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Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review)

  • Authors:
    • Hua Feng
    • Yuanting Jin
    • Bin Wu
  • View Affiliations / Copyright

    Affiliations: College of Life Sciences, China Jiliang University, Hangzhou, Zhejiang 310018, P.R. China, Department of Neurosurgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, P.R. China
    Copyright: © Feng et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY_NC 4.0].
  • Article Number: 43
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    Published online on: May 7, 2025
       https://doi.org/10.3892/ijo.2025.5749
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Abstract

Cancer immunotherapy stimulates and enhances antitumor immune responses to eliminate cancer cells. Neoantigens, which originate from specific mutations within tumor cells, are key targets in cancer immunotherapy. Neoantigens manifest as abnormal peptide fragments or protein segments that are uniquely expressed in tumor cells, making them highly immunogenic. As a result, they activate the immune system, particularly T cell‑mediated immune responses, effectively identifying and eliminating tumor cells. Certain tumor‑associated antigens that are abnormally expressed in normal host proteins in cancer cells are promising targets for immunotherapy. Neoantigens derived from mutated proteins in cancer cells offer true cancer specificity and are often highly immunogenic. Furthermore, most neoantigens are unique to each patient, highlighting the need for personalized treatment strategies. The precise identification and screening of neoantigens are key for improving treatment efficacy and developing individualized therapeutic plans. The neoantigen prediction process involves somatic mutation identification, human leukocyte antigen (HLA) typing, peptide processing and peptide‑HLA binding prediction. The present review summarizes the major current methods used for neoantigen screening, available computational tools and the advantages and limitations of various techniques. Additionally, the present review aimed to summarize experimental strategies for validating the immunogenicity of the predicted neoantigens, which will determine whether these neoantigens can effectively trigger immune responses, as well as challenges encountered during neoantigen screening, providing relevant recommendations for the optimization of neoantigen‑based immunotherapy.
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View References

1 

Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I and Jemal A: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 74:229–263. 2024. View Article : Google Scholar : PubMed/NCBI

2 

World Health Organization: Global cancer burden growing, amidst mounting need for services. 2024.

3 

Siegel RL, Kratzer TB, Giaquinto AN, Sung H and Jemal A: Cancer statistics, 2025. CA Cancer J Clin. 75:10–45. 2025. View Article : Google Scholar : PubMed/NCBI

4 

de Visser KE and Joyce JA: The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth. Cancer Cell. 41:374–403. 2023. View Article : Google Scholar : PubMed/NCBI

5 

Cai Z, Yin Y, Shen C, Wang J, Yin X, Chen Z, Zhou Y and Zhang B: Comparative effectiveness of preoperative, postoperative and perioperative treatments for resectable gastric cancer: A network meta-analysis of the literature from the past 20 years. Surg Oncol. 27:563–574. 2018. View Article : Google Scholar : PubMed/NCBI

6 

Krall JA, Reinhardt F, Mercury OA, Pattabiraman DR, Brooks MW, Dougan M, Lambert AW, Bierie B, Ploegh HL, Dougan SK and Weinberg RA: The systemic response to surgery triggers the outgrowth of distant immune-controlled tumors in mouse models of dormancy. Sci Transl Med. 10:eaan34642018. View Article : Google Scholar : PubMed/NCBI

7 

Yu WD, Sun G, Li J, Xu J and Wang X: Mechanisms and therapeutic potentials of cancer immunotherapy in combination with radiotherapy and/or chemotherapy. Cancer Lett. 452:66–70. 2019. View Article : Google Scholar

8 

Pich O, Muiños F, Lolkema MP, Steeghs N, Gonzalez-Perez A and Lopez-Bigas N: The mutational footprints of cancer therapies. Nat Genet. 51:1732–1740. 2019. View Article : Google Scholar : PubMed/NCBI

9 

Sonis ST: Mucositis: The impact, biology and therapeutic opportunities of oral mucositis. Oral Oncol. 45:1015–1020. 2009. View Article : Google Scholar : PubMed/NCBI

10 

Xiao H, Xiong L, Song X, Jin P, Chen L, Chen X, Yao H, Wang Y and Wang L: Angelica sinensis polysaccharides ameliorate stress-induced premature senescence of hematopoietic cell via protecting bone marrow stromal cells from oxidative injuries caused by 5-fluorouracil. Int J Mol Sci. 18:22652017. View Article : Google Scholar : PubMed/NCBI

11 

Wang Y, Probin V and Zhou DH: Cancer therapy-induced residual bone marrow injury-mechanisms of induction and implication for therapy. Curr Cancer Ther Rev. 2:271–279. 2006. View Article : Google Scholar : PubMed/NCBI

12 

Ryan JL: Ionizing radiation: The good, the bad, and the ugly. J Invest Dermatol. 132:985–993. 2012. View Article : Google Scholar : PubMed/NCBI

13 

Dracham CB, Shankar A and Madan R: Radiation induced secondary malignancies: A review article. Radiat Oncol J. 36:85–94. 2018. View Article : Google Scholar : PubMed/NCBI

14 

Gupta K, Walton R and Kataria SP: Chemotherapy-induced nausea and vomiting: Pathogenesis, recommendations, and new trends. Cancer Treat Res Commun. 26:1002782021. View Article : Google Scholar

15 

Spears N, Lopes F, Stefansdottir A, Rossi V, De Felici M, Anderson RA and Klinger FG: Ovarian damage from chemotherapy and current approaches to its protection. Hum Reprod Update. 25:673–693. 2019. View Article : Google Scholar : PubMed/NCBI

16 

Urruticoechea A, Alemany R, Balart J, Villanueva A, Viñals F and Capellá G: Recent advances in cancer therapy: An overview. Curr Pharm Des. 16:3–10. 2010. View Article : Google Scholar

17 

Cavalcanti IDL and Soares JCS: Advances in cancer treatment: From systemic chemotherapy to targeted therapy. Springer Nature. 1–109. 2021.

