{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T10:28:43Z","timestamp":1793269723419,"version":"4.5.0"},"reference-count":91,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":106,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","award":["A21998"],"award-info":[{"award-number":["A21998"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000268","name":"Biotechnology and Biological Sciences Research Council","doi-asserted-by":"publisher","award":["BM\/M012387\/1"],"award-info":[{"award-number":["BM\/M012387\/1"]}],"id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Precis. Onc."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Non-small cell lung cancer (NSCLC) has poor survival even with modern checkpoint inhibitor therapies. Personalised vaccines based on short peptide neoantigens containing tumour mutations are an attractive precision medicine strategy, but identifying therapeutically relevant neoantigens remains challenging, with existing methods yielding positive responses in only 6% of candidates tested. We developed an immunopeptidomics approach to improve neoantigen identification in 24 NSCLC patients (15 adenocarcinoma, 9 squamous cell carcinoma). We directly identified one neoantigen and using whole exome sequencing, transcriptomics and mass spectrometry-based immunopeptidomics, we filtered predicted neoantigens based on observed cohort HLA peptide presentation. This approach achieved positive functional responses in 5 of 6 patients tested (83% success rate) with 13% of putative neoantigens (9 out of 70) eliciting strong responses. Bayesian modelling of our initial rules-based neoantigen selection further revealed patient specific peptide presentation patterns and propensities. Our findings demonstrate that incorporating donor-specific HLA peptide presentation data substantially improves neoantigen identification success rates and immune response specificity, advancing personalised cancer vaccine development.<\/jats:p>","DOI":"10.1038\/s41698-026-01539-2","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T19:54:14Z","timestamp":1781121254000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Immunopeptidomics-guided identification of functional neoantigens in non-small cell lung cancer"],"prefix":"10.1038","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1467-9643","authenticated-orcid":false,"given":"Ben","family":"Nicholas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0023-8679","authenticated-orcid":false,"given":"Alistair","family":"Bailey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4388-7265","authenticated-orcid":false,"given":"Katy J.","family":"McCann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Wood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0400-2376","authenticated-orcid":false,"given":"Eve","family":"Currall","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2306-4974","authenticated-orcid":false,"given":"Peter","family":"Johnson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1097-0222","authenticated-orcid":false,"given":"Tim","family":"Elliott","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3619-1657","authenticated-orcid":false,"given":"Christian","family":"Ottensmeier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2995-2959","authenticated-orcid":false,"given":"Paul","family":"Skipp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,10]]},"reference":[{"key":"1539_CR1","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1038\/s41571-023-00798-3","volume":"20","author":"A Leiter","year":"2023","unstructured":"Leiter, A. et al. The global burden of lung cancer: current status and future trends. Nature reviews clinical oncology 20, 624\u2013639 (2023).","journal-title":"Nature reviews clinical oncology"},{"key":"1539_CR2","doi-asserted-by":"publisher","first-page":"4585","DOI":"10.1158\/1078-0432.CCR-15-3101","volume":"22","author":"JF Gainor","year":"2016","unstructured":"Gainor, J. F. et al. EGFR mutations and ALK rearrangements are associated with low response rates to PD-1 pathway blockade in non small cell lung cancer: a retrospective analysis. Clin. Cancer Res. 22, 4585\u20134593 (2016).","journal-title":"Clin. Cancer Res."