Abstract
Human tumour cells express mutated and non-mutated proteins that can be processed and presented by these cells as peptides bound to human leukocyte antigen (HLA). Some of these peptides are recognized by cognate T cell receptors as ‘non-self’, leading to specific killing of tumour cells by T cells. This process is fundamental to the success of cancer immunotherapy, which exploits the ability of the immune system to eliminate transformed cells. Mutated antigens (neoantigens) have been implicated in the remarkable therapeutic efficacy of immune checkpoint inhibitors (ICIs), which boost endogenous antitumour immune responses. In recent years, the combination of ICIs with personalized cancer vaccines that target neoantigens and other tumour-specific antigens has emerged as a new therapeutic strategy. However, the robust immune pressure that ICIs exert on cancer cells inevitably amplifies the phenomenon of immune editing, which can allow cancer cells to develop resistance mechanisms that subvert surveillance by the immune system. Diminished antigenicity can be due to defects in the antigen processing and presentation machinery, such as HLA-I/II loss of heterozygosity and loss of functional β2-microglobulin. This poses a considerable challenge for combination therapies that include ICIs and for the design of cancer-specific vaccines.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$32.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Pishesha, N., Harmand, T. J. & Ploegh, H. L. A guide to antigen processing and presentation. Nat. Rev. Immunol. 22, 751–764 (2022).
Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
Schoenfeld, A. J. & Hellmann, M. D. Acquired resistance to immune checkpoint inhibitors. Cancer Cell 37, 443–455 (2020).
Matsushita, H. et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature 482, 400–404 (2012). This seminal study provides direct evidence of cancer immunoediting in mice, showing that T cell responses eliminate highly antigenic tumour clones, leading to the outgrowth of less immunogenic variants under immune pressure.
Balachandran, V. P. et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551, 512–516 (2017).
Beyranvand Nejad, E., Welters, M. J., Arens, R. & van der Burg, S. H. The importance of correctly timing cancer immunotherapy. Expert Opin. Biol. Ther. 17, 87–103 (2017).
Anagnostou, V. et al. Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer. Cancer Discov. 7, 264–276 (2017). This study shows that acquired resistance to immune checkpoint inhibitors in non-small-cell lung cancer can result from the loss of neoantigens that initially elicited T cell responses. Analysis of matched tumours before and after treatment revealed that immune pressure drives clonal elimination or chromosomal deletions, reshaping the neoantigen landscape.
Jenkins, R. W., Barbie, D. A. & Flaherty, K. T. Mechanisms of resistance to immune checkpoint inhibitors. Br. J. Cancer 118, 9–16 (2018).
O’Donnell, J. S., Teng, M. W. L. & Smyth, M. J. Cancer immunoediting and resistance to T cell-based immunotherapy. Nat. Rev. Clin. Oncol. 16, 151–167 (2019).
Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019).
Hansen, T. H. & Bouvier, M. MHC class I antigen presentation: learning from viral evasion strategies. Nat. Rev. Immunol. 9, 503–513 (2009).
Yewdell, J. W., Reits, E. & Neefjes, J. Making sense of mass destruction: quantitating MHC class I antigen presentation. Nat. Rev. Immunol. 3, 952–961 (2003).
Axelrod, M. L., Cook, R. S., Johnson, D. B. & Balko, J. M. Biological consequences of MHC-II expression by tumor cells in cancer. Clin. Cancer Res. 25, 2392–2402 (2019).
Seliger, B., Kloor, M. & Ferrone, S. HLA class II antigen-processing pathway in tumors: molecular defects and clinical relevance. Oncoimmunology 6, e1171447 (2017).
Gfeller, D. & Bassani-Sternberg, M. Predicting antigen presentation-what could we learn from a million peptides? Front. Immunol. 9, 1716 (2018).
Joffre, O. P., Segura, E., Savina, A. & Amigorena, S. Cross-presentation by dendritic cells. Nat. Rev. Immunol. 12, 557–569 (2012).
Gurevich, I. et al. Active dissemination of cellular antigens by DCs facilitates CD8+ T-cell priming in lymph nodes. Eur. J. Immunol. 47, 1802–1818 (2017).
Wherry, E. J., Puorro, K. A., Porgador, A. & Eisenlohr, L. C. The induction of virus-specific CTL as a function of increasing epitope expression: responses rise steadily until excessively high levels of epitope are attained. J. Immunol. 163, 3735–3745 (1999).
