. Additionally, GSE29142 [ 25 ] employed the Phalanx Human OneArray platform to examine 19 samples, comprising 10 healthy control samples without diabetes and 9 PBMC samples from individuals with diabetes. All data analyses were conducted in R 4.2.3, and boxplots were generated using ggplot2.

Screening for shared genes in mononuclear cells and immune cell infiltration

Differential gene expression analysis was performed on two datasets by comparing expression values across different groups using the linear modeling module of Bioconductor for microarray data [ 26 ]. Normalization and log 2 transformation were applied to each dataset, and differentially expressed genes (DEGs) were identified. The most significant genes were visualized using heatmaps. CIBERSORT, a deconvolution algorithm [ 18 ], was utilized to describe the cellular composition of tissues based on the gene expression patterns. To determine the abundance of immune cells in PBMCs of OC patients and diabetic patients, the LM22 matrix was used as a reference [ 27 ]. The output of CIBERSORT, which assesses the reliability of results for all samples, was generated using Monte Carlo sampling [ 28 ]to obtain deconvoluted P values. Furthermore, to elucidate the interplay between DM and OC, we screened for common genes in monocytes between the OC-associated targets and DM-associated targets.

Construction of protein‒protein interaction networks and identification of hub genes

PPIs represent a major component of cellular biochemical reaction networks. Evaluating PPI networks and understanding their functions is a critical objective for gaining insights into cellular machinery processes in both cellular and molecular systems biology [ 29 ].To construct the Protein‒Protein interaction network, the STRING database ( https://cn.string-db.org/ ) was utilized. The following parameters were set: in the network display options, disconnected nodes were hidden. To obtain more comprehensive protein interaction information and generate a more complex network, the minimum required interaction score was set to a moderate confidence level of 0.4. The data were imported into Cytoscape 3.8.2 software to generate the gene network diagram. Subsequently, network topological analysis was performed using the Cytoscape plugin “cytoHubba”. The top ten nodes were selected based on their degree ranking for further analysis.

Functional enrichment analysis

GO is a prominent bioinformatics tool used for annotating genes and analyzing the biological processes associated with these genes. GO enrichment analysis of molecular function (MF), cellular components (CC), and biological process (BP) categories reveals overrepresented or underrepresented GO terms within a given set of genes [ 30 ]. KEGG is a major database resource that allows the understanding of high-level functions and biological systems based on large-scale molecular datasets generated from high-throughput experimental techniques [ 31 ]. Therefore, the R programming language packages ‘clusterProfiler’, ‘org.hs.eg.db’, ‘EnrichPlot’, and ‘ggplot2’ were employed to conduct GO and KEGG enrichment analysis on the shared targets of the aforementioned mononuclear cells, facilitating further analysis of the common genes [ 32 , 33 ].

Evaluation of crucial genes correlations and survival analyses

Survival analysis entails the methodological approach of analyzing and inferring the survival time of organisms or individuals based on data acquired from experiments or surveys. It involves studying the relationship between survival time, outcomes, various influencing factors, and their magnitudes [ 34 ]. The GEPIA database is an online platform for interactive analysis of gene expression profiles, which is an RNA sequencing data platform ( http://gepia.cancer-pku.cn/ ). It incorporates data from 9,736 tumor tissues and 8,587 normal tissues from both TCGA and GTEx databases [ 35 ]. By navigating to the “Box Plots” module in the GEPIA database, one can retrieve the differential expression of the top ten core genes in ovarian cancer compared to normal tissues.Utilizing the TCGA database ( https://kmplot.com/analysis/ ), Kaplan-Meier analysis was conducted to evaluate the prognostic value of the top ten core genes between high and low expression groups. Univariate Cox regression analysis was performed to examine the relationship between the expression of the top ten core genes and overall survival (OS), adjusting for age and tumor stage.Subsequently, Pearson correlation coefficient analysis was employed to investigate the interrelations among the core genes in different sample types. The Pearson correlation coefficient (r) values were calculated using the “corrplot” package in the R programming language, and a heatmap was generated for visualization.

Identification of pivotal gene-associated TFs and miRNAs

NetworkAnalyst is an online analysis platform for gene expression profiling and curation analysis that integrates advanced statistical methods and innovative data visualization systems. It enables differential analysis, functional analysis, and network analysis of differential analysis results [ 36 , 37 ]. TFs are proteins that bind to specific DNA sequences and control gene expression, making them crucial for molecular understanding [ 38 ]. We obtained the core gene-TF network topology graph through ENCODE. Additionally, miRNAs negatively affect protein expression by destabilizing the stability and translation efficiency of target mature messenger RNAs. Therefore, we analyzed the core gene-miRNA network topology graph using MiRTarBase [ 39 ]. Ultimately, Cytoscape was utilized to visualize the networks depicting the interactions between TF and genes and miRNA and genes.

Immunohistochemical staining

To validate the protein levels of key genes in ovarian tumor samples, we selected 4 cases of non-malignant ovarian samples and 4 cases of untreated high-grade serous ovarian cancer samples. Additionally, there were 4 cases of non-malignant ovarian samples combined with diabetes and 4 cases of high-grade serous ovarian cancer samples combined with diabetes. The study has been approved by the Ethics Committee of the First Affiliated Hospital of Dalian Medical University. Ovarian tumor samples, after fixation in formalin and paraffin embedding, were sectioned, deparaffinized in xylene, and hydrated in graded ethanol. Subsequently, antigen retrieval was performed in 100 °C citrate buffer, followed by peroxidase blocking of the sections. The sections were then washed with PBS. The slices were incubated with primary antibodies against HSP90AA1 (1:100, rabbit, proteintech, 10654-1-AP) and HAPA8 (1:100, rabbit, proteintech, 13171-1-AP) overnight at 4 °C. After washing with PBS, the sections were incubated with enzyme-labeled goat anti-mouse/rabbit IgG polymer, stained with 3,3’-diaminobenzidine (DAB), and counterstained with hematoxylin. The samples were dehydrated, covered with cover slips, and images were captured using a Leica Microscope Imaging System (Leica, DE).

