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Identification of tumor antigens with immunopeptidomics

Abstract

The identification of actionable tumor antigens is indispensable for the development of several cancer immunotherapies, including T cell receptor–transduced T cells and patient-specific mRNA or peptide vaccines. Most known tumor antigens have been identified through extensive molecular characterization and are considered canonical if they derive from protein-coding regions of the genome. By eluting human leukocyte antigen-bound peptides from tumors and subjecting these to mass spectrometry analysis, the peptides can be identified by matching the resulting spectra against reference databases. Recently, mass-spectrometry-based immunopeptidomics has enabled the discovery of noncanonical antigens—antigens derived from sequences outside protein-coding regions or generated by noncanonical antigen-processing mechanisms. Coupled with transcriptomics and ribosome profiling, this method enables the identification of thousands of noncanonical peptides, of which a substantial fraction may be detected exclusively in tumors. Spectral matching against the immense noncanonical reference may generate false positives. However, sensitive mass spectrometry, analytical validation and advanced bioinformatics solutions are expected to uncover the full landscape of presented antigens and clinically relevant targets.

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Fig. 1: The HLA-I processing and presentation pathway, along with the processes that can potentially generate noncanonical peptides.
Fig. 2: Noncanonical (and canonical) antigen identification and selection for immunotherapeutic applications, integrating information from multiple -omics levels and from publicly available datasets.
Fig. 3: Computational approaches to proteogenomics-directed immunopeptidomics analyses.

References

  1. 1.

    Kloetzel, P. M. Antigen processing by the proteasome. Nat. Rev. Mol. Cell Biol. 2, 179–187 (2001).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  2. 2.

    Coulie, P. G. et al. A mutated intron sequence codes for an antigenic peptide recognized by cytolytic T lymphocytes on a human melanoma. Proc. Natl Acad. Sci. USA 92, 7976–7980 (1995).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  3. 3.

    Roche, P. A. & Furuta, K. The ins and outs of MHC class II-mediated antigen processing and presentation. Nat. Rev. Immunol. 15, 203–216 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  4. 4.

    Yewdell, J. W., Reits, E. & Neefjes, J. Making sense of mass destruction: quantitating MHC class I antigen presentation. Nat. Rev. Immunol. 3, 952–961 (2003).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  5. 5.

    Schumacher, T. N., Scheper, W. & Kvistborg, P. Cancer neoantigens. Annu. Rev. Immunol. 37, 173–200 (2019).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  6. 6.

    Bianchi, V., Harari, A. & Coukos, G. Neoantigen-specific adoptive cell therapies for cancer: making T-cell products more personal. Front. Immunol. 11, 1215 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. 7.

    Curran, M. A. & Glisson, B. S. New hope for therapeutic cancer vaccines in the era of immune checkpoint modulation. Annu. Rev. Med. 70, 409–424 (2019).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  8. 8.

    Haen, S. P., Löffler, M. W., Rammensee, H.-G. & Brossart, P. Towards new horizons: characterization, classification and implications of the tumour antigenic repertoire. Nat. Rev. Clin. Oncol. 17, 595–610 (2020).

    PubMed  Article  PubMed Central  Google Scholar 

  9. 9.

    Kruger, S. et al. Advances in cancer immunotherapy 2019: latest trends. J. Exp. Clin. Cancer Res. 38, 268 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Christofi, T., Baritaki, S., Falzone, L., Libra, M. & Zaravinos, A. Current perspectives in cancer immunotherapy. Cancers (Basel) 11, 1472 (2019).

    Article  CAS  Google Scholar 

  11. 11.

    Laumont, C. M. et al. Global proteogenomic analysis of human MHC class I-associated peptides derived from non-canonical reading frames. Nat. Commun. 7, 10238 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. 12.

    Sebestyen, E. et al. Large-scale analysis of genome and transcriptome alterations in multiple tumors unveils novel cancer-relevant splicing networks. Genome Res. 26, 732–744 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  13. 13.

    Zhao, Q. et al. Proteogenomics uncovers a vast repertoire of shared tumor-specific antigens in ovarian cancer. Cancer Immunol. Res. 8, 544–555 (2020).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  14. 14.

    Ouspenskaia, T. et al. Thousands of novel unannotated proteins expand the MHC I immunopeptidome in cancer. Preprint at bioRxiv https://doi.org/10.1101/2020.02.12.945840 (2020).

