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Identification of neoantigens for individualized therapeutic cancer vaccines

Nature Reviews Drug Discovery volume 21, pages 261–282 (2022)Cite this article

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Abstract

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.

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Fig. 1: Engineering individualized neoantigen vaccines.
Fig. 2: Mechanisms of neoantigen-mediated tumour control.
Fig. 3: TCR diversity and degeneracy.
Fig. 4: Impact of clonality on neoantigen recognition.
Fig. 5: A context-based classification of neoantigens.
Fig. 6: Tumour rejection antigens.

References

  1. Wölfel, T. et al. A p16INK4a-insensitive CDK4 mutant targeted by cytolytic T lymphocytes in a human melanoma. Science 269, 1281–1284 (1995).

    PubMed  Google Scholar 

  2. 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).

    CAS  PubMed  Google Scholar 

  3. Pieper, R. et al. Biochemical identification of a mutated human melanoma antigen recognized by CD4+ T cells. J. Exp. Med.189, 757–766 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 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).

    CAS  PubMed  Google Scholar 

  5. 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).

    PubMed  Google Scholar 

  6. Gubin, M. M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).

    PubMed  PubMed Central  Google Scholar 

  8. Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949.e16 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–206 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).

    CAS  PubMed  Google Scholar 

  15. Castle, J. C. et al. Exploiting the mutanome for tumor vaccination. Cancer Res. 72, 1081–1091 (2012).

    CAS  PubMed  Google Scholar 

  16. 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).

    CAS  PubMed  Google Scholar 

  17. Tran, E. et al. Cancer immunotherapy based on mutation-specific CD4+ T cells in a patient with epithelial cancer. Science 344, 641–645 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Türeci, Ö. et al. Targeting the heterogeneity of cancer with individualized neoepitope vaccines. Clin. Cancer Res. 22, 1885–1896 (2016).

    PubMed  Google Scholar 

  19. Tran, E. et al. Immunogenicity of somatic mutations in human gastrointestinal cancers. Science 350, 1387–1390 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Parkhurst, M. R. et al. Unique neoantigens arise from somatic mutations in patients with gastrointestinal cancers. Cancer Discov. 9, 1022–1035 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).

    CAS  PubMed  Google Scholar 

  24. 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).

    PubMed Central  Google Scholar 

  25. 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).

    CAS  PubMed  Google Scholar 

  26. 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).

    CAS  PubMed  Google Scholar 

  27. Linnemann, C. et al. High-throughput identification of antigen-specific TCRs by TCR gene capture. Nat. Med. 19, 1534–1541 (2013).

    CAS  PubMed  Google Scholar 

  28. Ali, M. et al. Induction of neoantigen-reactive T cells from healthy donors. Nat. Protoc. 14, 1926–1943 (2019).

    CAS  PubMed  Google Scholar 

  29. Strønen, E. et al. Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science 352, 1337–1341 (2016).

    PubMed  Google Scholar 

  30. 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).

    CAS  Google Scholar 

  31. 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).

    CAS  PubMed  Google Scholar 

  32. Kreiter, S. et al. Mutant MHC class II epitopes drive therapeutic immune responses to cancer. Nature 520, 692–696 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 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).

    CAS  PubMed  Google Scholar 

  34. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 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).

    PubMed  PubMed Central  Google Scholar 

  36. 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).

    PubMed  PubMed Central  Google Scholar 

  37. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 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).

    CAS  PubMed  Google Scholar 

  39. Hirosue, S. & Dubrot, J. Modes of antigen presentation by lymph node stromal cells and their immunological implications. Front. Immunol. 6, 446 (2015).

    PubMed  PubMed Central  Google Scholar 

  40. 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).

    CAS  PubMed  Google Scholar 

  41. Flament, H. et al. Modeling the specific CD4+ T cell response against a tumor neoantigen. J. Immunol. 194, 3501–3512 (2015).

    CAS  PubMed  Google Scholar 

  42. 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).

    CAS  PubMed  Google Scholar 

  43. 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).