RNA isolation and reverse transcription-quantitative polymerase chain reaction

Total RNA was isolated from ovarian tumor tissues using RNA extraction reagent (Amoy Diagnostics). cDNA was reverse transcribed using a reverse transcriptase kit (Promega). Subsequently, amplification reactions were performed using a reaction system composed of 10 µl SYBR-Green qPCR Master Mix, 1 µl cDNA template, 7.8 µl DEPC water, and 0.6 µl each of forward and reverse primers. The thermal cycling conditions for the PCR reaction were as follows: an initial denaturation at 95 °C for 2 min, followed by denaturation at 95 °C for 15 s, annealing at 59 °C for 20 s, and extension at 60 °C for 40 s. GAPDH was used as the reference gene, and the relative expression level was calculated using the 2-ΔΔCt method. The primer sequences for the target genes and the reference gene were as follows: HSP90AA1, forward, 5ʹ-TAT AAG GCA GGC GCG GGG GT-3ʹ, reverse, 5ʹ-TGC ACC AGC CTG CAA AGC TTC C-3ʹ; HSPA8, forward, 5ʹ-TTG CTG CTC TTG GAT GTC-3ʹ, reverse, 5ʹ-TGT GTC TGC TTG GTA GGA-3ʹ; SOD1, forward, 5ʹ-GCC GAT GTG TCT ATT GAA G-3ʹ, reverse, 5ʹ-AGC GTT TCC TGT CTT TGT-3ʹ; SREBF2, forward, 5ʹ-GGA GAC CAG GAA GAA GAG A-3ʹ, reverse, 5ʹ-CAC CAC CGA CAG ATG ATG-3ʹ; GATA2, forward, 5ʹ-ACG ACA ACC ACC ACC TTA-3ʹ, reverse, 5ʹ-TTC TTG CTC TTC TTG GAC TT-3ʹ; GAPDH, forward, 5′- TAT GAC AAC AGC CTC AAG AT-3′, reverse, 5′- AGT CCT TCC ACG ATA CCA-3′.

Statistical analysis

The calculations and statistical analyses were performed using R 4.2.2 software and GraphPad Prism 9. The comparison between two groups was conducted using the Wilcoxon rank-sum test, while the Kruskal‒Wallis test was employed for comparisons involving more than two groups. Survival analysis was carried out using the Kaplan‒Meier method with log-rank test. The correlation analysis of key genes was performed using the Pearson method. p  < 0.05 was considered to indicate statistical significance.

Identification of the gene expression profile of monocytes in ovarian cancer

The scRNA-seq data of ovarian cancer used in this study were obtained from 59,324 cells from tumor samples in the GSE184880 dataset. After logarithmic normalization, the single-cell sequencing dataset of OC samples was analyzed, revealing good integration across 12 samples with no apparent batch effects (Fig.  2 A), making it suitable for subsequent analysis. We selected the top 2,000 highly variable genes and annotated the top 10 highly variable genes simultaneously (Fig.  2 B). The “FindNeighbors” and “FindClusters” functions from the “Seurat” package were used to perform unsupervised clustering analysis on the filtered cells, with a resolution ranging from 0.01 to 3. PCA was used for dimensionality reduction, and 16 PCs with a p value < 0.05 were selected for further analysis (Fig.  2 C). Ultimately, we obtained 29 clusters, which were visualized using t-SNE plots (Fig.  2 D). From these 29 clusters, a total of 28,466 differentially expressed marker genes were identified (Table S1 ), and the relative expression levels of these marker genes in each cluster are displayed in a heatmap (Fig.  2 E). Subsequently, with reference to known cell type marker genes in the CellMarker database, we annotated these cell clusters using the Single R algorithm, ultimately identifying eight cell types: NK cells, T cells, epithelial cells, smooth muscle cells, monocytes, B cells, endothelial cells, and tissue stem cells (Fig.  2 F). Clusters 5, 6, 7, 14, and 19 were annotated as monocytes. Among the annotated cell clusters, a total of 16,617 differentially expressed genes were identified (Table S2 ), and the relative gene expression levels in each cell cluster were visualized in a heatmap (Fig.  2 G).

figure 2

Identification of OC Subtypes. (A) t-SNE plot of 12 samples from the GSE184880 dataset after dimensionality reduction and batch correction; (B) top 10 differentially expressed genes in ovarian cancer; (C) Seurat clustering results with resolutions ranging from 0 to 3, where different colors represent different resolutions and larger dots indicate a higher number of cells in the subgroups; (D) t-SNE plot of the 29 cell clusters classified based on scRNA-seq data; (E) heatmap showing the relative expression levels of marker genes in the 29 cell clusters; (F) t-SNE plot indicating the identification of various cell subtypes; (G) heatmap displaying the relative expression levels of marker genes in different cell subtypes

Identification of the monocyte cell population in the gene expression atlas of Diabetes using scRNA-seq

The PBMC scRNA-seq data GSE165816 for diabetes were downloaded from the official GEO website, and the selected samples are shown in Table  1 . The data preprocessing steps: including cell filtering using the “Seurat” package to remove low-quality cells, logarithmic normalization, and selection of the top 2,000 highly variable genes with their top ten labeled as highly variable genes. Unsupervised clustering analysis was performed on the filtered cells, and an appropriate resolution was chosen. PCA was employed for dimensionality reduction, and 14 PCs with a p value < 0.05 were selected for further analysis (Fig.  3 A). Ultimately, 15 clusters were obtained and visualized using t-SNE (Fig.  3 B). From these clusters, a total of 5,875 differentially expressed marker genes were identified (Table S3 ). Subsequently, using reference marker genes available in the CellMarker database, the cell clusters were annotated using the Single R algorithm, resulting in the identification of three cell types: NK cells, monocytes, and T cells (Fig.  3 C). Among these, Clusters 1, 2, 3, 4, 5, 6, 9, 11, 12, and 13 were annotated as monocytes. A total of 4,161 differentially expressed genes were identified within the annotated cell clusters (Table S4 ), and the relative gene expression of each cell cluster was visualized in the form of a heatmap (Fig.  3 D).

figure 3

PBMC Gene Expression Profiles of Monocytes in Diabetic Peripheral Blood. (A) Seurat clustering results with a resolution ranging from 0 to 3. Different colors represent different resolutions, and larger dots indicate subpopulations with a higher number of cells. (B) t-SNE plots illustrating the classification of 15 cell clusters based on scRNA-seq data. (C) t-SNE plots used for identifying distinct cell subtypes. (D) Heatmap displaying the relative expression of marker genes in various cell subtypes

Immune cells infiltrating both OC and DM samples

CIBERSORT analysis was conducted on human mRNA expression data from the GSE40595 and GSE29142 studies to investigate immune cell infiltration and establish the correlation between OC samples and diabetic samples, involving 22 immune cell types. The analysis revealed that OC samples were similar to normal samples in terms of infiltrating immune cell types, which included plasma cells, T cells follicular helper, monocytes, mast cells resting, and neutrophils (Fig.  4 A; p  < 0.05, p  < 0.01, p  < 0.001). Moreover, diabetic peripheral blood PBMC samples showed infiltration patterns similar to those of normal samples, particularly in terms of activated NK cells, monocytes, and resting mast cells (Fig.  4 B; p  < 0.05). Monocytes and resting mast cells were identified as immune cell infiltrates common to both OC and DM.