},{"key":"1539_CR3","doi-asserted-by":"publisher","first-page":"9010","DOI":"10.1200\/JCO.2018.36.15_suppl.9010","volume":"36","author":"J Mazieres","year":"2018","unstructured":"Mazieres, J. et al. Efficacy of immune-checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC) patients harboring activating molecular alterations (ImmunoTarget). J. Clin. Oncol. 36, 9010\u20139010 (2018).","journal-title":"J. Clin. Oncol."},{"key":"1539_CR4","doi-asserted-by":"publisher","first-page":"2109","DOI":"10.1056\/NEJMoa1616288","volume":"376","author":"M Jamal-Hanjani","year":"2017","unstructured":"Jamal-Hanjani, M. et al. Tracking the evolution of non\u2013small-cell lung cancer. N. Engl. J. Med. 376, 2109\u20132121 (2017).","journal-title":"N. Engl. J. Med."},{"key":"1539_CR5","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1038\/s41586-019-1032-7","volume":"567","author":"R Rosenthal","year":"2019","unstructured":"Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature. 567, 479\u2013485 (2019).","journal-title":"Nature."},{"key":"1539_CR6","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms13404","volume":"7","author":"M Bassani-Sternberg","year":"2016","unstructured":"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).","journal-title":"Nat. Commun."},{"key":"1539_CR7","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1111\/imm.13578","volume":"168","author":"B Nicholas","year":"2023","unstructured":"Nicholas, B. et al. Identification of neoantigens in oesophageal adenocarcinoma. Immunology 168, 420\u2013431 (2023).","journal-title":"Immunology"},{"key":"1539_CR8","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1186\/s40425-019-0769-8","volume":"7","author":"A Newey","year":"2019","unstructured":"Newey, A. et al. Immunopeptidomics of colorectal cancer organoids reveals a sparse HLA class I neoantigen landscape and no increase in neoantigens with interferon or MEK-inhibitor treatment. J. Immunother. Cancer 7, 309 (2019).","journal-title":"J. Immunother. Cancer"},{"key":"1539_CR9","doi-asserted-by":"publisher","unstructured":"Marcu, A. et al. The HLA Ligand Atlas - A resource of natural HLA ligands presented on benign tissues. Preprint at https:\/\/doi.org\/10.1101\/778944 (2020).","DOI":"10.1101\/778944"},{"key":"1539_CR10","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1093\/bioinformatics\/btv639","volume":"32","author":"M Andreatta","year":"2016","unstructured":"Andreatta, M. & Nielsen, M. Gapped sequence alignment using artificial neural networks: application to the MHC class i system. Bioinformatics 32, 511\u2013517 (2016).","journal-title":"Bioinformatics"},{"key":"1539_CR11","doi-asserted-by":"publisher","first-page":"e7448","DOI":"10.1371\/journal.pone.0007448","volume":"4","author":"AS Juncker","year":"2009","unstructured":"Juncker, A. S. et al. Systematic characterisation of cellular localisation and expression profiles of proteins containing MHC ligands. PLoS ONE 4, e7448 (2009).","journal-title":"PLoS ONE"},{"key":"1539_CR12","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.3389\/fimmu.2017.01367","volume":"8","author":"M M\u00fcller","year":"2017","unstructured":"M\u00fcller, M., Gfeller, D., Coukos, G. & Bassani-Sternberg, M. \u2018Hotspots\u2019 of antigen presentation revealed by human leukocyte antigen ligandomics for neoantigen prioritization. Front. Immunol 8, 1367 (2017).","journal-title":"Front. Immunol"},{"key":"1539_CR13","doi-asserted-by":"publisher","first-page":"101708","DOI":"10.1016\/j.smim.2022.101708","volume":"66","author":"D Gfeller","year":"2023","unstructured":"Gfeller, D., Liu, Y. & Racle, J. Contemplating immunopeptidomes to better predict them. Semin. Immunol. 66, 101708 (2023).","journal-title":"Semin. Immunol."},{"key":"1539_CR14","doi-asserted-by":"publisher","first-page":"818\u2013","DOI":"10.1016\/j.cell.2020.09.015","volume":"183","author":"DK Wells","year":"2020","unstructured":"Wells, D. K. et al. Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction. Cell 183, 818\u2013\u2013834.e13 (2020).","journal-title":"Cell"},{"key":"1539_CR15","doi-asserted-by":"publisher","first-page":"bbac141","DOI":"10.1093\/bib\/bbac141","volume":"23","author":"PR Buckley","year":"2022","unstructured":"Buckley, P. R. et al. Evaluating performance of existing computational models in predicting CD8+ t cell pathogenic epitopes and cancer neoantigens. Brief. Bioinform 23, bbac141 (2022).","journal-title":"Brief. Bioinform"},{"key":"1539_CR16","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1038\/s43018-021-00197-6","volume":"2","author":"JJ Gartner","year":"2021","unstructured":"Gartner, J. J. et al. A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types. Nat. Cancer 2, 563\u2013574 (2021).","journal-title":"Nat. Cancer"},{"key":"1539_CR17","doi-asserted-by":"publisher","first-page":"2650","DOI":"10.1016\/j.immuni.2023.09.002","volume":"56","author":"M M\u00fcller","year":"2023","unstructured":"M\u00fcller, M. et al. Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction. Immunity 56, 2650\u20132663.e6 (2023).","journal-title":"Immunity"},{"key":"1539_CR18","doi-asserted-by":"publisher","first-page":"eadv6445","DOI":"10.1126\/sciadv.adv6445","volume":"11","author":"Y Cai","year":"2025","unstructured":"Cai, Y. et al. Immunopeptidomics-guided discovery and characterization of neoantigens for personalized cancer immunotherapy. Sci. Adv. 11, eadv6445 (2025).","journal-title":"Sci. Adv."},{"key":"1539_CR19","doi-asserted-by":"publisher","first-page":"1360","DOI":"10.1038\/s41587-024-02420-y","volume":"43","author":"F Huber","year":"2024","unstructured":"Huber, F. et al. A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy. Nat. Biotechnol. 43, 1360\u20131372 (2024).","journal-title":"Nat. Biotechnol."},{"key":"1539_CR20","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1158\/2326-6066.CIR-19-0401","volume":"8","author":"J Hundal","year":"2020","unstructured":"Hundal, J. et al. pVACtools: a computational toolkit to identify and visualize cancer neoantigens. Cancer Immunol. Res. 8, 409\u2013420 (2020).","journal-title":"Cancer Immunol. Res."},{"key":"1539_CR21","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1038\/nature12477","volume":"500","author":"LB Alexandrov","year":"2013","unstructured":"Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415\u2013421 (2013).","journal-title":"Nature"},{"key":"1539_CR22","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/nature12634","volume":"502","author":"C Kandoth","year":"2013","unstructured":"Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333\u2013339 (2013).","journal-title":"Nature"},{"key":"1539_CR23","unstructured":"Gori, K. & Baez-Ortega, A. Sigfit: flexible Bayesian inference of mutational signatures. 372896 https:\/\/www.biorxiv.org\/content\/10.1101\/372896v2 (2020). 10.1101\/372896 (2020)."},{"key":"1539_CR24","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1038\/s41586-020-1943-3","volume":"578","author":"LB Alexandrov","year":"2020","unstructured":"Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94\u2013101 (2020).","journal-title":"Nature"},{"key":"1539_CR25","doi-asserted-by":"publisher","first-page":"D941","DOI":"10.1093\/nar\/gky1015","volume":"47","author":"JG Tate","year":"2019","unstructured":"Tate, J. G. et al. COSMIC: The catalogue of somatic mutations in cancer. Nucleic Acids Res. 47, D941\u2013D947 (2019).","journal-title":"Nucleic Acids Res."},{"key":"1539_CR26","doi-asserted-by":"publisher","first-page":"e26476","DOI":"10.7554\/eLife.26476","volume":"6","author":"J Racle","year":"2017","unstructured":"Racle, J., Jonge, K., de, Baumgaertner, P., Speiser, D. E. & Gfeller, D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. eLife 6, e26476 (2017).","journal-title":"eLife"},{"key":"1539_CR27","doi-asserted-by":"publisher","first-page":"eadj6367","DOI":"10.1126\/sciadv.adj6367","volume":"9","author":"JB Nilsson","year":"2023","unstructured":"Nilsson, J. B. et al. Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning. Sci. Adv 9, eadj6367 (2023).","journal-title":"Sci. Adv"},{"key":"1539_CR28","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1016\/j.cell.2017.10.001","volume":"171","author":"N McGranahan","year":"2017","unstructured":"McGranahan, N. et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171, 1259\u20131271.e11 (2017).","journal-title":"Cell"},{"key":"1539_CR29","doi-asserted-by":"publisher","unstructured":"Gfeller, D. et al. The length distribution and multiple specificity of naturally presented HLA-I ligands. J. Immunol. https:\/\/doi.org\/10.4049\/jimmunol.1800914 (2018).","DOI":"10.4049\/jimmunol.1800914"},{"key":"1539_CR30","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-016-0264-5","volume":"8","author":"J Hundal","year":"2016","unstructured":"Hundal, J. et al. pVAC-seq: A genome-guided in silico approach to identifying tumor neoantigens. Genome Med. 8, 11 (2016).","journal-title":"Genome Med."},{"key":"1539_CR31","doi-asserted-by":"publisher","first-page":"1260419","DOI":"10.1126\/science.1260419","volume":"347","author":"M Uhl\u00e9n","year":"2015","unstructured":"Uhl\u00e9n, M. Tissue-based map of the human proteome. Science 347, 1260419 (2015).","journal-title":"Science"},{"key":"1539_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v080.i01","volume":"80","author":"P-C B\u00fcrkner","year":"2017","unstructured":"B\u00fcrkner, P.-C. brms: An R Package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1\u201328 (2017).","journal-title":"J. Stat. Softw."},{"key":"1539_CR33","doi-asserted-by":"publisher","first-page":"11737","DOI":"10.1073\/pnas.0306294101","volume":"101","author":"M Howarth","year":"2004","unstructured":"Howarth, M., Williams, A., Tolstrup, A. B. & Elliott, T. Tapasin enhances MHC class I peptide presentation according to peptide half-life. Proc. Natl. Acad. Sci. USA 101, 11737\u201311742 (2004).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"1539_CR34","doi-asserted-by":"publisher","first-page":"3465","DOI":"10.1073\/pnas.1222342110","volume":"110","author":"LH Boyle","year":"2013","unstructured":"Boyle, L. H. et al. Tapasin-related protein TAPBPR is an additional component of the MHC class I presentation pathway. Proc. Natl. Acad. Sci. USA 110, 3465\u20133470 (2013).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"1539_CR35","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1074\/mcp.M114.042812","volume":"14","author":"M Bassani-Sternberg","year":"2015","unstructured":"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\u2013673 (2015).","journal-title":"Mol. Cell. Proteom."},{"key":"1539_CR36","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1038\/nm0398-321","volume":"4","author":"SA Rosenberg","year":"1998","unstructured":"Rosenberg, S. A. et al. Immunologic and therapeutic evaluation of a synthetic peptide vaccine for the treatment of patients with metastatic melanoma. Nat. Med. 4, 321\u2013327 (1998).","journal-title":"Nat. Med."},{"key":"1539_CR37","first-page":"5818","volume":"62","author":"T Weinschenk","year":"2002","unstructured":"Weinschenk, T. et al. Integrated functional genomics approach for the design of patient-individual antitumor Vaccines1. Cancer Res. 62, 5818\u20135827 (2002).","journal-title":"Cancer Res."},{"key":"1539_CR38","doi-asserted-by":"publisher","first-page":"eabc8697","DOI":"10.1126\/science.abc8697","volume":"371","author":"EH-C Hsiue","year":"2021","unstructured":"Hsiue, E. H.-C. et al. Targeting a neoantigen derived from a common TP53 mutation. Science 371, eabc8697 (2021).","journal-title":"Science"},{"key":"1539_CR39","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1158\/0008-5472.CAN-11-3722","volume":"72","author":"JC Castle","year":"2012","unstructured":"Castle, J. C. et al. Exploiting the mutanome for tumor vaccination. Cancer Res. 72, 1081\u20131091 (2012).","journal-title":"Cancer Res."