18 

Baudino TA: Targeted cancer therapy: The next generation of cancer treatment. Curr Drug Discov Technol. 12:3–20. 2015. View Article : Google Scholar

19 

Lollini PL, Cavallo F, Nanni P and Forni G: Vaccines for tumour prevention. Nat Rev Cancer. 6:204–216. 2006. View Article : Google Scholar : PubMed/NCBI

20 

Pinho MP, Sundarasetty BS, Bergami-Santos PC, Steponavicius-Cruz K, Ferreira AK, Stripecke R and Barbuto JAM: Dendritic-tumor cell hybrids induce tumor-specific immune responses more effectively than the simple mixture of dendritic and tumor cells. Cytotherapy. 18:570–580. 2016. View Article : Google Scholar

21 

Guzhova IV and Margulis BA: HSP70-based anti-cancer immunotherapy. Hum Vaccin Immunother. 12:2529–2535. 2016. View Article : Google Scholar : PubMed/NCBI

22 

Howell LM and Forbes NS: Bacteria-based immune therapies for cancer treatment. Semin Cancer Biol. 86:1163–1178. 2022. View Article : Google Scholar

23 

Eshhar Z: The T-body approach: Redirecting T cells with antibody specificity. Handb Exp Pharmacol. 181:329–342. 2008. View Article : Google Scholar

24 

Dai R, Liu M, Nik Nabil WN, Xi Z and Xu H: Mycomedicine: A unique class of natural products with potent anti-tumour bioactivities. Molecules. 26:11132021. View Article : Google Scholar : PubMed/NCBI

25 

Lau TTY, Sefid Dashti ZJ, Titmuss E, Pender A, Topham JT, Bridgers J, Loree JM, Feng X, Pleasance ED, Renouf DJ, et al: The neoantigen landscape of the coding and noncoding cancer genome space. J Mol Diagn. 24:609–618. 2022. View Article : Google Scholar : PubMed/NCBI

26 

Lybaert L, Lefever S, Fant B, Smits E, De Geest B, Breckpot K, Dirix L, Feldman SA, van Criekinge W, Thielemans K, et al: Challenges in neoantigen-directed therapeutics. Cancer Cell. 41:15–40. 2023. View Article : Google Scholar

27 

Sia D, Villanueva A, Friedman SL and Llovet JM: Liver cancer cell of origin, molecular class, and effects on patient prognosis. Gastroenterology. 152:745–761. 2017. View Article : Google Scholar

28 

Sánchez-Danés A and Blanpain C: Deciphering the cells of origin of squamous cell carcinomas. Nat Rev Cancer. 18:549–561. 2018. View Article : Google Scholar

29 

Kim J, Lee BJ, Moon S, Lee H, Lee J, Kim BS, Jung K, Seo H and Chung Y: Strategies to overcome hurdles in cancer immunotherapy. Biomater Res. 28:00802024. View Article : Google Scholar

30 

Farhadi Rad H, Tahmasebi H, Javani S, Hemati M, Zakerhamidi D, Hosseini M, Alibabaei F, Banihashemian SZ, Oksenych V and Eslami M: Microbiota and cytokine modulation: Innovations in enhancing anticancer immunity and personalized cancer therapies. Biomedicines. 12:27762024. View Article : Google Scholar

31 

Ritu, Chandra P and Das A: Immune checkpoint targeting antibodies hold promise for combinatorial cancer therapeutics. Clin Exp Med. 23:4297–4322. 2023. View Article : Google Scholar : PubMed/NCBI

32 

Luo Q, Zhang L, Luo C and Jiang M: Emerging strategies in cancer therapy combining chemotherapy with immunotherapy. Cancer Lett. 454:191–203. 2019. View Article : Google Scholar : PubMed/NCBI

33 

Weiner GJ: Building better monoclonal antibody-based therapeutics. Nat Rev Cancer. 15:361–370. 2015. View Article : Google Scholar :

34 

Wei G, Zhang H, Zhao H, Wang J, Wu N, Li L, Wu J and Zhang D: Emerging immune checkpoints in the tumor microenvironment: Implications for cancer immunotherapy. Cancer Lett. 511:68–76. 2021. View Article : Google Scholar : PubMed/NCBI

35 

Oka Y, Tsuboi A, Oji Y, Kawase I and Sugiyama H: WT1 peptide vaccine for the treatment of cancer. Curr Opin Immunol. 20:211–220. 2008. View Article : Google Scholar : PubMed/NCBI

36 

Castle JC, Kreiter S, Diekmann J, Löwer M, van de Roemer N, de Graaf J, Selmi A, Diken M, Boegel S, Paret C, et al: Exploiting the mutanome for tumor vaccination. Cancer Res. 72:1081–1091. 2012. View Article : Google Scholar

37 

Rosenberg SA and Restifo NP: Adoptive cell transfer as personalized immunotherapy for human cancer. Science. 348:62–68. 2015. View Article : Google Scholar : PubMed/NCBI

38 

Zhang J and Wang L: The emerging world of TCR-T cell trials against cancer: A systematic review. Technol Cancer Res Treat. 18:15330338198310682019. View Article : Google Scholar : PubMed/NCBI

39 

Zhao QJ, Jiang Y, Xiang SX, Kaboli PJ, Shen J, Zhao YS, Wu X, Du FK, Li MX, Cho CH, et al: Engineered TCR-T cell immunotherapy in anticancer precision medicine: Pros and cons. Front Immunol. 12:6587532021. View Article : Google Scholar : PubMed/NCBI

40 

Sterner RC and Sterner RM: CAR-T cell therapy: Current limitations and potential strategies. Blood Cancer J. 11:692021. View Article : Google Scholar : PubMed/NCBI

41 

Daher M, Melo Garcia L, Li Y and Rezvani K: CAR-NK cells: The next wave of cellular therapy for cancer. Clin Transl Immunology. 10:e12742021. View Article : Google Scholar : PubMed/NCBI

42 

Chen XT, Yang J, Wang LF and Liu BR: Personalized neoantigen vaccination with synthetic long peptides: Recent advances and future perspectives. Theranostics. 10:6011–6023. 2020. View Article : Google Scholar : PubMed/NCBI

43 

Li L, Goedegebuure SP and Gillanders WE: Preclinical and clinical development of neoantigen vaccines. Ann Oncol. 28(Suppl 12): xii11–xii17. 2017. View Article : Google Scholar

44 

Lybaert L, Thielemans K, Feldman SA, van der Burg SH, Bogaert C and Ott PA: Neoantigen-directed therapeutics in the clinic: Where are we? Trends Cancer. 9:503–519. 2023. View Article : Google Scholar

45 

Luo Y, Zhou H, Mizutani M, Mizutani N, Liu C, Xiang R and Reisfeld RA: A DNA vaccine targeting Fos-related antigen 1 enhanced by IL-18 induces long-lived T-cell memory against tumor recurrence. Cancer Res. 65:3419–3427. 2005. View Article : Google Scholar : PubMed/NCBI

46 

Ribatti D: The concept of immune surveillance against tumors: The first theories. Oncotarget. 8:7175–7180. 2017. View Article : Google Scholar

47 

Gross L: Intradermal immunization of C3H mice against a sarcoma that originated in an animal of the same line. Cancer Res. 3:326–333. 1943.