Monocytes originate from bone marrow cells and represent a subset of cells present in peripheral blood [ 40 ]. Both PBMC collected from blood and those existing within the tumor environment are circulating immune cells in the periphery. Monocytes predominantly circulate in the bloodstream, and there’s a remarkable similarity between the monocytes identified within the cancer stroma and those present in the bloodstream [ 40 ].To further investigate this, we compared the putative 1748 putative differentially expressed monocyte genes in OC samples with the probable 1477 monocyte genes in diabetic PBMC samples based on the findings from scRNA-seq analysis. By intersecting the differentially expressed genes, we identified a total of 119 common differentially expressed genes (Fig.  4 C). Subsequently, we intersected the 119 common differentially expressed genes with the differentially expressed genes specific to OC (Fig.  4 D) and DM (Fig.  4 E) in the mRNA expression data. The resulting genes from this intersection were visualized in a heatmap based on their expression levels.

figure 4

Simultaneous involvement of mononuclear cells in both DM and OC pathogenesis. (A) Immune cell composition analysis using CIBERSORT in GSE40595; (B) immune cell composition analysis using CIBERSORT in GSE29142. The x-axis represents immune cell types, while the y-axis represents the relative abundance of different samples; (C) Venn diagram demonstrating the overlapping genes among differentially expressed genes in monocytes between GSE184880 and GSE165816. A total of 119 common genes were identified. (D) Heatmap depicting the intersection of differentially expressed genes in GSE40595 with the 119 common genes; (E) heatmap illustrating the intersection of differentially expressed genes in GSE29142 with the 119 common genes. Each column represents a specific gene, while each row corresponds to a sample or condition. Data were presented as the mean ± SD. * p  < 0.05, ** p  < 0.01, *** p  < 0.001

Functional analysis of critical module genes

The 119 duplicated genes in monocytes were uploaded to the STRING database, and the resulting free nodes were eliminated to construct the PPI network, which was visualized using Cytoscape. The PPI network consisted of 87 nodes and 328 edges (Fig.  5 A). Using the cytoHubba plug-in in Cytoscape with a target connectivity threshold of 30 (triple the median value), the top 10 hub genes were identified: HSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5. To examine the expression levels of these hub genes, a heatmap was generated using mRNA expression data. Among these genes, HSP90AA1 and UBA52 exhibited significant differences between DM samples and normal samples (Fig.  5 B; p  < 0.05), while PPP1CC and UBA52 showed significant differences between OC samples and normal samples (Fig.  5 C; p  < 0.05). The 119 common genes were subjected to GO enrichment analysis using R language. The analysis revealed the following findings (Table S5 ). In the biological process (BP) category, the common genes were primarily associated with neutrophil degranulation, neutrophil activation involved in immune response, protein targeting, RNA catabolic process, mRNA catabolic process, protein nitrosylation, peptidyl-cysteine S-nitrosylation, pattern recognition receptor signaling pathway, nuclear-transcribed mRNA catabolic process, and spliceosomal snRNP assembly (Fig.  5 D). Under high glucose conditions, ovarian cancer cells generate an inflammatory response, leading to an elevation in neutrophil levels, while the levels of functional lymphocytes often decrease. Neutrophils induce various cytokines and contribute to angiogenesis and the growth of ovarian cancer [ 41 ]. In the molecular function (MF) category, the common genes were mainly enriched in GDP-dissociation inhibitor activity, MHC class II protein complex binding, oxidoreductase activity (acting on a sulfur group of donors), scaffold protein binding, hyaluronic acid binding, protein disulfide oxidoreductase activity, telomerase RNA binding, MHC protein complex binding, glycolipid binding, and translation repressor activity (Fig.  5 E) MHC class II proteins induce apoptosis in ovarian cancer cells by presenting insulin or insulin-related antigens [ 42 ]. In the cell component (CC) category, the common genes were predominantly associated with secretory granule lumen, cytoplasmic vesicle lumen, vesicle lumen, ficolin-1-rich granule, ficolin-1-rich granule lumen, vacuolar lumen, focal adhesion, cell-substrate junction, methylosome, and specific granule (Fig.  5 F). The secretory granule lumen regulates inflammation and immunity in mice and suppresses the occurrence and development of ovarian cancer [ 43 ]. Furthermore, these common genes were subjected to KEGG pathway enrichment analysis. The results indicated that the common genes were primarily involved in tight junction, the IL-17 signaling pathway, acute myeloid leukemia, regulation of actin cytoskeleton, shigellosis, Salmonella infection, Yersinia infection, ubiquitin-mediated proteolysis, spliceosome, and legionellosis (Fig.  5 G, Table S6 ).

figure 5

Identification and functional enrichment analysis of hub genes. (A) Visualization of the PPI network using Cytoscape 3.8.2 software. Each node represents a protein, and each edge represents the relationship between two proteins. The size and color intensity of a node indicate its importance in the network. (B) Bar plot showing the intersection of differentially expressed genes in GSE29142 with the 119 shared genes in monocytes. (C) Bar plot depicting the intersection of differentially expressed genes in GSE40595 with the 119 shared genes in monocytes. (D) GO enrichment analysis of the 119 shared genes, including biological processes. (E) GO enrichment analysis of the 119 shared genes, focusing on molecular functions. (F) GO enrichment analysis of the 119 shared genes, highlighting cellular components. (G) KEGG enrichment analysis of the 119 common genes involved. BP, biological process; MF, molecular function; CC, cellular component. * p  < 0.05

Prognostic relevance of hub genes in OC

We performed an analysis of gene expression profiles in ovarian cancer samples and normal tissues using GEPIA. This analysis, based on TCGA and GTEx datasets, revealed significant differences ( p  < 0.05) in five out of the ten core genes, including HSPA8, PSMA5, RPL13A, RPSA, and SOD1. Additionally, the expression levels of HSPA8 and SOD1 were higher in high-grade serous ovarian cancer samples compared to non-malignant ovarian tumor tissues, while RPL13A, PSMA5, and RPSA showed the opposite trend (Fig.  6 A). To assess the prognostic value of the selected core genes in ovarian cancer, we plotted specific survival curves using the TCGA database (Fig.  6 B). Kaplan–Meier curve analysis revealed a significant correlation ( p  < 0.05) between low expression of HSP90AA1, HSPA8, PSMA5, and SOD1 and prolonged overall survival (OS) in ovarian cancer patients. Conversely, low expression of RPL13A was significantly associated with shorter OS ( p  < 0.05). However, the expression of ITGAM, PPP1CC, RPSA, and SNRPD2 showed no significant correlation with OS in OC patients (Fig.  6 B). Furthermore, we conducted Pearson correlation coefficient analysis to assess the reproducibility and correlation of these ten core genes in two GEO datasets. The analysis revealed positive correlations among HSP90AA1, ITGAM, PPP1CC, PSMA5, SNRPD2, SOD1, and UBA52 in the GSE40595 dataset (Fig.  6 C). Additionally, in GSE29142, we observed a negative correlation between HSP90AA1, ITGAM, PPP1CC, and other core genes. On the other hand, positive correlations were observed among PSMA5, SNRPD2, SOD1, and UBA52 (Fig.  6 D).

