presentation on hepatitis

Oct 01, 2012

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Hepatitis. RM SBI3U Mr. Watts. Definition. HEPATITIS- inflammation of the liver. - Hepatitis is a viral infection in the liver that causes the liver to swell and become inflamed. -Many different forms of the hepatitis virus (A,B,C,D,E) which vary in severity. History.

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Hepatitis RM SBI3U Mr. Watts

Definition HEPATITIS- inflammation of the liver - Hepatitis is a viral infection in the liver that causes the liver to swell and become inflamed. -Many different forms of the hepatitis virus (A,B,C,D,E) which vary in severity

History - 1947 Mark McCallum released the first evidence of infectious hepatitis and serum hepatitis in his research. - 1967 Dr. Blumberg discovered the Australian antigen which lead to the discovery of hepatitis B, in 1969 Blumberg assisted in the development of the blood test used to detect hepatitis B as well as the vaccine. - Hepatitis C was discovered in 1989 - Hepatitis D was discovered in 1977 - Hepatitis E was discovered sometime between 1971-1976

Causes Hepatitis A: -found in feces -both hepatitis A & E are spread through fecal-oral contamination or close contact (unprotected sex) • Hepatitis B: • - found in blood and certain body fluids • spread when blood or fluid from infected person enters body of person not immune • spread by unprotected sex, sharing needles, and through infected mother to child.

Hepatitis C: • found in blood and certain body fluids • Spread when HCV infected blood/fluid enters another person • Spread through sharing needles or infected mother to child but rarely is spread through unprotected sex. • Hepatitis D: • person must also be infected with hepatitis B • Spread through sharing needles and/or exposure to blood/blood products.

Symptoms • Jaundice • Clay coloured stools • Abdominal Pain • Dark coloured urine • Loss of apetite • Other flu like symptoms

Effects • Hepatitis A: • Recovery from HAV = lifetime immunity • 15-50 days for symptoms to develop • Kills 2% of those over 50 • Patient is contagious until jaundice appears • Can cause liver failure • Hepatitis B: • - babies born to mothers with HBV have higher chance of developing chronic HBV later in life which can lead to cirrhosis and liver cancer. • Chronic HBV can attack the liver for years without detection • 90% of healthy adults will recovery and develop antibody against it.

Hepatitis C: • 70-80% of people progress to chronic infection • 1-5% of infected people die as a result of chronic liver diseases, diseases from liver damage, cancer of liver • Hepatitis D: • Infects those carrying hepatitis B • Up to 20% of hepatitis D infections are rapidly fatal • Doesn’t have necessary viral equipment to replicate itself, depends on HBV for replication • Hepatitis E: • - severity increases with age • Most found in developing countries • Death rate in pregnant women 15-20% • not long lasting

Diagnosis • All forms of hepatitis can be detected and diagnosed through a blood test. • Two tests are necessary to confirm diagnosis of hepatitis C and a measure of liver enzymes through a blood test and sometimes a biopsy.

Treatment • Hepatitis A&B: • -Hepatitis A is prevented by good sanitation • Both hepatitis A and B are treated with rest and relaxation and no alcohol for several months • people with chronic hep. B should get vaccinated for hep. A • Hepatitis C: • can be waited out depending on liver damage seen through biopsy • Doctors will monitor condition, antiviral medication exist to help fight hep C (peginterferonaifa, ribavirin) Hepatitis D: - Liver transplant is effective treatment for severe chronic hepatitis D. Hepatitis E: - get enough calories, drink enough fluids, and avoid harmful liver meds.

Future Outlook • 7 drugs approved in U.S. For chronic hepatitis B • Medical people working on more effective and less toxic antivirals • 2 new drugs, telaprevirand boceprevir have been found and are providing hope for more cures to hepatitis C.

Fun Time!! What is the difference between acute and chronic hepatitis? Acute lasts less than 6 months whereas chronic lasts longer than 6 months Who was the doctor who won the Nobel Prize for Medicine through his work with hepatitis B? Dr. Blumberg To be diagnosed with hepatitis D you must have also been diagnosed with? Hepatitis B The word hepatitis means? Inflammation of the liver. Name 3 symptoms of hepatitis Jaundice, clay coloured stool, abdominal pain, dark coloured urine, loss of appetite, flu like symptoms.

Fun Time Final Round What were Mark McCallum’s original names for hepatitis A&B? Infectious and serum hepatitis What 2 symptoms are only associated with chronic hepatitis? Jaundice and abdominal pain. In severe cases of hepatitis D what is the most effective treatment? Liver transplant

Works Cited Youngson, R.M. 2005, Encyclopedia of Family Health Third Edition, Tarrytown, NY, Marshall Cavendish Corporation Pubmed Health, Hepatitis, 11,11,11, www.ncbi.nlm.nih.gov/pubmedhealth/PMH0002139/ Health Canada, Hepatitis, 11,11,11, www.hc-sc.gc.ca/hc-ps/dc-ma/hep-eng.php Hepatitis B Foundation, Hepatitis B Reference Summary, 11, 11, 11, http://www.hepb.org/hepatitisbcd/modules/infectd/id450101/id459101g.html Public Health Agency of Canada, Hepatitis E Fact Sheet, 11, 11, 11, www.phac-aspc.gc.ca/hcai-iamss/bbp-pts/hepatitis/hep_e-eng.php Public Health Agency of Canada, Hepatitis G Fact Sheet, 11, 11, 11, http://www.phac-aspc.gc.ca/hcai-iamss/bbp-pts/hepatitis/hep_g-eng.php Public Health Agency of Canada, Hepatitis C Fact Sheet, 11, 11, 11, http://www.phac-aspc.gc.ca/hcai-iamss/bbp-pts/hepatitis/hep_c-eng.php Washington State Department of Health, Delta Hepatitis (hepatitis D), 11, 11, 11, www.doh.wa.gov/EHSPHL/factsheet/hepd.htm Hepatitis B Foundation, Meet Dr. Blumberg, 7, 12, 11 http://www.hepb.org/about/blumberg.htm

Picture Citations http://kirstyne.files.wordpress.com/2007/09/infection.gif http://www.beltina.org/pics/hepatitis_prevention.jpg http://www.mysafetysign.com/img/lg/S/Unsafe-Drinking-Water-Notice-Sign-S-2828.gif http://www.asianweek.com/wp-content/uploads/2010/08/Dr.Blumberg.jpg http://www.hongkiat.com/blog/wp-content/uploads/homersimpson-css.gif http://www.vaccineinformation.org/photos/hepbuta002.jpg

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• Review Article
• Published: 16 June 2023

Antigen presentation in cancer — mechanisms and clinical implications for immunotherapy

• Kailin Yang 1 ,
• Ahmed Halima 1 &
• Timothy A. Chan   ORCID: orcid.org/0000-0002-9265-0283 1 , 2 , 3 , 4  

Nature Reviews Clinical Oncology volume  20 ,  pages 604–623 ( 2023 ) Cite this article

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• Immunotherapy
• Predictive markers
• Tumour immunology

Over the past decade, the emergence of effective immunotherapies has revolutionized the clinical management of many types of cancers. However, long-term durable tumour control is only achieved in a fraction of patients who receive these therapies. Understanding the mechanisms underlying clinical response and resistance to treatment is therefore essential to expanding the level of clinical benefit obtained from immunotherapies. In this Review, we describe the molecular mechanisms of antigen processing and presentation in tumours and their clinical consequences. We examine how various aspects of the antigen-presentation machinery (APM) shape tumour immunity. In particular, we discuss genomic variants in HLA alleles and other APM components, highlighting their influence on the immunopeptidomes of both malignant cells and immune cells. Understanding the APM, how it is regulated and how it changes in tumour cells is crucial for determining which patients will respond to immunotherapy and why some patients develop resistance. We focus on recently discovered molecular and genomic alterations that drive the clinical outcomes of patients receiving immune-checkpoint inhibitors. An improved understanding of how these variables mediate tumour–immune interactions is expected to guide the more precise administration of immunotherapies and reveal potentially promising directions for the development of new immunotherapeutic approaches.

The clinical success of immune-checkpoint inhibitors has improved cancer care, although long-term durable remission is only achieved in a subset of patients.

Antigen processing and presentation by tumour cells are essential for long-lasting immune surveillance.

Alterations in the genes encoding MHC components and other parts of the antigen-presentation machinery are frequently found across several cancer types and are associated with both tumour development and the effectiveness of immunotherapies.

MHC-based antigen presentation exerts strong evolutionary pressure on the immunopeptidome, which in turn shapes the mutational landscape of the tumour genome.

Germline human leukocyte antigen diversity and somatic aberrations in the antigen-presentation machinery inform the therapeutic response to immune-checkpoint inhibitors.

Development of novel therapies based on an accurate understanding of antigen presentation in the setting of tumour–immune dynamics is crucial to the development of improved therapeutic approaches.

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Introduction.

