A proteogenomic approach to target neoantigens in solid tumors

References

• Ruggles KV, Krug K, Wang X, et al. Methods, tools and current perspectives in proteogenomics *. 2017;959–981.
   Google Scholar
• Barbieri R, Guryev V, Brandsma C-A, et al., Horvatovich P. for clinical discovery and personalized medicine. 2016
   Google Scholar
• Wen B, Li K, Zhang Y, et al. Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis. [Internet] Nat Commun. 2020;111:1–14.
   PubMed Web of Science ®Google Scholar
• Shukla, S., Rooney, M., Rajasagi, M. et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat Biotechnol33, 1152–1158 (2015). https://doi.org/10.1038/nbt.3344
   PubMed Web of Science ®Google Scholar
• Nesvizhskii AI. Proteogenomics: concepts, applications, and computational strategies. Nature Methods. 2014;11(11):1114–1125.
   PubMed Web of Science ®Google Scholar
• Nesvizhskii AI, Van der Burg E. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. Attention, Perception & Psychophysics. 2011;73(11):2092–2123.
   PubMedGoogle Scholar
• Wormwood KL, Ngounou Wetie AG, Gomez MV, et al. Structural characterization and disulfide assignment of spider peptide Phα1β by mass spectrometry. J Am Soc Mass Spectrom. 2018;29(5):827–841. .
   PubMed Web of Science ®Google Scholar
• Yarchoan M, Johnson BA, Lutz ER, et al. Targeting neoantigens to augment antitumour immunity. Nat Rev Cancer. 2017;17(4):209–222.
   PubMed Web of Science ®Google Scholar
• Wirth TC, Kühnel F. Neoantigen targeting — dawn of a new Era in cancer immunotherapy ? 2017;8( December).
   Google Scholar
• Vigneron N. Human tumor antigens and cancer immunotherapy. 2015; 2015.
   Google Scholar
• Gubin MM, Artyomov MN, Mardis ER, et al. Tumor neoantigens : building a framework for personalized cancer immunotherapy. The Journal of Clinical Investigation. 2015;125(9):3413–3421.
   PubMed Web of Science ®Google Scholar
• Havel JJ The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy.
   Google Scholar
• Bobisse S, Foukas PG, Coukos G, et al. Neoantigen-based cancer immunotherapy. Ann Transl Med. 2016;4(14):262. http://doi.org/10.21037/atm.2016.06.17
   PubMed Web of Science ®Google Scholar
• Chruściel E, Urban-Wójciuk Z, Arcimowicz Ł, et al. Adoptive cell therapy—harnessing antigen-specific t cells to target solid tumours. Cancers (Basel). 2020;12(3):1–30. .
   Web of Science ®Google Scholar
• Zhou C, Zhu C, Liu Q. Toward in silico identification of tumor neoantigens in immunotherapy. Trends in Molecular Medicine. 2019;25(11):980–992.
   PubMed Web of Science ®Google Scholar
• Kanaseki T, Tokita S, Torigoe T. Proteogenomic discovery of cancer antigens : neoantigens and beyond. [Internet] Pathol Int. 2019;699:511–518.
   PubMed Web of Science ®Google Scholar
• Kalaora S, Barnea E, Merhavi-shoham E, et al. Use of HLA peptidomics and whole exome sequencing to identify human immunogenic neo-antigens. Oncotarget. 2016;7(5):5110–5117.
   PubMedGoogle Scholar
• Anagnostou V, Smith KN, Forde PM, et al. Evolution of neoantigen landscape during immune checkpoint blockade in non – small cell lung cancer. Cancer Discovery. 2017;7(March):264–277. .
   PubMedGoogle Scholar
• Keskin DB, Anandappa AJ, Sun J, et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nat. 2018. Available from. DOI: 10.1038/s41586-018-0792-9.
   Google Scholar
• Dou Y, Kawaler EA, Zhou DC, et al. Proteogenomic characterization of endometrial resource proteogenomic characterization of endometrial carcinoma. 2020;729–748.
   Google Scholar
• Ma S, Li X, Wang X, et al. Current progress in CAR-T cell therapy for solid tumors. Int J Biol Sci. 2019;15(12):2548–2560. .
   PubMed Web of Science ®Google Scholar
• Yan ZX, Li L, Wang W, et al. Clinical efficacy and tumor microenvironment influence in a dose-escalation study of anti-CD19 chimeric antigen receptor T cells in refractory B-cell non-Hodgkin’s lymphoma. Clin Cancer Res. 2019;25(23):6995–7003. .
   PubMed Web of Science ®Google Scholar
• Löffler MW, Mohr C, Bichmann L, et al. Multi-omics discovery of exome-derived neoantigens in hepatocellular carcinoma. 2019;1–16.
   Google Scholar
• Hintzsche J, Kim J, Yadav V, et al. IMPACT : a whole-exome sequencing analysis pipeline for integrating molecular profiles with actionable therapeutics in clinical samples. 2016;721–730.
   Google Scholar
• Li Y, Wang G, Tan X, et al. ProGeo-neo : a customized proteogenomic workflow for neoantigen prediction and selection.:1–26.
