• 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.

    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.

    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.

    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.

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

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

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

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

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

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 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.

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

    Article 

    Google Scholar 

  • 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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

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

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

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

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 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.

    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.

    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.

    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.

    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.

    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.

    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.

    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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