Kyewski, B. & Klein, L. A central role for central tolerance. Annu. Rev. Immunol. 24, 571–606 (2006).
Kaech, S. M., Wherry, E. J. & Ahmed, R. Effector and memory T-cell differentiation: implications for vaccine development. Nat. Rev. Immunol. 2, 251–262 (2002).
Liu, Q., Sun, Z. & Chen, L. Memory T cells: strategies for optimizing tumor immunotherapy. Protein Cell 11, 549–564 (2020).
Lanzavecchia, A. & Sallusto, F. Regulation of T cell immunity by dendritic cells. Cell 106, 263–266 (2001).
Wooldridge, L. et al. A single autoimmune T cell receptor recognizes more than a million different peptides. J. Biol. Chem. 287, 1168–1177 (2012).
Bessell, C. A. et al. Commensal bacteria stimulate antitumor responses via T cell cross-reactivity. JCI Insight 5, e135597 (2020).
Quezada, S. A. et al. Tumor-reactive CD4+ T cells develop cytotoxic activity and eradicate large established melanoma after transfer into lymphopenic hosts. J. Exp. Med. 207, 637–650 (2010).
Chen, D. S. & Mellman, I. Oncology meets immunology: the cancer-immunity cycle. Immunity 39, 1–10 (2013).
Bawden, E. G. et al. CD4+ T cell immunity against cutaneous melanoma encompasses multifaceted MHC II-dependent responses. Sci. Immunol. 9, eadi9517 (2024).
Bassani-Sternberg, M. et al. Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry. Nat. Commun. 7, 13404 (2016).
Marcu, A. et al. HLA ligand atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy. J. Immunother. Cancer 9, e002071 (2021).
Hoenisch Gravel, N. et al. TOF(IMS) mass spectrometry-based immunopeptidomics refines tumor antigen identification. Nat. Commun. 14, 7472 (2023).
Kraemer, A. I. et al. The immunopeptidome landscape associated with T cell infiltration, inflammation and immune editing in lung cancer. Nat. Cancer 4, 608–628 (2023).
Rijensky, N. M. et al. Identification of tumor antigens in the HLA peptidome of patient-derived xenograft tumors in mouse. Mol. Cell. Proteomics 19, 1360–1374 (2020).
Abelin, J. G. et al. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 46, 315–326 (2017).
Newey, A. et al. Immunopeptidomics of colorectal cancer organoids reveals a sparse HLA class I neoantigen landscape and no increase in neoantigens with interferon or MEK-inhibitor treatment. J. Immunother. Cancer 7, 309 (2019).
Bassani-Sternberg, M. et al. Soluble plasma HLA peptidome as a potential source for cancer biomarkers. Proc. Natl Acad. Sci. USA 107, 18769–18776 (2010).
Wahle, M. et al. IMBAS–MS discovers organ-specific HLA peptide patterns in plasma. Mol. Cell. Proteomics 23, 100689 (2024).
Jaeger, A. M. et al. Deciphering the immunopeptidome in vivo reveals new tumour antigens. Nature 607, 149–155 (2022).
Stopfer, L. E., Mesfin, J. M., Joughin, B. A., Lauffenburger, D. A. & White, F. M. Multiplexed relative and absolute quantitative immunopeptidomics reveals MHC I repertoire alterations induced by CDK4/6 inhibition. Nat. Commun. 11, 2760 (2020).
Goyal, A. et al. DNMT and HDAC inhibition induces immunogenic neoantigens from human endogenous retroviral element-derived transcripts. Nat. Commun. 14, 6731 (2023).
Chong, C. et al. Integrated proteogenomic deep sequencing and analytics accurately identify non-canonical peptides in tumor immunopeptidomes. Nat. Commun. 11, 1293 (2020).
Martin, M. V. et al. The neo-open reading frame peptides that comprise the tumor framome are a rich source of neoantigens for cancer immunotherapy. Cancer Immunol. Res. 12, 759–778 (2024).
Deutsch, E. W. et al. High-quality peptide evidence for annotating non-canonical open reading frames as human proteins. Preprint at bioRxiv https://doi.org/10.1101/2024.09.09.612016 (2024).