48 

Baldwin RW: Tumour-specific immunity against spontaneous rat tumours. Int J Cancer. 1:257–264. 1966. View Article : Google Scholar

49 

Dietrich CH, Allen JM, Lemmon AR, Lemmon EM, Takiya DM, Evangelista O, Walden KKO, Grady PGS and Johnson KP: Anchored hybrid enrichment-based phylogenomics of leafhoppers and treehoppers (Hemiptera: Cicadomorpha: Membracoidea). Insect Syst Divers. 1:57–72. 2017. View Article : Google Scholar

50 

Richters MM, Xia H, Campbell KM, Gillanders WE, Griffith OL and Griffith M: Best practices for bioinformatic characterization of neoantigens for clinical utility. Genome Med. 11:562019. View Article : Google Scholar

51 

Aljabali AAA, Hamzat Y, Alqudah A and Alzoubi L: Neoantigen vaccines: Advancing personalized cancer immunotherapy. Explor Immunol. 5:10031902025. View Article : Google Scholar

52 

Fennemann FL, de Vries IJM, Figdor CG and Verdoes M: Attacking tumors from all sides: Personalized multiplex vaccines to tackle intratumor heterogeneity. Front Immunol. 10:8242019. View Article : Google Scholar

53 

Peng M, Mo Y, Wang Y, Wu P, Zhang Y, Xiong F, Guo C, Wu X, Li Y, Li X, et al: Neoantigen vaccine: An emerging tumor immunotherapy. Mol Cancer. 18:1282019. View Article : Google Scholar : PubMed/NCBI

54 

Rojas LA, Sethna Z, Soares KC, Olcese C, Pang N, Patterson E, Lihm J, Ceglia N, Guasp P, Chu A, et al: Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer. Nature. 618:144–150. 2023. View Article : Google Scholar

55 

Ehx G and Perreault C: Discovery and characterization of actionable tumor antigens. Genome Med. 11:292019. View Article : Google Scholar

56 

Finn OJ: Human tumor antigens yesterday, today, and tomorrow. Cancer Immunol Res. 5:347–354. 2017. View Article : Google Scholar : PubMed/NCBI

57 

Sotirov S and Dimitrov I: Tumor-derived antigenic peptides as potential cancer vaccines. Int J Mol Sci. 25:49342024. View Article : Google Scholar

58 

Ott PA, Hu Z, Keskin DB, Shukla SA, Sun J, Bozym DJ, Zhang W, Luoma A, Giobbie-Hurder A, Peter L, et al: An immunogenic personal neoantigen vaccine for patients with melanoma. Nature. 547:217–221. 2017. View Article : Google Scholar : PubMed/NCBI

59 

Hu Z, Leet DE, Allesøe RL, Oliveira G, Li S, Luoma AM, Liu J, Forman J, Huang T, Iorgulescu JB, et al: Personal neoantigen vaccines induce persistent memory T cell responses and epitope spreading in patients with melanoma. Nat Med. 27:515–525. 2021. View Article : Google Scholar :

60 

Keskin DB, Anandappa AJ, Sun J, Tirosh I, Mathewson ND, Li S, Oliveira G, Giobbie-Hurder A, Felt K, Gjini E, et al: Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature. 565:234–239. 2019. View Article : Google Scholar :

61 

Ali OA, Lewin SA, Dranoff G and Mooney DJ: Vaccines combined with immune checkpoint antibodies promote cytotoxic T-cell activity and tumor eradication. Cancer Immunol Res. 4:95–100. 2016. View Article : Google Scholar

62 

Karyampudi L, Lamichhane P, Scheid AD, Kalli KR, Shreeder B, Krempski JW, Behrens MD and Knutson KL: Accumulation of memory precursor CD8 T cells in regressing tumors following combination therapy with vaccine and anti-PD-1 antibody. Cancer Res. 74:2974–2985. 2014. View Article : Google Scholar

63 

Robert C, Schachter J, Long GV, Arance A, Grob JJ, Mortier L, Daud A, Carlino MS, McNeil C, Lotem M, et al: Pembrolizumab versus ipilimumab in advanced melanoma. N Engl J Med. 372:2521–2532. 2015. View Article : Google Scholar

64 

Qiu Z, Chen Z, Zhang C and Zhong W: Achievements and futures of immune checkpoint inhibitors in non-small cell lung cancer. Exp Hematol Oncol. 8:192019. View Article : Google Scholar :

65 

Zou W, Wolchok JD and Chen L: PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: Mechanisms, response biomarkers, and combinations. Sci Transl Med. 8:328rv42016. View Article : Google Scholar : PubMed/NCBI

66 

Kantoff PW, Higano CS, Shore ND, Berger ER, Small EJ, Penson DF, Redfern CH, Ferrari AC, Dreicer R, Sims RB, et al: Sipuleucel-T immunotherapy for castration-resistant prostate cancer. N Engl J Med. 363:411–422. 2010. View Article : Google Scholar : PubMed/NCBI

67 

Higano CS, Armstrong AJ, Sartor AO, Vogelzang NJ, Kantoff PW, McLeod DG, Pieczonka CM, Penson DF, Shore ND, Vacirca J, et al: Real-world outcomes of sipuleucel-T treatment in PROCEED, a prospective registry of men with metastatic castration-resistant prostate cancer. Cancer. 125:4172–4180. 2019. View Article : Google Scholar

68 

Lin G, Elkashif A, Saha C, Coulter JA, Dunne NJ and McCarthy HO: Key considerations for a prostate cancer mRNA vaccine. Crit Rev Oncol Hematol. 208:1046432025. View Article : Google Scholar : PubMed/NCBI

69 

Blum JS, Wearsch PA and Cresswell P: Pathways of antigen processing. Annu Rev Immunol. 31:443–473. 2013. View Article : Google Scholar : PubMed/NCBI

70 

Babbitt BP, Allen PM, Matsueda G, Haber E and Unanue ER: Binding of immunogenic peptides to Ia histocompatibility molecules. Nature. 317:359–361. 1985. View Article : Google Scholar

71 

Abdel-Aal ABM, Lakshminarayanan V, Thompson P, Supekar N, Bradley JM, Wolfert MA, Cohen PA, Gendler SJ and Boons GJ: Immune and anticancer responses elicited by fully synthetic aberrantly glycosylated MUC1 tripartite vaccines modified by a TLR2 or TLR9 agonist. Chembiochem. 15:1508–1513. 2014. View Article : Google Scholar

72 

Vlad AM, Kettel JC, Alajez NM, Carlos CA and Finn OJ: MUC1 immunobiology: From discovery to clinical applications. Adv Immunol. 82:249–293. 2004. View Article : Google Scholar

73 

Arab A, Yazdian-Robati R and Behravan J: HER2-positive breast cancer immunotherapy: A focus on vaccine development. Arch Immunol Ther Exp (Warsz). 68:22020. View Article : Google Scholar

74 

Baxevanis CN, Sotiriadou NN, Gritzapis AD, Sotiropoulou PA, Perez SA, Cacoullos NT and Papamichail M: Immunogenic HER-2/neu peptides as tumor vaccines. Cancer Immunol Immunother. 55:85–95. 2006. View Article : Google Scholar

75 

Zanetti M: A second chance for telomerase reverse transcriptase in anticancer immunotherapy. Nat Rev Clin Oncol. 14:115–128. 2017. View Article : Google Scholar