   Google Scholar
• Bassani-sternberg M, Bra E, Audehm S, et al. Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry. 2016;(May).
   Google Scholar
• Yadav M, Jhunjhunwala S, Phung QT, et al. combining mass spectrometry and exome sequencing. 2014;( V).
   Google Scholar
• Freudenmann LK, Marcu A, Stevanović S. Mapping the tumour human leukocyte antigen (HLA) ligandome by mass spectrometry. Immunology. 2018;154(3):331–345.
   PubMed Web of Science ®Google Scholar
• Polyakova A, Kuznetsova K, Moshkovskii S. Proteogenomics meets cancer immunology: mass spectrometric discovery and analysis of neoantigens. Expert Rev Proteomics. 2015;12(5):533–541. doi:10.1586/14789450.2015.1070100
   Google Scholar
• Roudko V, Greenbaum B, Bhardwaj N. Computational prediction and validation of tumor-associated neoantigens. Front Immunol. 2020;11(January):1–11. http://doi.org/10.3389/fimmu.2020.00027
   Google Scholar
• Xu H, Luo X, Qian J, et al. FastUniq: A fast de novo duplicates removal tool for paired short reads. 2012;7( 12):1–6.
   Google Scholar
• Lai Z, Markovets A, Ahdesmaki M, et al. VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Res. 2016;44(11):e108. http://doi.org/10.1093/nar/gkw227
   Google Scholar
• Habegger L, Balasubramanian S, Chen DZ, et al. VAT : a computational framework to functionally annotate variants in personal genomes within a cloud-computing environment. Bioinformatics (Oxford, England). 2012;28(17):2267–2269. .
   PubMed Web of Science ®Google Scholar
• Redon R, Ishikawa S, Fitch KR, et al. Global variation in copy number in the human genome Richard. Nature. 2006;444(7118):444–454. .
   PubMed Web of Science ®Google Scholar
• Katsonis P, Koire A, Wilson SJ, et al. Single nucleotide variations: biological impact and theoretical interpretation. Protein Sci. 2014;23(12):1650–1666. .
   PubMed Web of Science ®Google Scholar
• Topham CM, Srinivasan N, Blundell TL. Prediction of the stability of protein mutants based on structural environment-dependent amino acid substitution and propensity tables. Protein Engineering. 1997;10(1):7–21.
   PubMedGoogle Scholar
• Turajlic S, Litchfield K, Xu H, et al. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis. Lancet Oncol. 2017;2045(17). [Internet]. DOI: 10.1016/S1470-2045(17)30516-8
   Google Scholar
• Jiang T, Shi T, Zhang H, et al. Tumor neoantigens: from basic research to clinical applications. J Hematol Oncol. 2019;12(1):93.[cited 6 Sep 2019]. Available from: http://doi.org/10.1186/s13045-019-0787-5
   Google Scholar
• Peng M, Mo Y, Wang Y, et al. Neoantigen vaccine : an emerging tumor immunotherapy. 2019;1–14.
   Google Scholar
• Iiizumi S, Ohtake J, Murakami N, et al. Identification of novel HLA class ii-restricted neoantigens derived from driver mutations.:1–14.
   Google Scholar
• Dietlein F, Weghorn D, Taylor-weiner A, et al. Identification of cancer driver genes based on nucleotide context. Nat Genet. 2020;52(2):208–218. .
   PubMed Web of Science ®Google Scholar
• Bailey MH, Tokheim C, Porta-Pardo E, et al. Comprehensive characterization of cancer driver genes and mutations. Cell. 2018;173(2):371–385.e18.
   PubMed Web of Science ®Google Scholar
• Gao Q, Liang W, Foltz SM, et al. Driver fusions and their implications in the development and treatment of human cancers Qingsong. Cell Reports. 2018;23(1):227–238. .
   PubMed Web of Science ®Google Scholar
• Smart AC, Margolis CA, Pimentel H, et al. Intron retention is a source of neoepitopes in cancer. Molecular Biology and Evolution. 2019;36(11):1056–1058. .
   PubMedGoogle Scholar
• Alfaro JA, Sinha A, Kislinger T, et al. Onco-proteogenomics: cancer proteomics joins forces with genomics. Nat Methods. 2014;11(11):1107–1113.
   PubMed Web of Science ®Google Scholar
• Zhang M, Fritsche J, Roszik J, et al. RNA editing derived epitopes function as cancer antigens to elicit immune responses. Nat Commun. 2018;9(1):3919. [cited 25 Sep 2018]. Available from: http://www.doi:10.1038/s41467-018-06405-9
   Google Scholar
• Yang IS, Kim S Analysis of whole transcriptome sequencing data : workflow and software. 2015;119–125.
   Google Scholar
• Lawrence MS, Stojanov P, Polak P, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013;499(7457):214–218. .
   PubMed Web of Science ®Google Scholar
• Finotello F, Di CB. Measuring differential gene expression with RNA-seq : challenges and strategies for data analysis. Briefings in Functional Genomics. 2014;14(2):130–142.