Uenaka, A. et al. Identification of a unique antigen peptide pRL1 on BALB/c RL male 1 leukemia recognized by cytotoxic T lymphocytes and its relation to the Akt oncogene. J. Exp. Med. 180, 1599–1607 (1994).
Coulie, P. G. et al. A mutated intron sequence codes for an antigenic peptide recognized by cytolytic T lymphocytes on a human melanoma. Proc. Natl Acad. Sci. USA 92, 7976–7980 (1995).
Robbins, P. F. et al. The intronic region of an incompletely spliced gp100 gene transcript encodes an epitope recognized by melanoma-reactive tumor-infiltrating lymphocytes. J. Immunol. 159, 303–308 (1997).
Lupetti, R. et al. Translation of a retained intron in tyrosinase-related protein (TRP) 2 mRNA generates a new cytotoxic T lymphocyte (CTL)-defined and shared human melanoma antigen not expressed in normal cells of the melanocytic lineage. J. Exp. Med. 188, 1005–1016 (1998).
Ouspenskaia, T. et al. Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer. Nat. Biotechnol. 40, 209–217 (2022).
Van Den Eynde, B. J. et al. A new antigen recognized by cytolytic T lymphocytes on a human kidney tumor results from reverse strand transcription. J. Exp. Med. 190, 1793–1800 (1999).
Ferreira, H. J. et al. Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides. Nat. Commun. 15, 2357 (2024).
Merlotti, A. et al. Noncanonical splicing junctions between exons and transposable elements represent a source of immunogenic recurrent neo-antigens in patients with lung cancer. Sci. Immunol. 8, eabm6359 (2023).
Peng, X. et al. Novel canonical and non-canonical viral antigens extend current targets for immunotherapy of HPV-driven cervical cancer. iScience 26, 106101 (2023).
Kalaora, S. et al. Identification of bacteria-derived HLA-bound peptides in melanoma. Nature 592, 138–143 (2021).
Kacen, A. et al. Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors. Nat. Biotechnol. 41, 239–251 (2023).
Wang, R. F., Parkhurst, M. R., Kawakami, Y., Robbins, P. F. & Rosenberg, S. A. Utilization of an alternative open reading frame of a normal gene in generating a novel human cancer antigen. J. Exp. Med. 183, 1131–1140 (1996).
Wang, R. F. et al. A breast and melanoma-shared tumor antigen: T cell responses to antigenic peptides translated from different open reading frames. J. Immunol. 161, 3598–3606 (1998).
Rosenberg, S. A. et al. Identification of BING-4 cancer antigen translated from an alternative open reading frame of a gene in the extended MHC class II region using lymphocytes from a patient with a durable complete regression following immunotherapy. J. Immunol. 168, 2402–2407 (2002).
Consortium, G. T. The GTEx consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
Angelova, M. et al. Evolution of metastases in space and time under immune selection. Cell 175, 751–765 e716 (2018).
Alban, T. J. et al. Neoantigen immunogenicity landscapes and evolution of tumor ecosystems during immunotherapy with nivolumab. Nat. Med. 30, 3209–3222 (2024). This study shows that nivolumab treatment in non-small-cell lung cancer drives immune selection against immunogenic neoantigen-bearing clones, highlighting how immune pressure shapes tumour evolution and treatment response.
Peto, J. Cancer epidemiology in the last century and the next decade. Nature 411, 390–395 (2001).
Jhunjhunwala, S., Hammer, C. & Delamarre, L. Antigen presentation in cancer: insights into tumour immunogenicity and immune evasion. Nat. Rev. Cancer 21, 298–312 (2021). This review maps every step from neoantigen generation to T cell recognition to explain how tumours lose HLA function under immune pressure, providing the mechanistic backbone for our discussion of immune escape.
Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).
Campoli, M. & Ferrone, S. HLA antigen changes in malignant cells: epigenetic mechanisms and biologic significance. Oncogene 27, 5869–5885 (2008).
Johnsen, A. K., Templeton, D. J., Sy, M. & Harding, C. V. Deficiency of transporter for antigen presentation (TAP) in tumor cells allows evasion of immune surveillance and increases tumorigenesis. J. Immunol. 163, 4224–4231 (1999).
Seliger, B. The link between MHC class I abnormalities of tumors, oncogenes, tumor suppressor genes, and transcription factors. J. Immunotoxicol. 11, 308–310 (2014).