    CAS  PubMed  Google Scholar 

  44. 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).

    CAS  PubMed  Google Scholar 

  45. Ruhland, M. K. et al. Visualizing synaptic transfer of tumor antigens among dendritic cells. Cancer Cell 37, 786–799.e5 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 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).

    CAS  PubMed  Google Scholar 

  47. 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).

    CAS  PubMed  Google Scholar 

  48. Rocha, B. & von Boehmer, H. Peripheral selection of the T cell repertoire. Science 251, 1225–1228 (1991).

    CAS  PubMed  Google Scholar 

  49. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Ramsdell, F., Lantz, T. & Fowlkes, B. J. A nondeletional mechanism of thymic self tolerance. Science 246, 1038–1041 (1989).

    CAS  PubMed  Google Scholar 

  51. 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).

    CAS  PubMed  Google Scholar 

  52. Blank, C. U. et al. Defining ‘T cell exhaustion’. Nat. Rev. Immunol. 19, 665–674 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Obst, R. The timing of T cell priming and cycling. Front. Immunol. 6, 563 (2015).

    PubMed  PubMed Central  Google Scholar 

  54. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 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).

    CAS  PubMed  Google Scholar 

  56. Hennecke, J. & Wiley, D. C. T cell receptor-MHC interactions up close. Cell 104, 1–4 (2001).

    CAS  PubMed  Google Scholar 

  57. 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).

    PubMed Central  Google Scholar 

  58. 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).

    PubMed  PubMed Central  Google Scholar 

  59. Cafri, G. et al. Memory T cells targeting oncogenic mutations detected in peripheral blood of epithelial cancer patients. Nat. Commun. 10, 449 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Malekzadeh, P. et al. Antigen experienced T cells from peripheral blood recognize p53 neoantigens. Clin. Cancer Res. 26, 1267–1276 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Wooldridge, L. et al. A single autoimmune T cell receptor recognizes more than a million different peptides. J. Biol. Chem. 287, 1168–1177 (2012).

    CAS  PubMed  Google Scholar 

  64. Birnbaum, M. E. et al. Deconstructing the peptide-MHC specificity of T cell recognition. Cell 157, 1073–1087 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 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).

    PubMed  PubMed Central  Google Scholar 

  66. Leng, Q., Tarbe, M., Long, Q. & Wang, F. Pre-existing heterologous T-cell immunity and neoantigen immunogenicity. Clin. Transl. Immunol. 9, e01111 (2020).

    Google Scholar 

  67. 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.

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 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.

    PubMed  Google Scholar 

  69. Schumacher, T. et al. A vaccine targeting mutant IDH1 induces antitumour immunity. Nature 512, 324–327 (2014).

    CAS  PubMed  Google Scholar 

  70. 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.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Greenman, C. et al. Patterns of somatic mutation in human cancer genomes. Nature 446, 153–158 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 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).

    CAS  PubMed  Google Scholar 

  73. Xie, Y. et al. Naive tumor-specific CD4+ T cells differentiated in vivo eradicate established melanoma. J. Exp. Med. 207, 651–667 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Oh, D. Y. et al. Intratumoral CD4+ T cells mediate anti-tumor cytotoxicity in human bladder cancer. Cell 181, 1612–1625.e13 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 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).

    CAS  PubMed  Google Scholar 

  77. 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).

    CAS  PubMed  Google Scholar 

  78. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Brossart, P. The role of antigen spreading in the efficacy of immunotherapies. Clin. Cancer Res. 26, 4442–4447 (2020).

    CAS  PubMed  Google Scholar 

  80. 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).

    CAS  PubMed  Google Scholar 

  81. Sahin, U. & Türeci, Ö. Personalized vaccines for cancer immunotherapy. Science 359, 1355–1360 (2018).

    CAS  PubMed  Google Scholar 

  82. Chen, D. S. & Mellman, I. Elements of cancer immunity and the cancer–immune set point. Nature 541, 321–330 (2017).

    CAS  PubMed  Google Scholar 

  83. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 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).

