Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Advances of mRNA vaccine in tumor: a maze of opportunities and challenges
Biomarker Research Open Access 18 January 2023
A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer
Journal of Ovarian Research Open Access 07 January 2023
Neoantigens: promising targets for cancer therapy
Signal Transduction and Targeted Therapy Open Access 06 January 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
Wölfel, T. et al. A p16INK4a-insensitive CDK4 mutant targeted by cytolytic T lymphocytes in a human melanoma. Science 269, 1281–1284 (1995).
Chiari, R. et al. Two antigens recognized by autologous cytolytic T lymphocytes on a melanoma result from a single point mutation in an essential housekeeping gene. Cancer Res. 59, 5785–5792 (1999).
Pieper, R. et al. Biochemical identification of a mutated human melanoma antigen recognized by CD4+ T cells. J. Exp. Med.189, 757–766 (1999).
Wang, R. F., Wang, X., Atwood, A. C., Topalian, S. L. & Rosenberg, S. A. Cloning genes encoding MHC class II-restricted antigens: mutated CDC27 as a tumor antigen. Science 284, 1351–1354 (1999).
van Rooij, N. et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J. Clin. Oncol. 31, e439–e442 (2013).
Gubin, M. M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014).
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).
Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).
Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949.e16 (2017).
Robbins, P. F. et al. Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nat. Med. 19, 747–752 (2013).
Prickett, T. D. et al. Durable complete response from metastatic melanoma after transfer of autologous T cells recognizing 10 mutated tumor antigens. Cancer Immunol. Res. 4, 669–678 (2016).
Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–206 (2019).
Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).
Castle, J. C. et al. Exploiting the mutanome for tumor vaccination. Cancer Res. 72, 1081–1091 (2012).
Lu, Y.-C. et al. Mutated PPP1R3B is recognized by T cells used to treat a melanoma patient who experienced a durable complete tumor regression. J. Immunol. 190, 6034–6042 (2013).
Tran, E. et al. Cancer immunotherapy based on mutation-specific CD4+ T cells in a patient with epithelial cancer. Science 344, 641–645 (2014).
Türeci, Ö. et al. Targeting the heterogeneity of cancer with individualized neoepitope vaccines. Clin. Cancer Res. 22, 1885–1896 (2016).
Tran, E. et al. Immunogenicity of somatic mutations in human gastrointestinal cancers. Science 350, 1387–1390 (2015).
Parkhurst, M. R. et al. Unique neoantigens arise from somatic mutations in patients with gastrointestinal cancers. Cancer Discov. 9, 1022–1035 (2019).
Lo, W. et al. Immunologic recognition of a shared p53 mutated neoantigen in a patient with metastatic colorectal cancer. Cancer Immunol. Res. 7, 534–543 (2019).
Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).
Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).
Yossef, R. et al. Enhanced detection of neoantigen-reactive T cells targeting unique and shared oncogenes for personalized cancer immunotherapy. JCI Insight 3, e122467 (2018).
Simon, P. et al. Functional TCR retrieval from single antigen-specific human T cells reveals multiple novel epitopes. Cancer Immunol. Res. 2, 1230–1244 (2014).
Lu, Y.-C. et al. An efficient single-cell RNA-seq approach to identify neoantigen-specific T cell receptors. Mol. Ther. 26, 379–389 (2018).
Linnemann, C. et al. High-throughput identification of antigen-specific TCRs by TCR gene capture. Nat. Med. 19, 1534–1541 (2013).
Ali, M. et al. Induction of neoantigen-reactive T cells from healthy donors. Nat. Protoc. 14, 1926–1943 (2019).
Strønen, E. et al. Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science 352, 1337–1341 (2016).
Brady, M. S., Eckels, D. D., Ree, S. Y., Schultheiss, K. E. & Lee, J. S. MHC class II-mediated antigen presentation by melanoma cells. J. Immunother. Emphas. Tumor Immunol. 19, 387–397 (1996).
Arnold, P. Y. et al. The majority of immunogenic epitopes generate CD4+ T cells that are dependent on MHC class II-bound peptide-flanking residues. J. Immunol. 169, 739–749 (2002).
Kreiter, S. et al. Mutant MHC class II epitopes drive therapeutic immune responses to cancer. Nature 520, 692–696 (2015).
Sallusto, F., Cella, M., Danieli, C. & Lanzavecchia, A. Dendritic cells use macropinocytosis and the mannose receptor to concentrate macromolecules in the major histocompatibility complex class II compartment. Downregulation by cytokines and bacterial products. J. Exp. Med. 182, 389–400 (1995).
Albert, M. L. et al. Immature dendritic cells phagocytose apoptotic cells via αvβ5 and CD36, and cross-present antigens to cytotoxic T lymphocytes. J. Exp. Med. 188, 1359–1368 (1998).
Lecoultre, M., Dutoit, V. & Walker, P. R. Phagocytic function of tumor-associated macrophages as a key determinant of tumor progression control: a review. J. Immunother. Cancer 8, e001408 (2020).
Platt, C. D. et al. Mature dendritic cells use endocytic receptors to capture and present antigens. Proc. Natl Acad. Sci. USA 107, 4287–4292 (2010).
Junker, F., Gordon, J. & Qureshi, O. Fc gamma receptors and their role in antigen uptake, presentation, and T cell activation. Front. Immunol. 11, 1393 (2020).
Sixt, M. et al. The conduit system transports soluble antigens from the afferent lymph to resident dendritic cells in the T cell area of the lymph node. Immunity 22, 19–29 (2005).
Hirosue, S. & Dubrot, J. Modes of antigen presentation by lymph node stromal cells and their immunological implications. Front. Immunol. 6, 446 (2015).
Saeki, H., Moore, A. M., Brown, M. J. & Hwang, S. T. Cutting edge. Secondary lymphoid-tissue chemokine (SLC) and CC chemokine receptor 7 (CCR7) participate in the emigration pathway of mature dendritic cells from the skin to regional lymph nodes. J. Immunol. 162, 2472–2475 (1999).
Flament, H. et al. Modeling the specific CD4+ T cell response against a tumor neoantigen. J. Immunol. 194, 3501–3512 (2015).