Restifo, N. P. et al. Loss of functional beta 2-microglobulin in metastatic melanomas from five patients receiving immunotherapy. J. Natl Cancer Inst. 88, 100–108 (1996).
Bussey, K. A. & Brinkmann, M. M. Strategies for immune evasion by human tumor viruses. Curr. Opin. Virol. 32, 30–39 (2018).
Huber, F. et al. A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02420-y (2024). NeoDisc is an end-to-end clinical proteogenomic pipeline that integrates multiomics data and machine learning to improve neoantigen selection by accounting for antigen presentation defects, enhancing personalized cancer vaccine design.
Vivier, E. et al. Natural killer cell therapies. Nature 626, 727–736 (2024).
Berjis, A. et al. Pretreatment with IL-15 and IL-18 rescues natural killer cells from granzyme B-mediated apoptosis after cryopreservation. Nat. Commun. 15, 3937 (2024).
Andre, P. et al. Anti-NKG2A mAb is a checkpoint inhibitor that promotes anti-tumor immunity by unleashing both T and NK cells. Cell 175, 1731–1743 e1713 (2018).
Puttick, C. et al. MHC hammer reveals genetic and non-genetic HLA disruption in cancer evolution. Nat. Genet. 56, 2121–2131 (2024).
McGranahan, N. et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171, 1259–1271 e1211 (2017).
Schaafsma, E., Fugle, C. M., Wang, X. & Cheng, C. Pan-cancer association of HLA gene expression with cancer prognosis and immunotherapy efficacy. Br. J. Cancer 125, 422–432 (2021).
Paulson, K. G. et al. Acquired cancer resistance to combination immunotherapy from transcriptional loss of class I HLA. Nat. Commun. 9, 3868 (2018).
Dai, Z. X., Zhang, G. H., Zhang, X. H., Zhu, J. W. & Zheng, Y. T. A splice variant of HLA-A with a deletion of exon 3 expressed as nonmature cell-surface glycoproteins forms a heterodimeric structure with full-length HLA-A. Hum. Immunol. 75, 234–238 (2014).
Krangel, M. S. Secretion of HLA-A and -B antigens via an alternative RNA splicing pathway. J. Exp. Med. 163, 1173–1190 (1986).
Demaria, S. & Bushkin, Y. Soluble HLA proteins with bound peptides are released from the cell surface by the membrane metalloproteinase. Hum. Immunol. 61, 1332–1338 (2000).
Albitar, M. et al. Levels of soluble HLA-I and beta2M in patients with acute myeloid leukemia and advanced myelodysplastic syndrome: association with clinical behavior and outcome of induction therapy. Leukemia 21, 480–488 (2007).
Albitar, M. et al. Clinical relevance of soluble HLA-I and beta2-microglobulin levels in non-Hodgkin’s lymphoma and Hodgkin’s disease. Leuk. Res. 31, 139–145 (2007).
Nocito, M., Montalban, C., Gonzalez-Porque, P. & Villar, L. M. Increased soluble serum HLA class I antigens in patients with lymphoma. Hum. Immunol. 58, 106–111 (1997).
Shimura, T. et al. Clinical significance of soluble form of HLA class I molecule in Japanese patients with pancreatic cancer. Hum. Immunol. 62, 615–619 (2001).
Migliaresi, S. et al. Increased serum concentrations of soluble HLA-class I antigens in hepatitis C virus related mixed cryoglobulinaemia. Ann. Rheum. Dis. 59, 20–25 (2000).
Tsuchiya, N., Shiota, M., Yamaguchi, A. & Ito, K. Elevated serum level of soluble HLA class I antigens in patients with systemic lupus erythematosus. Arthritis Rheum. 39, 792–796 (1996).
Moore, C., Ehlayel, M., Inostroza, J., Leiva, L. E. & Sorensen, R. U. Elevated levels of soluble HLA class I (sHLA-I) in children with severe atopic dermatitis. Ann. Allergy Asthma Immunol. 79, 113–118 (1997).
Jinushi, M. et al. Impairment of natural killer cell and dendritic cell functions by the soluble form of MHC class I-related chain A in advanced human hepatocellular carcinomas. J. Hepatol. 43, 1013–1020 (2005).
Puppo, F. et al. Soluble human MHC class I molecules induce soluble Fas ligand secretion and trigger apoptosis in activated CD8+ Fas (CD95)+ T lymphocytes. Int. Immunol. 12, 195–203 (2000).