},{"key":"1539_CR40","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.ccell.2018.11.009","volume":"35","author":"AJ Levine","year":"2019","unstructured":"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\u201315 (2019).","journal-title":"Cancer Cell"},{"key":"1539_CR41","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1126\/science.1905840","volume":"253","author":"M Hollstein","year":"1991","unstructured":"Hollstein, M., Sidransky, D., Vogelstein, B. & Harris, C. C. p53 Mutations in human cancers. Science 253, 49\u201353 (1991).","journal-title":"Science"},{"key":"1539_CR42","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1038\/s43018-022-00418-6","volume":"3","author":"MJ Lin","year":"2022","unstructured":"Lin, M. J. et al. Cancer vaccines: the next immunotherapy frontier. Nat. Cancer 3, 911\u2013926 (2022).","journal-title":"Nat. Cancer"},{"key":"1539_CR43","doi-asserted-by":"publisher","first-page":"3975","DOI":"10.3390\/cancers14163975","volume":"14","author":"J Vadakekolathu","year":"2022","unstructured":"Vadakekolathu, J. et al. Multi-omic analysis of two common P53 mutations: proteins regulated by mutated P53 as potential targets for immunotherapy. Cancers 14, 3975 (2022).","journal-title":"Cancers"},{"key":"1539_CR44","doi-asserted-by":"publisher","first-page":"4690","DOI":"10.1172\/JCI88590","volume":"126","author":"H Pearson","year":"2016","unstructured":"Pearson, H. et al. MHC class Iassociated peptides derive from selective regions of the human genome. J. Clin. Investig. 126, 4690\u20134701 (2016).","journal-title":"J. Clin. Investig."},{"key":"1539_CR45","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1038\/s41565-018-0236-6","volume":"13","author":"L Restrepo-P\u00e9rez","year":"2018","unstructured":"Restrepo-P\u00e9rez, L., Joo, C. & Dekker, C. Paving the way to single-molecule protein sequencing. Nat. Nanotechnol. 13, 786\u2013796 (2018).","journal-title":"Nat. Nanotechnol."},{"key":"1539_CR46","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1038\/s41592-021-01143-1","volume":"18","author":"JA Alfaro","year":"2021","unstructured":"Alfaro, J. A. et al. The emerging landscape of single-molecule protein sequencing technologies. Nat. Methods 18, 604\u2013617 (2021).","journal-title":"Nat. Methods"},{"key":"1539_CR47","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-26046-9","volume":"12","author":"FLR Lucas","year":"2021","unstructured":"Lucas, F. L. R., Versloot, R. C. A., Yakovlieva, L., Walvoort, M. T. C. & Maglia, G. Protein identification by nanopore peptide profiling. Nat. Commun. 12, 5795 (2021).","journal-title":"Nat. Commun."},{"key":"1539_CR48","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1038\/s41592-023-01800-7","volume":"20","author":"K Motone","year":"2023","unstructured":"Motone, K. & Nivala, J. Not if but when nanopore protein sequencing meets single-cell proteomics. Nat. Methods 20, 336\u2013338 (2023).","journal-title":"Nat. Methods"},{"key":"1539_CR49","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1038\/s41565-023-01462-8","volume":"18","author":"P Martin-Baniandres","year":"2023","unstructured":"Martin-Baniandres, P. et al. Enzyme-less nanopore detection of post-translational modifications within long polypeptides. Nat. Nanotechnol. 18, 1335\u20131340 (2023).","journal-title":"Nat. Nanotechnol."},{"key":"1539_CR50","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1038\/s41586-024-07935-7","volume":"633","author":"K Motone","year":"2023","unstructured":"Motone, K. et al. Multi-pass, single-molecule nanopore reading of long protein strands with single-amino acid sensitivity. Nature 633, 662\u2013669 (2023).","journal-title":"Nature"},{"key":"1539_CR51","doi-asserted-by":"publisher","first-page":"eaau5516","DOI":"10.1126\/scitranslmed.aau5516","volume":"10","author":"CM Laumont","year":"2018","unstructured":"Laumont, C. M. et al. Noncoding regions are the main source of targetable tumor-specific antigens. Sci. Transl. Med 10, eaau5516 (2018).","journal-title":"Sci. Transl. Med"},{"key":"1539_CR52","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-14968-9","volume":"11","author":"C Chong","year":"2020","unstructured":"Chong, C. et al. Integrated proteogenomic deep sequencing and analytics accurately identify non-canonical peptides in tumor immunopeptidomes. Nat. Commun. 