Over the past decade, the success of immune-checkpoint inhibitors (ICIs) has transformed cancer care and substantially improved the survival outcomes of patients with cancer 1 . The clinical application of ICIs that target CTLA4, PD-1 or its ligand PD-L1 mobilizes the immune system to detect and eradicate tumour cells 2 , 3 . Efficacy of the anti-CTLA4 monoclonal antibody ipilimumab was originally demonstrated in patients with metastatic melanoma in 2010, resulting in FDA approval in 2011 (refs.  2 , 4 ). Several antibodies targeting PD-1 or PD-L1, including nivolumab and pembrolizumab, have since been approved for clinical use in an expanding list of tumour types, including melanoma, lung cancer, renal cell carcinoma, head and neck cancer, bladder cancer, oesophageal cancer, and certain types of breast cancer 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 . Initially used in patients with metastatic cancers, ICIs have now demonstrated clinical utility in the neoadjuvant, adjuvant and maintenance treatment settings 13 , 14 . However, therapeutic responses to ICIs at the individual level are highly variable, and durable long-term disease control is only achieved in a fraction of patients 15 , 16 , 17 . Furthermore, ICIs can cause both acute and chronic immune-related adverse events, including damage to multiple organ systems or even death in some patients 18 . Therefore, identifying biomarkers that accurately predict response to ICIs, with the goal of fine-tuning precise approaches to patient selection and understanding mechanisms of resistance, is an area of considerable research interest 19 , 20 . Research by us and subsequently others has shown that tumours with a high tumour mutational burden (TMB), including those with deficient mismatch repair (dMMR) or with high microsatellite instability (MSI-H), are more sensitive to ICIs targeting PD-1 than tumours with a low TMB 21 , 22 , 23 , 24 . FDA approvals of various anti-PD-1 antibodies for patients with high TMB or MSI-H solid tumours were among the first tissue-agnostic or site-agnostic approvals for any cancer therapy 25 . Strikingly, in 2022, single-agent therapy with the anti-PD-1 antibody dostarlimab generated a 100% complete response rate in a phase II trial involving patients with dMMR rectal adenocarcinoma 26 . Other biomarkers, such as PD-L1, have also entered clinical practice to guide treatment decision-making regarding the use of ICIs 27 , 28 , 29 .

Immunogenicity refers to the ability of an antigen to provoke an immune response 30 , 31 . In cancers, the proper recognition of tumour antigens presented on MHC class I (MHC I) by cytotoxic CD8 + T cells is a cornerstone of antitumour immunity 32 . The existence of both cancer cells and T cells in a tumour, known as the Hellstrom paradox, implicates the emergence of dysfunctional antigen presentation and recognition processes as a contributor to carcinogenesis 33 . Activation of CD8 + T cells first requires the priming of naive precursor T cells by antigen-presenting cells (APCs) followed by direct recognition of tumour cells via binding of cognate T cell receptors (TCRs) to tumour antigen–MHC I complexes 34 . This latter process generates an antigen-specific signal that is elicited through the binding of the TCR to the antigen–MHC I complex — commonly referred to as signal 1 in the immunology literature — that is required for durable adaptive antitumour immunity. The inhibitory roles of CTLA4, PD-1 and PD-L1 in T cell activation and proliferation underscore the importance of secondary signalling processes that regulate signal 1 for the clinical success of ICIs 12 . Signal 1 function is also crucially dependent on many germline and somatic factors that modulate the presentation of tumour antigens by MHC I 35 . In this Review, we focus on the role of tumour cell antigen presentation in the therapeutic response to ICIs. We survey the biological processes regulating the antigen-presentation machinery (APM) in cancer and discuss how these processes modulate immunopeptide processing and presentation to T cells. Finally, we discuss and summarize available data on the clinical consequences of altered antigen presentation in patients receiving ICIs.

Mechanisms of tumour cell antigen presentation

Mhc complexes.

Antigen processing and presentation enable the adaptive immune system to survey the host cell proteome and detect pathogens and mutations 36 , 37 . MHC I and MHC II are the two predominant MHCs, although other minor non-canonical types also exist. MHCs, encoded by the HLA genes in humans, are evolutionarily conserved across vertebrates, albeit with several highly polymorphic regions such as the peptide-binding groove 38 . MHC I is ubiquitously expressed on the surfaces of nucleated cells and comprises a heterodimer with a membrane-anchored heavy chain encoded by the classical HLA-A , HLA-B or HLA-C genes (subsequently referred to as MHC I genes), in association with β2-microglobulin (B2M) 39 . The peptides bound to MHC I molecules are usually in the range of 8–11 amino acid residues in length, and the peptide–MHC I complex is recognized by the TCR of CD8 + cytotoxic T cells 40 . In both non-malignant cells and cancer cells, peptides presented on MHC I are generated mostly from cytosolic proteins 41 ; however, in APCs, MHC I can also present peptides of an extracellular origin, a process known as cross-presentation 42 . By contrast, MHC II is composed of an α-subunit and a β-subunit anchored to the membrane, is encoded by the HLA-DP , HLA-DM , HLA-DO , HLA-DQ or HLA-DR genes, and is typically found on professional APCs and other haematopoietic cells 43 . MHC II molecules are able to present larger peptides of approximately 10–16 residues in length derived primarily from extracellular sources and are mostly recognized by CD4 + helper T cells 44 . MHC II complexes tend to accommodate a more diverse range of peptide ligands and are thus more promiscuous. Unlike the classical HLA genes, which are strikingly polymorphic, the human genome also contains several class I-like HLA genes, including HLA-E , HLA-F and HLA-G 45 , which have limited levels of polymorphism and encode non-classical MHC molecules that primarily modulate the function of immune cells 46 .

Antigen processing and presentation in tumour cells

Endogenous proteins are in a state of constant turnover with controlled degradation mediated by a process involving ubiquitination followed by proteasomal degradation 47 . Misfolded proteins and defective polypeptides generated from aberrant translation are also substrates for the proteasome, the core particle of which is a barrel-shaped megacomplex 48 . The cap complexes located at both ends of the core complex facilitate the entry of source proteins via deubiquitination and unfolding 49 . Peptide hydrolysis is then carried out by the proteasome and mediated by three catalytically active subunits, β1, β2 and β5, which are encoded by the proteasome 20S subunit genes PSMB6 , PSMB7 and PSMB5 , respectively 50 . The catalytic subunit forms an acyl–enzyme intermediate with the N-terminal part of the peptide, which is commonly resolved with another water molecule to generate the hydrolysis products. However, such acyl–enzyme intermediates can also be resolved to generate trans-spliced peptides via the involvement of α-NH 2 of the amino terminus of another peptide 51 , 52 . Both natural and spliced peptides have been reported as immunogenic ligands suitable for presentation by MHC I. However, the frequency at which trans-splicing occurs remains controversial 53 , 54 . Upon induction with cytokines, such as IFNγ, during infection and/or other inflammatory conditions, the constitutive catalytic β-subunits (β1, β2 and β5) of the proteasome are replaced by their inducible counterparts β1i, β2i and β5i (encoded by PSMB9 , PSMB10 and PSMB8 , respectively), thus forming the immunoproteasome 55 . In comparison with its non-immune counterpart, the immunoproteasome has distinct proteolytic features that generate peptides with hydrophobic or basic carboxyl termini and an enhanced affinity for MHC I 56 .

Following processing by the proteasome in the cytosol, peptides with a preferential size of 9–16 residues are translocated into the lumen of the endoplasmic reticulum (ER) via the transmembrane channel within the transporter associated with antigen processing (TAP) complex 57 . Two luminal enzymes, ER aminopeptidase 1 (ERAP1) and ERAP2, further trim the peptides to a length of 8–11 residues 58 . Assembly of the peptide–MHC I complex is then mediated by the peptide loading complex (PLC), comprising the MHC I molecules, TAP, tapasin, ERp57, calreticulin and calnexin 59 . Tapasin recruits newly synthesized MHC I complexes, and ERp57 and calreticulin promote proper folding of the recruited complexes. Appropriately trimmed candidate peptides from ERAP1 and ERAP2 are then loaded onto the polymorphic MHC I binding groove, followed by a specific selection process keeping the canonical high-affinity peptides for further optimization 60 . Additional quality control, which optimizes peptide–MHC I complex binding, is coordinated by tapasin and TAP binding protein-related protein (TAPBPR) 61 , 62 . Stable peptide–MHC I complexes with high levels of binding affinity then dissociate from the PLC and are transported to the cell surface via the Golgi complex. Bound peptides are then exposed to the extracellular space, enabling recognition by the cognate TCRs of CD8 + T cells and permitting immune surveillance 59 (Fig.  1 ).

Antigen processing in tumour cells begins with proteasome-mediated degradation of endogenous proteins, including misfolded and defective polypeptides. IFNγ induces expression of the proteasome β1i, β2i and β5i subunits, which form the immunoproteasome and confer distinct proteolytic preferences. Both the regular proteasomes and immunoproteasomes are involved in the formation of antigen peptides. The resulting peptides, with a size of 9–16 amino acid residues, are transported into the endoplasmic reticulum lumen by the transporter associated with antigen processing (TAP) complex. Newly synthesized MHC class I (MHC I) molecules form the peptide loading complex (PLC) along with β2-microglobulin (B2M), tapasin, ERp57 and calreticulin, which facilitate proper MHC I folding. Antigen peptides undergo additional trimming by endoplasmic reticulum aminopeptidase 1 (ERAP1) and ERAP2 followed by loading into the peptide-binding groove of the MHC I complex, and additional optimization mediated by TAP binding protein-related protein (TAPBPR). Stable peptide–MHC I complexes then dissociate from the PLC and are transported to the cell surface via the Golgi apparatus and transport vesicles, a process that is stimulated by IFNγ signalling. Once presented on the surface of tumour cells, the bound peptides can be recognized by the T cell receptors (TCRs) of CD8 + T cells to generate immune responses. Stars highlight parts of the antigen-presentation machinery that are altered in tumours and/or those that affect clinical outcomes in patients with cancer. JAK, Janus kinase.