    CAS  PubMed  Google Scholar 

  86. 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).

    PubMed  PubMed Central  Google Scholar 

  87. 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).

    PubMed  PubMed Central  Google Scholar 

  88. Beck, C., Schreiber, H. & Rowley, D. A. Role of TGF-β in immune-evasion of cancer. Microsc. Res. Tech. 52, 387–395 (2001).

    CAS  PubMed  Google Scholar 

  89. Efremova, M. et al. Targeting immune checkpoints potentiates immunoediting and changes the dynamics of tumor evolution. Nat. Commun. 9, 32 (2018).

    PubMed  PubMed Central  Google Scholar 

  90. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 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.

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Zapata, L. et al. Negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome. Genome Biol. 19, 67 (2018).

    PubMed  PubMed Central  Google Scholar 

  93. 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).

    PubMed  PubMed Central  Google Scholar 

  94. Campoli, M. & Ferrone, S. HLA antigen changes in malignant cells: epigenetic mechanisms and biologic significance. Oncogene 27, 5869–5885 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 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).

    CAS  PubMed  Google Scholar 

  96. Sade-Feldman, M. et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136 (2017).

    PubMed  PubMed Central  Google Scholar 

  97. Brastianos, P. K. et al. Genomic characterization of brain metastases reveals branched evolution and potential therapeutic targets. Cancer Discov. 5, 1164–1177 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Jiménez-Sánchez, A. et al. Heterogeneous tumor-immune microenvironments among differentially growing metastases in an ovarian cancer patient. Cell 170, 927–938.e20 (2017).

    PubMed  PubMed Central  Google Scholar 

  99. Angelova, M. et al. Evolution of metastases in space and time under immune selection. Cell 175, 751–765.e16 (2018).

    CAS  PubMed  Google Scholar 

  100. Kvistborg, P. et al. Anti-CTLA-4 therapy broadens the melanoma-reactive CD8+ T cell response. Sci. Transl Med. 6, 254ra128 (2014).

    PubMed  Google Scholar 

  101. 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).

    CAS  PubMed  Google Scholar 

  102. Matsushita, H. et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature 482, 400–404 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Joyce, J. A. & Fearon, D. T. T cell exclusion, immune privilege, and the tumor microenvironment. Science 348, 74–80 (2015).

    CAS  PubMed  Google Scholar 

  104. 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).

    PubMed  PubMed Central  Google Scholar 

  105. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 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).

    CAS  Google Scholar 

  107. Bessell, C. A. et al. Commensal bacteria stimulate antitumor responses via T cell cross-reactivity. JCI Insight 5, e135597 (2020).

    PubMed Central  Google Scholar 

  108. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. 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).

    PubMed Central  Google Scholar 

  110. McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Lu, T. et al. Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes. Sci. Immunol. 5, eaaz3199 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Łuksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017).

    PubMed  PubMed Central  Google Scholar 

  113. Miao, D. et al. Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors. Nat. Genet. 50, 1271–1281 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Goodman, A. M. et al. MHC-I genotype and tumor mutational burden predict response to immunotherapy. Genome Med. 12, 45 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. 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).

    CAS  PubMed  Google Scholar 

  116. 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).

    CAS  PubMed  Google Scholar 

  117. Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 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).

    PubMed  Google Scholar 

  119. Yang, W. et al. Immunogenic neoantigens derived from gene fusions stimulate T cell responses. Nat. Med. 25, 767–775 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 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).

    CAS  PubMed  Google Scholar 

  121. 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).

    CAS  PubMed  Google Scholar 

  122. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  123. Hilf, N. et al. Actively personalized vaccination trial for newly diagnosed glioblastoma. Nature 565, 240–245 (2019).

    CAS  PubMed  Google Scholar 

  124. 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).

    CAS  PubMed  Google Scholar 

  125. Keskin, D. B. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239 (2019).

    CAS  PubMed  Google Scholar 

  126. Chen, F. et al. Neoantigen identification strategies enable personalized immunotherapy in refractory solid tumors. J. Clin. Invest. 129, 2056–2070 (2019).