figure 6

Validation of hub gene expression and survival. (A) mRNA expression of hub genes in ovarian cancer tissues compared to normal tissues, indicated by asterisks. (B) Kaplan‒Meier survival analysis of 10 hub genes in mononuclear cells of OC patients, showing high and low expression groups. (C) Pearson correlation analysis of hub genes in the GSE40595 dataset; (D) pearson correlation analysis of HUB genes in the GSE29142 dataset. The color indicates the strength of the correlation. Correlation coefficients between 0 and 1 represent positive correlation, between − 1 and 0 represent negative correlation. The larger the absolute value of the coefficient, the stronger the correlation. * p  < 0.05

Construction of regulatory networks

Transcription factors (TFs) are proteins capable of binding to gene-specific sequences [ 44 ], and miRNAs are a class of small non-coding RNA molecules [ 44 ]. Both can regulate gene expression. In our study, we separately analyzed interactions within the NetworkAnalyst platform (including ENCODE and MiRTarBase databases) to construct a TF-mRNA-miRNA interaction network. By analyzing the interaction networks of TF-mRNA-miRNA, we identified 46 key transcription factors that can regulate core genes. Among them, 13 TFs can regulate HSP90AA1, 12 TFs can regulate HSPA8, and 6 TFs can regulate SOD1. In the regulatory network, SREBF2, GATA2, and SRF are particularly important, as they can regulate both HSP90AA1 and HSPA8 (Fig.  7 ). Additionally, we obtained a total of 294 miRNAs that can regulate core genes. By applying a target connectivity criterion of ≥ 1 for miRNAs, we eventually identified the top 27 miRNAs. Among them, 17 miRNAs can regulate HSP90AA1, and 15 miRNAs can regulate HSPA8. Specifically, miR-320a can regulate HSPA8 and SNRPD2, miR-378a-3p can regulate HSP90AA1, and miR-26a-5p can simultaneously regulate HSPA8, PPP1CC, UBA52, and SOD1 (Fig.  8 ). The topological tables for the regulatory networks of TF-mRNA-miRNA are presented in Tables S7 and S8.

figure 7

Network Analyst generated an interconnected regulatory interaction network of TF genes, in which blue circular nodes represent TFs and genes interacting with TFs are depicted as red circular nodes

figure 8

NetworkAnalyst generated an interconnected regulatory interaction network of miRNA-gene. The square nodes represent miRNAs, while the genes that interact with the miRNAs are depicted as circles. The size of the node area and the darkness of the color indicate their relative importance within the network

Expression of core proteins and transcription factors in ovarian cancer

We demonstrated differential expression of HSP90AA1 and HSPA8 in non-malignant ovarian tumor tissues, high-grade serous ovarian cancer samples, non-malignant ovarian samples concomitant with diabetes, and diabetes concomitant with high-grade serous ovarian cancer samples through Immunohistochemistry (IHC) and RT-qPCR. Additionally, we assessed the differential expression of the core protein SOD1 and the transcription factors SREBF2 and GTAT2 in these samples using RT-qPCR. The IHC and RT-qPCR results revealed that HSP90AA1 and HSPA8 were downregulated in non-malignant ovarian tumor tissues and significantly upregulated in high-grade serous ovarian cancer samples. Furthermore, there was no significant difference in non-malignant ovarian tumor tissues concomitant with diabetes, but a significant increase was observed in high-grade serous ovarian cancer samples (Fig.  9 A, P < 0.001), consistent with our genetic analysis. Subsequently, we further evaluated the relationship between the expression of the core protein SOD1 and the transcription factors SREBF2 and GTAT2 in different samples. RT-qPCR results showed that SOD1 exhibited consistent expression with HSP90AA1 and HSPA8, while SREBF2 and GTAT2 were highly expressed in non-malignant ovarian tumor tissues and significantly decreased in high-grade serous ovarian cancer samples. Moreover, there was no significant difference in non-malignant ovarian tumor tissues concomitant with diabetes, but a significant decrease was observed in high-grade serous ovarian cancer samples (Fig.  9 B, P < 0.001). In conclusion, these results suggest a significant increase in HSP90AA1 and HSPA8 concomitant with diabetes, and SREBF2 and GTAT2 may regulate the expression of HSP90AA1 and HSPA8.

figure 9

Expression of Core Proteins and Transcription Factors in Ovarian Tumors. (A) Immunohistochemical staining of HSP90AA1 and HSPA8 in age-matched high-grade serous ovarian tissues and non-malignant ovarian tumor tissues ( n  = 4). (B) Expression changes of core proteins HSP90AA1, HSPA8, and SOD1, as well as transcription factors SREBF2 and GTAT2 ( n  = 4). Control, Non-malignant ovarian tumor group; Control + DM, Non-malignant ovarian tumor group combined with DM; OC, High-grade serous ovarian cancer group; OC + DM, High-grade serous ovarian cancer group combined with DM. * p  < 0.05, ** p  < 0.01, *** p  < 0.001

The relationship between DM and OC is an area of research that has gained significant attention. Patients with diabetes often experience a chronic inflammatory state, which can lead to excessive secretion of inflammatory cytokines, growth factors, and interleukin-1 (IL-1) [ 45 , 46 ]. Chronic inflammation may influence the growth, invasion, and metastasis of ovarian cancer cells by activating various inflammatory mediators and cytokines. Additionally, inflammation can impact ovarian cancer development through mechanisms such as modulation of the tumor microenvironment and immune escape [ 47 , 48 ]. Hence, we explored potential interactions between monocytes in the OC microenvironment and PBMCs in individuals with DM from a unique perspective, aiming to identify common therapeutic targets.

In this study, we employed a network-based approach to investigate the gene expression profiles of two scRNA-seq datasets derived from OC and DM. Our analysis aimed to identify molecular targets that could serve as potential biomarkers for OC. Within the ovarian cancer scRNA-seq dataset, we identified eight major cell clusters. Moreover, analysis of the diabetes PBMC scRNA-seq dataset allowed us to identify three major cell clusters, with monocytes comprising a significant proportion of the cell clusters. Notably, our immune infiltration analysis of OC and DM RNA-seq data revealed substantial differences in monocyte infiltration between the two conditions. Monocytes are a central component to the innate immune response to pathogens [ 49 ]. There is literature indicating that in a mouse model with subcutaneous injection of human ovarian cancer cells, tumor volume significantly decreased in animals injected with interferon and monocytes at the early stage of tumor formation, and in some animals, tumors completely disappeared [ 50 ]. Stimulating the expression of CD44 in monocytes promotes apoptosis of ovarian cancer cells, favoring immune suppression [ 51 ]. Monocytes in the blood, besides their immune functions, also belong to a complex tissue control system (TCS). After simple and precise immunotherapy, stage IV ovarian cancer with liver metastasis completely regressed in the presence of diabetes [ 52 ]. Metformin downregulates the expression and ectonucleotidase activity of CD39 and CD73 on monocytes and various monocyte MDSC subpopulations in diabetic mice with OC, blocking the suppressive function of myeloid-derived suppressor cells (MDSC) and inducing apoptosis of ovarian cancer cells [ 53 ]. In non-obese diabetic-severe combined immunodeficiency (NOD-SCID) mice, monocytes modulated by bone marrow dendritic cells blocked with B7-H1 exhibited a more effective capacity to inhibit the growth of human ovarian cancer [ 54 ].