Priming of CD8 + T cells via cross-presentation

Productive priming of naive CD8 + T cells to tumour cell-associated antigens, which commonly occurs in the draining lymph nodes, is a prerequisite for the generation of antigen-specific effector responses 63 . APCs, such as dendritic cells (DCs), are able to take up released tumour antigens through phagocytosis, endocytosis or direct trogocytosis from live cancer cells 64 . In contrast to the MHC II-mediated antigen presentation to TCRs on naive CD4 + T cells, priming of CD8 + T cells via cross-presentation is dependent on MHC I 65 . After uptake, tumour-derived proteins located in the phagosomes, which are processed by DCs in the same ways as extracellular content, can undergo two intracellular processes that enable their processed peptides to reach the nascent MHC I complex in the ER: the cytosolic pathway or the vacuolar pathway 66 . For the cytosolic pathway, phagosomal rupture enables tumour-derived polypeptides to enter the cytosol 67 . These polypeptides then undergo hydrolysis in the proteasome followed by translocation into the ER through the TAP complex and PLC-mediated loading onto MHC I. Conversely, in the vacuolar pathway, the phagosomes containing tumour-derived proteins fuse directly with the ER, and their processed peptides are delivered directly to the loading system and onto MHC I without passing through the cytosol 68 . Cross-presentation of tumour antigens by DCs is essential to prime an endogenous CD8 + T cell-mediated immune response following the administration of ICIs 69 .

Variations in the APM

Immune escape is a key hallmark of tumorigenesis in an immunocompetent patient, and cancer cells can develop various strategies to evade immune surveillance and suppress antitumour immunity 70 . After priming with tumour antigens presented by DCs in the draining lymph nodes, activated CD8 + T cells traverse to the tumour and initiate cytotoxic killing by targeting the peptide–MHC I complexes on cancer cells 42 . Factors that alter antigen display on tumour cells, including germline variations and somatic aberrations in the genes encoding MHC I and other APM components, transcriptional modulation, post-translational modification, and epigenetic and epitranscriptomic regulation, can influence the strength of antigen-directed cytotoxicity by the immune system (Table  1 ).

Genetic variants in HLA alleles

The HLA locus on chromosome 6 is one of the most polymorphic regions in the human genome 41 . The three genes encoding the heavy chains of MHC I ( HLA-A , HLA-B and HLA-C ) include a total of six HLA-I allotypes per individual (one on each chromosome that is expressed in a co-dominant fashion) 40 . HLA diversity is selected for to enhance immune function under evolutionary pressures from infectious agents. Doherty and Zinkernagel postulated that this heterozygosity confers the advantage of better immunological function 37 , and HLA diversity enables more efficient immune-dependent control of many infectious diseases 71 , 72 , 73 , 74 . Given the unique preferences of each HLA-I molecule for specific antigenic peptides, patient-specific germline HLA genotypes have been shown to influence tumour antigen presentation and the extent of therapeutic responses to ICIs 75 , 76 . Patients with maximal heterozygosity at all three HLA-I loci have improved survival after administration of anti-CTLA4 and/or anti-PD-1 or anti-PD-L1 antibodies compared to those who are homozygous for at least one locus 77 . Research by several groups also indicates that HLA homozygosity is associated with inferior outcomes in patients with non-small-cell lung cancer (NSCLC) receiving ICIs as monotherapy 78 , 79 . A similar effect is observed in patients with urothelial carcinoma receiving intravesical Bacillus Calmette–Guerin infusions 80 . Conversely, a high level of HLA molecular diversity, as quantified by HLA evolutionary diversity (HED), is associated with improved clinical benefit from ICIs 81 . HED is calculated using the Grantham distance of the HLA alleles, an established metric that quantifies physiochemical differences between the coding sequences for each HLA gene 81 . Using a machine learning approach, we showed that HED is an independent contributor to ICI efficacy 82 . Multiple other groups have now validated the association between HLA diversity and improved clinical outcomes following ICI treatment, and this association has been seen in patients with many cancer types, including those with melanoma 81 , 83 , NSCLC 84 , gastrointestinal cancers 85 , renal cell carcinoma 86 and bladder cancer 80 , 87 (Table  1 ). Nonetheless, the contribution of HED to the efficacy of ICIs might not be as strong as that of TMB 81 , especially in high-TMB tumour types. In fact, the effects of HED are most apparent when TMB and HED are both considered 80 . Indeed, biomarkers that are based on assessments of complex biological features, such as antigen presentation, should not be used in isolation from other informative and potentially confounding variables. Moreover, HLA diversity seems to also strongly dictate clinical outcomes after bone marrow and solid organ transplantation, two situations in which HLA diversity would be hypothesized to have a fundamental role 88 , 89 . Importantly, examination of highly variable cohorts with approaches involving too many confounding biases and using error-prone HLA genotyping strategies (such as imputation) should be avoided.

Individual germline HLA-I alleles and supertypes, which are defined as a group of HLA-I alleles with similar peptide-binding features, can also inform on the likelihood of benefit from ICIs (Fig.  2a ). For example, in two independent cohorts of patients with melanoma, improved overall survival (OS) was associated with the HLA-B44 supertype (at a prevalence of 45%) and inferior OS was observed with the HLA-B62 supertype (at a prevalence of 15%) 77 . The B44 supertype, which features an electropositive binding pocket that preferentially displays peptides with negatively charged amino acid anchors, is associated with improved OS in patients with melanoma receiving ICIs, although this effect was not seen in patients receiving the same therapy for NSCLC 77 . This difference might reflect that mutations leading to glutamic acid substitutions have been shown to occur more often in melanoma than in NSCLC based on comparisons of the mutational landscapes of these tumours 90 . The same researchers also showed that stratifying patients with NSCLC harbouring HLA-B44 based on the presence of somatic mutations that lead to negatively charged glutamic acid anchors identifies a subpopulation that derives similar levels of benefit from ICIs as seen in patients with melanoma 90 . Molecular dynamics simulations using the HLA-B*15:01 allele from the HLA-B62 supertype demonstrated impaired interactions with TCRs 77 . Similar to HLA-B62 , HLA-A*03 is associated with inferior OS after ICIs in a number of tumour types, including bladder cancer and renal cell carcinoma 91 . Somatic loss of heterozygosity (LOH) in the HLA locus is another underlying mechanism leading to immune evasion by reducing antigen presentation. In a pan-cancer survey of samples from 610 patients with 15 different tumour types, HLA LOH was identified in 18%, ranging from 4% with liver cancer to 40% with head and neck squamous cell carcinoma (HNSCC) 92 . The majority of HLA LOH (76.4%) resulted in loss of all three HLA-I genes. These effects of evolutionary selection on antigen presentation are further corroborated by the strong correlation between HLA LOH, neoantigen burden and PD-L1 expression 92 . Somatic mutations in HLA-I genes offer another mechanism of immune evasion, although the extent of polymorphism at HLA loci poses a challenge to accurate mutational profiling 93 . Interrogation of tumour samples and matched non-malignant tissues using The Cancer Genome Atlas (TCGA) revealed non-silent somatic HLA mutations in 3.3% of patients and, interestingly, the highest prevalence was again seen in those with HNSCC (7.9%) 94 . Exon 4, which encodes the α3 domain of the MHC I heavy chain, the region that binds to the CD8 glycoprotein on cytotoxic T cells 95 , was the most commonly mutated. A positive association between HLA mutations and the transcriptional signature of effector lymphocyte infiltration is consistent with the existence of selective pressure on tumour cell antigen presentation imposed by the host immune system.

Both germline and somatic alterations in the genes encoding HLA class I (HLA-I) and other antigen-presentation machinery (APM) components can alter the display of antigens on the cell surfaces of tumour cells and therefore influence the antigen-directed T cell responses elicited by the immune system. a , The HLA locus is among the most polymorphic regions of the human genome. HLA-A , HLA-B and HLA-C (subsequently referred to as HLA-I) encode a total of six HLA-I alleles in each individual. Germline variations in these genes, characterized by the unique antigen preferences of each HLA-I molecule together with the combination of HLA-I allotypes, shape the vast diversity of peptide selection and presentation in each patient. A higher level of HLA evolutionary diversity (HED) is associated with clinical benefit among patients with solid tumours receiving immune-checkpoint inhibitors 81 . b , Transcriptional regulation of HLA-I is mediated by the IFNγ signalling pathway. IFNγ activates Janus kinase (JAK)–signal transducer and activator of transcription (STAT) signalling via IFNGR binding, leading to the induction of interferon regulatory factor 1 (IRF1) activity and overexpression of NLRC5, which subsequently bind with interferon-sensitive response element (ISRE) and cis-regulatory elements of W/S, X1, X2, and Y motifs (W/S-X-Y) upstream of HLA-I gene loci, respectively. Besides transcriptional modulation, mutations in genes encoding APM components, such as B2M , as well as those in the IFNγ signalling pathway have been reported in large-scale sequencing studies. Loss of heterozygosity (LOH) of one or more HLA-I alleles or at the B2M locus is another genetic mechanism that alters antigen presentation in tumour cells. c , Post-transcriptional regulation of antigen presentation involves the regulation of HLA-I mRNA translation and fine-tuning of MHC class I (MHC I) accessibility to T cells on the tumour cell surface. Both mRNAs and the MEX3 family of RNA-binding proteins directly affect the translation of MHC I complexes and other APM components such as transporter associated with antigen processing 2 (TAP2) and calreticulin. On the tumour cell membrane, signal peptide peptidase-like 3 (SPPL3) alters the glycosphingolipid repertoire to create a shield around the antigen–MHC I complex on cancer cells and inhibit T cell receptor (TCR) binding. Selective autophagy of antigen–MHC I complexes followed by lysosome-mediated degradation adds another layer of regulation capable of suppressing antigen presentation. d , Both epigenetic and epitranscriptomic mechanisms are important regulators of antigen processing and presentation in tumour cells. Polycomb repressive complex 2 (PRC2) enforces the bivalent status of the promoter regions of HLA-I and other APM genes, comprised of activating H3K4me3 and repressive H3K27me3. This epigenetic reprogramming suppresses both basal and cytokine-induced HLA-I expression. At the epitranscriptomic level, the METTL3–METTL14 complex deposits N 6 -methyladenosine (m 6 A) modifications on HLA-I mRNAs, thus promoting protein translation. By contrast, the mRNAs of several autophagy-related genes, which have been implicated in the regulation and surface display of antigen–MHC I complexes, are modified by m 6 A and then recognized by reader proteins such as YTHDF2, leading to mRNA degradation and reduced protein levels. B2M, β2-microglobulin; EZH2, enhancer of zeste homologue 2; miRNA, microRNA; UTR, untranslated region.