Spiotto, M. T. et al. Increasing tumor antigen expression overcomes “ignorance” to solid tumors via crosspresentation by bone marrow-derived stromal cells. Immunity 17, 737–747 (2002).
Allan, R. S. et al. Migratory dendritic cells transfer antigen to a lymph node-resident dendritic cell population for efficient CTL priming. Immunity 25, 153–162 (2006).
Harshyne, L. A., Watkins, S. C., Gambotto, A. & Barratt-Boyes, S. M. Dendritic cells acquire antigens from live cells for cross-presentation to CTL. J. Immunol. 166, 3717–3723 (2001).
Ruhland, M. K. et al. Visualizing synaptic transfer of tumor antigens among dendritic cells. Cancer Cell 37, 786–799.e5 (2020).
Albert, M. L., Sauter, B. & Bhardwaj, N. Dendritic cells acquire antigen from apoptotic cells and induce class I-restricted CTLs. Nature 392, 86–89 (1998).
Bode, K. et al. Dectin-1 binding to annexins on apoptotic cells induces peripheral immune tolerance via NADPH oxidase-2. Cell Rep. 29, 4435–4446.e9 (2019).
Rocha, B. & von Boehmer, H. Peripheral selection of the T cell repertoire. Science 251, 1225–1228 (1991).
Chen, W. et al. Conversion of peripheral CD4+CD25− naive T cells to CD4+CD25+ regulatory T cells by TGF-beta induction of transcription factor Foxp3. J. Exp. Med. 198, 1875–1886 (2003).
Ramsdell, F., Lantz, T. & Fowlkes, B. J. A nondeletional mechanism of thymic self tolerance. Science 246, 1038–1041 (1989).
Rocha, B., Grandien, A. & Freitas, A. A. Anergy and exhaustion are independent mechanisms of peripheral T cell tolerance. J. Exp. Med. 181, 993–1003 (1995).
Blank, C. U. et al. Defining ‘T cell exhaustion’. Nat. Rev. Immunol. 19, 665–674 (2019).
Obst, R. The timing of T cell priming and cycling. Front. Immunol. 6, 563 (2015).
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).
Abelin, J. G. et al. Defining HLA-II ligand processing and binding rules with mass spectrometry enhances cancer epitope prediction. Immunity 51, 766–779.e17 (2019).
Hennecke, J. & Wiley, D. C. T cell receptor-MHC interactions up close. Cell 104, 1–4 (2001).
Szeto, C., Lobos, C. A., Nguyen, A. T. & Gras, S. TCR recognition of peptide-MHC-I. rule makers and breakers. Int. J. Mol. Sci. 22, 68 (2020).
Bobisse, S. et al. Sensitive and frequent identification of high avidity neo-epitope specific CD8+T cells in immunotherapy-naive ovarian cancer. Nat. Commun. 9, 1092 (2018).
Cafri, G. et al. Memory T cells targeting oncogenic mutations detected in peripheral blood of epithelial cancer patients. Nat. Commun. 10, 449 (2019).
Malekzadeh, P. et al. Antigen experienced T cells from peripheral blood recognize p53 neoantigens. Clin. Cancer Res. 26, 1267–1276 (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).
Nelson, R. W. et al. T cell receptor cross-reactivity between similar foreign and self peptides influences naive cell population size and autoimmunity. Immunity 42, 95–107 (2015).
Wooldridge, L. et al. A single autoimmune T cell receptor recognizes more than a million different peptides. J. Biol. Chem. 287, 1168–1177 (2012).
Birnbaum, M. E. et al. Deconstructing the peptide-MHC specificity of T cell recognition. Cell 157, 1073–1087 (2014).
Cameron, B. J. et al. Identification of a Titin-derived HLA-A1-presented peptide as a cross-reactive target for engineered MAGE A3-directed T cells. Sci. Transl Med. 5, 197ra103 (2013).
Leng, Q., Tarbe, M., Long, Q. & Wang, F. Pre-existing heterologous T-cell immunity and neoantigen immunogenicity. Clin. Transl. Immunol. 9, e01111 (2020).
Balachandran, V. P. et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551, 512–516 (2017). This article highlights that neoantigen quality correlates with clinical outcome.
Chen, D. S. & Mellman, I. Oncology meets immunology: the cancer-immunity cycle. Immunity 39, 1–10 (2013). This article introduces the cancer-immunity cycle and proposes the associated biomarkers for tailoring individualized treatments.
Schumacher, T. et al. A vaccine targeting mutant IDH1 induces antitumour immunity. Nature 512, 324–327 (2014).
Alspach, E. et al. MHC-II neoantigens shape tumour immunity and response to immunotherapy. Nature 574, 696–701 (2019). This article underlines the relevance of MHC-II neoantigens for an efficient antitumour response.
Greenman, C. et al. Patterns of somatic mutation in human cancer genomes. Nature 446, 153–158 (2007).
Antony, P. A. et al. CD8+ T cell immunity against a tumor/self-antigen is augmented by CD4+ T helper cells and hindered by naturally occurring T regulatory cells. J. Immunol. 174, 2591–2601 (2005).
Xie, Y. et al. Naive tumor-specific CD4+ T cells differentiated in vivo eradicate established melanoma. J. Exp. Med. 207, 651–667 (2010).
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).
Oh, D. Y. et al. Intratumoral CD4+ T cells mediate anti-tumor cytotoxicity in human bladder cancer. Cell 181, 1612–1625.e13 (2020).
Robbins, P. F. et al. Multiple HLA class II-restricted melanocyte differentiation antigens are recognized by tumor-infiltrating lymphocytes from a patient with melanoma. J. Immunol. 169, 6036–6047 (2002).
Lehmann, P. V., Forsthuber, T., Miller, A. & Sercarz, E. E. Spreading of T-cell autoimmunity to cryptic determinants of an autoantigen. Nature 358, 155–157 (1992).
Lo, J. A. et al. Epitope spreading toward wild-type melanocyte-lineage antigens rescues suboptimal immune checkpoint blockade responses. Sci. Transl Med. 13, eabd8636 (2021).