By identifying the overlapping differentially expressed genes in monocytes between DM and OC, we were able to determine 10 core genes with differential expression, indicative of their relevance to the pathogenesis of both conditions. These genes include HSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5.Heat shock proteins (HSPs) play a protective role in shielding cells from oxidative stress, inflammation, and apoptosis [ 55 ]. Recent studies have indicated that activation of HSP90AA1 promotes tumor progression, invasion, and chemotherapy resistance [ 56 ]. HSP90AA1 acts as an extracellular secretory factor involved in inflammation, facilitating the malignant phenotype formation in tumor cells [ 57 ]. Inhibition of HSP90AA1 activity has been shown to reduce tumor necrosis factor (TNF) mRNA levels [ 58 ] and enhance glucose-stimulated insulin secretion and gene expression related to β-cell function [ 59 ]. Heat shock protein A8 (HSPA8) participates in cellular stress response, protein folding and assembly, protein transport, and protein degradation processes [ 60 ]. In the ovarian cancer tumor microenvironment, HSPA8 is implicated in modulating the immune response of monocytes/macrophages [ 61 ]. Furthermore, HSPA8 plays an important role in insulin secretion and insulin receptor signaling in DM [ 62 ]. Small nuclear ribonucleoprotein D2 (SNRPD2) consists of small nuclear ribonucleoproteins and small nuclear RNAs (snRNAs), playing a crucial role in the splicing process and gene expression regulation [ 63 ]. Aberrant expression or functional alterations of SNRPD2 in ovarian cancer may lead to splicing errors, affecting gene expression regulation in tumor cells [ 64 ]. UBA52 is a protein associated with ubiquitin, and its role has been reported in other pathologies. In traumatic brain injury, altered mRNA and protein levels of UBA52 have been observed [ 65 ]. Upregulated UBA52 has been found in the contexts of diabetic nephropathy and hepatoma cell apoptosis. Additionally, UBA52 deficiency in mice is associated with decreased protein synthesis, cell cycle arrest, and death during embryonic development [ 66 ]. Superoxide dismutase 1 (SOD1) regulates the levels of superoxide originating from the mitochondrial intermembrane space, cytosol, and peroxisome [ 67 , 68 ]. Increased expression of SOD1 genes in animal models may decrease fasting blood glucose and hemoglobin A1c [ 69 ], and contribute to the survival of hypertrophied beta cells during chronic hyperglycemia. Conversely, genetic disruption of the SOD1 gene causes glucose intolerance and impairs beta cell function [ 70 ]. SOD1 is a critical determinant of platinum resistance in ovarian cancer and represents a target for overcoming this resistance [ 71 ].Small nucleolar RNAs (snoRNAs) are noncoding RNAs that form ribonucleoproteins involved in guiding covalent modifications of ribosomal and small nuclear RNAs in the nucleus. Loss of Rpl13a snoRNAs alters mitochondrial metabolism, reduces reactive oxygen species levels, increases glucose-stimulated insulin secretion from pancreatic islets, and enhances systemic glucose tolerance [ 72 ]. Previous studies have reported that under normoxic conditions, the most stably expressed genes in ovarian cancer cells are GAPDH/TBP, while under hypoxic conditions, the most stable candidate housekeeping genes are RPL13A/SDHA [ 73 ]. ITGAM encodes the α chain of integrin αMβ2 (CD11b). CD11b forms the leukocyte adhesion molecule β2 integrin with CD18, known as macrophage differentiation antigen-1 (Mac-1) [ 74 ]. The expression of Mac-1 and ICIAM-1 in the proliferative diabetic retina suggests the involvement of adhesion molecules in the pathogenesis of diabetic microvascular complications [ 75 ]. Furthermore, under pathological conditions, Mac-1 serves as a key adhesion molecule that facilitates cancer progression and mediates the adhesion of tumor cells to the endothelium of blood vessels [ 76 ]. The PPPC family has been shown to play essential roles in tumor cell proliferation [ 77 ], metastasis [ 78 ], and resistance to chemotherapy [ 79 ]. Additionally, GYS1 and PPP1CC have been reported to improve insulin resistance by regulating miR-140-5p in diabetes [ 80 ].Proteasome alpha subunits (PSMAs) have been implicated in the malignant progression of various human cancers [ 81 ]. In survival analyses, PSMA1-7 showed significant prognostic value in breast, lung, and gastric cancer. Furthermore, potential correlations between PSMAs and survival outcomes have been observed in ovarian cancer, colorectal cancer, and melanoma using Kaplan‒Meier Plotter [ 82 , 83 ].

With each new scientific discovery, the structure of the GO resource continually evolves to incorporate biological knowledge of gene functions and to constantly improve its ability to reflect the latest state of biological understanding [ 84 ]. GO analysis was performed using the “clusterProfiler” package, and the analysis involved three ontological categories processes: biological process (molecular activity), cellular component (gene regulatory function), and molecular function (activity at the molecular level), utilizing the GO database as a source of information. Among the top GO terms in the biological process category were positive regulation of neutrophil degranulation and neutrophil activation involved in the immune response. Neutrophils serve as the first responders to inflammation and infection, while monocytes belong to the neutrophil species. It has been reported in the literature that treatment of orthotopic mouse ovarian cancer tumors with an anti-TGFBI antibody reduced peritoneal tumor size, increased tumor monocytes, and activated β3-expressing unconventional T cells [ 85 ]. Furthermore, alleviating the symptoms and complications associated with diabetes can be achieved by inhibiting inflammatory monocyte infiltration and altering macrophage characteristics [ 86 ].In the cellular component analysis, two major GO pathways were identified: secretory granule lumen and cytoplasmic vesicle lumen. These pathways play crucial roles in the development and progression of ovarian cancer and diabetes, including regulation of intracellular substance storage, transport, transfer, invasion, and formation of drug resistance [ 87 ]. In the context of diabetes, they are involved in processes related to insulin secretion, insulin resistance, intracellular substance transport and secretion associated with glucose metabolism and insulin secretion regulation [ 88 ].According to the molecular function analysis, the top GO terms were GDP-dissociation inhibitor activity and MHC class II protein complex binding. In ovarian cancer, MHC class II protein complex binding is associated with immune surveillance and immune evasion. Reduced expression or dysfunctional MHC class II protein complex binding prevents effective recognition and elimination of ovarian cancer cells by the immune system, promoting tumor development and metastasis [ 89 ]. In diabetic mice on a high-fat diet, the MHC II immune peptidome underwent quantitative and qualitative changes, highlighting the link between glycation reactions and alterations in MHC II antigen presentation, which may contribute to the development of type 2 diabetes complications [ 90 ]. KEGG pathway enrichment analysis of candidate target genes revealed their association with 211 signaling pathways, among which the interleukin-17 (IL-17) signaling pathway is a well-known cancer signaling pathway [ 91 ]. There are research reports suggesting that IL-17 may specifically modulate inflammatory monocytes during the later phases of the inflammatory response [ 92 ].