  15. 15.

    Chen, J. et al. Pervasive functional translation of noncanonical human open reading frames. Science 367, 1140–1146 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  16. 16.

    Ilyas, S. & Yang, J. C. Landscape of tumor antigens in T cell immunotherapy. J. Immunol. 195, 5117–5122 (2015).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  17. 17.

    Caballero, O. L. & Chen, Y. T. Cancer/testis (CT) antigens: potential targets for immunotherapy. Cancer Sci. 100, 2014–2021 (2009).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  18. 18.

    Tio, D. et al. Expression of cancer/testis antigens in cutaneous melanoma: a systematic review. Melanoma Res. 29, 349–357 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  19. 19.

    Schooten, E., Di Maggio, A., van Bergen En Henegouwen, P. M. P. & Kijanka, M. M. MAGE-A antigens as targets for cancer immunotherapy. Cancer Treat. Rev. 67, 54–62 (2018).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  20. 20.

    D’Angelo, S. P. et al. Antitumor activity associated with prolonged persistence of adoptively transferred NY-ESO-1 c259T cells in synovial. Sarcoma 8, 944–957 (2018).

    Google Scholar 

  21. 21.

    Rapoport, A. P. et al. NY-ESO-1–specific TCR–engineered T cells mediate sustained antigen-specific antitumor effects in myeloma. Nat. Med. 21, 914–921 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  22. 22.

    Robbins, P. F. et al. A pilot trial using lymphocytes genetically engineered with an NY-ESO-1–reactive T-cell receptor: long-term follow-up and correlates with response. Clin. Cancer Res. 21, 1019–1027 (2015).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  23. 23.

    Laumont, C. M. & Perreault, C. Exploiting non-canonical translation to identify new targets for T cell-based cancer immunotherapy. Cell. Mol. Life Sci. 75, 607–621 (2018).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  24. 24.

    Moreau-Aubry, A. et al. A processed pseudogene codes for a new antigen recognized by a CD8+ T cell clone on melanoma. J. Exp. Med. 191, 1617–1623 (2000).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. 25.

    Li, L.-J., Leng, R.-X., Fan, Y.-G., Pan, H.-F. & Ye, D.-Q. Translation of noncoding RNAs: focus on lncRNAs, pri-miRNAs, and circRNAs. Exp. Cell Res. 361, 1–8 (2017).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  26. 26.

    Charpentier, M. et al. IRES-dependent translation of the long non coding RNA meloe in melanoma cells produces the most immunogenic MELOE antigens. Oncotarget 7, 59704–59713 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Roulois, D. et al. DNA-demethylating agents target colorectal cancer cells by inducing viral mimicry by endogenous transcripts. Cell 162, 961–973 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. 28.

    Chiappinelli, K. B. et al. Inhibiting DNA methylation causes an interferon response in cancer via dsRNA including endogenous retroviruses. Cell 162, 974–986 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  29. 29.

    Attermann, A. S., Bjerregaard, A. M., Saini, S. K., Gronbaek, K. & Hadrup, S. R. Human endogenous retroviruses and their implication for immunotherapeutics of cancer. Ann. Oncol. 29, 2183–2191 (2018).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  30. 30.

    Vigneron, N. et al. An antigenic peptide produced by peptide splicing in the proteasome. Science 304, 587–590 (2004).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  31. 31.

    Delong, T. et al. Pathogenic CD4 T cells in type 1 diabetes recognize epitopes formed by peptide fusion. Science 351, 711–714 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  32. 32.

    Yewdell, J. W. & Holly, J. DRiPs get molecular. Curr. Opin. Immunol. 64, 130–136 (2020).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  33. 33.

    Welters, M. J. et al. Induction of tumor-specific CD4+ and CD8+ T-cell immunity in cervical cancer patients by a human papillomavirus type 16 E6 and E7 long peptides vaccine. Clin. Cancer Res. 14, 178–187 (2008).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  34. 34.

    Morgan, R. A. et al. Cancer regression and neurological toxicity following anti-MAGE-A3 TCR gene therapy. J. Immunother. 36, 133–151 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. 35.

    Skipper, J. C. et al. Mass-spectrometric evaluation of HLA-A*0201-associated peptides identifies dominant naturally processed forms of CTL epitopes from MART-1 and gp100. Int J. Cancer 82, 669–677 (1999).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  36. 36.