   PubMed Web of Science ®Google Scholar
• Richters MM, Xia H, Campbell KM, Gillanders WE, et al. Best practices for bioinformatic characterization of neoantigens for clinical utility. Genome Med. 2019;11(1):56.[cited 28 Aug 2019]. Available from: http://doi.org/10.1186/s13073-019-0666-2
   Google Scholar
• Shiina T, Hosomichi K, Inoko H, et al. The HLA genomic loci map : expression, interaction, diversity and disease. Journal of Human Genetics. 2009;54(January):15–39.
   PubMedGoogle Scholar
• Castro A, Ozturk K, Pyke RM, et al. Elevated neoantigen levels in tumors with somatic mutations in the HLA-A, HLA-B, HLA-C and B2M genes. BMC Med Genomics. 2019;12\(Suppl 6):107. [cited 25 Jul 2019]. Available from: http://doi.org/10.1186/s12920-019-0544-1.
   Google Scholar
• Marty R, Kaabinejadian S, Rossell D, et al. MHC-I genotype restricts the oncogenic mutational landscape. Cell. 2017;171(6):1272–1283. .
   PubMed Web of Science ®Google Scholar
• Brogdon J, Eckels DD, Davies C, et al. A site for CD4 binding in the β 1 domain of the MHC class II protein HLA-DR1. 2020
   Google Scholar
• Mcgranahan N, Rosenthal R, Hiley CT, et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell. 2017;171(6):1259–1271.
   PubMed Web of Science ®Google Scholar
• Vasaikar S, Huang C, Wang X, et al. Proteogenomic analysis of human colon cancer resource proteogenomic analysis of human colon cancer reveals new therapeutic opportunities. 2019;1035–1049.
   Google Scholar
• Chen R, Fulton KM, Twine SM, et al. Identification of MHC peptides using mass spectrometry for neoantigen discovery and cancer vaccine development. 2019;1–16.
   Google Scholar
• Kanaseki T, Torigoe T. Proteogenomics: advances in cancer antigen research. [Internet] Immunol Med. 2019;422:65–70.
   PubMedGoogle Scholar
• Hayes SA, Clarke S, Pavlakis N, et al. The role of proteomics in the age of immunotherapies. [Internet] Mamm Genome. 2018; 00:0.
   Web of Science ®Google Scholar
• Chong C, Marino F, Pak H, et al. High-throughput and sensitive immunopeptidomics platform reveals profound interferon ␥ -mediated remodeling of the Human Leukocyte Antigen (HLA). 2018;533–548.
   Google Scholar
• Lanoix J, Durette C, Courcelles M, et al. Comparison of the MHC I immunopeptidome repertoire of B-cell lymphoblasts using two isolation methods. Proteomics. 2018;18(12):1–13. .
   Web of Science ®Google Scholar
• Pfammatter S, Bonneil E, McManus FP, et al. A novel differential ion mobility device expands the depth of proteome coverage and the sensitivity of multiplex proteomic measurements. Mol Cell Proteomics. 2018;17(10):2051–2067. .
   PubMed Web of Science ®Google Scholar
• Li S, DeCourcy A, Tang H Constrained De Novo sequencing of neo-epitope peptides using tandem mass spectrometry. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). 2018; 10812 LNBI:138–153.
   Google Scholar
• Caron E, Kowalewski DJ, Koh CC, et al. Analysis of major histocompatibility complex (MHC) immunopeptidomes using mass spectrometry. Mol Cell Proteomics. 2015;14(12):3105–3117.
   PubMed Web of Science ®Google Scholar
• Cox J, Mann M. Quantitative, high-resolution proteomics for data-driven systems biology. Annu Rev Biochem. 2011;80(1):273–299.
   PubMedGoogle Scholar
• Dou Y, Kawaler EA, Cui Zhou D, et al. Proteogenomic characterization of endometrial carcinoma. Cell. 2020;180(4):729–748.e26.
   PubMed Web of Science ®Google Scholar
• Creech AL, Ting YS, Goulding SP, et al. The role of mass spectrometry and proteogenomics in the advancement of HLA epitope prediction. 2018;1700259.
   Google Scholar
• Bhattacharya R, Sivakumar A, Tokheim C, et al. Evaluation of machine learning methods to predict peptide binding to MHC Class I proteins. bioRxiv [Internet]. 2017;154757. [cited 27 Jul 2017]. Available from: https://www.biorxiv.org/content/early/2017/07/27/154757
   Google Scholar
• Fleri W, Paul S, Dhanda SK, et al. The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design. Front Immunol. 2017;8:278. [cited 14 Mar 2017]. Available from: http://doi.org/10.3389/fimmu.2017.00278
   Google Scholar
• Zhang L, Chen Y, Wong HS, et al. TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules. PLoS One. 2012;7(2):e30483. doi:10.1371/journal.pone.0030483.
   Google Scholar
• Turtle CJ, Hanafi LA, Berger C, et al. CD19 CAR-T cells of defined CD4+:CD8+ composition in adult B cell ALL patients. J Clin Invest. 2016;126(6):2123–2138. .
   PubMed Web of Science ®Google Scholar
• Lu Y, Robbins PF Targeting neoantigens for cancer immunotherapy. 2016;28( 7):365–370.
   Google Scholar