Besides MHC I, neoantigens presented by MHC II also have an essential role in antitumour immunity, thus highlighting the clinical significance of CD4 + T cell activity mediated by antigen presentation on MHC II 96 . CD4 + T cells exposed to antigen–MHC II complexes promote the activation of CD8 + cytotoxic T cells during treatment with ICIs. Using computational approaches, the size of the immunogenic mutational burden presented by MHC II was shown to predict responsiveness to ICIs in patients with NSCLC or melanoma 97 . The fact that most MHC II-restricted neopeptides are non-overlapping with MHC I-restricted neoantigens supports a complementary role of MHC II in establishing antitumour activity 96 . Interestingly, HLA-II HED might have implications for diseases and therapies with an autoimmune component. For example, low HLA-II HED is associated with disease progression and inferior OS in patients with aplastic anaemia 98 . Elsewhere, investigators have shown that combined measures of MHC I to MHC II HED ratio provide an independent marker that is positively associated with disease-free survival and OS after allogeneic stem cell transplantation in patients with acute myeloid leukaemia 99 .

Modulation of HLA expression

HLA expression, and that of other APM components, can be regulated through numerous mechanisms, including at the transcriptional, translational and post-translational levels (Fig.  2b ). IFNγ, secreted by activated T cells in an inflamed tumour microenvironment, has an important role in the transcriptional regulation of HLA expression 100 , 101 . HLA expression, probably through modulation of antigen presentation, can influence clinical outcomes. In a cohort of 2,863 patients with colorectal cancer, high levels of HLA-B and HLA-C expression were identified as independent prognostic biomarkers for favourable OS 102 . The promoter regions of HLA genes are comprised of several cis-regulatory domains: the upstream region with enhancer A and interferon-sensitive response element (ISRE), which provide the binding sites for NF-κB and interferon regulatory factor 1 (IRF1), respectively, and the downstream nodule of W/S, X1, X2 and Y motifs (W/S-X-Y) that can be activated by NLRC5, an MHC class I transactivator (CITA) 59 . IFNγ activates the Janus kinase (JAK)–signal transducer and activator of transcription (STAT) signalling pathway to induce IRF1 activity and stimulates the expression of NLRC5 (ref.  103 ). IRF1 and NLRC5 subsequently upregulate the transcription of HLA-I genes through direct binding to their regulatory cassettes. In a pan-cancer analysis of TCGA datasets, NLRC5 expression is highly correlated with the expression of HLA-I, B2M and other APM components such as TAP1 (ref.  104 ). Among all APM-related genes, NLRC5 had the highest prevalence of genetic and epigenetic aberrations, including somatic mutations, copy number loss and promoter methylation, and reduced NLRC5 expression leads to impaired HLA-I expression, reduced CD8 + T cell activation and inferior OS outcomes. Reactivation of DUX4, an early embryonic transcription factor, suppresses HLA-I expression by blocking IFNγ signalling in a range of solid tumours 105 . Conversely, NF-κB promotes the transcription of HLA-I genes 106 , and forced expression of the NF-κB subunit p65 together with IRF1 reconstitutes the processing and presentation of tumour antigens to cytotoxic T cells 107 .

MicroRNAs (miRNAs) downregulate target mRNAs through complementary binding to the 3′-untranslated region (UTR) or open-reading frame (ORF) 108 . Several miRNAs, such as miR-148a-3p, miR-125a-5p and miR-27a, suppress MHC I expression in tumour cells and thus inhibit the cytotoxic antitumour activity of T cells 109 , 110 (Fig.  2c ). Mechanistically, miR-148a-3p binds directly to the 3′-UTR of HLA-A , HLA- B and HLA- C mRNAs. Binding sites for miR-125a-5p and miR-27a can be found on the mRNAs encoding TAP2 and calreticulin, respectively. The MEX3 family of RNA-binding ubiquitin ligase proteins exerts another layer of post-transcriptional regulation; for example, MEX3B binds to the 3′-UTR of HLA-A mRNA and suppresses its translation, thus inhibiting the cytotoxic killing of melanoma cells by T cells 111 , and MEX3C promotes the deadenylation and degradation of HLA-I mRNAs through ubiquitin-mediated modulation of intracellular CNOT7 activity 112 .

At the post-translational level, macroautophagy is an essential regulator of MHC I expression and, in mouse models of pancreatic ductal adenocarcinoma, the autophagy cargo receptor NBR1 induces selective autophagy of MHC I complexes 113 . Removal of MHC I from the cell surface impairs the antitumour activity of CD8 + T cells, while inhibition of autophagy reinstates antigen presentation and CD8 + T cell recognition. In addition to autophagy, tumour cells can co-opt ER-associated protein degradation to channel nascent MHC I heavy chains into the cytoplasm for degradation, thus suppressing antigen loading 114 . On the cell membrane, overexpression of myelin and lymphocyte protein 2 (MAL2) engages endocytosis and promotes the turnover of peptide-loaded MHC I complexes 115 . Alterations in the glycosphingolipid content, regulated by transmembrane protease signal peptide peptidase-like 3 (SPPL3), create a phospholipid shield around the antigen–MHC I complex on cancer cells that hinders the ability of TCRs on CD8 + T cells to gain access 116 .

Alterations in other APM components

In addition to genes encoding HLAs, over a dozen genes encode other components of the APM, including the various components needed for epitope processing, transport and presentation. A comprehensive analysis of samples from 18 solid tumour types revealed recurrent point mutations in B2M , the invariant chain of the MHC I complex, with the highest prevalence observed in patients with stomach cancer (5.7%) followed by those with colorectal cancer and cervical cancer 117 (Fig.  2b ). The overall mutation rate in B2M is lower than that of HLA-I genes, although both are enriched in specimens with a high level of immune cytolytic activity, suggesting the existence of selective pressures on antigen presentation during tumour evolution 117 . Microdeletions and/or insertions in the repetitive nucleotide motifs of B2M , which potentially provide an alternative mechanism for inactivation, are most commonly heterozygous and demonstrate strong associations with MSI in patients with colorectal cancer and melanoma, linking the presence of a high neoantigen burden with immune escape through altered antigen presentation 17 , 118 . The extent to which such disruption of antigen presentation accelerates tumorigenesis in this setting is not yet known. Although mutations in B2M have been found in a few patients with acquired resistance to ICIs, these events are rare 17 , 119 . B2M LOH events are known to occur, yet the functional consequences of this single copy loss on APM function are not known 120 and corresponding mutation of the other allele remains very rare. Genetic inactivation of B2M , through mutations, insertions or deletions, has been found in 29% of patients with diffuse large B cell lymphoma (DLBCL), leading to aberrant MHC I expression 121 . Somatic inactivation of B2M or HLA-I loci occurs in up to 80% of DLBCL that lack surface MHC I expression, while 70% of those positive for MHC I expression harbour monoallelic HLA-I alterations 122 . These alterations probably contribute to the development of DLBCL by enabling immune evasion.

Alterations in other APM components, although less frequent than those in B2M or HLA-I genes, have been reported in numerous other cancers. Point mutations located close to the ATP-binding site of TAP1 give rise to a loss of antigen presentation in a small-cell lung cancer cell line 123 . Mutations in calreticulin are found in essential thrombocythaemia or primary myelofibrosis, leading to impaired PLC function and reduced surface antigen display on MHC I 124 , 125 . Attenuation of the expression of the inducible catalytic β-subunits β1i, β2i and β5i of the immunoproteasome in NSCLC cells defines a mesenchymal phenotype and results in a depleted repertoire of MHC I-bound peptides as well as in increased risk of both disease recurrence and metastasis 126 . Conversely, overexpression of the immunoproteasome genes PSMB8 or PSMB9 was associated with better OS and enhanced immune cell infiltration in TCGA melanoma cohort and predicted response to ICIs in two cohorts of such patients receiving anti-CTLA4 or anti-PD-1 antibodies 127 .