    Wolf, B. et al. Safety and tolerability of adoptive cell therapy in cancer. Drug Saf. 42, 315–334 (2019).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  37. 37.

    Purcell, A. W., Ramarathinam, S. H. & Ternette, N. Mass spectrometry-based identification of MHC-bound peptides for immunopeptidomics. Nat. Protoc. 14, 1687–1707 (2019).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  38. 38.

    Caron, E. et al. Analysis of major histocompatibility complex (MHC) immunopeptidomes using mass spectrometry. Mol. Cell. Proteom. 14, 3105–3117 (2015).

    Article  CAS  Google Scholar 

  39. 39.

    Ritz, D., Kinzi, J., Neri, D. & Fugmann, T. Data-independent acquisition of HLA class I peptidomes on the Q exactive mass spectrometer platform. Proteomics 17, 1700177 (2017).

    Article  CAS  Google Scholar 

  40. 40.

    Gillet, L. C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteom. 11, O111.016717 (2012).

    Article  CAS  Google Scholar 

  41. 41.

    Brunner, A.-D. et al. Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation. Preprint at bioRxiv 2020.2012.2022.423933 (2020).

  42. 42.

    Tsou, C. C. et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat. Methods 12, 258–264 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  43. 43.

    Muntel, J. et al. Surpassing 10 000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy. Mol. Omics 15, 348–360 (2019).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  44. 44.

    Gessulat, S. et al. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat. Methods 16, 509–518 (2019).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  45. 45.

    Croft, N. P. et al. Kinetics of antigen expression and epitope presentation during virus infection. PLoS Pathog. 9, e1003129 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. 46.

    Hassan, C. et al. Accurate quantitation of MHC-bound peptides by application of isotopically labeled peptide MHC complexes. J. Proteom. 109, 240–244 (2014).

    Article  CAS  Google Scholar 

  47. 47.

    Croft, N. P., Purcell, A. W. & Tscharke, D. C. Quantifying epitope presentation using mass spectrometry. Mol. Immunol. 68, 77–80 (2015).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  48. 48.

    Tan, C. T., Croft, N. P., Dudek, N. L., Williamson, N. A. & Purcell, A. W. Direct quantitation of MHC-bound peptide epitopes by selected reaction monitoring. Proteomics 11, 2336–2340 (2011).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  49. 49.

    Kapp, E. A. et al. An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: sensitivity and specificity analysis. Proteomics 5, 3475–3490 (2005).

  50. 50.

    Kapp, E. & Schutz, F. Overview of tandem mass spectrometry (MS/MS) database search algorithms. Curr. Protoc. Protein Sci. 49, 25.2.1–25.2.19 (2007).

    Article  Google Scholar 

  51. 51.

    Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  52. 52.

    Zhang, J. et al. PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification. Mol. Cell. Proteom. 11, M111.010587 (2012).

    Article  CAS  Google Scholar 

  53. 53.

    Shan, P. & Tran, H. Integrating database search and de novo sequencing for immunopeptidomics with DIA approach. J. Biomol. Tech. 30, S23 (2019).

    PubMed Central  Google Scholar 

  54. 54.

    Faridi, P., Purcell, A. W. & Croft, N. P. In immunopeptidomics we need a sniper instead of a shotgun. Proteomics 18, e1700464 (2018).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  55. 55.

    Thompson, A. et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895–1904 (2003).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  56. 56.

    Pfammatter, S. et al. Extending the comprehensiveness of immunopeptidome analyses using isobaric peptide labeling. Anal. Chem. 92, 9194–9204 (2020).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  57. 57.

    Ramarathinam, S. H. et al. A peptide-signal amplification strategy for the detection and validation of neoepitope presentation on cancer biopsies. Preprint at bioRxiv https://doi.org/10.1101/2020.06.12.145276 (2020).

  58. 58.

    Stopfer, L. E., Mesfin, J. M., Joughin, B. A., Lauffenburger, D. A. & White, F. M. Multiplexed relative and absolute quantitative immunopeptidomics reveals MHC I repertoire alterations induced by CDK4/6 inhibition. Nat. Commun. 11, 2760 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  59. 59.

    d’Atri, V. et al. Adding a new separation dimension to MS and LC–MS: what is the utility of ion mobility spectrometry? J. Sep. Sci. 41, 20–67 (2018).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  60. 60.