Campoli, M. & Ferrone, S. Tumor escape mechanisms: potential role of soluble HLA antigens and NK cells activating ligands. Tissue Antigens 72, 321–334 (2008).
Sade-Feldman, M. et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136 (2017). This study identifies loss of B2M as a common mechanism of resistance to immune checkpoint blockade in metastatic melanoma, with B2M loss enriched in non-responders and associated with poorer survival.
Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).
Gettinger, S. et al. Impaired HLA class I antigen processing and presentation as a mechanism of acquired resistance to immune checkpoint inhibitors in lung cancer. Cancer Discov. 7, 1420–1435 (2017).
Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).
Zhao, Y. et al. B2M gene expression shapes the immune landscape of lung adenocarcinoma and determines the response to immunotherapy. Immunology 164, 507–523 (2021).
Gavrielatou, N. et al. Digital spatial profiling links beta-2-microglobulin expression with immune checkpoint blockade outcomes in head and neck squamous cell carcinoma. Cancer Res. Commun. 3, 558–563 (2023).
Shrout, J. et al. β2Microglobulin mRNA expression levels are prognostic for lymph node metastasis in colorectal cancer patients. Br. J. Cancer 98, 1999–2005 (2008).
Zhang, H. et al. B2M overexpression correlates with malignancy and immune signatures in human gliomas. Sci. Rep. 11, 5045 (2021).
Tang, F. et al. Impact of beta-2 microglobulin expression on the survival of glioma patients via modulating the tumor immune microenvironment. CNS Neurosci. Ther. 27, 951–962 (2021).
Eugene, J. et al. The inhibitory receptor CD94/NKG2A on CD8+ tumor-infiltrating lymphocytes in colorectal cancer: a promising new druggable immune checkpoint in the context of HLAE/β2m overexpression. Mod. Pathol. 33, 468–482 (2020).
Ikeda, H., Old, L. J. & Schreiber, R. D. The roles of IFNγ in protection against tumor development and cancer immunoediting. Cytokine Growth Factor Rev. 13, 95–109 (2002).
Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).
Gao, J. et al. Loss of IFN-gamma pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell 167, 397–404 e399 (2016).
Shin, D. S. et al. Primary resistance to PD-1 blockade mediated by JAK1/2 mutations. Cancer Discov. 7, 188–201 (2017).
Kriegsman, B. A. et al. Frequent loss of IRF2 in cancers leads to immune evasion through decreased MHC class I antigen presentation and increased PD-L1 expression. J. Immunol. 203, 1999–2010 (2019).
Marijt, K. A. & van Hall, T. To TAP or not to TAP: alternative peptides for immunotherapy of cancer. Curr. Opin. Immunol. 64, 15–19 (2020).
Sokol, L. et al. Loss of tapasin correlates with diminished CD8+ T-cell immunity and prognosis in colorectal cancer. J. Transl. Med. 13, 279 (2015).
Fucikova, J., Spisek, R., Kroemer, G. & Galluzzi, L. Calreticulin and cancer. Cell Res. 31, 5–16 (2021).
Callahan, M. K., Garg, M. & Srivastava, P. K. Heat-shock protein 90 associates with N-terminal extended peptides and is required for direct and indirect antigen presentation. Proc. Natl Acad. Sci. USA 105, 1662–1667 (2008).
Koumantou, D. et al. Editing the immunopeptidome of melanoma cells using a potent inhibitor of endoplasmic reticulum aminopeptidase 1 (ERAP1). Cancer Immunol. Immunother. 68, 1245–1261 (2019).
Milner, E. et al. The effect of proteasome inhibition on the generation of the human leukocyte antigen (HLA) peptidome. Mol. Cell. Proteomics 12, 1853–1864 (2013).
Rana, P. S., Ignatz-Hoover, J. J. & Driscoll, J. J. Targeting proteasomes and the MHC class I antigen presentation machinery to treat cancer, infections and age-related diseases. Cancers 15, 5632 (2023).
Jaeger, A. M. et al. Rebalancing protein homeostasis enhances tumor antigen presentation. Clin. Cancer Res. 25, 6392–6405 (2019).
Vansteenkiste, J. F. et al. Efficacy of the MAGE-A3 cancer immunotherapeutic as adjuvant therapy in patients with resected MAGE-A3-positive non-small-cell lung cancer (MAGRIT): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 17, 822–835 (2016).