11, 1293 (2020).","journal-title":"Nat. Commun."},{"key":"1539_CR53","doi-asserted-by":"publisher","first-page":"108815","DOI":"10.1016\/j.celrep.2021.108815","volume":"34","author":"MV Ruiz Cuevas","year":"2021","unstructured":"Ruiz Cuevas, M. V. et al. Most non-canonical proteins uniquely populate the proteome or immunopeptidome. Cell Rep. 34, 108815 (2021).","journal-title":"Cell Rep."},{"key":"1539_CR54","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1261\/rna.035667.112","volume":"19","author":"WR Jeck","year":"2012","unstructured":"Jeck, W. R. et al. Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 19, 141\u2013157 (2012).","journal-title":"RNA"},{"key":"1539_CR55","doi-asserted-by":"publisher","first-page":"101039","DOI":"10.1016\/j.mcpro.2025.101039","volume":"24","author":"Y Horokhovskyi","year":"2025","unstructured":"Horokhovskyi, Y. et al. An automated workflow to address proteome complexity and the large search space problem in proteomics and HLA-I immunopeptidomics. Mol. Cell Proteom. 24, 101039 (2025).","journal-title":"Mol. Cell Proteom."},{"key":"1539_CR56","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-46408-3","volume":"15","author":"HJ Ferreira","year":"2024","unstructured":"Ferreira, H. J. et al. Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides. Nat. Commun. 15, 2357 (2024).","journal-title":"Nat. Commun."},{"key":"1539_CR57","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1038\/s41587-021-01021-3","volume":"40","author":"T Ouspenskaia","year":"2021","unstructured":"Ouspenskaia, T. et al. Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer. Nat. Biotechnol. 40, 209\u2013217 (2021).","journal-title":"Nat. Biotechnol."},{"key":"1539_CR58","doi-asserted-by":"publisher","first-page":"eadk3487","DOI":"10.1126\/science.adk3487","volume":"388","author":"ZA Ely","year":"2025","unstructured":"Ely, Z. A. et al. Pancreatic cancerrestricted cryptic antigens are targets for T cell recognition. Science 388, eadk3487 (2025).","journal-title":"Science"},{"key":"1539_CR59","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.cell.2014.12.033","volume":"160","author":"MS Rooney","year":"2015","unstructured":"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\u201361 (2015).","journal-title":"Cell"},{"key":"1539_CR60","doi-asserted-by":"publisher","first-page":"1549","DOI":"10.1038\/s41591-019-0592-2","volume":"25","author":"K Joshi","year":"2019","unstructured":"Joshi, K. et al. Spatial heterogeneity of the T cell receptor repertoire reflects the mutational landscape in lung cancer. Nat. Med. 25, 1549\u20131559 (2019).","journal-title":"Nat. Med."},{"key":"1539_CR61","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1038\/s43018-020-0066-y","volume":"1","author":"E Ghorani","year":"2020","unstructured":"Ghorani, E. et al. The T cell differentiation landscape is shaped by tumour mutations in lung cancer. Nat. Cancer 1, 546\u2013561 (2020).","journal-title":"Nat. Cancer"},{"key":"1539_CR62","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1038\/nature23003","volume":"547","author":"U Sahin","year":"2017","unstructured":"Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222\u2013226 (2017).","journal-title":"Nature"},{"key":"1539_CR63","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1038\/s41586-018-0810-y","volume":"565","author":"N Hilf","year":"2019","unstructured":"Hilf, N. et al. Actively personalized vaccination trial for newly diagnosed glioblastoma. Nature 565, 240\u2013245 (2019).","journal-title":"Nature"},{"key":"1539_CR64","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1038\/s41586-023-06063-y","volume":"618","author":"LA Rojas","year":"2023","unstructured":"Rojas, L. A. et al. Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer. Nature 618, 144\u2013150 (2023).","journal-title":"Nature"},{"key":"1539_CR65","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.1038\/s41586-024-08508-4","volume":"639","author":"Z Sethna","year":"2025","unstructured":"Sethna, Z. et al. RNA neoantigen vaccines prime long-lived CD8+ T cells in pancreatic cancer. Nature 639, 1042\u20131051 (2025).","journal-title":"Nature"},{"key":"1539_CR66","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s00251-005-0781-7","volume":"57","author":"M Nielsen","year":"2005","unstructured":"Nielsen, M., Lundegaard, C., Lund, O. & Ke\u015fmir, C. The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics 57, 33\u201341 (2005).","journal-title":"Immunogenetics"},{"key":"1539_CR67","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s00262-003-0480-x","volume":"53","author":"H Singh-Jasuja","year":"2004","unstructured":"Singh-Jasuja, H., Emmerich, N. P. N. & Rammensee, H.-G. The T\u00fcbingen approach: identification, selection, and validation of tumor-associated HLA peptides for cancer therapy. Cancer Immunol. Immunother. 53, 187\u2013195 (2004).","journal-title":"Cancer Immunol. Immunother."},{"key":"1539_CR68","doi-asserted-by":"publisher","first-page":"eado6491","DOI":"10.1126\/sciadv.ado6491","volume":"10","author":"S Tokita","year":"2024","unstructured":"Tokita, S. et al. Identification of immunogenic HLA class I and II neoantigens using surrogate immunopeptidomes. Sci. Adv. 10, eado6491 (2024).","journal-title":"Sci. Adv."},{"key":"1539_CR69","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giab008","volume":"10","author":"P Danecek","year":"2021","unstructured":"Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience 10, giab008 (2021).","journal-title":"GigaScience"},{"key":"1539_CR70","unstructured":"Picard Toolkit (Broad Institute, 2019)."},{"key":"1539_CR71","unstructured":"Van der Auwera, G.A. & O'Connor, B.D. Genomics in the cloud (1st digital ed.) Sebastopol, CA 95472 (O'Reilly, 2020)."},{"key":"1539_CR72","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1093\/bioinformatics\/btaa538","volume":"37","author":"EM Ross","year":"2021","unstructured":"Ross, E. M., Haase, K., Van Loo, P. & Markowetz, F. Allele-specific multi-sample copy number segmentation in ASCAT. Bioinformatics 37, 1909\u20131911 (2021).","journal-title":"Bioinformatics"},{"key":"1539_CR73","doi-asserted-by":"publisher","unstructured":"Benjamin, D. et al. Calling somatic SNVs and indels with Mutect2. Preprint at https:\/\/doi.org\/10.1101\/861054 (2019).","DOI":"10.1101\/861054"},{"key":"1539_CR74","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1101\/gr.129684.111","volume":"22","author":"DC Koboldt","year":"2012","unstructured":"Koboldt, D. C. et al. VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568\u2013576 (2012).","journal-title":"Genome Res."},{"key":"1539_CR75","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1038\/s41592-018-0051-x","volume":"15","author":"S Kim","year":"2018","unstructured":"Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591\u2013594 (2018).","journal-title":"Nat. Methods"},{"key":"1539_CR76","doi-asserted-by":"crossref","unstructured":"Bonfield, J. K. et al. HTSlib: C library for reading\/writing high-throughput sequencing data. GigaScience 10, (2021).","DOI":"10.1093\/gigascience\/giab007"},{"key":"1539_CR77","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-016-0974-4","volume":"17","author":"W McLaren","year":"2016","unstructured":"McLaren, W. et al. The ensembl variant effect predictor. Genome Biol. 17, 122 (2016).","journal-title":"Genome Biol."},{"key":"1539_CR78","doi-asserted-by":"publisher","first-page":"i884","DOI":"10.1093\/bioinformatics\/bty560","volume":"34","author":"S Chen","year":"2018","unstructured":"Chen, S., Zhou, Y., Chen, Y. & Gu, J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884\u2013i890 (2018).","journal-title":"Bioinformatics"},{"key":"1539_CR79","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1038\/s41587-019-0201-4","volume":"37","author":"D Kim","year":"2019","unstructured":"Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907\u2013915 (2019).","journal-title":"Nat. Biotechnol."},{"key":"1539_CR80","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-019-1910-1","volume":"20","author":"S Kovaka","year":"2019","unstructured":"Kovaka, S. et al. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol. 20, 278 (2019).","journal-title":"Genome Biol."