    Pfammatter, S. et al. A novel differential ion mobility device expands the depth of proteome coverage and the sensitivity of multiplex proteomic measurements. Mol. Cell. Proteom. 17, 2051–2067 (2018).

    Article  CAS  Google Scholar 

  61. 61.

    Pfammatter, S., Bonneil, E. & Thibault, P. Improvement of quantitative measurements in multiplex proteomics using high-field asymmetric waveform spectrometry. J. Proteome Res. 15, 4653–4665 (2016).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  62. 62.

    Meier, F. et al. Online parallel accumulation-serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer. Mol. Cell. Proteom. 17, 2534–2545 (2018).

    Article  CAS  Google Scholar 

  63. 63.

    Nesvizhskii, A. I. Proteogenomics: concepts, applications and computational strategies. Nat. Methods 11, 1114–1125 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  64. 64.

    Zhang, M. et al. RNA editing derived epitopes function as cancer antigens to elicit immune responses. Nat. Commun. 9, 3919 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  65. 65.

    Wei, Z. et al. The landscape of tumor fusion neoantigens: a pan-cancer. Anal. iScience 21, 249–260 (2019).

    Article  Google Scholar 

  66. 66.

    Löffler, M. W. et al. Multi-omics discovery of exome-derived neoantigens in hepatocellular carcinoma. Genome Med. 11, 28 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Kalaora, S. et al. Use of HLA peptidomics and whole exome sequencing to identify human immunogenic neo-antigens. Oncotarget 7, 5110–5117 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    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).

    Article  CAS  Google Scholar 

  69. 69.

    Khodadoust, M. S. et al. Antigen presentation profiling reveals recognition of lymphoma immunoglobulin neoantigens. Nature 543, 723–727 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  70. 70.

    Bassani-Sternberg, M. et al. Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry. Nat. Commun. 7, 13404 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  71. 71.

    Binz, P. A. et al. Proteomics Standards Initiative extended FASTA format. J. Proteome Res. 18, 2686–2692 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  72. 72.

    Eng, J. K. & Deutsch, E. W. Extending comet for global amino acid variant and post-translational modification analysis using the PSI extended FASTA format. Proteomics 72, e1900362 (2020).

    Article  CAS  Google Scholar 

  73. 73.

    Elias, J. E. & Gygi, S. P. Target-decoy search strategy for mass spectrometry-based proteomics. Methods Mol. Biol. 604, 55–71 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  74. 74.

    Gupta, N., Bandeira, N., Keich, U. & Pevzner, P. A. Target-decoy approach and false discovery rate: when things may go wrong. J. Am. Soc. Mass Spectrom. 22, 1111–1120 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  75. 75.

    Tanner, S. et al. Improving gene annotation using peptide mass spectrometry. Genome Res. 17, 231–239 (2007).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  76. 76.

    Chong, C. et al. Integrated proteogenomic deep sequencing and analytics accurately identify non-canonical peptides in tumor immunopeptidomes. Nat. Commun. 11, 1293 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  77. 77.

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

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  78. 78.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  79. 79.

    Attig, J. et al. LTR retroelement expansion of the human cancer transcriptome and immunopeptidome revealed by de novo transcript assembly. Genome Res. 29, 1578–1590 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  80. 80.

    Kong, Y. et al. Transposable element expression in tumors is associated with immune infiltration and increased antigenicity. Nat. Commun. 10, 5228 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  81. 81.

    Shraibman, B., Melamed Kadosh, D., Barnea, E. & Admon, A. HLA peptides derived from tumor antigens induced by inhibition of DNA methylation for development of drug-facilitated immunotherapy. Mol. Cell. Proteom. 15, 3058–3070 (2016).

    Article  CAS  Google Scholar 

  82. 82.

    Calviello, L. et al. Detecting actively translated open reading frames in ribosome profiling data. Nat. Methods 13, 165–170 (2016).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  83. 83.

    Erhard, F. et al. Improved Ribo-seq enables identification of cryptic translation events. Nat. Methods 15, 363–366 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  84. 84.

    Ingolia, N. T. et al. Ribosome profiling reveals pervasive translation outside of annotated protein-coding genes. Cell Rep. 8, 1365–1379 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  85. 85.

    Slavoff, S. A. et al. Peptidomic discovery of short open reading frame–encoded peptides in human cells. Nat. Chem. Biol. 9, 59–64 (2013).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  86. 86.

    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).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  87. 87.