76 

Slingluff CL Jr, Chianese-Bullock KA, Bullock TNJ, Grosh WW, Mullins DW, Nichols L, Olson W, Petroni G, Smolkin M and Engelhard VH: Immunity to melanoma antigens: From self-tolerance to immunotherapy. Adv Immunol. 90:243–295. 2006. View Article : Google Scholar

77 

Guo Q, Wang J, Xiao J, Wang L, Hu X, Yu W, Song G, Lou J and Chen JF: Heterogeneous mutation pattern in tumor tissue and circulating tumor DNA warrants parallel NGS panel testing. Mol Cancer. 17:1312018. View Article : Google Scholar :

78 

Hollingsworth RE and Jansen K: Turning the corner on therapeutic cancer vaccines. NPJ Vaccines. 4:72019. View Article : Google Scholar

79 

Malacopol AT and Holst PJ: Cancer vaccines: Recent insights and future directions. Int J Mol Sci. 25:112562024. View Article : Google Scholar : PubMed/NCBI

80 

Leisegang M, Engels B, Schreiber K, Yew PY, Kiyotani K, Idel C, Arina A, Duraiswamy J, Weichselbaum RR, Uckert W, et al: Eradication of large solid tumors by gene therapy with a T-cell receptor targeting a single cancer-specific point mutation. Clin Cancer Res. 22:2734–2743. 2016. View Article : Google Scholar

81 

Chen P, Fang QX, Chen DB and Chen HS: Neoantigen vaccine: An emerging immunotherapy for hepatocellular carcinoma. World J Gastrointest Oncol. 13:673–683. 2021. View Article : Google Scholar

82 

Pan RY, Chung WH, Chu MT, Chen SJ, Chen HC, Zheng L and Hung SI: Recent development and clinical application of cancer vaccine: Targeting neoantigens. J Immunol Res. 2018:43258742018. View Article : Google Scholar

83 

Vormehr M, Türeci Ö and Sahin U: Harnessing tumor mutations for truly individualized cancer vaccines. Annu Rev Med. 70:395–407. 2019. View Article : Google Scholar : PubMed/NCBI

84 

Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SAJR, Behjati S, Biankin AV, Bignell GR, Bolli N, Borg A, Børresen-Dale AL, et al: Signatures of mutational processes in human cancer. Nature. 500:415–421. 2013. View Article : Google Scholar : PubMed/NCBI

85 

Turajlic S, Litchfield K, Xu H, Rosenthal R, McGranahan N, Reading JL, Wong YNS, Rowan A, Kanu N, Al Bakir M, et al: Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: A pan-cancer analysis. Lancet Oncol. 18:1009–1021. 2017. View Article : Google Scholar : PubMed/NCBI

86 

Yang W, Lee KW, Srivastava RM, Kuo F, Krishna C, Chowell D, Makarov V, Hoen D, Dalin MG, Wexler L, et al: Immunogenic neoantigens derived from gene fusions stimulate T cell responses. Nat Med. 25:767–775. 2019. View Article : Google Scholar :

87 

Zhang J, White NM, Schmidt HK, Fulton RS, Tomlinson C, Warren WC, Wilson RK and Maher CA: INTEGRATE: Gene fusion discovery using whole genome and transcriptome data. Genome Res. 26:108–118. 2016. View Article : Google Scholar :

88 

Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, Walsh LA, Postow MA, Wong P, Ho TS, et al: Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 371:2189–2199. 2014. View Article : Google Scholar

89 

Chen DS and Mellman I: Elements of cancer immunity and the cancer-immune set point. Nature. 541:321–330. 2017. View Article : Google Scholar

90 

Borden ES, Buetow KH, Wilson MA and Hastings KT: Cancer neoantigens: Challenges and future directions for prediction, prioritization, and validation. Front Oncol. 12:8368212022. View Article : Google Scholar

91 

Anczuków O and Krainer AR: Splicing-factor alterations in cancers. RNA. 22:1285–1301. 2016. View Article : Google Scholar : PubMed/NCBI

92 

Kahles A, Lehmann KV, Toussaint NC, Hüser M, Stark SG, Sachsenberg T, Stegle O, Kohlbacher O and Sander C; Cancer Genome Atlas Research Network, Gunnar Rätsch: Comprehensive analysis of alternative splicing across tumors from 8,705 patients. Cancer Cell. 34:211–224 e6. 2018. View Article : Google Scholar

93 

Cheng R, Xu Z, Luo M, Wang P, Cao H, Jin X, Zhou W, Xiao L and Jiang Q: Identification of alternative splicing-derived cancer neoantigens for mRNA vaccine development. Brief Bioinform. 23:bbab5532022. View Article : Google Scholar

94 

Weller C, Bartok O, McGinnis CS, Palashati H, Chang TG, Malko D, Shmueli MD, Nagao A, Hayoun D, Murayama A, et al: Translation dysregulation in cancer as a source for targetable antigens. Cancer Cell. S1535-6108(25)00082-02025.Epub ahead of print.

95 

Carreno BM, Magrini V, Becker-Hapak M, Kaabinejadian S, Hundal J, Petti AA, Ly A, Lie WR, Hildebrand WH, Mardis ER and Linette GP: Cancer immunotherapy. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science. 348:803–808. 2015. View Article : Google Scholar :

96 

Sahin U, Derhovanessian E, Miller M, Kloke BP, Simon P, Löwer M, Bukur V, Tadmor AD, Luxemburger U, Schrörs B, et al: Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature. 547:222–226. 2017. View Article : Google Scholar : PubMed/NCBI

97 

Hacohen N, Fritsch EF, Carter TA, Lander ES and Wu CJ: Getting personal with neoantigen-based therapeutic cancer vaccines. Cancer Immunol Res. 1:11152013. View Article : Google Scholar

98 

Capietto AH, Hoshyar R and Delamarre L: Sources of cancer neoantigens beyond single-nucleotide variants. Int J Mol Sci. 23:101312022. View Article : Google Scholar : PubMed/NCBI

99 

Schumacher T, Bunse L, Pusch S, Sahm F, Wiestler B, Quandt J, Menn O, Osswald M, Oezen I, Ott M, et al: A vaccine targeting mutant IDH1 induces antitumour immunity. Nature. 512:324–327. 2014. View Article : Google Scholar : PubMed/NCBI

100 

Wang QJ, Yu Z, Griffith K, Hanada KI, Restifo NP and Yang JC: Identification of T-cell receptors targeting KRAS-mutated human tumors. Cancer Immunol Res. 4:204–214. 2016. View Article : Google Scholar

101 

Chheda ZS, Kohanbash G, Okada K, Jahan N, Sidney J, Pecoraro M, Yang X, Carrera DA, Downey KM, Shrivastav S, et al: Novel and shared neoantigen derived from histone 3 variant H3.3K27M mutation for glioma T cell therapy. J Exp Med. 215:141–157. 2018. View Article : Google Scholar :