Epigenetic and epitranscriptomic regulation

Epigenetic mechanisms capable of regulating APM components include aberrant DNA methylation, post-translational histone modifications and altered chromatin organization (Fig.  2d ). Epigenetic silencing can reduce HLA expression and promote immune evasion during tumorigenesis. DNA methylation of HLA genes can contribute to the variations in allelic HLA-I expression in individuals and provides a mechanism to generate diversity in antigen presentation 128 . Promoter hypermethylation of HLA-I genes has been observed in a tumour-specific manner in an analysis of gastrointestinal cancers 129 . Promoter hypermethylation reduces the level of HLA-I expression and modulates antigen presentation by downregulating the overall number of MHC complexes 129 . In cancer cells that lack cell surface expression of classical HLAs, polycomb repressive complex 2 (PRC2) maintains a bivalent chromatin state with activating H3K4me3 and repressive H3K27me3 at the promoter regions of HLA-I and other APM genes such as PSMB8 , PSMB9 , TAP1 and TAP2 . This chromatin state leads to the suppression of gene expression both at basal levels and after cytokine-mediated induction 130 . Alterations leading to changes in PRC2 function also include recurrent alterations in enhancer of zeste homologue 2 (EZH2), the catalytic subunit of PRC2, in DLBCLs harbouring a loss of both MHC I and MHC II expression 131 . Introducing Ezh2 Y641F/N mutations, which alter the enzymatic activity and enhance the production of trimethylated H3K27, into a mouse model of lymphoma leads to a reduction in MHC expression and T cell infiltration, and pharmacological targeting of EZH2 in human DLBCL cell lines restores defective MHC expression. Moreover, interrogation of hepatocellular carcinomas included in TCGA revealed that EZH2 overexpression is associated with attenuated expression of HLA-I genes and B2M and results in an immunosuppressive microenvironment and inferior clinical outcomes 132 .

N 6 -Methyladenosine (m 6 A), the most abundant endogenous modification of eukaryotic mRNAs, regulates mRNA stability and translation by recruiting reader proteins 133 . HLA-I mRNAs are methylated at m 6 A, and such epitranscriptomic regulation by the m 6 A methyltransferase proteins METTL3 and METTL14 promotes the translation of HLA-I 134 . m 6 A methylation also regulates autophagy through methylation of the mRNAs of several important genes, including ULK1 (encoding Unc-51-like kinase 1), ATG5 (encoding autophagy protein 5) and ATG7 , leading to the recruitment of reader protein YTH domain-containing family protein 2 (YTHDF2) followed by mRNA degradation and reduced protein expression 135 , 136 . Given the key role of autophagy in regulating cell surface expression of MHC I, further elucidating the regulatory role and importance of m 6 A methylation in tumour cell antigen presentation will become increasingly important. In DCs, RNA methylation regulates the cross-presentation of tumour neoantigens and the cross-priming of CD8 + T cells via the m 6 A reader protein YTHDF1 (ref.  137 ). Mechanistically, the transcripts of lysosomal proteases, such as cathepsins, are marked with m 6 A and subsequently recognized by YTHDF1, which stimulates the translation of these enzymes to facilitate antigen processing.

Immunopeptidome alterations

The repertoire of short (8–16 amino acids in length) peptides presented at the cell surfaces by MHC I and MHC II are collectively referred to as the immunopeptidome 41 . T cells scan the immunopeptidome for non-self peptides that might indicate the presence of an infection or malignant process, leading to the eradication of abnormal cells through immune-mediated cytotoxic activity. Efforts to systemically characterize the constitutive immunopeptidome of non-malignant human tissues, including the Human Immunopeptidome Project and MHC Ligand Atlas, have generated some useful data 138 , 139 . The HLA-A , HLA-B and HLA-C allotypes contribute unevenly to the peptide repertoire across different non-malignant tissues, and such diversity is further exemplified by the preferences of each individual HLA-I haplotype for a distinct set of antigens 140 . Somatic mutations and oncogenic viruses generate neoantigens that differentiate tumour cells from their non-malignant counterparts and enable polyclonal T cell responses to occur 15 , 22 , 76 , 141 , 142 , 143 , 144 . Novel epitopes can be generated from many types of mutations, including somatic missense mutations, insertions and deletions, frameshifts, nonsense mutations, and splice site mutations. In certain scenarios, neoantigens derived from tumour-specific gene fusions, such as DEK – AFF2 identified in a patient with HNSCC with an exceptional response to anti-PD-1 antibodies, emerged as a particularly immunogenic component of the immunopeptidome that informs therapeutic response 145 . Profiling using an integrated genomic and proteomic strategy enabled the discovery of unique repertoires of neoepitopes linked to MHC I and MHC II in patients with lymphoma 146 . Unlike responses to wild-type tumour-associated antigens such as Melan-A 147 , the immune responses to somatic mutation-derived neoantigens are theoretically not limited by central tolerance and might therefore have substantially greater therapeutic potential 148 , 149 . In fact, increasing excitement exists surrounding the use of neoantigens in a new generation of cancer vaccines. Early data indicate that neoantigen-targeting vaccines can be very active, especially when used alongside ICIs 150 , 151 , 152 , 153 . These data indicate that vaccines that are able to reshape the immunopeptidome can be used to educate productive immunological responses against cancers.

Survey methodologies

Mass spectrometry (MS)-based immunopeptidomics has been a cornerstone of attempts to comprehensively profile MHC-bound peptides in a high-throughput manner 154 , 155 (Fig.  3a ). Peptide–MHC complexes are immunoprecipitated and the peptides are then eluted and subjected to liquid chromatography and tandem MS, which enables the identification of thousands of peptide epitopes from cancer cell lines and tumour specimens 146 , 156 , 157 . Efforts from a global collaboration led to the creation of the SysteMHC Atlas, a public data base of quality-controlled immunopeptidomic data offering tumour type-specific and MHC allele-specific annotation 158 . Through further coupling with DNA sequence aberrations identified using next-generation sequencing, the development of the Cancer Antigen Atlas (caAtlas) added more comprehensive information on post-translational modifications and cancer associations for MHC-bound antigens 159 . Improvements in MS strategies have fuelled an ongoing evolution in immunopeptidome profiling strategies. For example, absolute quantification of tumour antigens has been made possible using heavy isotope-coded peptide standards to determine the endogenous concentration of melanoma antigens 160 . An elegant attempt to introduce an inducible affinity into the mouse H2-K1 gene (encoding an MHC I protein) and target this allele to genetically engineered mouse models revealed cancer-specific patterns of in vivo antigen presentation 161 . Interestingly, MHC peptidomics identified bacterial peptides presented by tumour cell MHC molecules in samples from patients with melanomas 162 . Further improvements in our understanding of the context-dependent interactions between tumour cells and the immune system provided by immunopeptidome survey approaches are expected to prove useful as we enhance our understanding of how the APM influences responses to cancer treatment.

The immunopeptidome is defined as the complete repertoire of short peptides presented at the cellular surface by both MHC class I (MHC I) and MHC II molecules. Understanding the dynamics of the immunopeptidome and its roles in the complex interplay between tumour cells and the immune microenvironment is essential to predicting therapeutic responses to immune-checkpoint inhibitors and other immunotherapies. a , Mass spectrometry (MS) analyses for profiling of MHC-bound peptides enriched from immunoprecipitation, coupled with next-generation sequencing, have generated a comprehensive landscape of neoantigens from patient-derived specimens. This effort is corroborated by the development of sophisticated computational methods enabling more precise annotation and interpretation of experimental data. Newer tools based on machine learning are being utilized to predict antigen binding and the probability of MHC-mediated presentation. b , Immunoediting highlights the existence of dynamic interactions between tumour cells and the immune cell population, in which antigen presentation-mediated T cell activation has an important role. The three key steps of immunoediting include elimination, equilibrium and escape, which are influenced by both the immunogenicity of specific antigens and intratumoural heterogeneity. Presentation of immunogenic neoantigens on tumour cells leads to their recognition by and activation of CD8 + T cells and cytotoxic killing. However, subclones harbouring non-immunogenic antigens or immunogenic antigens that are not adequately presented by MHC I might evade the immune system. In addition, mutations in genes encoding HLA class I and other antigen-presentation machinery components can hinder the presentation of immunogenic peptides. These mechanisms contribute to escape from immunosurveillance and therapeutic resistance. c , MHC-mediated antigen presentation shapes the evolutionary dynamics of the tumour immunopeptidome. This interaction is influenced by additional factors, including HLA evolutionary diversity (HED), tumour mutational burden (TMB), number of neopeptides, number of viral peptides and T cell receptor (TCR) clonality. HED is defined as the Grantham distance between HLA alleles, an established metric that quantifies physiochemical differences between protein sequences for each HLA gene 81 . HPLC, high-performance liquid chromatography.

The development of computational methods for accurate annotation and interpretation of experimental data is equally as important for characterization of the immunopeptidome 163 . Machine learning-based tools, such as NNAlign_MA, NetMHCpan and MHCflurry, have enabled high-throughput spectral analysis, motif deconvolution and prediction of MHC antigen presentation based on ligandome data generated from MS 164 , 165 , 166 . Fine-tuning the crosstalk between experimental approaches and computational methods enhances the scope and accuracy of antigen prediction that can be achieved 167 , 168 . Trained on MS data from 95 HLA-I monoallelic cell lines and integrated with transcript abundance and peptide processing, HLAthena provides allele-specific and length-specific prediction of MHC I epitopes 169 . Similarly, MARIA utilizes deep learning methods to integrate peptide ligand sequences from MS, expression of antigen-encoding genes, and protease cleavage signatures and enables the identification of immunogenic MHC II epitopes in a diverse range of cancer types 170 . Expanding the annotation to novel or unannotated ORFs and other cryptic ORFs, which can be detected using ribosome profiling, helps the discovery of an unexplored pool of tumour-specific, MHC I-bound peptides through the development of computational tools, including Peptide-PRISM and nuORFdb 171 , 172 . Advances in both experimental and computational survey methodologies have enabled the robust identification of neoantigens with therapeutic significance at the individual patient level 173 . Building upon improved vaccine delivery platforms using peptides or mRNAs, neoantigen-based vaccines have been shown to induce clinically meaningful CD4 + and CD8 + T cell responses in patients with melanoma or glioblastoma in early phase trials 150 , 151 , 174 , 175 . Such approaches focusing on patient-specific neoantigens can also be combined with ICIs, and further optimization of survey methodologies will improve therapy design.