We have also identified associations between diseases based on TF-genes and miRNA-genes interactions. TFs are proteins that bind to specific gene sequences, known as promoters, and regulate gene transcription and expression [ 44 ]. Extensive research has revealed the regulatory roles of several TFs in the pathogenesis of OC and DM [ 93 ]. Notably, SREBF2, GATA2, PPARG, NFIC, ELK4, RELA, E2F1, and SRF have been implicated as TFs associated with various types of OC. For instance, the downregulation of sterol regulatory element binding protein 2 (SREBF2) inhibits the serine protease 8 (PRSS8)/sodium channel epithelial 1alpha subunit (SCNN1A) axis, leading to reduced cell proliferation, migration, and epithelial-mesenchymal transition in OC [ 94 ]. Moreover, the GATA2 gene has been identified as a prognostic factor in stromal-related studies of colon cancer [ 95 ], and it has also been implicated as a molecular signature in ovarian cancer through a network medicine perspective [ 96 ]. Regarding the visualization of gene-miRNA interactions, miR-320a, miR-378a-3p, miR-26a-5p, miR-92a-3p, and miR-484 have been associated with the pathogenesis of OC. For example, miR-320a promotes the proliferation and invasion of epithelial ovarian cancer cells by targeting RASSF8 [ 97 ]. Additionally, decreased expression of miR-378a-3p has been closely linked to an unfavorable prognosis in ovarian cancer patients, as it inhibits cell proliferation and promotes apoptosis [ 98 ]. These findings contribute to our understanding of the connection between DM and OC.

Conclusions

This study unravels potential mechanisms and common therapeutic targets in monocytes between OC and DM, shedding light on the pathogenesis of both diseases. Through bioinformatics analysis, immune infiltration analysis, and survival analysis, it was confirmed that the identified key targets could be crucial treatment targets. These findings may provide a basis for clinical application of targeted treatments in patients with concurrent ovarian cancer and diabetes mellitus.

Data Availability

The study utilized publicly available datasets for analysis. Specifically, the single-cell databases GSE184880 and GSE165816, as well as transcriptome data from GSE40595 and GSE29142, were sourced from the GEO platform ( https://www.ncbi.nlm.nih.gov/geo/ ).

Abbreviations

Diabetes mellitus

  • Ovarian cancer

Protein‒protein interaction

Gene Ontology

Kyoto Encyclopedia of Genes and Genomes

Immunohistochemistry

qPCR-Real-time quantitative PCR

Peripheral blood mononuclear cells

1-Insulin growth factor-1

seq-Single-cell RNA sequencing

Tumor microenvironment

Gene Expression Omnibus

National Center for Biotechnology Information

Differentially expressed genes

Molecular function

Cellular components

Biological process

Overall survival

3,3’-diaminobenzidine

Transcription factors

1-Interleukin-1

Tissue control system

Myeloid-derived suppressor cells

Heat shock proteins

Tumor necrosis factor

Heat shock protein A8

Small nuclear ribonucleoprotein D2

Small nuclear ribonucleoproteins and small nuclear RNAs

Superoxide dismutase 1

Small nucleolar RNAs

αchain of integrin αMβ2

1-Macrophage differentiation antigen-1

Proteasome alpha subunits

17-Interleukin-17

Sterol regulatory element binding protein 2

Serine protease 8

Sodium channel epithelial 1alpha subunit

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Acknowledgements

The authors would like to thank all members of the Laboratory of the Dalian Medical University for their technical assistance.

This work was supported by grants from the National Natural Science Foundation of China (No.81873195), the Liaoning Revitalization Talents Program (XLYC1907113), the Natural Science Foundation of Liaoning Province (2023010109-JH2/1013), the Distinguished Young Scholars in Dalian (2022RJ19), and the Dalian Medical University Foundation for Teaching Reform Project of Undergraduate Innovative Talents Training (111906010210).

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Zhihao Zhao and Qilin Wang have contributed equally to this work.

Authors and Affiliations

Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, China

Zhihao Zhao, Qilin Wang, Junnan Ma, Xue Sui, Hyok Chol Choe, Peng Chen & Lin Zhang

Department of Pathology, the First Hospital of Dalian Medical University, Dalian, Liaoning Province, 116027, China

Institute of Innovation and Applied Research in Chinese Medicine, Department of Rheumatology of The First Hospital, Hunan University of Chinese Medicine, Changsha, Hunan, China

Department of Clinical Medicine, Sinuiju Medical University, Sinuiju, Democratic People’s Republic of Korea

Hyok Chol Choe

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Contributions

LZ, XG contributed to the conceptual framework and revised the manuscript. ZHZ and QLW is responsible for the experimental design, operation, writing and revision. JNM, XS, FZ, CHC and PC collected experimental data and performed statistical analysis. All authors contributed to the article and approved the submitted version.

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Correspondence to Xue Gao or Lin Zhang .

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Supplementary Material 1:

Differentially expressed marker genes identified across 29 clusters in the GSE184880 dataset

Supplementary Material 2:

Five clusters were categorized as monocytes in the GSE184880 dataset

Supplementary Material 3:

Differentially expressed marker genes identified in the GSE165816 dataset

Supplementary Material 4:

Ten clusters were categorized as monocytes in the GSE184880 dataset

Supplementary Material 5:

GO functional enrichment assessment was conducted on the PPI network regulating diabetes and ovarian cancer

Supplementary Material 6:

The top 10 enriched KEGG pathways of the differentially expressed proteins

Supplementary Material 7:

The topological tables of TF genes

Supplementary Material 8:

The topological tables of miRNA genes

Supplementary Material 9:

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Zhao, Z., Wang, Q., Zhao, F. et al. Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer. BMC Genomics 25 , 1 (2024). https://doi.org/10.1186/s12864-023-09893-2

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By Mayo Clinic staff

Researchers at Mayo Clinic Comprehensive Cancer Center spent 2023 studying the biology of cancer and new ways to predict, prevent, diagnose and treat the disease. Their discoveries are creating hope and transforming the quality of life for people with cancer today and in the future. Here are some highlights from their research over the past year:

Mayo Clinic researchers link ovarian cancer to bacteria colonization in the microbiome.

A specific colonization of microbes in the reproductive tract is commonly found in people with ovarian cancer, according to a study from the Mayo Clinic  Center for Individualized Medicine . Published in  Scientific Reports  and led by  Marina Walther-Antonio, Ph.D. , a Mayo Clinic researcher, and Abigail Asangba, Ph.D., the discovery strengthens the evidence that the bacterial component of the microbiome — a community of microorganisms that also consists of viruses, yeasts and fungi — is an important indicator for early detection, diagnosis and prognosis of ovarian cancer . The study also suggests that a higher accumulation of pathogenic microbes plays a role in treatment outcomes and could be a potential indicator for predicting a patient's prognosis and response to therapy.  Read more .