Brossart, P. The role of antigen spreading in the efficacy of immunotherapies. Clin. Cancer Res. 26, 4442–4447 (2020).
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.e24 (2020).
Sahin, U. & Türeci, Ö. Personalized vaccines for cancer immunotherapy. Science 359, 1355–1360 (2018).
Chen, D. S. & Mellman, I. Elements of cancer immunity and the cancer–immune set point. Nature 541, 321–330 (2017).
Ahmadzadeh, M. et al. Tumor-infiltrating human CD4+ regulatory T cells display a distinct TCR repertoire and exhibit tumor and neoantigen reactivity. Sci. Immunol. 4, eaao4310 (2019).
Golding, A., Darko, S., Wylie, W. H., Douek, D. C. & Shevach, E. M. Deep sequencing of the TCR-β repertoire of human forkhead box protein 3 (FoxP3)+ and FoxP3− T cells suggests that they are completely distinct and non-overlapping. Clin. Exp. Immunol. 188, 12–21 (2017).
Sotomayor, E. M. et al. Cross-presentation of tumor antigens by bone marrow-derived antigen-presenting cells is the dominant mechanism in the induction of T-cell tolerance during B-cell lymphoma progression. Blood 98, 1070–1077 (2001).
Pontes-de-Carvalho, L., Mengel, J., Figueiredo, C. A. & Alcântara-Neves, N. M. Antigen mimicry between infectious agents and self or environmental antigens may lead to long-term regulation of inflammation. Front. Immunol. 4, 314 (2013).
Taube, J. M. et al. Colocalization of inflammatory response with B7-H1 expression in human melanocytic lesions supports an adaptive resistance mechanism of immune escape. Sci. Transl Med. 4, 127ra37 (2012).
Beck, C., Schreiber, H. & Rowley, D. A. Role of TGF-β in immune-evasion of cancer. Microsc. Res. Tech. 52, 387–395 (2001).
Efremova, M. et al. Targeting immune checkpoints potentiates immunoediting and changes the dynamics of tumor evolution. Nat. Commun. 9, 32 (2018).
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).
Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019). This article integrates genomic features of tumours with immune infiltrates and analyses neoantigen-dependent immune escape.
Zapata, L. et al. Negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome. Genome Biol. 19, 67 (2018).
van den Eynden, J., Jiménez-Sánchez, A., Miller, M. L. & Larsson, E. Lack of detectable neoantigen depletion signals in the untreated cancer genome. Nat. Genet. 51, 1741–1748 (2019).
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.-S. & 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).
Sade-Feldman, M. et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136 (2017).
Brastianos, P. K. et al. Genomic characterization of brain metastases reveals branched evolution and potential therapeutic targets. Cancer Discov. 5, 1164–1177 (2015).
Jiménez-Sánchez, A. et al. Heterogeneous tumor-immune microenvironments among differentially growing metastases in an ovarian cancer patient. Cell 170, 927–938.e20 (2017).
Angelova, M. et al. Evolution of metastases in space and time under immune selection. Cell 175, 751–765.e16 (2018).
Kvistborg, P. et al. Anti-CTLA-4 therapy broadens the melanoma-reactive CD8+ T cell response. Sci. Transl Med. 6, 254ra128 (2014).
Novellino, L. et al. Identification of a mutated receptor-like protein tyrosine phosphatase κ as a novel, class II HLA-restricted melanoma antigen. J. Immunol. 170, 6363–6370 (2003).
Matsushita, H. et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature 482, 400–404 (2012).
Joyce, J. A. & Fearon, D. T. T cell exclusion, immune privilege, and the tumor microenvironment. Science 348, 74–80 (2015).
De Plaen, E. et al. Immunogenic (tum−) variants of mouse tumor P815: cloning of the gene of tum− antigen P91A and identification of the tum− mutation. Proc. Natl Acad. Sci. USA 85, 2274–2278 (1988).
Dubey, P. et al. The immunodominant antigen of an ultraviolet-induced regressor tumor is generated by a somatic point mutation in the DEAD box helicase p68. J. Exp. Med. 185, 695–706 (1997).
Samowitz, W. S. et al. Microsatellite instability in sporadic colon cancer is associated with an improved prognosis at the population level. Cancer Epidemiol. Biomark. Prev. 10, 917–923 (2001).
Bessell, C. A. et al. Commensal bacteria stimulate antitumor responses via T cell cross-reactivity. JCI Insight 5, e135597 (2020).
Pihlgren, M., Dubois, P. M., Tomkowiak, M., Sjögren, T. & Marvel, J. Resting memory CD+ T cells are hyperreactive to antigenic challenge in vitro J. Exp. Med. 184, 2141–2151 (1996).
Ghorani, E. et al. Differential binding affinity of mutated peptides for MHC class I is a predictor of survival in advanced lung cancer and melanoma. Ann. Oncol. 29, 271–279 (2017).
McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).
Lu, T. et al. Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes. Sci. Immunol. 5, eaaz3199 (2020).
Łuksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017).
Miao, D. et al. Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors. Nat. Genet. 50, 1271–1281 (2018).
Goodman, A. M. et al. MHC-I genotype and tumor mutational burden predict response to immunotherapy. Genome Med. 12, 45 (2020).
Subudhi, S. K. et al. Neoantigen responses, immune correlates, and favorable outcomes after ipilimumab treatment of patients with prostate cancer. Sci. Transl Med. 12, eaaz3577 (2020).
Turajlic, S. et al. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis. Lancet Oncol. 18, 1009–1021 (2017).
Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).
Cimen Bozkus, C. et al. Immune checkpoint blockade enhances shared neoantigen-induced T-cell immunity directed against mutated calreticulin in myeloproliferative neoplasms. Cancer Discov. 9, 1192–1207 (2019).
Yang, W. et al. Immunogenic neoantigens derived from gene fusions stimulate T cell responses. Nat. Med. 25, 767–775 (2019).
Boyman, O., Kovar, M., Rubinstein, M. P., Surh, C. D. & Sprent, J. Selective stimulation of T cell subsets with antibody-cytokine immune complexes. Science 311, 1924–1927 (2006).