MHC-mediated evolutionary pressure in tumours

Immune evasion is a key hallmark of cancer 70 . With ongoing immunosurveillance, immune cells are able to create selective pressure throughout tumour initiation and progression 176 . This dynamic process is crucially dependent on the recognition of antigenic alterations presented on the surfaces of tumour cells, a process modulated by MHCs and the APM (Fig.  3b ). The six unique HLA-I alleles carried by each patient dictate a preferential subgroup of the immunopeptidome that is effectively presented as supported by the development of an MHC I affinity-based scoring scheme 177 . The characterization of 1,018 recurrent oncogenic mutations across 9,176 patients with cancer demonstrates that the frequency of specific oncogenic mutations is inversely correlated with the probability of their presentation on MHC I. At a composite level, neoantigens that are poorly presented by MHC I are more likely to define the mutational profiles of recurrent tumour specimens. A similar study aiming to develop an affinity-based MHC II genotype score indicated that MHC II imparts stronger selection pressure on patient-specific oncogenic mutations but also permits less variability in mutation presentation at the intertumoural level compared with MHC I-restricted neoantigens 178 . Consistent with different roles in T cell activation, MHC II-mediated presentation is mostly bimodal, meaning that a mutation is either presented by MHC II in most patients or in almost none, whereas mutations selected by MHC I tend to be presented by highly variable fractions of patients.

The observation that common driver mutations are poorly presented by both MHC I and MHC II suggests that immunological pressures can shape tumour genomes by applying evolutionary selection during tumour growth 179 . However, multiple other forces also probably have a role. Common hotspot mutations in cancers are associated with distinct mutational signatures with low levels of immunogenicity, and these processes might confound the signals for neoantigen depletion 180 , 181 . A ‘free fitness’ model stems from the rate-limiting processes leading to the emergence of fitness advantages during cancer evolution and reveals a fine balance between neoantigen immunogenicity and tumorigenic potential 182 . The theory of immunoediting delineates how the immune system sculpts developing tumours in a three-step process: elimination, equilibrium and escape 183 . Data from 2017 on the tumour characteristics of rare long-term survivors of pancreatic ductal adenocarcinoma offer a unique angle on the role of neoantigens in immunoediting; tumour samples from these patients were enriched with high-quality neoantigens, with neoantigen quality defined as the calculated probability of TCR binding 184 , 185 . Durable T cell responses accompany these high-quality neoantigens in long-term survivors, while metastatic progression is associated with fewer high-quality neoantigens, consistent with the influence of immunoediting on clonal evolution via lymphocyte-mediated killing of the more immunogenic tumour cells 186 . T cell cross-reactivity between neoantigens and microbial epitopes, which have been implicated in promoting immune-mediated tumour killing through neoantigen molecular mimicry, was described here and by several others 184 , 187 , 188 . The clinical utility of this finding was further showcased by a neoantigen fitness model based on the probability of MHC-mediated presentation, homology to known microbial antigens and subsequent recognition by T cells, which could help to predict the survival of patients with melanoma or NSCLC after treatment with ICIs 185 . The basis of these observations is not fully understood. Certain sequence classes might have been selected for owing to physicochemical properties that render them more readily recognizable by TCRs compared to others.

The implications of antigen presentation for immunoediting are further exemplified in an in-depth case study from the ADAPTeR trial, in which nivolumab was evaluated in treatment-naive patients with metastatic clear cell renal cell carcinoma (ccRCC). Upon enrolment, patient ADR015 presented with a recurrent tumour located in the surgical bed, bone metastases and nodal disease. The patient had a tonsillar metastatic lesion resected prior to receiving nivolumab and had a mixed treatment response (a best response of stable disease and a progression-free survival (PFS) duration of 8.4 months) followed by intracranial disease progression 189 . Initial results from whole-exome sequencing of metastatic deposits collected post-mortem ran counter to the typical association between a high TMB or dMMR and ICI response 24 , 190 , 191 , 192 . This unexpected observation can probably be explained by genetic divergence between nivolumab-resistant sites (brain metastases and pretreatment resected tonsillar metastasis), which had a high TMB (median 10.8 mut/Mb), and nivolumab-responsive disease (nodal metastases) with a median TMB of 1.3 mut/Mb. This discrepancy can probably be explained by the presence of biallelic inactivation of B2M via mutation and LOH in addition to the loss of MLH1 in the nivolumab-resistant lesions, supporting a likely path of immune-mediated selection in which subclonal loss of MLH1 leading to high levels of neoantigens was followed by B2M loss and suppression of antigen presentation.

Alternative sources of MHC-presented epitopes

Several non-traditional sources can contribute to the tumour cell immunopeptidome, which can influence tumour characteristics. Human endogenous retroviruses (HERVs) are important components of the human genome, accounting for approximately 8% of total genomic DNA 193 . HERVs are epigenetically silenced in most non-malignant tissues but can become aberrantly activated in tumour cells during carcinogenesis 194 , 195 , 196 . Pan-cancer interrogation of TCGA datasets revealed a conserved pattern of HERV activation, correlating with high levels of cytolytic activity 117 . The expression of HERVs correlates with patient survival, most prominently in those with ccRCC 197 . Antigens derived from HERVs can be presented on MHC complexes and recognized by T cells to induce immune responses 197 , 198 . Quantification of HERV expression in the ADAPTeR trial revealed that the transcription of ccRCC-specific HERVs can indirectly inform ICI response 189 .

Besides HERVs, several oncogenic viruses, such as Epstein–Barr virus, hepatitis B virus, hepatitis C virus and human papilloma virus, have been associated with T cell activation, suggesting a role in antigen-specific antitumour immunity 117 . Numerous bacterial species are known to colonize and replicate in human tumours 199 , 200 . The composition and diversity of the tumour microbiota can help regulate disease progression and therapeutic response 201 , 202 . Data from an immunopeptidomic study involving patients with melanoma demonstrated that antigens derived from intracellular bacteria can be presented on tumour cell MHC I and MHC II molecules 162 . These bacterial peptides displayed on tumour cells are immunogenic and can elicit specific immune response by mobilizing IFNγ-secreting tumour-infiltrating lymphocytes 162 . In mouse models of lung cancer, the local microbiota can stimulate the development of a cancer-promoting microenvironment via the activation of tissue-resident γδ T cells and the IL-17 signalling pathway 201 . Intracellular bacteria can also reshape the actin cytoskeleton of circulating tumour cells and thereby promote distal metastasis 203 . Commensal bacteria, such as those found in the gut microbiota, have been linked with a response to ICIs 204 , 205 , and determining the clinical significance of the intracellular bacteria present within tumour cells is expected to reveal the role of this new class of tumour antigen in immunotherapy 206 . Further evaluations of these alternative sources of MHC epitopes will expand our understanding of the composition and function of the tumour immunopeptidome and its role in cancer evolution and response to immunotherapies 207 .

Linking antigen presentation with clinical response to ICIs

Modulation of antigen presentation by both germline and somatic mechanisms can regulate antitumour immunity and influence the clinical efficacy of ICIs. Homozygosity for at least one HLA-I locus was associated with worse OS and PFS outcomes in a cohort of 170 patients with NSCLC receiving single-agent ICIs, and this effect was more prominent among patients ≥65 years of age, with high ECOG status (defined as ≥2) or with a PD-L1 tumour proportion score of ≥50% 79 . As mentioned in an earlier section, germline HLA diversity can be quantified using HED 81 . Mechanistically, high HED might increase the diversity of displayed viral, self and neoantigen peptides (Fig.  3c ). The clinical significance of HED in ICI response has been independently validated, and combining HED with TMB improves the predictive potential of either feature alone 80 , 81 , 83 , 84 , 85 , 86 , 87 . This observation emphasizes that germline factors do not work in isolation but rather in concert with other factors that can either promote or impair the activity of ICIs 15 , 78 , 82 , 208 , 209 , 210 . This consideration might explain the apparent interpatient variations in the observable contribution of germline factors to antitumour immunity.

Somatic aberrations in the genes encoding APM components enable tumour cells to exploit the processes of antigen processing and presentation to evade immunosurveillance, leading to resistance to ICIs 179 . Genomic profiling of specimens from four patients with relapsed melanoma after an initial response to pembrolizumab, together with their matched pretreatment tumours, revealed resistance-specific truncating mutations in B2M in one patient and loss-of-function mutations in JAK1 or JAK2 in two others 17 . Homozygous frameshift B2M deletions resulted in loss of outer-membrane localization of the MHC I heavy chain in the recurrent specimen compared to the baseline tumour sample. A similar study involving 14 ICI-resistant lung cancer specimens identified homozygous B2M loss in one sample and downregulation of B2M expression in two others 119 . Elsewhere, an epidemiological survey of B2M mutations suggested enrichment in MSI-H colorectal carcinomas although patients with a combination of B2M mutations and MSI-H were still able to derive clinical benefit from ICIs, suggesting that loss of B2M might decrease but not eliminate T cell-mediated killing 211 , 212 , 213 . Loss-of-function B2M mutations are an efficient method of compromising antigen presentation on MHC I; nonetheless, they are also relatively rare compared to those involving HLA-I based on data from large-scale profiling efforts such as TCGA 117 . The exact reason for such an observation remains unclear, although this might be related to the small size of this gene and the potential role of B2M in directly promoting tumorigenesis 214 , 215 . Moreover, complete inactivation of B2M can result in the activation of natural killer (NK) cells and other types of innate immune cells 216 .