    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).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  88. 88.

    Warren, E. H. et al. An antigen produced by splicing of noncontiguous peptides in the reverse order. Science 313, 1444–1447 (2006).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  89. 89.

    Dalet, A. et al. An antigenic peptide produced by reverse splicing and double asparagine deamidation. Proc. Natl Acad. Sci. USA 108, E323–E331 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  90. 90.

    Michaux, A. et al. A spliced antigenic peptide comprising a single spliced amino acid is produced in the proteasome by reverse splicing of a longer peptide fragment followed by trimming. J. Immunol. 192, 1962–1971 (2014).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  91. 91.

    Hanada, K., Yewdell, J. W. & Yang, J. C. Immune recognition of a human renal cancer antigen through post-translational protein splicing. Nature 427, 252–256 (2004).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  92. 92.

    Liepe, J. et al. A large fraction of HLA class I ligands are proteasome-generated spliced peptides. Science 354, 354–358 (2016).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  93. 93.

    Faridi, P. et al. A subset of HLA-I peptides are not genomically templated: evidence for cis- and trans-spliced peptide ligands. Sci. Immunol. 3, eaar3947 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  94. 94.

    Liepe, J., Sidney, J., Lorenz, F. K. M., Sette, A. & Mishto, M. Mapping the MHC class I–spliced immunopeptidome of cancer cells. Cancer Immunol. Res. 7, 62–76 (2019).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  95. 95.

    Paes, W. et al. Contribution of proteasome-catalyzed peptide cis-splicing to viral targeting by CD8+ T cells in HIV-1 infection. Proc. Natl Acad. Sci. USA 116, 24748–24759 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  96. 96.

    Faridi, P. et al. Spliced peptides and cytokine-driven changes in the immunopeptidome of melanoma. Cancer Immunol. Res. 8, 1322–1334 (2020).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  97. 97.

    Mylonas, R. et al. Estimating the contribution of proteasomal spliced peptides to the HLA-I ligandome. Mol. Cell. Proteom. 17, 2347–2357 (2018).

    Article  CAS  Google Scholar 

  98. 98.

    Rolfs, Z., Solntsev, S. K., Shortreed, M. R., Frey, B. L. & Smith, L. M. Global identification of post-translationally spliced peptides with neo-fusion. J. Proteome Res. 18, 349–358 (2019).

    PubMed  CAS  PubMed Central  Google Scholar 

  99. 99.

    Erhard, F., Dölken, L., Schilling, B. & Schlosser, A. Identification of the cryptic HLA-I immunopeptidome. Cancer Immunol. Res. 8, 1018–1026 (2020).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  100. 100.

    Vigneron, N., Ferrari, V., Stroobant, V., Abi Habib, J. & Van den Eynde, B. J. Peptide splicing by the proteasome. J. Biol. Chem. 292, 21170–21179 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  101. 101.

    Dalet, A., Vigneron, N., Stroobant, V., Hanada, K. & Van den Eynde, B. J. Splicing of distant peptide fragments occurs in the proteasome by transpeptidation and produces the spliced antigenic peptide derived from fibroblast growth factor-5. J. Immunol. 184, 3016–3024 (2010).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  102. 102.

    Henry, V. J., Bandrowski, A. E., Pepin, A. S., Gonzalez, B. J. & Desfeux, A. OMICtools: an informative directory for multi-omic data analysis. Database (Oxford) 2014, bau069 (2014).

    Article  CAS  Google Scholar 

  103. 103.

    Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 46, W537–W544 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  104. 104.

    Nesvizhskii, A. I. et al. Dynamic spectrum quality assessment and iterative computational analysis of shotgun proteomic data: toward more efficient identification of post-translational modifications, sequence polymorphisms, and novel peptides. Mol. Cell. Proteom. 5, 652–670 (2006).

    Article  CAS  Google Scholar 

  105. 105.

    Andreatta, M. et al. MS-Rescue: a computational pipeline to increase the quality and yield of immunopeptidomics experiments. Proteomics 19, e1800357 (2019).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  106. 106.

    Rolfs, Z., Müller, M., Shortreed, M. R., Smith, L. M. & Bassani-Sternberg, M. Comment on ‘A subset of HLA-I peptides are not genomically templated: evidence for cis- and trans-spliced peptide ligands’. Sci. Immunol. 4, eaaw1622 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  107. 107.

    McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168, 613–628 (2017).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  108. 108.