Linnemann, C. et al. High-throughput epitope discovery reveals frequent recognition of neo-antigens by CD4+ T cells in human melanoma. Nat. Med. 21, 81–85 (2015).
Carreno, B. M. et al. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science 348, 803–808 (2015).
Hilf, N. et al. Actively personalized vaccination trial for newly diagnosed glioblastoma. Nature 565, 240–245 (2019).
Fang, Y. et al. A pan-cancer clinical study of personalized neoantigen vaccine monotherapy in treating patients with various types of advanced solid tumors. Clin. Cancer Res. 26, 4511–4520 (2020).
Keskin, D. B. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239 (2019).
Chen, F. et al. Neoantigen identification strategies enable personalized immunotherapy in refractory solid tumors. J. Clin. Invest. 129, 2056–2070 (2019).
Blass, E. & Ott, P. A. Advances in the development of personalized neoantigen-based therapeutic cancer vaccines. Nat. Rev. Clin. Oncol. 18, 215–229 (2021).
Supabphol, S., Li, L., Goedegebuure, S. P. & Gillanders, W. E. Neoantigen vaccine platforms in clinical development: understanding the future of personalized immunotherapy. Expert Opin. Investig. Drugs 30, 529–541 (2021).
Lee, K. L. et al. Efficient tumor clearance and diversified immunity through neoepitope vaccines and combinatorial immunotherapy. Cancer Immunol. Res. 7, 1359–1370 (2019).
Bassani-Sternberg, M., Pletscher-Frankild, S., Jensen, L. J. & Mann, M. Mass spectrometry of human leukocyte antigen class I peptidomes reveals strong effects of protein abundance and turnover on antigen presentation. Mol. Cell. Proteom. 14, 658–673 (2015).
Anagnostou, V. et al. Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer. Cancer Discov. 7, 264–276 (2017).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
Schoenberger, S. P., Toes, R. E., van der Voort, E. I., Offringa, R. & Melief, C. J. T-cell help for cytotoxic T lymphocytes is mediated by CD40–CD40L interactions. Nature 393, 480–483 (1998).
Zhao, W. & Sher, X. Systematically benchmarking peptide-MHC binding predictors: from synthetic to naturally processed epitopes. PLoS Comput. Biol. 14, e1006457 (2018).
Paul, S. et al. Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system. PLoS Comput. Biol. 16, e1007757 (2020).
Jurtz, V. et al. NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 3360–3368 (2017).
O’Donnell, T. J. et al. MHCflurry: open-source class I MHC binding affinity prediction. Cell Syst. 7, 129–132.e4 (2018).
Bassani-Sternberg, M. et al. Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity. PLoS Comput. Biol. 13, e1005725 (2017).
Gfeller, D. et al. The length distribution and multiple specificity of naturally presented HLA-I ligands. J. Immunol. 201, 3705–3716 (2018).
Mei, S. et al. A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction. Brief. Bioinform. 21, 1119–1135 (2020).
Nielsen, M. et al. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS ONE 2, e796 (2007).
Blaha, D. T. et al. High-throughput stability screening of neoantigen/HLA complexes improves immunogenicity predictions. Cancer Immunol. Res. 7, 50–61 (2019).
Rasmussen, M. et al. Pan-specific prediction of peptide-MHC class I complex stability, a correlate of T cell immunogenicity. J. Immunol. 197, 1517–1524 (2016).
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).
Chowell, D. et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359, 582–587 (2018).
Marty, R. et al. MHC-I genotype restricts the oncogenic mutational landscape. Cell 171, 1272–1283.e15 (2017).
Marty Pyke, R. et al. Evolutionary pressure against MHC class II binding cancer mutations. Cell 175, 416–428.e13 (2018).
Keşmir, C., Nussbaum, A. K., Schild, H., Detours, V. & Brunak, S. Prediction of proteasome cleavage motifs by neural networks. Protein Eng. 15, 287–296 (2002).
Stranzl, T., Larsen, M. V., Lundegaard, C. & Nielsen, M. NetCTLpan: pan-specific MHC class I pathway epitope predictions. Immunogenetics 62, 357–368 (2010).
Bjerregaard, A.-M. et al. An analysis of natural T cell responses to predicted tumor neoepitopes. Front. Immunol. 8, 1566 (2017).
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.e4 (2019).
Trolle, T. & Nielsen, M. NetTepi: an integrated method for the prediction of T cell epitopes. Immunogenetics 66, 449–456 (2014).
Jørgensen, K. W., Rasmussen, M., Buus, S. & Nielsen, M. NetMHCstab — predicting stability of peptide-MHC-I complexes; impacts for cytotoxic T lymphocyte epitope discovery. Immunology 141, 18–26 (2014).
Jurtz, V. I. et al. NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks. Preprint at bioRxiv https://doi.org/10.1101/433706v01 (2018).
Lanzarotti, E., Marcatili, P. & Nielsen, M. T-cell receptor cognate target prediction based on paired α and β chain sequence and structural CDR loop similarities. Front. Immunol. 10, 2080 (2019).
Roth, A. et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11, 396–398 (2014).
Miller, C. A. et al. SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution. PLoS Comput. Biol. 10, e1003665 (2014).
Levine, A. J., Jenkins, N. A. & Copeland, N. G. The roles of initiating truncal mutations in human cancers: the order of mutations and tumor cell type matters. Cancer Cell 35, 10–15 (2019).
Gejman, R. S. et al. Rejection of immunogenic tumor clones is limited by clonal fraction. eLife 7, e41090 (2018).
Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).
McGranahan, N. et al. Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Sci. Transl Med. 7, 283ra54 (2015).
Tate, J. G. et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 47, D941–D947 (2019).
Liu, S.-H. et al. DriverDBv3: a multi-omics database for cancer driver gene research. Nucleic Acids Res. 48, D863–D870 (2020).
Tran, E. et al. T-cell transfer therapy targeting mutant KRAS in cancer. N. Engl. J. Med. 375, 2255–2262 (2016).