    PubMed  PubMed Central  Google Scholar 

  127. Blass, E. & Ott, P. A. Advances in the development of personalized neoantigen-based therapeutic cancer vaccines. Nat. Rev. Clin. Oncol. 18, 215–229 (2021).

    PubMed  PubMed Central  Google Scholar 

  128. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. Lee, K. L. et al. Efficient tumor clearance and diversified immunity through neoepitope vaccines and combinatorial immunotherapy. Cancer Immunol. Res. 7, 1359–1370 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. 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).

    CAS  Google Scholar 

  131. Anagnostou, V. et al. Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer. Cancer Discov. 7, 264–276 (2017).

    CAS  PubMed  Google Scholar 

  132. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    CAS  PubMed  Google Scholar 

  133. 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).

    CAS  PubMed  Google Scholar 

  134. Zhao, W. & Sher, X. Systematically benchmarking peptide-MHC binding predictors: from synthetic to naturally processed epitopes. PLoS Comput. Biol. 14, e1006457 (2018).

    PubMed  PubMed Central  Google Scholar 

  135. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  136. 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).

    CAS  PubMed  Google Scholar 

  137. O’Donnell, T. J. et al. MHCflurry: open-source class I MHC binding affinity prediction. Cell Syst. 7, 129–132.e4 (2018).

    PubMed  Google Scholar 

  138. 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).

    PubMed  PubMed Central  Google Scholar 

  139. Gfeller, D. et al. The length distribution and multiple specificity of naturally presented HLA-I ligands. J. Immunol. 201, 3705–3716 (2018).

    CAS  PubMed  Google Scholar 

  140. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  141. 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).

    PubMed  PubMed Central  Google Scholar 

  142. Blaha, D. T. et al. High-throughput stability screening of neoantigen/HLA complexes improves immunogenicity predictions. Cancer Immunol. Res. 7, 50–61 (2019).

    CAS  PubMed  Google Scholar 

  143. 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).

    CAS  PubMed  Google Scholar 

  144. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. Chowell, D. et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359, 582–587 (2018).

    CAS  PubMed  Google Scholar 

  146. Marty, R. et al. MHC-I genotype restricts the oncogenic mutational landscape. Cell 171, 1272–1283.e15 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  147. Marty Pyke, R. et al. Evolutionary pressure against MHC class II binding cancer mutations. Cell 175, 416–428.e13 (2018).

    PubMed  Google Scholar 

  148. 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).

    PubMed  Google Scholar 

  149. Stranzl, T., Larsen, M. V., Lundegaard, C. & Nielsen, M. NetCTLpan: pan-specific MHC class I pathway epitope predictions. Immunogenetics 62, 357–368 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Bjerregaard, A.-M. et al. An analysis of natural T cell responses to predicted tumor neoepitopes. Front. Immunol. 8, 1566 (2017).

    PubMed  PubMed Central  Google Scholar 

  151. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Trolle, T. & Nielsen, M. NetTepi: an integrated method for the prediction of T cell epitopes. Immunogenetics 66, 449–456 (2014).

    CAS  PubMed  Google Scholar 

  153. 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).

    PubMed  Google Scholar 

  154. 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).

    Article  Google Scholar 

  155. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. Roth, A. et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11, 396–398 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. 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).

    PubMed  PubMed Central  Google Scholar 

  158. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  159. Gejman, R. S. et al. Rejection of immunogenic tumor clones is limited by clonal fraction. eLife 7, e41090 (2018).

    PubMed  PubMed Central  Google Scholar 

  160. Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).

    CAS  PubMed  Google Scholar 

  161. 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).

    PubMed  PubMed Central  Google Scholar 

  162. Tate, J. G. et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 47, D941–D947 (2019).

    CAS  PubMed  Google Scholar 

  163. Liu, S.-H. et al. DriverDBv3: a multi-omics database for cancer driver gene research. Nucleic Acids Res. 48, D863–D870 (2020).