Besides B2M , genetic aberrations in HLA-I alleles are another type of event that promotes immune escape: using the computational tool LOHHLA to determine HLA allele-specific copy number in a cohort of 100 patients with NSCLC, the prevalence of LOH at the HLA loci was determined to be 40%, which was correlated with a high subclonal neoantigen burden and upregulation of cytolytic activity 217 . To integrate multiple predictive biomarkers, a machine learning random forest model with 16 input features (RF16) was developed through integration of TMB, HED and LOH in HLA-I, among other genomic, demographic and clinical features, and was validated using the MSK-IMPACT cohort of 1,479 patients with 1 of 16 cancer types receiving ICIs 82 . RF16 improved the accuracy of predictions of ICI response relative to those based on TMB only, thus highlighting the power of such approaches to guide clinical decision-making in patients who are eligible for ICIs. These data also demonstrate that the clinical consequences of APM-related variables for antitumour immunity are influenced by a combination of physiological covariates such as serum albumin, haemoglobin levels and platelet count. Indeed, the effects of mechanistic biomarkers can be influenced by various physiological parameters.

The differential expression of HLA-I and HLA-II genes can occur in tumour type-specific patterns and result in altered tumour immunogenicity. Interrogation of 33 tumour types from TCGA revealed upregulation of HLA-I genes in most tumours that were immunogenically active based on immune expression signatures 218 , 219 . HLA-II genes were overexpressed in tumours of an inflammatory immune subtype that was associated with the best clinical outcomes 218 . From a therapeutic perspective, the loss of MHC I expression on melanoma cells (as determined by immunohistochemistry) occurs in 43% of patients and is associated with underlying transcriptional repression of HLA-I genes or B2M . These tumours are associated with resistance to anti-CTLA4 antibodies but not to anti-PD-1 antibodies 101 . Another study involving patients with melanoma showed that those with durable responses to anti-PD-1 antibodies as monotherapy all have tumours with intact MHC I expression, and those with tumours harbouring limited MHC I expression have a microenvironment enriched with myeloid cells but with reduced T cell infiltration 220 . Nonetheless, whether data from larger cohorts will validate this observation remains unclear and reduced MHC expression is currently not used in the clinic for prediction purposes. Moreover, the differential effects of MHC loss observed following treatment with anti-CTLA4 versus anti-PD-1 antibodies are difficult to explain. Additional genomic and molecular features, including MHC II expression and tumour heterogeneity, sample purity, and ploidy, have all been found to affect clinical responses to anti-PD-1 antibodies in a cohort of 144 patients with melanoma 221 . However, these conclusions are probably underpowered and those relating to subtype-specific effects have not been validated. Others have found that TMB generally predicts response to ICIs in patients with melanoma regardless of the subtype. Another interesting observation to emerge from this cohort is that expression of MHC II on >1% of melanoma cells can be detected in 30% of patients and is associated with clinical response to anti-PD-1 but not to anti-CTLA4 antibodies 221 . This observation might reflect that the contributions of MHC I and MHC II to ICI response involve distinct signalling pathways. Despite being traditionally thought to be restricted to professional APCs such as DCs, MHC II expression by tumour cells has been recognized over the past few years and found to predict favourable clinical outcomes 222 . Mitogen-activated protein kinase (MAPK) has been implicated in the repression of MHC II expression in tumour cells via an epigenetic mechanism, and combined inhibition of MAPK kinase (MEK) and histone deacetylase induces the expression of MHC II in NSCLC cell lines 223 . Tumour-specific expression of MHC II predicts improved response rates, PFS and OS in patients with melanoma and in those with classical Hodgkin lymphoma receiving anti-PD-1 or anti-PD-L1 antibodies 224 , 225 . Tumour-specific MHC II expression is also associated with clinical benefit from anti-PD-1 or anti-PD-L1 antibodies in patients with HER2-negative primary breast cancer 226 . Consistent with the importance of MHC II in responsiveness to ICIs is the observation that MHC II neoantigens might have a role in immune-mediated tumour rejection 96 , 227 . At the mechanistic level, data from preclinical models have shed new light on the role of CD4 + T cells in the development of antitumour immunity, which has been historically underestimated 228 . IL-21 derived from CD4 + T cells is essential for the formation of a subset of CD8 + cells, characterized by CX3CR1 expression, which has strong cytolytic effects on tumour cells 229 . Compared with CD8 + T cells, intratumoural CD4 + T cells have lower levels of clonal expansion. Both the expression of MHC II-restricted neoantigens on tumour cells and the proper activation of CD4 + T cells are required for spontaneous and ICI-induced antitumour immune responses in animal models, thus supporting the therapeutic significance of MHC II-mediated neoantigen presentation 96 .

An evolutionary perspective

The interaction between tumours and the immune microenvironment maintains a constant selective pressure that drives cancer cell evolution. MHC-mediated antigen presentation forms a molecular foundation for immune recognition and tumour cell killing. Genomic aberrations that attenuate the processing and/or presentation of neoantigens on the surfaces of tumour cells provide selective potential during tumour initiation and progression; for example, HLA LOH is linked with subclonal expansion, a high neoantigen burden and immune activity 217 . Furthermore, in tumours with intact HLA-I alleles together with high or heterogeneous levels of immune infiltration, suppression of neoantigen expression through promoter hypermethylation can provide an alternative route for immune escape 230 . Chemotherapy is often administered concurrently with ICIs, especially to patients with metastatic disease; therefore, the role of chemotherapy in tumour–immune evolution has gained increased recognition 231 . Chemotherapy can directly stimulate the tumour immune microenvironment through upregulation of immune-related genes and by depleting regulatory T cells 232 , 233 . Elevated somatic mutations in tumours treated with chemotherapeutic agents, such as cisplatin, cyclophosphamide and etoposide, at certain doses can promote neoantigen production and presentation, leading to T cell-mediated cytotoxicity of tumour cells, although chemotherapies might also lead to immunosuppression via bone marrow suppression, particularly at higher doses 234 .

Quantifying the efficacy of neoantigen-mediated immunoediting and elimination can inform the development of computational approaches to predict responses to ICIs 235 . Data from studies utilizing bulk and single-cell approaches to evaluate response and resistance to ICIs have shed new light on the complex tumour–immune dynamics after therapy. A multi-omic analysis of 115 multi-region ccRCC specimens before and after treatment with nivolumab revealed enhanced immune cell infiltration and activation among responders, and maintenance of the expanded TCR clones initially characterized in pretreatment samples can predict therapeutic response 189 . The tumour genomic landscape sculpts the TCR repertoire of the immune microenvironment through positive selection and expansion of the responsive T cell clones, which, in turn, creates a selective pressure during immunoediting that removes mutations in tumour cells owing to recognition and destruction by cytotoxic T cells 236 . Assessments of a combination of complementary factors that include alterations in the APM and those associated with immune infiltration and response (including TCR clonality and immunoediting) might be an effective strategy to predict disease recurrence and highlight the importance of an integrated view of antigen presentation-mediated tumour evolution 237 .

Engineering antigen presentation and antigen targeting

Beyond anti-CTLA4 and anti-PD-1 or anti-PD-L1 antibodies, an improved understanding of neoantigen processing and presentation can provide novel insights into the development of innovative therapeutic options such as second-generation ICIs, rational combination strategies, cancer vaccines and adoptive cellular therapies 173 (Fig.  4 ). Given that multiple immune checkpoints can limit activation of signal 1, combination therapies with multiple ICIs would be expected to improve the ability of T cells to target tumour cells above what can currently be achieved with anti-PD-1 antibodies alone. Indeed, adding relatlimab, a monoclonal antibody targeting lymphocyte activation gene 3 (LAG3), to nivolumab has been shown to extend the PFS durations of patients with treatment-naive advanced-stage melanoma compared to nivolumab alone, which led to the FDA approval of this combination therapy in 2022 (ref.  238 ). This approval probably, at least partly, reflects the more favourable safety profile of this combination, with 18.9% of patients having grade 3–4 treatment-related adverse events, which is much lower than that seen with ipilimumab plus nivolumab (59%) in the same setting 239 . Besides agents targeting LAG3, clinical trials investigating therapeutics targeting other novel inhibitory immune checkpoints, such as T cell immunoglobulin and mucin domain-containing protein 3 (TIM3), B7 molecules, and natural killer group protein 2A (NKG2A), either as monotherapy or in combination with established therapies, are currently under way (such as NCT02817633 and NCT04752215 ) 240 . Determining the extent to which APM-dependent, T cell-mediated killing of tumour cells is enhanced compared to what can be achieved with ICI monotherapy will be an important step. Secondary mechanisms, such as NK cell engagement or myeloid cell reprogramming, might either additively or synergistically contribute to the T cell-dependent effects mediated by currently available ICIs. Combinations of targeted therapies and ICIs present another option with the potential to provide synergistic activity by reversing the molecular mechanisms of immune suppression. For example, inhibitors targeting Wee1-like protein kinase (WEE1) upregulate immune signalling and antigen presentation by activating HERV expression and the double-stranded RNA viral defence pathway, leading to greater sensitivity to ICIs in preclinical models 241 . In tumours with dysregulated APM, for which the clinical efficacy of first-generation ICIs might be limited, exploiting the therapeutic value of such novel targets might offer a valuable avenue for clinical success. For example, the anti-NKG2A monoclonal antibody monalizumab promotes antitumour immunity by unleashing the effector function of NK cells 242 .