Chen, H. et al. Comprehensive assessment of computational algorithms in predicting cancer driver mutations. Genome Biol. 21, 43 (2020).
Bozic, I. et al. Accumulation of driver and passenger mutations during tumor progression. Proc. Natl Acad. Sci. USA 107, 18545–18550 (2010).
Charoentong, P. et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 18, 248–262 (2017).
Claeys, A., Luijts, T., Marchal, K. & van den Eynden, J. Low immunogenicity of common cancer hot spot mutations resulting in false immunogenic selection signals. PLoS Genet. 17, e1009368 (2021).
McGranahan, N. & Swanton, C. Neoantigen quality, not quantity. Sci. Transl Med. 11, eaax7918 (2019).
Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).
Mei, R. et al. Genome-wide detection of allelic imbalance using human SNPs and high-density DNA arrays. Genome Res. 10, 1126–1137 (2000).
Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).
Georgi, B., Voight, B. F. & Bućan, M. From mouse to human: evolutionary genomics analysis of human orthologs of essential genes. PLoS Genet. 9, e1003484 (2013).
Blomen, V. A. et al. Gene essentiality and synthetic lethality in haploid human cells. Science 350, 1092–1096 (2015).
Buus, S. et al. Sensitive quantitative predictions of peptide-MHC binding by a ‘Query by Committee’ artificial neural network approach. Tissue Antigens 62, 378–384 (2003).
Hoof, I. et al. NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics 61, 1–13 (2008).
Sarkizova, S. et al. A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nat. Biotechnol. 38, 199–209 (2020).
Riley, T. P. et al. Structure based prediction of neoantigen immunogenicity. Front. Immunol. 10, 2047 (2019).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Phloyphisut, P., Pornputtapong, N., Sriswasdi, S. & Chuangsuwanich, E. MHCSeqNet: a deep neural network model for universal MHC binding prediction. BMC Bioinformatics 20, 270 (2019).
Bulik-Sullivan, B. et al. Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nat. Biotechnol. 37, 55–63 (2019).
Chen, B. et al. Predicting HLA class II antigen presentation through integrated deep learning. Nat. Biotechnol. 37, 1332–1343 (2019).
Wu, J. et al. DeepHLApan: a deep learning approach for neoantigen prediction considering both HLA-peptide binding and immunogenicity. Front. Immunol. 10, 2559 (2019).
Zou, J. et al. A primer on deep learning in genomics. Nat. Genet. 51, 12–18 (2019).
Di, W. et al. Multiregion sequencing reveals the genetic heterogeneity and evolutionary history of osteosarcoma and matched pulmonary metastases. Cancer Res. 79, 7–20 (2019).
Leong, T. L. et al. Deep multi-region whole-genome sequencing reveals heterogeneity and gene-by-environment interactions in treatment-naive, metastatic lung cancer. Oncogene 38, 1661–1675 (2019).
Gerlinger, M. et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46, 225–233 (2014).
Haile, S. et al. Sources of erroneous sequences and artifact chimeric reads in next generation sequencing of genomic DNA from formalin-fixed paraffin-embedded samples. Nucleic Acids Res. 47, e12 (2019).
Robinson, D. R. et al. Integrative clinical genomics of metastatic cancer. Nature 548, 297–303 (2017).
Aguilar-Mahecha, A. et al. The identification of challenges in tissue collection for biomarker studies: the Q-CROC-03 neoadjuvant breast cancer translational trial experience. Mod. Pathol. 30, 1567–1576 (2017).
Chen, M. & Zhao, H. Next-generation sequencing in liquid biopsy: cancer screening and early detection. Hum. Genomics 13, 34 (2019).
ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).
Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).
Benjamin, D. et al. Calling somatic SNVs and indels with Mutect2. Preprint at bioRxiv https://doi.org/10.1101/861054 (2019).
Wang, K. et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 38, e178 (2010).
Aran, D., Sirota, M. & Butte, A. J. Systematic pan-cancer analysis of tumour purity. Nat. Commun. 6, 8971 (2015).
Dagogo-Jack, I. & Shaw, A. T. Tumour heterogeneity and resistance to cancer therapies. Nat. Rev. Clin. Oncol. 15, 81–94 (2018).
Li, Y. et al. Patterns of somatic structural variation in human cancer genomes. Nature 578, 112–121 (2020).
Garcia-Garijo, A., Fajardo, C. A. & Gros, A. Determinants for neoantigen identification. Front. Immunol. 10, 1392 (2019).
Zhou, W.-J. et al. NeoPeptide: an immunoinformatic database of T-cell-defined neoantigens. Database 2019, baz128 (2019).
Tan, X. et al. dbPepNeo: a manually curated database for human tumor neoantigen peptides. Database 2020, baaa004 (2020).
Reker, D., Schneider, P., Schneider, G. & Brown, J. B. Active learning for computational chemogenomics. Future Med. Chem. 9, 381–402 (2017).
Eraslan, G., Avsec, Ž., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389–403 (2019).
Hollingsworth, R. E. & Jansen, K. Turning the corner on therapeutic cancer vaccines. NPJ Vaccines 4, 7 (2019).
Roy, S., Sethi, T. K., Taylor, D., Kim, Y. J. & Johnson, D. B. Breakthrough concepts in immune-oncology: cancer vaccines at the bedside. J. Leukoc. Biol. 108, 1455–1489 (2020).
D’Alise, A. M. et al. Adenoviral vaccine targeting multiple neoantigens as strategy to eradicate large tumors combined with checkpoint blockade. Nat. Commun. 10, 2688 (2019).
Leoni, G. et al. A genetic vaccine encoding shared cancer neoantigens to treat tumors with microsatellite instability. Cancer Res. 80, 3972–3982 (2020).
US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03639714 (2021).
US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03953235 (2020).
Duperret, E. K. et al. A synthetic DNA, multi-neoantigen vaccine drives predominately MHC class I CD8+ T-cell responses, impacting tumor challenge. Cancer Immunol. Res. 7, 174–182 (2019).
Aurisicchio, L. et al. Poly-specific neoantigen-targeted cancer vaccines delay patient derived tumor growth. J. Exp. Clin. Cancer Res. 38, 78 (2019).