102 

Wang Y, Shi T, Song X, Liu B and Wei J: Gene fusion neoantigens: Emerging targets for cancer immunotherapy. Cancer Lett. 506:45–54. 2021. View Article : Google Scholar

103 

Wei Z, Zhou C, Zhang Z, Guan M, Zhang C, Liu Z and Liu Q: The landscape of tumor fusion neoantigens: A pan-cancer analysis. iScience. 21:249–260. 2019. View Article : Google Scholar : PubMed/NCBI

104 

Dai X, Theobard R, Cheng H, Xing M and Zhang J: Fusion genes: A promising tool combating against cancer. Biochim Biophys Acta Rev Cancer. 1869:149–160. 2018. View Article : Google Scholar : PubMed/NCBI

105 

Smith CC, Selitsky SR, Chai S, Armistead PM, Vincent BG and Serody JS: Alternative tumour-specific antigens. Nat Rev Cancer. 19:465–478. 2019. View Article : Google Scholar :

106 

Diederichs S, Bartsch L, Berkmann JC, Fröse K, Heitmann J, Hoppe C, Iggena D, Jazmati D, Karschnia P, Linsenmeier M, et al: The dark matter of the cancer genome: Aberrations in regulatory elements, untranslated regions, splice sites, non-coding RNA and synonymous mutations. EMBO Mol Med. 8:442–457. 2016. View Article : Google Scholar :

107 

Laumont CM, Vincent K, Hesnard L, Audemard É, Bonneil É, Laverdure JP, Gendron P, Courcelles M, Hardy MP, Côté C, et al: Noncoding regions are the main source of targetable tumor-specific antigens. Sci Transl Med. 10:eaau55162018. View Article : Google Scholar

108 

Huang D, Zhu X, Ye S, Zhang J, Liao J, Zhang N, Zeng X, Wang J, Yang B, Zhang Y, et al: Tumour circular RNAs elicit anti-tumour immunity by encoding cryptic peptides. Nature. 625:593–602. 2024. View Article : Google Scholar

109 

Robbins PF, Lu YC, El-Gamil M, Li YF, Gross C, Gartner J, Lin JC, Teer JK, Cliften P, Tycksen E, et al: Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nat Med. 19:747–752. 2013. View Article : Google Scholar

110 

van Buuren MM, Calis JJ and Schumacher TN: High sensitivity of cancer exome-based CD8 T cell neo-antigen identification. Oncoimmunology. 3:e288362014. View Article : Google Scholar : PubMed/NCBI

111 

Biswas N, Chakrabarti S, Padul V, Jones LD and Ashili S: Designing neoantigen cancer vaccines, trials, and outcomes. Front Immunol. 14:11054202023. View Article : Google Scholar :

112 

Roudko V, Greenbaum B and Bhardwaj N: Computational prediction and validation of tumor-associated neoantigens. Front Immunol. 11:272020. View Article : Google Scholar : PubMed/NCBI

113 

Yadav M, Jhunjhunwala S, Phung QT, Lupardus P, Tanguay J, Bumbaca S, Franci C, Cheung TK, Fritsche J, Weinschenk T, et al: Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature. 515:572–576. 2014. View Article : Google Scholar : PubMed/NCBI

114 

Chen S, Zhou Y, Chen Y and Gu J: fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 34:i884–i890. 2018. View Article : Google Scholar :

115 

Chen S: Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. Imeta. 2:e1072023. View Article : Google Scholar : PubMed/NCBI

116 

Li H and Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 25:1754–1760. 2009. View Article : Google Scholar : PubMed/NCBI

117 

Langmead B and Salzberg SL: Fast gapped-read alignment with Bowtie 2. Nat Methods. 9:357–359. 2012. View Article : Google Scholar : PubMed/NCBI

118 

Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M and Gingeras TR: STAR: Ultrafast universal RNA-seq aligner. Bioinformatics. 29:15–21. 2013. View Article : Google Scholar

119 

Kim D, Langmead B and Salzberg SL: HISAT: A fast spliced aligner with low memory requirements. Nat Methods. 12:357–360. 2015. View Article : Google Scholar :

120 

Pertea M, Kim D, Pertea GM, Leek JT and Salzberg SL: Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc. 11:1650–1667. 2016. View Article : Google Scholar : PubMed/NCBI

121 

Kim D, Paggi JM, Park C, Bennett C and Salzberg SL: Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 37:907–915. 2019. View Article : Google Scholar : PubMed/NCBI

122 

Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R and Salzberg SL: TopHat2: Accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14:R362013. View Article : Google Scholar : PubMed/NCBI

123 

Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G and Durbin R; 1000 Genome Project Data Processing Subgroup: The sequence alignment/map format and SAMtools. Bioinformatics. 25:2078–2079. 2009. View Article : Google Scholar : PubMed/NCBI

124 

Shumate A, Wong B, Pertea G and Pertea M: Improved transcriptome assembly using a hybrid of long and short reads with StringTie. PLoS Comput Biol. 18:e10097302022. View Article : Google Scholar : PubMed/NCBI

125 

van der Auwera G and O'Connor BD: Genomics in the cloud: Using docker, GATK, and WDL in Terra. O'Reilly Media, Incorporated; 2020

126 

Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, Miller CA, Mardis ER, Ding L and Wilson RK: VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22:568–576. 2012. View Article : Google Scholar

127 

Lai Z, Markovets A, Ahdesmaki M, Chapman B, Hofmann O, McEwen R, Johnson J, Dougherty B, Barrett JC and Dry JR: VarDict: A novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Res. 44:e1082016. View Article : Google Scholar : PubMed/NCBI

128 

Kim S, Scheffler K, Halpern AL, Bekritsky MA, Noh E, Källberg M, Chen X, Kim Y, Beyter D, Krusche P and Saunders CT: Strelka2: Fast and accurate calling of germline and somatic variants. Nat Methods. 15:591–594. 2018. View Article : Google Scholar : PubMed/NCBI

129 

Haas BJ, Dobin A, Stransky N, Li B, Yang X, Tickle T, Bankapur A, Ganote C, Doak TG, Pochet N, et al: STAR-fusion: Fast and accurate fusion transcript detection from RNA-Seq. bioRxiv: 120295. 2017.