Kirkwood, J. M. et al. High-dose interferon alfa-2b significantly prolongs relapse-free and overall survival compared with the GM2-KLH/QS-21 vaccine in patients with resected stage IIB-III melanoma: results of intergroup trial E1694/S9512/C509801. J. Clin. Oncol. 19, 2370–2380 (2001).
Lin, M. J. et al. Cancer vaccines: the next immunotherapy frontier. Nat. Cancer 3, 911–926 (2022).
Hilf, N. et al. Actively personalized vaccination trial for newly diagnosed glioblastoma. Nature 565, 240–245 (2019).
Wood, C. et al. An adjuvant autologous therapeutic vaccine (HSPPC-96; vitespen) versus observation alone for patients at high risk of recurrence after nephrectomy for renal cell carcinoma: a multicentre, open-label, randomised phase III trial. Lancet 372, 145–154 (2008).
Testori, A. et al. Phase III comparison of vitespen, an autologous tumor-derived heat shock protein gp96 peptide complex vaccine, with physician’s choice of treatment for stage IV melanoma: the C-100-21 study group. J. Clin. Oncol. 26, 955–962 (2008).
Cui, C., Ott, P. A. & Wu, C. J. Advances in vaccines for melanoma. Hematol. Oncol. Clin. North Am. 38, 1045–1060 (2024).
Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).
Carreno, B. M. et al. Cancer immunotherapy. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science 348, 803–808 (2015).
LaCasse, C. J. et al. Th-1 lymphocytes induce dendritic cell tumor killing activity by an IFN-γ-dependent mechanism. J. Immunol. 187, 6310–6317 (2011).
Alspach, E. et al. MHC-II neoantigens shape tumour immunity and response to immunotherapy. Nature 574, 696–701 (2019).
Espinosa-Carrasco, G. et al. Intratumoral immune triads are required for immunotherapy-mediated elimination of solid tumors. Cancer Cell 42, 1202–1216 e1208 (2024).
Kruse, B. et al. CD4+ T cell-induced inflammatory cell death controls immune-evasive tumours. Nature 618, 1033–1040 (2023).
Ott, P. A. et al. A phase Ib trial of personalized neoantigen therapy plus anti-PD-1 in patients with advanced melanoma, non-small cell lung cancer, or bladder cancer. Cell 183, 347–362 e324 (2020).
Hu, Z. et al. Personal neoantigen vaccines induce persistent memory T cell responses and epitope spreading in patients with melanoma. Nat. Med. 27, 515–525 (2021).
Weber, J. S. et al. Individualised neoantigen therapy mRNA-4157 (V940) plus pembrolizumab versus pembrolizumab monotherapy in resected melanoma (KEYNOTE-942): a randomised, phase 2b study. Lancet 403, 632–644 (2024). This randomized phase IIb trial represents a seminal study demonstrating that the combination of the personalized mRNA-based neoantigen vaccine mRNA-4157 (V940) with pembrolizumab improves recurrence-free survival compared with pembrolizumab alone in patients with resected high-risk melanoma, highlighting the promise of individualized mRNA vaccines in the adjuvant treatment of melanoma.
Rojas, L. A. et al. Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer. Nature 618, 144–150 (2023).
Sellars, M. C., Wu, C. J. & Fritsch, E. F. Cancer vaccines: building a bridge over troubled waters. Cell 185, 2770–2788 (2022).
Lybaert, L. et al. Challenges in neoantigen-directed therapeutics. Cancer Cell 41, 15–40 (2023). This review article discusses the key challenges in developing neoantigen-directed cancer therapies, including antigen identification, tumour heterogeneity, immune evasion and strategies to improve the effectiveness of personalized immunotherapies.
Leko, V. & Rosenberg, S. A. Identifying and targeting human tumor antigens for T cell-based immunotherapy of solid tumors. Cancer Cell 38, 454–472 (2020). This review presents a concise roadmap for using next-generation sequencing and immunopeptidomics to identify tumour antigens and develop personalized T cell therapies for solid cancers.
Fotakis, G., Rieder, D., Haider, M., Trajanoski, Z. & Finotello, F. NeoFuse: predicting fusion neoantigens from RNA sequencing data. Bioinformatics 36, 2260–2261 (2020).