US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT04015700 (2021).
Harrison, R. P., Ruck, S., Rafiq, Q. A. & Medcalf, N. Decentralised manufacturing of cell and gene therapy products: learning from other healthcare sectors. Biotechnol. Adv. 36, 345–357 (2018).
Kagermann, H. in Management of Permanent Change (eds Albach, H., Meffert, H. Pinkwart, A. & Reichwald, A.) 23–45 (Springer, 2015).
Theobald, M. (ed.) Current Immunotherapeutic Strategies in Cancer (Springer, 2020).
Britten, C. M. et al. The regulatory landscape for actively personalized cancer immunotherapies. Nat. Biotechnol. 31, 880–882 (2013).
Vormehr, M., Türeci, Ö. & Sahin, U. Harnessing tumor mutations for truly individualized cancer vaccines. Annu. Rev. Med. 70, 395–407 (2019).
Vormehr, M. et al. Mutanome engineered RNA immunotherapy: towards patient-centered tumor vaccination. J. Immunol. Res. 2015, 595363 (2015).
US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/results?term=neoantigen+AND+vaccine&recrs=abdef&cond=Cancer (2021).
Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).
Gfeller, D. & Bassani-Sternberg, M. Predicting antigen presentation-what could we learn from a million peptides? Front. Immunol. 9, 1716 (2018).
Chen, R., Fulton, K. M., Twine, S. M. & Li, J. Identification of MHC peptides using mass spectrometry for neoantigen discovery and cancer vaccine development. Mass Spectrom. Rev. 40, 110–125 (2019).
Laumont, C. M. et al. Noncoding regions are the main source of targetable tumor-specific antigens. Sci. Transl Med. 10, eaau5516 (2018).
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).
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).
Fritsch, E. F. et al. HLA-binding properties of tumor neoepitopes in humans. Cancer Immunol. Res. 2, 522–529 (2014).
Bentzen, A. K. et al. T cell receptor fingerprinting enables in-depth characterization of the interactions governing recognition of peptide-MHC complexes. Nat. Biotechnol. 36, 1191–1196 (2018).
Ogishi, M. & Yotsuyanagi, H. Quantitative prediction of the landscape of T cell epitope immunogenicity in sequence space. Front. Immunol. 10, 827 (2019).
Devlin, J. R. et al. Structural dissimilarity from self drives neoepitope escape from immune tolerance. Nat. Chem. Biol. 16, 1269–1276 (2020).
Ochs, K. et al. K27M-mutant histone-3 as a novel target for glioma immunotherapy. Oncoimmunology 6, e1328340 (2017).
Chheda, Z. S. et al. Novel and shared neoantigen derived from histone 3 variant H3.3K27M mutation for glioma T cell therapy. J. Exp. Med. 215, 141–157 (2017).
Holmström, M. O. et al. The JAK2V617F mutation is a target for specific T cells in the JAK2V617F-positive myeloproliferative neoplasms. Leukemia 31, 495–498 (2017).
Chandran, S. et al. T cell receptor gene therapy for a public neoantigen derived from mutated PIK3CA, a dominant driver oncogene in breast and endometrial cancers [abstract CN01-03]. Mol. Cancer Ther. 18, CN01-03 (2019).
Veatch, J. R. et al. Tumor-infiltrating BRAFV600E-specific CD4+ T cells correlated with complete clinical response in melanoma. J. Clin. Invest. 128, 1563–1568 (2018).
Linard, B. et al. A ras-mutated peptide targeted by CTL infiltrating a human melanoma lesion. J. Immunol. 168, 4802 (2002).
Robbins, P. F. et al. A mutated beta-catenin gene encodes a melanoma-specific antigen recognized by tumor infiltrating lymphocytes. J. Exp. Med. 183, 1185–1192 (1996).
van der Lee, D. I. et al. Mutated nucleophosmin 1 as immunotherapy target in acute myeloid leukemia. J. Clin. Invest. 129, 774–785 (2019).
Sæterdal, I. et al. Frameshift-mutation-derived peptides as tumor-specific antigens in inherited and spontaneous colorectal cancer. Proc. Natl Acad. Sci. USA 98, 13255 (2001).
Inderberg, E. M. et al. T cell therapy targeting a public neoantigen in microsatellite instable colon cancer reduces in vivo tumor growth. Oncoimmunology 6, e1302631 (2017).
Bosch, G. J., Joosten, A. M., Kessler, J. H., Melief, C. J. & Leeksma, O. C. Recognition of BCR-ABL positive leukemic blasts by human CD4+ T cells elicited by primary in vitro immunization with a BCR-ABL breakpoint peptide. Blood 88, 3522–3527 (1996).
Clark, R. E. et al. Direct evidence that leukemic cells present HLA-associated immunogenic peptides derived from the BCR-ABL b3a2 fusion protein. Blood 98, 2887–2893 (2001).
Makita, M. et al. Leukemia-associated fusion proteins, dek-can and bcr-abl, represent immunogenic HLA-DR-restricted epitopes recognized by fusion peptide-specific CD4+ T lymphocytes. Leukemia 16, 2400–2407 (2002).
Cai, A. et al. Mutated BCR-ABL generates immunogenic T-cell epitopes in CML patients. Clin. Cancer Res. 18, 5761–5772 (2012).
Worley, B. S. et al. Antigenicity of fusion proteins from sarcoma-associated chromosomal translocations. Cancer Res. 61, 6868–6875 (2001).
Sato, Y. et al. Detection and induction of CTLs specific for SYT-SSX-derived peptides in HLA-A24+ patients with synovial sarcoma. J. Immunol. 169, 1611–1618 (2002).
Gambacorti-Passerini, C. et al. Human CD4 lymphocytes specifically recognize a peptide representing the fusion region of the hybrid protein pml/RAR alpha present in acute promyelocytic leukemia cells. Blood 81, 1369–1375 (1993).
van den Broeke, L. T., Pendleton, C. D., Mackall, C., Helman, L. J. & Berzofsky, J. A. Identification and epitope enhancement of a PAX-FKHR fusion protein breakpoint epitope in alveolar rhabdomyosarcoma cells created by a tumorigenic chromosomal translocation inducing CTL capable of lysing human tumors. Cancer Res. 66, 1818–1823 (2006).