    CAS  PubMed  Google Scholar 

  164. Tran, E. et al. T-cell transfer therapy targeting mutant KRAS in cancer. N. Engl. J. Med. 375, 2255–2262 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  165. Chen, H. et al. Comprehensive assessment of computational algorithms in predicting cancer driver mutations. Genome Biol. 21, 43 (2020).

    PubMed  PubMed Central  Google Scholar 

  166. Bozic, I. et al. Accumulation of driver and passenger mutations during tumor progression. Proc. Natl Acad. Sci. USA 107, 18545–18550 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  167. 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).

    CAS  PubMed  Google Scholar 

  168. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  169. McGranahan, N. & Swanton, C. Neoantigen quality, not quantity. Sci. Transl Med. 11, eaax7918 (2019).

    PubMed  Google Scholar 

  170. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  171. Mei, R. et al. Genome-wide detection of allelic imbalance using human SNPs and high-density DNA arrays. Genome Res. 10, 1126–1137 (2000).

    CAS  PubMed  Google Scholar 

  172. Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  173. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  174. Blomen, V. A. et al. Gene essentiality and synthetic lethality in haploid human cells. Science 350, 1092–1096 (2015).

    CAS  PubMed  Google Scholar 

  175. 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).

    CAS  PubMed  Google Scholar 

  176. Hoof, I. et al. NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics 61, 1–13 (2008).

    PubMed  PubMed Central  Google Scholar 

  177. 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).

    CAS  PubMed  Google Scholar 

  178. Riley, T. P. et al. Structure based prediction of neoantigen immunogenicity. Front. Immunol. 10, 2047 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  179. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    CAS  PubMed  Google Scholar 

  180. Phloyphisut, P., Pornputtapong, N., Sriswasdi, S. & Chuangsuwanich, E. MHCSeqNet: a deep neural network model for universal MHC binding prediction. BMC Bioinformatics 20, 270 (2019).

    PubMed  PubMed Central  Google Scholar 

  181. Bulik-Sullivan, B. et al. Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nat. Biotechnol. 37, 55–63 (2019).

    CAS  Google Scholar 

  182. Chen, B. et al. Predicting HLA class II antigen presentation through integrated deep learning. Nat. Biotechnol. 37, 1332–1343 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  183. Wu, J. et al. DeepHLApan: a deep learning approach for neoantigen prediction considering both HLA-peptide binding and immunogenicity. Front. Immunol. 10, 2559 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  184. Zou, J. et al. A primer on deep learning in genomics. Nat. Genet. 51, 12–18 (2019).

    CAS  PubMed  Google Scholar 

  185. 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).

    Google Scholar 

  186. 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).

    CAS  PubMed  Google Scholar 

  187. Gerlinger, M. et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46, 225–233 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  188. 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).

    PubMed  Google Scholar 

  189. Robinson, D. R. et al. Integrative clinical genomics of metastatic cancer. Nature 548, 297–303 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  190. 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).

    CAS  PubMed  Google Scholar 

  191. Chen, M. & Zhao, H. Next-generation sequencing in liquid biopsy: cancer screening and early detection. Hum. Genomics 13, 34 (2019).

    PubMed  PubMed Central  Google Scholar 

  192. ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).

    Google Scholar 

  193. Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).

    CAS  PubMed  Google Scholar 

  194. Benjamin, D. et al. Calling somatic SNVs and indels with Mutect2. Preprint at bioRxiv https://doi.org/10.1101/861054 (2019).

    Article  Google Scholar 

  195. Wang, K. et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 38, e178 (2010).

    PubMed  PubMed Central  Google Scholar 

  196. Aran, D., Sirota, M. & Butte, A. J. Systematic pan-cancer analysis of tumour purity. Nat. Commun. 6, 8971 (2015).

    CAS  PubMed  Google Scholar 

  197. Dagogo-Jack, I. & Shaw, A. T. Tumour heterogeneity and resistance to cancer therapies. Nat. Rev. Clin. Oncol. 15, 81–94 (2018).