Artificial intelligence is forging a new future for colorectal cancer and other digestive system diseases.

Colonoscopy remains the gold standard in detecting and preventing colorectal cancer , but the procedure has limitations. Some studies suggest that more than half of post-colonoscopy colon cancer cases arise from lesions missed at patients' previous colonoscopies. In 2022, Michael Wallace, M.D. , a Mayo Clinic gastroenterologist, published the results  of an international, multicenter study testing the impact of adding artificial intelligence (AI) to routine colonoscopies. His team, including James East, M.D. , a Mayo Clinic gastroenterologist, and other researchers from the U.S., the U.K., Italy, Germany and Ireland, found that incorporating AI into colonoscopies reduced the risk of missing polyps by 50%.  Read more .

A big step forward: Bringing DNA sequencing data to routine patient care.

The Tapestry study , an extensive genomic sequencing clinical research study, aims to complete exome sequencing (sequencing the protein-coding regions of a genome) for 100,000 Mayo Clinic patients. The results will be integrated into patients’ electronic health records for three hereditary conditions, and the amassed data will contribute to a research dataset stored within the Mayo Clinic Cloud on the Omics Data Platform. The overall hope of Tapestry is to accelerate discoveries in individualized medicine to tailor prevention, diagnosis and treatment to a patient's unique genetic makeup. It is poised to advance evidence that exome sequencing, when applied to a diverse and comprehensive general population, can proficiently identify carriers of genetic variants that put them at higher risk for a disease, allowing them to take preventive measures.  Read more .

Patients with multiple tumors in one breast may not need a mastectomy.

Patients who have multiple tumors in one breast may be able to avoid a mastectomy if surgeons can remove the tumors while leaving enough breast tissue, according to research led by the  Alliance in Clinical Trials in Oncology  and  Mayo Clinic Comprehensive Cancer Center . Patients would receive breast-conserving therapy — a  lumpectomy  followed by whole-breast  radiation therapy — rather than mastectomy . The study is published in the  Journal of Clinical Oncology . Historically, women with multiple tumors in one breast have been advised to have a mastectomy. Now, patients can be offered a less invasive option with faster recovery, resulting in better patient satisfaction and cosmetic outcomes, says  Judy Boughey, M.D. , lead author, Mayo Clinic breast surgical oncologist and the W.H. Odell Professor of Individualized Medicine. Read more .

Staging pancreatic cancer early with minimally invasive surgery shows positive results in patient prognosis.

A study published in the  Journal of the American College of Surgeons  reveals that performing a minor surgical procedure on patients newly diagnosed with  pancreatic cancer  helps to identify cancer spread early and determine the stage of cancer. The researchers add that the surgery ideally should be performed before the patient begins chemotherapy. "This is an important study because it supports that staging laparoscopy may help determine a patient's prognosis and better inform treatment so that patients avoid unhelpful or potentially harmful surgical therapy," says  Mark Truty, M.D. , a Mayo Clinic surgical oncologist who led the research.  Read more .

Mayo Clinic study reveals proton beam therapy may shorten breast cancer treatment.

In a trial published in  The Lancet Oncology , Mayo Clinic Comprehensive Cancer Center researchers uncovered evidence supporting a shorter treatment time for people with breast cancer . The study compared two separate dosing schedules of pencil-beam scanning proton therapy , known for its precision in targeting cancer cells while preserving healthy tissue to reduce the risk of side effects. The investigators found that both 25-day and 15-day proton therapy schedules resulted in excellent cancer control while sparing surrounding non-cancerous tissue. Further, complication rates were comparable between the two study groups. "We can now consider the option of 15 days of therapy for patients based on the similar treatment outcomes observed," says  Robert Mutter, M.D. , a Mayo Clinic radiation oncologist and physician-scientist. Read more .

Harnessing the immune system to fight ovarian cancer.

Mayo Clinic research is biomanufacturing an experimental, cell-based ovarian cancer vaccine and combining it with immunotherapy to study a "one-two punch" approach to halting ovarian cancer progression. This research begins with a blood draw from people with advanced  ovarian cancer  whose tumors have returned after standard surgery and chemotherapy. White blood cells are extracted from the blood, biomanufactured to become dendritic cells and returned to the patient. Dendritic cells act as crusaders that march through the body, triggering the immune system to recognize and fight cancer. "We're building on an earlier phase 1 clinical trial  that showed promising results  in terms of survival after the dendritic cell-based vaccine," says  Matthew Block, M.D., Ph.D. , co-principal investigator and Mayo Clinic medical oncologist. "Of the 18 evaluable patients in the phase 1 study, 11 had cancer return, but seven of them — 40% — have been cancer-free for almost 10 years. We typically expect 90% of patients in this condition to have the cancer return."  Read more .

New gene markers detect Lynch syndrome-associated colorectal cancer.

Researchers from Mayo Clinic Comprehensive Cancer Center and Mayo Clinic Center for Individualized Medicine have discovered new genetic markers to identify Lynch syndrome-associated colorectal cancer with high accuracy. Studies are underway to determine if these genetic markers are in stool samples and, if so, how this could lead to a non-invasive screening option for people with  Lynch syndrome . The research was published in Cancer Prevention Research , a journal of the American Association for Cancer Research. "This is an exciting finding that brings us closer to the reality that clinicians may soon be able to offer a non-invasive cancer screening option to patients with the highest risk of getting cancer," says  Jewel Samadder, M.D. , co-lead author of the paper and a Mayo Clinic gastroenterologist. Read more .

Mayo Clinic prepares to biomanufacture a new CAR-T cell therapy for B-cell blood cancers.

Mayo Clinic research has developed a new type of  chimeric antigen receptor-T cell therapy (CAR-T cell therapy)  aimed at killing B-cell blood cancers that have returned and are no longer responding to treatment. This pioneering technology, designed and developed in the lab of  Hong Qin, M.D., Ph.D. , a Mayo Clinic cancer researcher, killed B-cell tumors grown in the laboratory and tumors implanted in mouse models. The preclinical findings are published in  Cancer Immunology, Immunotherapy . "This study shows our experimental CAR-T cell therapy targets several blood cancers, specifically chronic lymphocytic leukemia," says Dr. Qin. "Currently, there are six different CAR-T cell therapies approved for treatment of relapsed blood cancers. While the results are impressive, not everyone responds to this treatment. Our goal is to provide novel cell therapies shaped to each patient's individual need."  Read more .

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https://www.wsj.com/articles/startups-are-using-ai-to-predict-responses-to-cancer-drugs-d7fb06eb

Startups Are Using AI to Predict Responses to Cancer Drugs

More accurate prognoses and treatments tailored to patients—entrepreneurs claim ai can be harnessed for breakthroughs in beating cancer. one founder is using lab-grown human organs in combination with ai to test drug efficacy..

research articles ovarian cancer

Biomedical startups are using artificial intelligence to predict the response patients will have to cancer treatments, aiming to increase the success of drugs in clinical trials and tailor therapies to individuals.