130 

Yang H and Wang K: Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Nat Protoc. 10:1556–1566. 2015. View Article : Google Scholar : PubMed/NCBI

131 

Mayakonda A, Lin DC, Assenov Y, Plass C and Koeffler HP: Maftools: Efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 28:1747–1756. 2018. View Article : Google Scholar : PubMed/NCBI

132 

Love MI, Huber W and Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15:5502014. View Article : Google Scholar : PubMed/NCBI

133 

Gubin MM, Artyomov MN, Mardis ER and Schreiber RD: Tumor neoantigens: Building a framework for personalized cancer immunotherapy. J Clin Invest. 125:3413–3421. 2015. View Article : Google Scholar : PubMed/NCBI

134 

Shiina T, Hosomichi K, Inoko H and Kulski JK: The HLA genomic loci map: expression, interaction, diversity and disease. J Hum Genet. 54:15–39. 2009. View Article : Google Scholar : PubMed/NCBI

135 

Klein J and Sato A: The HLA system. First of two parts. N Engl J Med. 343:702–709. 2000. View Article : Google Scholar : PubMed/NCBI

136 

Neefjes J, Jongsma M, Paul P and Bakke O: Towards a systems understanding of MHC class I and MHC class II antigen presentation. Nat Rev Immunol. 11:823–836. 2011. View Article : Google Scholar : PubMed/NCBI

137 

Aurisicchio L, Pallocca M, Ciliberto G and Palombo F: The perfect personalized cancer therapy: Cancer vaccines against neoantigens. J Exp Clin Cancer Res. 37:862018. View Article : Google Scholar : PubMed/NCBI

138 

Bontadini A: HLA techniques: Typing and antibody detection in the laboratory of immunogenetics. Methods. 56:471–476. 2012. View Article : Google Scholar : PubMed/NCBI

139 

De Mattos-Arruda L, Vazquez M, Finotello F, Lepore R, Porta E, Hundal J, Amengual-Rigo P, Ng CKY, Valencia A, Carrillo J, et al: Neoantigen prediction and computational perspectives towards clinical benefit: Recommendations from the ESMO precision medicine working group. Ann Oncol. 31:978–990. 2020. View Article : Google Scholar : PubMed/NCBI

140 

Hosomichi K, Shiina T, Tajima A and Inoue I: The impact of next-generation sequencing technologies on HLA research. J Hum Genet. 60:665–673. 2015. View Article : Google Scholar : PubMed/NCBI

141 

Danzer M, Niklas N, Stabentheiner S, Hofer K, Pröll J, Stückler C, Raml E, Polin H and Gabriel C: Rapid, scalable and highly automated HLA genotyping using next-generation sequencing: A transition from research to diagnostics. BMC Genomics. 14:2212013. View Article : Google Scholar : PubMed/NCBI

142 

Smith AG, Pereira S, Jaramillo A, Stoll ST, Khan FM, Berka N, Mostafa AA, Pando MJ, Usenko CY, Bettinotti MP, et al: Comparison of sequence-specific oligonucleotide probe vs next generation sequencing for HLA-A, B, C, DRB1, DRB3/B4/B5, DQA1, DQB1, DPA1, and DPB1 typing: Toward single-pass high-resolution HLA typing in support of solid organ and hematopoietic cell transplant programs: Toward single-pass high-resolution HLA typing in support of solid organ and hematopoietic cell transplant programs. HLA. 94:296–306. 2019. View Article : Google Scholar : PubMed/NCBI

143 

Wittig M, Anmarkrud JA, Kässens JC, Koch S, Forster M, Ellinghaus E, Hov JR, Sauer S, Schimmler M, Ziemann M, et al: Development of a high-resolution NGS-based HLA-typing and analysis pipeline. Nucleic Acids Res. 43:e702015. View Article : Google Scholar :

144 

Boegel S, Löwer M, Schäfer M, Bukur T, de Graaf J, Boisguérin V, Türeci O, Diken M, Castle JC and Sahin U: HLA typing from RNA-seq sequence reads. Genome Med. 4:1022012. View Article : Google Scholar : PubMed/NCBI

145 

Warren RL, Choe G, Freeman DJ, Castellarin M, Munro S, Moore R and Holt RA: Derivation of HLA types from shotgun sequence datasets. Genome Med. 4:952012. View Article : Google Scholar : PubMed/NCBI

146 

Warren RL: HLA predictions from long sequence read alignments, streamed directly into HLAminer. arXiv: 2209.09155. 2022.

147 

Kim HJ and Pourmand N: HLA typing from RNA-seq data using hierarchical read weighting [corrected]. PLoS One. 8:e678852013. View Article : Google Scholar

148 

Liu C, Yang X, Duffy B, Mohanakumar T, Mitra RD, Zody MC and Pfeifer JD: ATHLATES: Accurate typing of human leukocyte antigen through exome sequencing. Nucleic Acids Res. 41:e1422013. View Article : Google Scholar :

149 

Szolek A, Schubert B, Mohr C, Sturm M, Feldhahn M and Kohlbacher O: OptiType: Precision HLA typing from next-generation sequencing data. Bioinformatics. 30:3310–3316. 2014. View Article : Google Scholar

150 

Shukla SA, Rooney MS, Rajasagi M, Tiao G, Dixon PM, Lawrence MS, Stevens J, Lane WJ, Dellagatta JL, Steelman S, et al: Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat Biotechnol. 33:1152–1158. 2015. View Article : Google Scholar : PubMed/NCBI

151 

Huang Y, Yang J, Ying D, Zhang Y, Shotelersuk V, Hirankarn N, Sham PC, Lau YL and Yang W: HLAreporter: A tool for HLA typing from next generation sequencing data. Genome Med. 7:252015. View Article : Google Scholar :

152 

Nariai N, Kojima K, Saito S, Mimori T, Sato Y, Kawai Y, Yamaguchi-Kabata Y, Yasuda J and Nagasaki M: HLA-VBSeq: Accurate HLA typing at full resolution from whole-genome sequencing data. BMC Genomics. 16(Suppl 2): S72015. View Article : Google Scholar : PubMed/NCBI

153 

Xie C, Yeo ZX, Wong M, Piper J, Long T, Kirkness EF, Biggs WH, Bloom K, Spellman S, Vierra-Green C, et al: Fast and accurate HLA typing from short-read next-generation sequence data with xHLA. Proc Natl Acad Sci USA. 114:8059–8064. 2017. View Article : Google Scholar : PubMed/NCBI

154 

Kawaguchi S, Higasa K, Shimizu M, Yamada R and Matsuda F: HLA-HD: An accurate HLA typing algorithm for nextgeneration sequencing data. Hum Mutat. 38:788–797. 2017. View Article : Google Scholar : PubMed/NCBI

155 

Kawaguchi S, Higasa K, Yamada R and Matsuda F: Comprehensive HLA typing from a current allele database using next-generation sequencing data. Methods Mol Biol. 1802:225–233. 2018. View Article : Google Scholar

156 

Kawaguchi S and Matsuda F: High-definition genomic analysis of HLA genes via comprehensive HLA allele genotyping. Methods Mol Biol. 2131:31–38. 2020. View Article : Google Scholar : PubMed/NCBI

157 

Ka S, Lee S, Hong J, Cho Y, Sung J, Kim HN, Kim HL and Jung J: HLAscan: Genotyping of the HLA region using next-generation sequencing data. BMC Bioinformatics. 18:2582017. View Article : Google Scholar :

158 

Hayashi S, Yamaguchi R, Mizuno S, Komura M, Miyano S, Nakagawa H and Imoto S: ALPHLARD: A Bayesian method for analyzing HLA genes from whole genome sequence data. BMC Genomics. 19:7902018. View Article : Google Scholar : PubMed/NCBI

159 

Bai Y, Wang D and Fury W: PHLAT: Inference of high-resolution HLA types from RNA and whole exome sequencing. Methods Mol Biol. 1802:193–201. 2018. View Article : Google Scholar : PubMed/NCBI

160 

Orenbuch R, Filip I, Comito D, Shaman J, Pe'er I and Rabadan R: arcasHLA: High-resolution HLA typing from RNAseq. Bioinformatics. 36:33–40. 2020. View Article : Google Scholar :

161 

Bauer DC, Zadoorian A, Wilson LOW; Melbourne Genomics Health Alliance; Thorne NP: Evaluation of computational programs to predict HLA genotypes from genomic sequencing data. Brief Bioinform. 19:179–187. 2018.