},{"key":"1539_CR81","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1038\/s41588-018-0283-9","volume":"51","author":"J Hundal","year":"2019","unstructured":"Hundal, J. et al. Accounting for proximal variants improves neoantigen prediction. Nat. Genet. 51, 175\u2013179 (2019).","journal-title":"Nat. Genet."},{"key":"1539_CR82","unstructured":"R. Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria, 2018)."},{"key":"1539_CR83","doi-asserted-by":"publisher","first-page":"1687","DOI":"10.1038\/s41596-019-0133-y","volume":"14","author":"AW Purcell","year":"2019","unstructured":"Purcell, A. W., Ramarathinam, S. H. & Ternette, N. Mass spectrometrybased identification of MHC-bound peptides for immunopeptidomics. Nat. Protoc. 14, 1687\u20131707 (2019).","journal-title":"Nat. Protoc."},{"key":"1539_CR84","doi-asserted-by":"publisher","unstructured":"Bailey, A. et al. Characterization of the class i MHC peptidome resulting from DNCB exposure of HaCaT cells. Toxicol. Sci. https:\/\/doi.org\/10.1093\/toxsci\/kfaa184 (2020).","DOI":"10.1093\/toxsci\/kfaa184"},{"key":"1539_CR85","doi-asserted-by":"publisher","first-page":"D543","DOI":"10.1093\/nar\/gkab1038","volume":"50","author":"Y Perez-Riverol","year":"2021","unstructured":"Perez-Riverol, Y. et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50, D543\u2013D552 (2021).","journal-title":"Nucleic Acids Res."},{"key":"1539_CR86","doi-asserted-by":"publisher","first-page":"M111010587","DOI":"10.1074\/mcp.M111.010587","volume":"11","author":"J Zhang","year":"2012","unstructured":"Zhang, J. et al. PEAKS DB: De novo sequencing assisted database search for sensitive and accurate peptide identification. Mol. Cell. Proteom. 11, M111010587 (2012).","journal-title":"Mol. Cell. Proteom."},{"key":"1539_CR87","doi-asserted-by":"publisher","first-page":"8247","DOI":"10.1073\/pnas.1705691114","volume":"114","author":"NH Tran","year":"2017","unstructured":"Tran, N. H., Zhang, X., Xin, L., Shan, B. & Li, M. De novo peptide sequencing by deep learning. Proc. Natl. Acad. Sci. USA 114, 8247\u20138252 (2017).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"1539_CR88","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1074\/mcp.TIR117.000383","volume":"17","author":"C Chong","year":"2018","unstructured":"Chong, C. et al. High-throughput and sensitive immunopeptidomics platform reveals profound interferon\u03b3-mediated remodeling of the human leukocyte antigen (HLA) ligandome*. Mol. Cell. Proteom. 17, 533\u2013548 (2018).","journal-title":"Mol. Cell. Proteom."},{"key":"1539_CR89","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.21105\/joss.01686","volume":"4","author":"H Wickham","year":"2019","unstructured":"Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).","journal-title":"J. Open Source Softw."},{"key":"1539_CR90","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1038\/s41587-019-0289-6","volume":"37","author":"J Racle","year":"2019","unstructured":"Racle, J. et al. Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes. Nat. Biotechnol. 37, 1283\u20131286 (2019).","journal-title":"Nat. Biotechnol."},{"key":"1539_CR91","unstructured":"Jessen, L. E. PepTools - an R-Package for Making Immunoinformatics Accessible (2018)."}],"container-title":["npj Precision Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41698-026-01539-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41698-026-01539-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41698-026-01539-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T11:03:06Z","timestamp":1790247786000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41698-026-01539-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,10]]},"references-count":91,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1539"],"URL":"https:\/\/doi.org\/10.1038\/s41698-026-01539-2","relation":{},"ISSN":["2397-768X"],"issn-type":[{"value":"2397-768X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,10]]},"assertion":[{"value":"21 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"359"}}