    Marcu, A. et al. The HLA Ligand Atlas. A resource of natural HLA ligands presented on benign tissues. J. Immunother. Cancer 9, e002071 (2019).

    Article  Google Scholar 

  109. 109.

    Schatton, T. et al. Identification of cells initiating human melanomas. Nature 451, 345–349 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  110. 110.

    Lang, D., Mascarenhas, J. B. & Shea, C. R. Melanocytes, melanocyte stem cells, and melanoma stem cells. Clin. Dermatol. 31, 166–178 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  111. 111.

    Kassiotis, G. & Stoye, J. P. Immune responses to endogenous retroelements: taking the bad with the good. Nat. Rev. Immunol. 16, 207–219 (2016).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  112. 112.

    Rycaj, K. et al. Cytotoxicity of human endogenous retrovirus K–specific T cells toward autologous ovarian. Cancer Cells 21, 471–483 (2015).

    CAS  Google Scholar 

  113. 113.

    Saini, S. K. et al. Human endogenous retroviruses form a reservoir of T cell targets in hematological cancers. Nat. Commun. 11, 5660 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  114. 114.

    Mullins, C. S. & Linnebacher, M. Endogenous retrovirus sequences as a novel class of tumor-specific antigens: an example of HERV-H env encoding strong CTL epitopes. Cancer Immunol. Immun. 61, 1093–1100 (2012).

    Article  CAS  Google Scholar 

  115. 115.

    Tu, X. et al. Human leukemia antigen-A*0201-restricted epitopes of human endogenous retrovirus W family envelope (HERV-W env) induce strong cytotoxic T lymphocyte responses. Virol. Sin. 32, 280–289 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  116. 116.

    Belgnaoui, S. M., Gosden, R. G., Semmes, O. J. & Haoudi, A. Human LINE-1 retrotransposon induces DNA damage and apoptosis in cancer cells. Cancer Cell Int. 6, 13 (2006).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  117. 117.

    Scott, E. C. et al. A hot L1 retrotransposon evades somatic repression and initiates human colorectal cancer. Genome Res. 26, 745–755 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  118. 118.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  119. 119.

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

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  120. 120.

    Ebrahimi-Nik, H. et al. Mass spectrometry driven exploration reveals nuances of neoepitope-driven tumor rejection. JCI Insight 5, e129152 (2019).

    Article  Google Scholar 

  121. 121.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  122. 122.

    Jackson, R. et al. The translation of non-canonical open reading frames controls mucosal immunity. Nature 564, 434–438 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  123. 123.

    Muller, M., Gfeller, D., Coukos, G. & Bassani-Sternberg, M. ‘Hotspots’ of antigen presentation revealed by human leukocyte antigen ligandomics for neoantigen prioritization. Front. Immunol. 8, 1367 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  124. 124.

    Schittenhelm, R. B. et al. A comprehensive analysis of constitutive naturally processed and presented HLA-C*04:01 (Cw4)-specific peptides. Tissue Antigen. 83, 174–179 (2014).

    Article  CAS  Google Scholar 

  125. 125.

    Schuster, H. et al. The immunopeptidomic landscape of ovarian carcinomas. Proc. Natl Acad. Sci. USA 114, E9942–E9951 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  126. 126.

    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).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  127. 127.

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

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  128. 128.

    Shraibman, B. et al. Identification of tumor antigens among the HLA peptidomes of glioblastoma tumors and plasma. Mol. Cell. Proteom. 17, 2132–2145 (2018).

    Article  CAS  Google Scholar 

  129. 129.

    Ternette, N. et al. Immunopeptidomic profiling of HLA-A2-positive triple negative breast cancer identifies potential immunotherapy target antigens. Proteomics 18, 1700465 (2018).

    PubMed Central  Article  CAS  Google Scholar 

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Acknowledgements

This study was supported by the Ludwig Institute for Cancer Research and grant KFS-4680-02-2019-R from the Swiss Cancer League (M.B.-S.). This work was also supported by grants from Cancera, Mats Paulssons and by a gift from the Biltema Foundation that was administered by the ISREC Foundation, Lausanne, Switzerland. The figures were originally created with BioRender.com.

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Correspondence to Michal Bassani-Sternberg.

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Chong, C., Coukos, G. & Bassani-Sternberg, M. Identification of tumor antigens with immunopeptidomics. Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-01038-8

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