    CAS  PubMed  Google Scholar 

  198. Li, Y. et al. Patterns of somatic structural variation in human cancer genomes. Nature 578, 112–121 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  199. Garcia-Garijo, A., Fajardo, C. A. & Gros, A. Determinants for neoantigen identification. Front. Immunol. 10, 1392 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  200. Zhou, W.-J. et al. NeoPeptide: an immunoinformatic database of T-cell-defined neoantigens. Database 2019, baz128 (2019).

    PubMed  PubMed Central  Google Scholar 

  201. Tan, X. et al. dbPepNeo: a manually curated database for human tumor neoantigen peptides. Database 2020, baaa004 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  202. Reker, D., Schneider, P., Schneider, G. & Brown, J. B. Active learning for computational chemogenomics. Future Med. Chem. 9, 381–402 (2017).

    CAS  PubMed  Google Scholar 

  203. Eraslan, G., Avsec, Ž., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389–403 (2019).

    CAS  PubMed  Google Scholar 

  204. Hollingsworth, R. E. & Jansen, K. Turning the corner on therapeutic cancer vaccines. NPJ Vaccines 4, 7 (2019).

    PubMed  PubMed Central  Google Scholar 

  205. 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).

    CAS  PubMed  Google Scholar 

  206. 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).

    PubMed  PubMed Central  Google Scholar 

  207. Leoni, G. et al. A genetic vaccine encoding shared cancer neoantigens to treat tumors with microsatellite instability. Cancer Res. 80, 3972–3982 (2020).

    CAS  PubMed  Google Scholar 

  208. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03639714 (2021).

  209. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03953235 (2020).

  210. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  211. Aurisicchio, L. et al. Poly-specific neoantigen-targeted cancer vaccines delay patient derived tumor growth. J. Exp. Clin. Cancer Res. 38, 78 (2019).

    PubMed  PubMed Central  Google Scholar 

  212. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT04015700 (2021).

  213. 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).

    PubMed  Google Scholar 

  214. Kagermann, H. in Management of Permanent Change (eds Albach, H., Meffert, H. Pinkwart, A. & Reichwald, A.) 23–45 (Springer, 2015).

  215. Theobald, M. (ed.) Current Immunotherapeutic Strategies in Cancer (Springer, 2020).

  216. Britten, C. M. et al. The regulatory landscape for actively personalized cancer immunotherapies. Nat. Biotechnol. 31, 880–882 (2013).

    CAS  PubMed  Google Scholar 

  217. Vormehr, M., Türeci, Ö. & Sahin, U. Harnessing tumor mutations for truly individualized cancer vaccines. Annu. Rev. Med. 70, 395–407 (2019).

    CAS  PubMed  Google Scholar 

  218. Vormehr, M. et al. Mutanome engineered RNA immunotherapy: towards patient-centered tumor vaccination. J. Immunol. Res. 2015, 595363 (2015).

    PubMed  PubMed Central  Google Scholar 

  219. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/results?term=neoantigen+AND+vaccine&recrs=abdef&cond=Cancer (2021).

  220. 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).

    CAS  PubMed  Google Scholar 

  221. Gfeller, D. & Bassani-Sternberg, M. Predicting antigen presentation-what could we learn from a million peptides? Front. Immunol. 9, 1716 (2018).

    PubMed  PubMed Central  Google Scholar 

  222. 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).

    PubMed  Google Scholar 

  223. Laumont, C. M. et al. Noncoding regions are the main source of targetable tumor-specific antigens. Sci. Transl Med. 10, eaau5516 (2018).

    CAS  PubMed  Google Scholar 

  224. 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).

    CAS  PubMed  Google Scholar 

  225. 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).

    PubMed  PubMed Central  Google Scholar 

  226. Fritsch, E. F. et al. HLA-binding properties of tumor neoepitopes in humans. Cancer Immunol. Res. 2, 522–529 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  227. 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).