Yotnda, P. et al. Cytotoxic T cell response against the chimeric ETV6-AML1 protein in childhood acute lymphoblastic leukemia. J. Clin. Invest. 102, 455–462 (1998).
Zamora, A. E. et al. Pediatric patients with acute lymphoblastic leukemia generate abundant and functional neoantigen-specific CD8+ T cell responses. Sci. Transl Med. 11, eaat8549 (2019).
Gourraud, P.-A. et al. HLA diversity in the 1000 genomes dataset. PLoS ONE 9, e97282 (2014).
Jensen, K. K. et al. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology 154, 394–406 (2018).
Racle, J. et al. Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes. Nat. Biotechnol. 37, 1283–1286 (2019).
Calis, J. J. A. et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput. Biol. 9, e1003266 (2013).
Rech, A. J. et al. Tumor immunity and survival as a function of alternative neopeptides in human cancer. Cancer Immunol. Res. 6, 276–287 (2018).
Rubinsteyn, A. et al. Computational pipeline for the PGV-001 neoantigen vaccine trial. Front. Immunol. 8, 1807 (2018).
Kodysh, J. & Rubinsteyn, A. in Bioinformatics for Cancer Immunotherapy: Methods and Protocols (ed. Boegel, S.) 147–160 (Springer, 2020).
Bjerregaard, A.-M., Nielsen, M., Hadrup, S. R., Szallasi, Z. & Eklund, A. C. MuPeXI: prediction of neo-epitopes from tumor sequencing data. Cancer Immunol. Immunother. 66, 1123–1130 (2017).
Besser, H., Yunger, S., Merhavi-Shoham, E., Cohen, C. J. & Louzoun, Y. Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides. J. Immunother. Cancer 7, 135 (2019).
Smith, C. C. et al. Machine-learning prediction of tumor antigen immunogenicity in the selection of therapeutic epitopes. Cancer Immunol. Res. 7, 1591–1604 (2019).
Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).
Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28, 1811–1817 (2012).
Larson, D. E. et al. SomaticSniper: identification of somatic point mutations in whole genome sequencing data. Bioinformatics 28, 311–317 (2012).
Radenbaugh, A. J. et al. RADIA: RNA and DNA integrated analysis for somatic mutation detection. PLoS ONE 9, e111516 (2014).
Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at arXiv https://arxiv.org/abs/1207.3907 (2012).
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Rimmer, A. et al. Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications. Nat. Genet. 46, 912–918 (2014).
Jones, D. et al. cgpCaVEManWrapper: simple execution of CaVEMan in order to detect somatic single nucleotide variants in NGS data. Curr. Protoc. Bioinformatics 56, 15.10.1–15.10.18 (2016).
Raine, K. M. et al. cgpPindel: identifying somatically acquired insertion and deletion events from paired end sequencing. Curr. Protoc. Bioinformatics 52, 15.7.1–15.7.12 (2015).
Wala, J. A. et al. SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res. 28, 581–591 (2018).
Fan, Y. et al. MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data. Genome Biol. 17, 178 (2016).
Moncunill, V. et al. Comprehensive characterization of complex structural variations in cancer by directly comparing genome sequence reads. Nat. Biotechnol. 32, 1106–1112 (2014).
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
Sahraeian, S. M. E. et al. Deep convolutional neural networks for accurate somatic mutation detection. Nat. Commun. 10, 1041 (2019).
Geyer, R. J., Tobet, R., Berlin, R. D. & Srivastava, P. K. Immune response to mutant neo-antigens: cancer’s lessons for aging. Oncoimmunology 2, e26382 (2013).
Prehn, R. T. & Main, J. M. Immunity to methylcholanthrene-induced sarcomas. J. Natl Cancer Inst. 18, 769–778 (1957).
Smith, C. C. et al. Alternative tumour-specific antigens. Nat. Rev. Cancer 19, 465–478 (2019).
van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).
Roudko, V. et al. Shared immunogenic poly-epitope frameshift mutations in microsatellite unstable tumors. Cell 183, 1634–1649.e17 (2020).
Litchfield, K. et al. Escape from nonsense-mediated decay associates with anti-tumor immunogenicity. Nat. Commun. 11, 3800 (2020).
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013). This article illustrates the prevalence of somatic point mutations across human cancers.
Shtivelman, E., Lifshitz, B., Gale, R. P. & Canaani, E. Fused transcript of abl and bcr genes in chronic myelogenous leukaemia. Nature 315, 550–554 (1985).
Gao, Q. et al. Driver fusions and their implications in the development and treatment of human cancers. Cell Rep. 23, 227–238.e3 (2018).
Dai, X., Theobard, R., Cheng, H., Xing, M. & Zhang, J. Fusion genes: a promising tool combating against cancer. Biochim. Biophys. Acta Rev. Cancer 1869, 149–160 (2018).
Smart, A. C. et al. Intron retention is a source of neoepitopes in cancer. Nat. Biotechnol. 36, 1056–1058 (2018).
Kahles, A. et al. Comprehensive analysis of alternative splicing across tumors from 8,705 patients. Cancer Cell 34, 211–224.e6 (2018).
Jayasinghe, R. G. et al. Systematic analysis of splice-site-creating mutations in cancer. Cell Rep. 23, 270–281.e3 (2018).
Ehx, G. et al. Atypical acute myeloid leukemia-specific transcripts generate shared and immunogenic MHC class-I-associated epitopes. Immunity 54, 737–752.e10 (2021).
Bigot, J. et al. Splicing patterns in SF3B1 mutated uveal melanoma generate shared immunogenic tumor-specific neo-epitopes. Cancer Discov. 11, 1938–1951 (2021).
Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24–29 (2019).
International Society for Pharmaceutical Engineering. GAMP 5 guide: compliant GxP computerized systems, ISPE https://ispe.org/publications/guidance-documents/gamp-5 (2008).