Rieder, D. et al. nextNEOpi: a comprehensive pipeline for computational neoantigen prediction. Bioinformatics 38, 1131–1132 (2022).
Muller, M. et al. Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction. Immunity 56, 2650–2663.e6 (2023).
Wells, D. K. et al. Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction. Cell 183, 818–834.e13 (2020).
Muller, M., Gfeller, D., Coukos, G. & Bassani-Sternberg, M. ‘Hotspots’ of antigen presentation revealed by human leukocyte antigen ligandomics for neoantigen prioritization. Front. Immunol. 8, 1367 (2017).
Tokita, S. et al. Identification of immunogenic HLA class I and II neoantigens using surrogate immunopeptidomes. Sci. Adv. 10, eado6491 (2024).
Hundal, J. et al. pVACtools: a computational toolkit to identify and visualize cancer neoantigens. Cancer Immunol. Res. 8, 409–420 (2020).
Racle, J. et al. Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes. Immunity 56, 1359–1375.e13 (2023).
Reynisson, B. et al. Improved prediction of MHC II antigen presentation through integration and motif deconvolution of mass spectrometry MHC eluted ligand data. J. Proteome Res. 19, 2304–2315 (2020).
Stronen, E. et al. Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science 352, 1337–1341 (2016).
Nathanson, T. et al. Somatic mutations and neoepitope homology in melanomas treated with CTLA-4 blockade. Cancer Immunol. Res. 5, 84–91 (2017).
Duan, F. et al. Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity. J. Exp. Med. 211, 2231–2248 (2014).
Richman, L. P., Vonderheide, R. H. & Rech, A. J. Neoantigen dissimilarity to the self-proteome predicts immunogenicity and response to immune checkpoint blockade. Cell Syst. 9, 375–382 e374 (2019).
Luksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017).
Gfeller, D. et al. Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes. Cell Syst. 14, 72–83 e75 (2023).
Li, G., Iyer, B., Prasath, V. B. S., Ni, Y. & Salomonis, N. DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity. Brief. Bioinform. 22, bbab160 (2021).
Albert, B. A. et al. Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity. Nat. Mach. Intell. 5, 861–872 (2023).
Łuksza, M. et al. Neoantigen quality predicts immunoediting in survivors of pancreatic cancer. Nature 606, 389–395 (2022).
O’Brien, H. et al. A modular protein language modelling approach to immunogenicity prediction. PLoS Comput. Biol. 20, e1012511 (2024).
Hundal, J. et al. pVAC-Seq: a genome-guided in silico approach to identifying tumor neoantigens. Genome Med. 8, 11 (2016).
Zhou, C. et al. pTuneos: prioritizing tumor neoantigens from next-generation sequencing data. Genome Med. 11, 67 (2019).
Gartner, J. J. et al. A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types. Nat. Cancer 2, 563–574 (2021).
Chuwdhury, G. S. et al. ImmuneMirror: a machine learning-based integrative pipeline and web server for neoantigen prediction. Brief. Bioinform. 25, bbae024 (2024).
Shim, J. H. et al. HLA-corrected tumor mutation burden and homologous recombination deficiency for the prediction of response to PD-(L)1 blockade in advanced non-small-cell lung cancer patients. Ann. Oncol. 31, 902–911 (2020).
Shukla, S. A. et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 33, 1152–1158 (2015).
Harari, A. et al. A personalized neoantigen vaccine in combination with platinum-based chemotherapy induces a T-cell response coinciding with a complete response in endometrial carcinoma. Cancers 13, 5801 (2021).
Sultan, H. et al. Neoantigen-specific cytotoxic Tr1 CD4 T cells suppress cancer immunotherapy. Nature 632, 182–191 (2024).
Reis, B. et al. Tumor beta2-microglobulin and HLA-A expression is increased by immunotherapy and can predict response to CIT in association with other biomarkers. Front. Immunol. 15, 1285049 (2024).
Montesion, M. et al. Somatic HLA class I loss is a widespread mechanism of immune evasion which refines the use of tumor mutational burden as a biomarker of checkpoint inhibitor response. Cancer Discov. 11, 282–292 (2021). This pan-cancer study reveals that HLA-I LOH is a common immune evasion mechanism with a nonlinear relationship to tumour mutational burden and is a significant negative predictor of immune checkpoint inhibitor outcomes, particularly in non-small-cell lung cancer.