    CAS  Google Scholar 

  228. Ogishi, M. & Yotsuyanagi, H. Quantitative prediction of the landscape of T cell epitope immunogenicity in sequence space. Front. Immunol. 10, 827 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  229. Devlin, J. R. et al. Structural dissimilarity from self drives neoepitope escape from immune tolerance. Nat. Chem. Biol. 16, 1269–1276 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  230. Ochs, K. et al. K27M-mutant histone-3 as a novel target for glioma immunotherapy. Oncoimmunology 6, e1328340 (2017).

    PubMed  PubMed Central  Google Scholar 

  231. 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).

    PubMed  Google Scholar 

  232. 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).

    PubMed  Google Scholar 

  233. 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).

    Google Scholar 

  234. 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).

    PubMed  PubMed Central  Google Scholar 

  235. Linard, B. et al. A ras-mutated peptide targeted by CTL infiltrating a human melanoma lesion. J. Immunol. 168, 4802 (2002).

    CAS  PubMed  Google Scholar 

  236. 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).

    CAS  PubMed  Google Scholar 

  237. van der Lee, D. I. et al. Mutated nucleophosmin 1 as immunotherapy target in acute myeloid leukemia. J. Clin. Invest. 129, 774–785 (2019).

    PubMed  PubMed Central  Google Scholar 

  238. 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).

    PubMed  PubMed Central  Google Scholar 

  239. 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).

    PubMed  PubMed Central  Google Scholar 

  240. 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).

    CAS  PubMed  Google Scholar 

  241. 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).

    CAS  PubMed  Google Scholar 

  242. 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).

    CAS  PubMed  Google Scholar 

  243. Cai, A. et al. Mutated BCR-ABL generates immunogenic T-cell epitopes in CML patients. Clin. Cancer Res. 18, 5761–5772 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  244. Worley, B. S. et al. Antigenicity of fusion proteins from sarcoma-associated chromosomal translocations. Cancer Res. 61, 6868–6875 (2001).

    CAS  PubMed  Google Scholar 

  245. 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).

    CAS  PubMed  Google Scholar 

  246. 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).

    CAS  PubMed  Google Scholar 

  247. 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).

    PubMed  Google Scholar 

  248. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  249. 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).

    PubMed  PubMed Central  Google Scholar 

  250. Gourraud, P.-A. et al. HLA diversity in the 1000 genomes dataset. PLoS ONE 9, e97282 (2014).

    PubMed  PubMed Central  Google Scholar 

  251. Jensen, K. K. et al. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology 154, 394–406 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  252. Racle, J. et al. Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes. Nat. Biotechnol. 37, 1283–1286 (2019).

    CAS  PubMed  Google Scholar 

  253. Calis, J. J. A. et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput. Biol. 9, e1003266 (2013).

    PubMed  PubMed Central  Google Scholar 

  254. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  255. Rubinsteyn, A. et al. Computational pipeline for the PGV-001 neoantigen vaccine trial. Front. Immunol. 8, 1807 (2018).

    PubMed  PubMed Central  Google Scholar 

  256. Kodysh, J. & Rubinsteyn, A. in Bioinformatics for Cancer Immunotherapy: Methods and Protocols (ed. Boegel, S.) 147–160 (Springer, 2020).

  257. 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).

    CAS  PubMed  Google Scholar 

  258. 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).

    PubMed  PubMed Central  Google Scholar 

  259. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  260. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  261. Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28, 1811–1817 (2012).

    CAS  PubMed  Google Scholar 

  262. Larson, D. E. et al. SomaticSniper: identification of somatic point mutations in whole genome sequencing data. Bioinformatics 28, 311–317 (2012).

    CAS  PubMed  Google Scholar 

  263. Radenbaugh, A. J. et al. RADIA: RNA and DNA integrated analysis for somatic mutation detection. PLoS ONE 9, e111516 (2014).

    PubMed  PubMed Central  Google Scholar 

  264. Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at arXiv https://arxiv.org/abs/1207.3907 (2012).

  265. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  266. Rimmer, A. et al. Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications. Nat. Genet. 46, 912–918 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  267. 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).

    Google Scholar 

  268. 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).

    Google Scholar 

  269. Wala, J. A. et al. SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res. 28, 581–591 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  270. 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).