This work was supported by a European Research Council Advanced Grant to U.S. (ERC-AdG 789256). The authors thank K. Chu for proofreading of the manuscript and helpful comments. They further thank the ERC, the German Federal Ministry of Education and Research (BMBF) and the Deutsche Forschungsgemeinschaft (DFG) for supporting their research in this field.
B.S., M.L., Ö.T. and U.S. are inventors on patents related to some of the technologies described in this article. Ö.T. is shareholder and CMO at BioNTech. U.S. is co-founder, shareholder and CEO at BioNTech. F.L. declares no competing interests.
Peer review information
Nature Reviews Drug Discovery thanks William Gillanders, Michal Bassani-Sternberg and Robert Petit for their contribution to the peer review of this work.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Major histocompatibility complex (MHC)-bound peptides that arise from tumour-specific mutations.
- Adoptive T cell transfer
Immunotherapy in which T cells are taken from the patient’s tumour tissue or blood, expanded in vitro and then transferred back to the patient to support the immune system’s natural fight against the cancer.
A mutation that causes changes in the amino acid sequence of a protein.
The fraction of tumour subclones that harbour a given mutation.
Protein complex formed by β2-microglobulin and an α-chain encoded in the HLA-A, HLA-B and HLA-C loci in human. Major histocompatibility complex class I (MHC-I) is expressed on the cell surface of all nucleated cells and presents intracellularly synthesized peptides to CD8+ T cells.
Protein complex formed by an α- and a β-chain that are encoded in the HLA-DR, HLA-DP and HLA-DQ loci in human. Major histocompatibility complex class II (MHC-II) is mainly expressed on the cell surface of specialized antigen-presenting cells and presents mainly extracellular peptides to CD4+ T cells.
Set of all non-synonymous somatic mutations occurring in a tumour.
- Central immune tolerance
Elimination of self-reactive T cells in the thymus.
- Peripheral tolerance
Elimination or suppression of autoreactive T cells or B cell clones that escaped to the periphery.
A hyporesponsive state in which an antigen-experienced T cell is functionally impaired and does not adequately respond to cognate antigen exposure.
- TCR diversity
The ability of a single peptide–major histocompatibility (MHC) to engage antigen-specific T cells with diverse T cell receptor (TCR) α/β chains.
- TCR degeneracy
The ability of a single T cell receptor (TCR) to recognize diverse peptide–major compatibility complex (MHC) complexes.
- Heterologous immunity
Cross-reactive T cell immunity induced by an unrelated antigen, often existent before tumour onset.
- Immune surveillance
A hypothesis that assumes that immune cells monitor, identify and eliminate pre-malignant or malignant cells in the body.
- Loss of heterozigosity
(LOH). Loss of one of the allele copies in a locus with two different alleles.
- Neoantigen features
Features or algorithm that can be used to rank neoantigen candidates.
- Differential agretopicity index
(DAI). Difference in major histocompatibility complex class I (MHC-I) binding affinity between neoepitope and corresponding non-mutated peptide.
- Clonal mutations
Mutations that are present in all subclones of a tumour. In practice, the definition of clonal versus subclonal mutation is not standardized and depends on the experimental setting and bioinformatics tools, as these provide a numerical estimation of clonality (e.g. PyClone).
A hypothesis that describes the transition between immune protection against tumour development and tumour outgrowth in three phases: elimination, equilibrium and escape.
Immune responses induced by vaccination as in the case of ignored neoepitopes.
Induction of immune responses without vaccination as in the case of guarding and restrained neoepitopes.
- Antigen spreading
Expansion of an immune response to secondary epitopes or other antigens that were not targeted by immunotherapy.
- Computational neoantigen prediction pipelines
Computational tools for neoantigen prediction, starting with mutation calling or a set of called mutations and covering the translation into mutated peptide sequences and ranking of neoantigen candidates by a neoantigen feature.
Process that identifies the transcripts to which a RNA sequence read is most likely related, but does not specify how each nucleotide matches the reference as in a normal alignment.
- Receiver operating characteristic (ROC) curve
A graphical plot that reflects the quality of a classifier by showing the true positive versus false positive rate across varying thresholds.
- TAP protein complex
Protein complex of transporter associated with antigen processing 1 (TAP1), and TAP2, which imports peptides from the cytosol into the endoplasmic reticulum.
- Basic Local Alignment Search Tool
(BLAST). A tool to find local regions of similarity between biological sequences. It enables comparison of a sequence of interest against a database of sequences and identification of the sequences with highest local similarities.
- Artificial neural network
Computing system that is inspired by biological neural networks and that applies tasks based on learned patterns.
- Variant allele frequency
The fraction of sequence reads observed covering a mutation divided by the overall number of reads at that locus.
- Truncal mutations
Mutations that occur early during tumour evolution.
- Branched mutations
Mutations that occur later during tumour evolution and that are only present in a subset of tumour cells.
- Loss function
A function that calculates how well or poorly an algorithm models the given data by comparing the predicted values with the actual values.
- Mutation calling process
Mutation calling strategy in which the overlap (consensus) from at least two different mutation callers is used to define the final set of mutational events.
- Sequence alignment
Mapping of sequencing reads to a reference genome to determine the genomic loci of origin.
- Dark matter
DNA segments in the genome with unknown function.
- Anchor residues
Positions in a major histocompatibility complex (MHC) epitope with specific amino acid preference.
Rights and permissions
About this article
Cite this article
Lang, F., Schrörs, B., Löwer, M. et al. Identification of neoantigens for individualized therapeutic cancer vaccines. Nat Rev Drug Discov 21, 261–282 (2022). https://doi.org/10.1038/s41573-021-00387-y
This article is cited by
Advances of mRNA vaccine in tumor: a maze of opportunities and challenges
Biomarker Research (2023)
A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer
Journal of Ovarian Research (2023)
Immunology and immunotherapy of cholangiocarcinoma
Nature Reviews Gastroenterology & Hepatology (2023)
Neoantigens: promising targets for cancer therapy
Signal Transduction and Targeted Therapy (2023)
Neoantigen-targeted CD8+ T cell responses with PD-1 blockade therapy