162 

Kiyotani K, Mai TH and Nakamura Y: Comparison of exome-based HLA class I genotyping tools: Identification of platform-specific genotyping errors. J Hum Genet. 62:397–405. 2017. View Article : Google Scholar

163 

Schumacher TN and Schreiber RD: Neoantigens in cancer immunotherapy. Science. 348:69–74. 2015. View Article : Google Scholar : PubMed/NCBI

164 

Ferrari G, Kostyu DD, Cox J, Dawson DV, Flores J, Weinhold KJ and Osmanov S: Identification of highly conserved and broadly cross-reactive HIV type 1 cytotoxic T lymphocyte epitopes as candidate immunogens for inclusion in Mycobacterium bovis BCG-vectored HIV vaccines. AIDS Res Hum Retroviruses. 16:1433–1443. 2000. View Article : Google Scholar : PubMed/NCBI

165 

Kessler JH, Benckhuijsen WE, Mutis T, Melief CJM, van der Burg SH and Drijfhout JW: Competition-based cellular peptide binding assay for HLA class I. Curr Protoc Immunol. Chapter 18: Unit 18.12. 2004. View Article : Google Scholar

166 

Wulf M, Hoehn P and Trinder P: Identification and validation of T-cell epitopes using the IFN-gamma ELISPOT assay. Methods Mol Biol. 524:439–446. 2009. View Article : Google Scholar : PubMed/NCBI

167 

Yang B, Jeang J, Yang A, Wu TC and Hung CF: DNA vaccine for cancer immunotherapy. Hum Vaccin Immunother. 10:3153–3164. 2014. View Article : Google Scholar

168 

Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, Wheeler DK, Sette A and Peters B: The immune epitope database (IEDB): 2018 Update. Nucleic Acids Res. 47(D1): D339–D343. 2019. View Article : Google Scholar :

169 

Rammensee H, Bachmann J, Emmerich NP, Bachor OA and Stevanović S: SYFPEITHI: Database for MHC ligands and peptide motifs. Immunogenetics. 50:213–219. 1999. View Article : Google Scholar

170 

Bhasin M, Singh H and Raghava GPS: MHCBN: A comprehensive database of MHC binding and non-binding peptides. Bioinformatics. 19:665–666. 2003. View Article : Google Scholar : PubMed/NCBI

171 

Blass E and Ott PA: Advances in the development of personalized neoantigen-based therapeutic cancer vaccines. Nat Rev Clin Oncol. 18:215–229. 2021. View Article : Google Scholar : PubMed/NCBI

172 

Garcia-Garijo A, Fajardo CA and Gros A: Determinants for neoantigen identification. Front Immunol. 10:13922019. View Article : Google Scholar : PubMed/NCBI

173 

Falk K, Rötzschke O, Stevanović S, Jung G and Rammensee HG: Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature. 351:290–296. 1991. View Article : Google Scholar : PubMed/NCBI

174 

Rötzschke O, Falk K, Stevanović S, Jung G, Walden P and Rammensee HG: Exact prediction of a natural T cell epitope. Eur J Immunol. 21:2891–2894. 1991. View Article : Google Scholar : PubMed/NCBI

175 

Nielsen M, Lundegaard C, Worning P, Lauemøller SL, Lamberth K, Buus S, Brunak S and Lund O: Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 12:1007–1017. 2003. View Article : Google Scholar : PubMed/NCBI

176 

Nielsen M and Andreatta M: NetMHCpan-3.0; Improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets. Genome Med. 8:332016. View Article : Google Scholar : PubMed/NCBI

177 

Peters B and Sette A: Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics. 6:1322005. View Article : Google Scholar : PubMed/NCBI

178 

Kim Y, Sidney J, Pinilla C, Sette A and Peters B: Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. BMC Bioinformatics. 10:3942009. View Article : Google Scholar : PubMed/NCBI

179 

Lundegaard C, Lund O and Nielsen M: Prediction of epitopes using neural network based methods. J Immunol Methods. 374:26–34. 2011. View Article : Google Scholar :

180 

Schueler-Furman O, Altuvia Y, Sette A and Margalit H: Structure-based prediction of binding peptides to MHC class I molecules: Application to a broad range of MHC alleles. Protein Sci. 9:1838–1846. 2000. View Article : Google Scholar : PubMed/NCBI

181 

Kumar N and Mohanty D: MODPROPEP: A program for knowledge-based modeling of protein-peptide complexes. Nucleic Acids Res. 35(Web Server Issue): W549–W555. 2007. View Article : Google Scholar : PubMed/NCBI

182 

Hattotuwagama CK, Doytchinova IA and Flower DR: Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: In silico bioinformatic step-by-step guide using quantitative structure-activity relationships. Methods Mol Biol. 409:227–245. 2007. View Article : Google Scholar

183 

Luo H, Ye H, Ng HW, Shi L, Tong W, Mendrick DL and Hong H: Machine learning methods for predicting HLA-peptide binding activity. Bioinform Biol Insights. 9(Suppl 3): S21–S29. 2015.

184 

Zhang H, Lund O and Nielsen M: The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: Application to MHC-peptide binding. Bioinformatics. 25:1293–1299. 2009. View Article : Google Scholar :

185 

Wang M, Lei C, Wang J, Li Y and Li M: TripHLApan: Predicting HLA molecules binding peptides based on triple coding matrix and transfer learning. Brief Bioinform. 25:bbae1542024. View Article : Google Scholar

186 

Boehm KM, Bhinder B, Raja VJ, Dephoure N and Elemento O: Predicting peptide presentation by major histocompatibility complex class I: An improved machine learning approach to the immunopeptidome. BMC Bioinformatics. 20:72019. View Article : Google Scholar

187 

Mei S, Li F, Xiang D, Ayala R, Faridi P, Webb GI, Illing PT, Rossjohn J, Akutsu T, Croft NP, et al: Anthem: A user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules. Brief Bioinform. 22:bbaa4152021. View Article : Google Scholar

188 

Reche PA, Glutting JP and Reinherz EL: Prediction of MHC class I binding peptides using profile motifs. Hum Immunol. 63:701–709. 2002. View Article : Google Scholar : PubMed/NCBI