    PubMed  PubMed Central  Google Scholar 

  271. Moncunill, V. et al. Comprehensive characterization of complex structural variations in cancer by directly comparing genome sequence reads. Nat. Biotechnol. 32, 1106–1112 (2014).

    CAS  PubMed  Google Scholar 

  272. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  273. Sahraeian, S. M. E. et al. Deep convolutional neural networks for accurate somatic mutation detection. Nat. Commun. 10, 1041 (2019).

    PubMed  PubMed Central  Google Scholar 

  274. 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).

    PubMed  PubMed Central  Google Scholar 

  275. Prehn, R. T. & Main, J. M. Immunity to methylcholanthrene-induced sarcomas. J. Natl Cancer Inst. 18, 769–778 (1957).

    CAS  PubMed  Google Scholar 

  276. Smith, C. C. et al. Alternative tumour-specific antigens. Nat. Rev. Cancer 19, 465–478 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  277. van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).

    PubMed  PubMed Central  Google Scholar 

  278. Roudko, V. et al. Shared immunogenic poly-epitope frameshift mutations in microsatellite unstable tumors. Cell 183, 1634–1649.e17 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  279. Litchfield, K. et al. Escape from nonsense-mediated decay associates with anti-tumor immunogenicity. Nat. Commun. 11, 3800 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  280. 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.

    CAS  PubMed  PubMed Central  Google Scholar 

  281. 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).

    CAS  PubMed  Google Scholar 

  282. Gao, Q. et al. Driver fusions and their implications in the development and treatment of human cancers. Cell Rep. 23, 227–238.e3 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  283. 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).

    CAS  PubMed  Google Scholar 

  284. Smart, A. C. et al. Intron retention is a source of neoepitopes in cancer. Nat. Biotechnol. 36, 1056–1058 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  285. Kahles, A. et al. Comprehensive analysis of alternative splicing across tumors from 8,705 patients. Cancer Cell 34, 211–224.e6 (2018).

    CAS  PubMed  Google Scholar 

  286. Jayasinghe, R. G. et al. Systematic analysis of splice-site-creating mutations in cancer. Cell Rep. 23, 270–281.e3 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  287. 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).

    CAS  PubMed  Google Scholar 

  288. Bigot, J. et al. Splicing patterns in SF3B1 mutated uveal melanoma generate shared immunogenic tumor-specific neo-epitopes. Cancer Discov. 11, 1938–1951 (2021).

    CAS  PubMed  Google Scholar 

  289. Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24–29 (2019).

    CAS  PubMed  Google Scholar 

  290. International Society for Pharmaceutical Engineering. GAMP 5 guide: compliant GxP computerized systems, ISPE https://ispe.org/publications/guidance-documents/gamp-5 (2008).

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Acknowledgements

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.

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Authors and Affiliations

  1. TRON Translational Oncology, Mainz, Germany

    Franziska Lang, Barbara Schrörs & Martin Löwer

  2. BioNTech, Mainz, Germany

    Özlem Türeci & Ugur Sahin

  3. University Medical Center, Johannes Gutenberg University, Mainz, Germany

    Ugur Sahin

Authors
  1. Franziska Lang
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  2. Barbara Schrörs
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  3. Martin Löwer
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  4. Özlem Türeci
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  5. Ugur Sahin
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The authors contributed equally to all aspects of the article.

Corresponding author

Correspondence to Ugur Sahin.

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Competing interests

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.

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Supplementary information

Glossary

Neoepitopes

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.

Non-synonymous

A mutation that causes changes in the amino acid sequence of a protein.

Clonality

The fraction of tumour subclones that harbour a given mutation.

MHC-I

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.

MHC-II

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.

Mutanome

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.

Anergy

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).

Immunoediting

A hypothesis that describes the transition between immune protection against tumour development and tumour outgrowth in three phases: elimination, equilibrium and escape.

Antigenicity

Immune responses induced by vaccination as in the case of ignored neoepitopes.

Immunogenicity

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.

Pseudoalignment

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.

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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

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