Kwak, Y. et al. Differential prognostic impact of CD8+ T cells based on human leucocyte antigen I and PD-L1 expression in microsatellite-unstable gastric cancer. Br. J. Cancer 122, 1399–1408 (2020).
Na, H. Y. et al. Expression of human leukocyte antigen class I and β2-microglobulin in colorectal cancer and its prognostic impact. Cancer Sci. 112, 91–100 (2021).
Tsang, J. Y. et al. Co-expression of HLA-I loci improved prognostication in HER2+ breast cancers. Cancer Immunol. Immunother. 69, 799–811 (2020).
Zhao, J. et al. The prevalence of HLA-I LOH in Chinese pan-cancer patients and genomic features of patients harboring HLA-I LOH. Hum. Mutat. 42, 1254–1264 (2021).
Lozac’hmeur, A. et al. Detecting HLA loss of heterozygosity within a standard diagnostic sequencing workflow for prognostic and therapeutic opportunities. npj Precis. Oncol. 8, 174 (2024).
Lim, W. C. et al. Divergent HLA variations and heterogeneous expression but recurrent HLA loss-of- heterozygosity and common HLA-B and TAP transcriptional silencing across advanced pediatric solid cancers. Front. Immunol. 14, 1265469 (2023).
Pyke, R. M. et al. A machine learning algorithm with subclonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity. Nat. Commun. 13, 1925 (2022). This study introduces DASH, a machine learning algorithm that accurately detects HLA loss of heterozygosity from tumour-normal sequencing data, revealing that HLA LOH is a common immune evasion mechanism present in 18% of patients across multiple cancers.
Lau, D. et al. Integration of tumor extrinsic and intrinsic features associates with immunotherapy response in non-small cell lung cancer. Nat. Commun. 13, 4053 (2022).
Filip, I. et al. Pervasiveness of HLA allele-specific expression loss across tumor types. Genome Med. 15, 8 (2023).
Acknowledgements
This study was supported by the Ludwig Institute for Cancer Research, by grant no. KFS-5637-08-2022 from the Swiss Cancer Research Foundation (to M.B.-S.), the Swiss National Science Foundation PRIMA (grant no. PR00P3_193079 to M.B.-S.) and the Swiss Bridge Foundation Award (to M.B.-S.). The authors thank the members of the Immunopeptidomics Lab at the Department of Oncology, University Hospital of Lausanne, for their constructive discussions and valuable input.
Author information
Authors and Affiliations
Contributions
The authors contributed equally to all aspects of the article.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Reviews Immunology thanks Robert Binder, Luigi Buonaguro and the other, anonymous, reviewer for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Glossary
- Cancer-immunity cycle
-
The stepwise process in which tumour antigens are released, presented, recognized and lead to T cell-mediated killing, generating new antigens that restart the cycle.
- Epitope spreading
-
The development of T cell reactivity to antigens distinct from those initially targeted by the vaccine.
- Immune escape
-
The ability of tumour cells to avoid recognition and destruction by the immune system through mechanisms such as antigen loss, immune suppression or alteration of immune signalling pathways.
- Immune landscapes
-
The overall composition and activation state of immune cells within a tumour or tissue microenvironment.
- Immune pressure
-
The selective pressure exerted by the immune system that promotes the survival of resistant tumour clones.
- Immune privileged
-
In the context of tumours, this refers to tumours that evade immune detection by lacking recognizable neoantigens, suppressing local immune responses or using mechanisms that inhibit T cell activation.
- Immunopeptidome
-
The set of peptides displayed on cell-surface human leukocyte antigen (HLA) molecules.
- Immunosurveillance
-
Continuous immune monitoring that detects and eliminates emerging malignant or infected cells.
- Indels
-
Small insertions or deletions of nucleotides in DNA that can disrupt reading frames or alter protein sequence.
- Shared tumour-associated antigens
-
Non-mutated proteins aberrantly expressed across many tumours that can be recognized by T cells.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Huber, F., Bassani-Sternberg, M. Defects in antigen processing and presentation: mechanisms, immune evasion and implications for cancer vaccine development. Nat Rev Immunol (2025). https://doi.org/10.1038/s41577-025-01208-8
Accepted:
Published:
DOI: https://doi.org/10.1038/s41577-025-01208-8
This article is cited by
-
Engineering adoptive cell therapy for solid tumors
Medical Oncology (2025)