Alterations in antigen processing and presentation owing to both germline and somatic alterations are essential to the therapeutic response and patient outcomes after immune-checkpoint inhibitor (ICI) treatment. Given the intertumoural heterogeneity among patients, a series of predictive biomarkers, including PD-1 and PD-L1 expression, tumour mutational burden (TMB), MHC class I (MHC I) expression, HLA evolutionary diversity (HED), HLA class I (HLA-I) loss of heterozygosity (LOH), B2M LOH, immunopeptidomics, human endogenous retroviruses (HERV) and the microbiome, have all been investigated regarding their effects on clinical responses to ICIs in patients with various cancer types. Advanced computational approaches that provide enhanced data integration through machine learning and/or deep learning have the potential to add to the clinical utility of these predictive tools. Proper stratification of patients with cancer based on their predicted therapeutic responses will guide the development of precision immunotherapies that deliver maximal clinical efficacy while minimizing the risk of adverse events. IHC, immunohistochemistry.

Chimeric antigen receptor (CAR) T cells targeting CD19 are effective in patients with advanced-stage forms of several haematological malignancies, including B cell leukaemias and diffuse large B cell lymphomas 243 . In mouse models of solid tumours, synthetic peptide-centric CARs enable cross-MHC targeting of antigens derived from intracellular oncoproteins that are essential to tumorigenesis 244 , although this potential has yet to be translated into clinically effective therapies. In addition, investigating the epitopes derived from activated HERVs and commensals present in the microbiota could reveal potential opportunities to develop high-avidity T cell-based immunotherapies 206 , 245 . Compared to CAR T cells, TCR-engineered T cells have the advantage of a natural sensitivity to antigens presented only at low densities on tumour cells 246 . However, many tumour-reactive TCRs generate weaker responses to target peptide–MHC complexes compared to those achievable with CAR T cells, and traditional strategies designed to increase TCR affinity might lead to substantial off-target toxicities 247 . Isolating low-affinity peptide–MHC complexes that nonetheless generate robust activation signals via approaches such as catch-bond engineering provides one example of a method for selecting effective tumour-reactive TCRs while also limiting cross-reactivity 248 . An alternative strategy to modulate antigen processing and presentation is to develop proteolysis-targeting chimeras, also known as protein degraders, which are a class of heterobifunctional small molecules that brings an endogenous E3 ubiquitin ligase into close proximity with a target protein and induces subsequent ubiquitin-mediated protein degradation 249 . These degraders can enhance target protein-derived peptide presentation on MHC I and have entered early phase clinical testing (for example, NCT05654623 and NCT04886622 ), thus providing the potential for therapeutic synergy with bispecific antibodies that recruit T cells to tumour cells and ICIs 250 .

Another therapeutic approach to exploit antigen processing and presentation is the development of TCR-like antibodies that specifically target peptide–MHC complexes 251 . In comparison with traditional antibody-based therapies, which are generally limited to protein targets expressed on the cell surface, TCR-like antibodies recognize peptides derived from intracellular proteins presented by specific MHC molecules. The generation of various fusion proteins with the MHC I heavy chain and B2M separated by a flexible linker region has enabled the cost-effective preparation of peptide–MHC I complexes 252 . Coupled with positive and negative selection via antibody phage display, specific TCR-like antibodies capable of recognizing particular peptide–MHC I complexes can be identified 253 . Common screening strategies include using libraries of antigen-binding fragments and single-chain variable fragments (scFVs). At the biochemical level, engineered TCR-like antibodies can achieve approximately 1,000-fold higher levels of affinity for specific MHC complexes than those achieved by wild-type TCRs, suggesting substantial therapeutic potential 254 . Tebentafusp is a first-in-class molecule of the immune-mobilizing monoclonal T cell receptors against cancer (ImmTAC) class and is a fusion protein comprising a soluble affinity-enhanced TCR recognizing the glycoprotein 100 (gp100) peptide YLEPGPVTA presented on HLA-A*02:01 with an anti-CD3 scFV 255 . Tebentafusp recognizes the specific peptide–MHC-I complex on the target cell surface and subsequently recruits polyclonal T cells through the CD3 domain, leading to an antitumour immune response. In clinical trials, tebentafusp improved the OS of patients with HLA-A*02:01 -positive metastatic uveal melanoma relative to ICIs or dacarbazine chemotherapy (1-year OS 73% versus 59%, HR 0.51, 95% CI 0.37–0.71; P  < 0.001), with a manageable adverse effect profile, and was subsequently approved by the FDA for this indication in January 2022 (refs.  256 , 257 ). The clinical success of tebentafusp highlights the value of targeting MHC I-mediated antigen presentation to mobilize a specific antitumour immune response, which is particularly important in cancers such as metastatic uveal melanoma, which traditionally confer dismal outcomes with limited effective therapeutic options available 255 .

TCR-like antibodies can be used to create engineered T cells that target a unique peptide antigen that is either overexpressed on tumour cells or contains tumour-specific mutations. Using a human scFV library, the monoclonal antibody 2D2 was identified based on specific binding to a peptide derived from the New York Oesophageal Squamous Cell Carcinoma-1 gene (NY-ESO-1157-165), presented by HLA-A*02:01 (ref.  258 ). Given the specific expression of NY-ESO-1 in malignant cells, second-generation CAR T cells engineered to express the 2D2 scFV were able to recognize and eliminate NY-ESO-1-expressing tumours in mouse models of triple-negative breast cancer and melanoma. TCR-like antibodies can also be used to create bispecific antibodies to recruit activated T cells to tumour cells expressing common neoantigens 259 . Using the most common arginine-to-histidine mutation at codon 175 (R175H) of the tumour-suppressor gene TP53 as the target, the antibody variable fragment H2 was identified as being able to recognize the mutant peptide–MHC I complex containing this mutation with a dissociation constant of 86 nM. Fusion of H2 with an antibody fragment targeting the TCR–CD3 complex on T cells created a bispecific antibody with specific antitumour activity in vitro and in vivo. Similar bispecific antibodies have also been developed to target mutations derived from prominent target oncogenes such as RAS 260 . Bispecific molecules provide a useful method of recruiting T cells and promoting immune responses against tumours that lack MHC I-mediated antigen presentation. Many such agents are currently being tested in clinical trials (for example, NCT04082364 , NCT03321981 and NCT05577182 ) 261 .

Conclusions

The immune system is able to detect and eradicate malignant cells by recognizing non-self antigens. Antigen processing and presentation by both tumour cells and immune cells are crucial for the activation of T cells and durable clinical response to ICIs. Comprehensive genomic and immunopeptidomic studies of patient-derived tumour samples, both at baseline and after treatment with immunotherapies, have revealed the scale of aberrations in the APM, leading to altered antigen display and T cell recognition. Antigen presentation, in turn, is a source of strong evolutionary selection on tumours, shaped by the neoantigen repertoire. Studies have revealed the roles of both germline components of the APM and somatic aberrations in tumour cells in dictating responses to immunotherapy. The integration of genomic, molecular and clinical signals using sophisticated deep learning methods offers a more powerful and robust approach to identifying responders to immunotherapy. Nevertheless, several challenges remain. We have yet to understand how multiple changes in APM components combine to affect immunogenicity and how we can use such information as biomarkers for therapy 262 . Furthermore, regulation of the expression of various APM components in cancers is still not completely understood. Ongoing research efforts to understand the multifaceted dynamics of antigen presentation will undoubtedly be needed to enable more precise use of immunotherapy in patients with cancer.

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Acknowledgements

The authors are grateful for the support from NIH grants R35CA232097 (T.A.C.), R01CA205426 (T.A.C.), U54CA274513 (T.A.C.) and T32CA094186 (K.Y.), a Young Investigator Award from ASCO Conquer Cancer Foundation (K.Y.), a RSNA Research Resident Grant (K.Y.), and a Cleveland Clinic VeloSano Impact Award (K.Y.).

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Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, USA

Kailin Yang, Ahmed Halima & Timothy A. Chan

Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA

Timothy A. Chan

National Center for Regenerative Medicine, Cleveland, OH, USA

Case Comprehensive Cancer Center, Cleveland, OH, USA

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T.A.C. is a co-founder of and holds equity in Gritstone Oncology; holds equity in An2H; acknowledges grant funding from An2H, AstraZeneca, Bristol Myers Squibb, Eisai, Illumina and Pfizer; has served as an advisor for An2H, AstraZeneca, Bristol Myers Squibb, Eisai, Illumina and MedImmune; and holds ownership of intellectual property on using tumour mutational burden to predict immunotherapy response, which has been licensed to PGDx. K.Y. and A.H. declare no competing interests.

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Yang, K., Halima, A. & Chan, T.A. Antigen presentation in cancer — mechanisms and clinical implications for immunotherapy. Nat Rev Clin Oncol 20 , 604–623 (2023). https://doi.org/10.1038/s41571-023-00789-4

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Accepted : 25 May 2023

Published : 16 June 2023

Issue Date : September 2023

DOI : https://doi.org/10.1038/s41571-023-00789-4

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