As data accumulate from clinical trials and fields such as gene and protein research, AI is helping scientists sift through large volumes of information to uncover signatures that correlate with response—or resistance—to treatment. Startups are using it to predict which drugs are likely to work in clinical studies and create tests to help doctors choose treatments.

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IMAGES

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    Posted: April 7, 2022 Researchers have developed tiny "drug factories" that produce an immune-boosting molecule and can be implanted near tumors. The pinhead-sized beads eliminated tumors in mice with ovarian and colorectal cancer and will soon be tested in human studies. Trametinib Is a New Treatment Option for Rare Form of Ovarian Cancer

  5. Ovarian Cancer

    Ovarian cancer is the leading cause of death in women diagnosed with gynecological cancers. It is also the fifth most frequent cause of death in women, in general. [1] Most of the cases are diagnosed at an advanced stage, which leads to poor outcomes of this disease.

  6. Ovarian cancer: Current status and strategies for improving therapeutic

    1. INTRODUCTION Ovarian cancer (OC) is the deadliest cancer among women placing it with 4th place for all the fatal disease among women. Cancer statistics from 2019 show that the estimated number of new cases is 22 240 with deaths around 14 170 cases. 1 There are three histological types associated with the disease.

  7. Ovarian Cancer: An Integrated Review

    Becoming familiar with and educating women about risk factors and the elusive symptoms of ovarian cancer can increase patient autonomy and advocacy, as well as potentially improve patient outcomes for those affected by ovarian cancer.

  8. A real-world study on characteristics, treatments and outcomes in US

    Background Detailed epidemiologic descriptions of large populations of advanced stage ovarian cancer patients have been lacking to date. This study aimed to describe the patient characteristics, treatment patterns, survival, and incidence rates of health outcomes of interest (HOI) in a large cohort of advanced stage ovarian cancer patients in the United States (US). Methods This cohort study ...

  9. Advances in Ovarian Cancer Research

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    At the 2016 Ovarian Cancer Research Symposium, sponsored by the Rivkin Center for Ovarian Cancer and the American Association for Cancer Research, cutting edge research on these topics was presented, and the proceedings from the 2016 Ovarian Cancer Research Symposium are recently published in the journal Clinical Cancer Research. 4 Hereinafter ...

  15. Ovarian cancer

    Ovarian cancer is a global problem, is typically diagnosed at a late stage and has no effective screening strategy. Standard treatments for newly diagnosed cancer consist of cytoreductive surgery and platinum-based chemotherapy.

  16. Investigation on factors associated with ovarian cancer: an umbrella

    Journal of Ovarian Research 14, Article number: 153 ( 2021 ) Cite this article 6827 Accesses 26 Citations 27 Altmetric Metrics Abstract Following cervical and uterine cancer, ovarian cancer (OC) has the third rank in gynecologic cancers. It often remains non-diagnosed until it spreads throughout the pelvis and abdomen.

  17. Ovarian Cancer Research Highlights

    Ovarian Cancer Research Highlights Ovarian cancer causes more deaths in women living in the United States than any other cancer of the female reproductive system. The American Cancer Society's (ACS) research programs help find answers to critical questions: How can ovarian cancer be diagnosed early?

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    Ovarian cancer screening could be paired with DNA-based pan-cancer screening strategies or combined with site-specific blood tests that are being developed to detect colorectal adenomas, and ...

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    PDF The association between systemic immune-inflammation index and in vitro fertilization outcomes in women with polycystic ovary syndrome: a cohort study As a novel prognostic and inflammatory marker, the systemic immune-inflammation index (SII) has come to the foreground in recent years.

  20. Evolutionary perspectives, heterogeneity and ovarian cancer: a

    Ovarian cancer is composed of a complex system of cells best described by features such as clonal evolution, spatial and temporal genetic heterogeneity, and development of drug resistance, thus making it the most lethal gynecologic cancer. Seminal work on cancer as an evolutionary process has a long history; however, recent cost-effective large-scale molecular profiling has started to provide ...

  21. Ovarian Cancer: Prevention, Detection and Treatment of the Disease and

    Ovarian cancer is the sixth most common cancer worldwide among women in developed countries and the most lethal of all gynecologic malignancies. ( 1) Currently, most women have advanced stage disease at the time of diagnosis.

  22. Ovarian cancer articles within British Journal of Cancer

    The untapped potential of ascites in ovarian cancer research and treatment. Caroline Elizabeth Ford, Bonnita Werner & Kristina Warton; Article 27 April 2020 | Open Access.

  23. Familial risks of ovarian cancer by age at diagnosis, proband ...

    Ovarian cancer is a heterogeneous disease. Data regarding familial risks for specific proband, age at diagnosis and histology are limited. Such data can assist genetic counseling and help elucidate etiologic differences among various histologic types of ovarian malignancies. By using the Swedish Family-Cancer Database, we calculated relative risks (RRs) for detailed family histories using a ...

  24. Cell State of Origin Impacts Development of Distinct Endometriosis

    The origin of ovarian cancer has been the subject of intense debate for over two decades ().It was only recently that most researchers agreed on a unique feature of ovarian carcinoma: most ovarian carcinoma histotypes arise from cells that are not native to the ovary ().HGSOC likely originates from the fallopian tube epithelium (FTE; ref. 12), whereas ENOC and CCOC are thought to arise from ...

  25. Test Using Routine Pap Smears Could Diagnose Ovarian Cancer Early

    Based on the findings, the researchers designed a diagnostic test known as the Early oVArian cancer test, or the EVA test, which uses samples collected during Pap smears. The EVA test has a sensitivity of 75% and specificity of 96%. The researchers noted that larger and longer prospective studies are needed to determine whether the EVA test can ...

  26. Salpingectomy and the Risk of Ovarian Cancer in Ontario

    Epithelial ovarian cancer is the fifth leading cause of cancer death among women in Canada, with a 5-year survival rate of 45%. 1 High-grade serous cancer is the most common subtype, typically presenting at an advanced stage; thus, the case fatality rate is high. 2 There has been little progress in screening for early detection; apart from oral ...

  27. Single-cell and transcriptomic analyses reveal the influence of

    There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC.

  28. Cancer research highlights from 2023

    Mayo Clinic research is biomanufacturing an experimental, cell-based ovarian cancer vaccine and combining it with immunotherapy to study a "one-two punch" approach to halting ovarian cancer progression. This research begins with a blood draw from people with advanced ovarian cancer whose tumors have returned after standard surgery and ...

  29. Startups Are Using AI to Predict Responses to Cancer Drugs

    Photo: Giro Studios. Biomedical startups are using artificial intelligence to predict the response patients will have to cancer treatments, aiming to increase the success of drugs in clinical ...