189 

Reche PA, Glutting JP, Zhang H and Reinherz EL: Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics. 56:405–419. 2004. View Article : Google Scholar

190 

Reche PA and Reinherz EL: Prediction of peptide-MHC binding using profiles. Methods Mol Biol. 409:185–200. 2007. View Article : Google Scholar

191 

Buus S, Lauemøller SL, Worning P, Kesmir C, Frimurer T, Corbet S, Fomsgaard A, Hilden J, Holm A and Brunak S: Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach. Tissue Antigens. 62:378–384. 2003. View Article : Google Scholar : PubMed/NCBI

192 

Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O and Nielsen M: NetMHC-3.0: Accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11. Nucleic Acids Res. 36(Web Server Issue): W509–W512. 2008. View Article : Google Scholar

193 

Andreatta M and Nielsen M: Gapped sequence alignment using artificial neural networks: Application to the MHC class I system. Bioinformatics. 32:511–517. 2016. View Article : Google Scholar

194 

Sidney J, Assarsson E, Moore C, Ngo S, Pinilla C, Sette A and Peters B: Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res. 4:22008. View Article : Google Scholar :

195 

Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B and Nielsen M: NetMHCpan-4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J Immunol. 199:3360–3368. 2017. View Article : Google Scholar

196 

Hoof I, Peters B, Sidney J, Pedersen LE, Sette A, Lund O, Buus S and Nielsen M: NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics. 61:1–13. 2009. View Article : Google Scholar

197 

Reynisson B, Alvarez B, Paul S, Peters B and Nielsen M: NetMHCpan-4.1 and NetMHCIIpan-4.0: Improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 48(W1): W449–W454. 2020. View Article : Google Scholar

198 

Bassani-Sternberg M, Chong C, Guillaume P, Solleder M, Pak H, Gannon PO, Kandalaft LE, Coukos G and Gfeller D: Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity. PLoS Comput Biol. 13:e10057252017. View Article : Google Scholar : PubMed/NCBI

199 

Gfeller D, Schmidt J, Croce G, Guillaume P, Bobisse S, Genolet R, Queiroz L, Cesbron J, Racle J and Harari A: Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes. Cell Syst. 14:72–83.e5. 2023. View Article : Google Scholar

200 

Liu G, Li D, Li Z, Qiu S, Li W, Chao CC, Yang N, Li H, Cheng Z, Song X, et al: PSSMHCpan: A novel PSSM-based software for predicting class I peptide-HLA binding affinity. Gigascience. 6:1–11. 2017. View Article : Google Scholar

201 

O'Donnell TJ, Rubinsteyn A and Laserson U: MHCflurry 2.0: Improved pan-allele prediction of MHC class I-presented peptides by incorporating antigen processing. Cell Syst. 11:42–48.e7. 2020. View Article : Google Scholar

202 

O'Donnell TJ, Rubinsteyn A, Bonsack M, Riemer AB, Laserson U and Hammerbacher J: MHCflurry: Open-source class I MHC binding affinity prediction. Cell Syst. 7:129–132.e4. 2018. View Article : Google Scholar

203 

Phloyphisut P, Pornputtapong N, Sriswasdi S and Chuangsuwanich E: MHCSeqNet: A deep neural network model for universal MHC binding prediction. BMC Bioinformatics. 20:2702019. View Article : Google Scholar : PubMed/NCBI

204 

Hu Y, Wang Z, Hu H, Wan F, Chen L, Xiong Y, Wang X, Zhao D, Huang W and Zeng J: ACME: Pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks. Bioinformatics. 35:4946–4954. 2019. View Article : Google Scholar

205 

Wu J, Wang W, Zhang J, Zhou B, Zhao W, Su Z, Gu X, Wu J, Zhou Z and Chen S: DeepHLApan: A deep learning approach for neoantigen prediction considering both HLA-peptide binding and immunogenicity. Front Immunol. 10:25592019. View Article : Google Scholar : PubMed/NCBI

206 

Sarkizova S, Klaeger S, Le PM, Li LW, Oliveira G, Keshishian H, Hartigan CR, Zhang W, Braun DA, Ligon KL, et al: A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nat Biotechnol. 38:199–209. 2020. View Article : Google Scholar

207 

Shao XM, Bhattacharya R, Huang J, Sivakumar IKA, Tokheim C, Zheng L, Hirsch D, Kaminow B, Omdahl A, Bonsack M, et al: High-throughput prediction of MHC class I and II neoantigens with MHCnuggets. Cancer Immunol Res. 8:396–408. 2020. View Article : Google Scholar

208 

Yang X, Zhao L, Wei F and Li J: DeepNetBim: Deep learning model for predicting HLA-epitope interactions based on network analysis by harnessing binding and immunogenicity information. BMC Bioinformatics. 22:2312021. View Article : Google Scholar : PubMed/NCBI

209 

Albert BA, Yang Y, Shao XM, Singh D, Smit KN, Anagnostou V and Karchin R: Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity. Nat Mach Intell. 5:861–872. 2023. View Article : Google Scholar : PubMed/NCBI

210 

Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O, Sahin U, Braxenthaler M, Gallazzi F, Protti MP, Sinigaglia F and Hammer J: Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol. 17:555–561. 1999. View Article : Google Scholar

211 

Singh H and Raghava GP: ProPred: Prediction of HLA-DR binding sites. Bioinformatics. 17:1236–1237. 2001. View Article : Google Scholar

212 

Wan J, Liu W, Xu Q, Ren Y, Flower DR and Li T: SVRMHC prediction server for MHC-binding peptides. BMC Bioinformatics. 7:4632006. View Article : Google Scholar

213 

Nielsen M, Lundegaard C and Lund O: Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics. 8:2382007. View Article : Google Scholar : PubMed/NCBI

214 

Nielsen M and Lund O: NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinformatics. 10:2962009. View Article : Google Scholar : PubMed/NCBI

215 

Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, Sette A, Peters B and Nielsen M: Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology. 154:394–406. 2018. View Article : Google Scholar

216 

Nielsen M, Lundegaard C, Blicher T, Peters B, Sette A, Justesen S, Buus S and Lund O: Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLoS Comput Biol. 4:e10001072008. View Article : Google Scholar : PubMed/NCBI

217 

Andreatta M, Karosiene E, Rasmussen M, Stryhn A, Buus S and Nielsen M: Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification. Immunogenetics. 67:641–650. 2015. View Article : Google Scholar :

218 

Kaabinejadian S, Barra C, Alvarez B, Yari H, Hildebrand WH and Nielsen M: Accurate MHC motif deconvolution of immunopeptidomics data reveals a significant contribution of DRB3, 4 and 5 to the total DR immunopeptidome. Front Immunol. 13:8354542022. View Article : Google Scholar :

219 

Pfeifer N and Kohlbacher O: Multiple instance learning allows MHC class II epitope predictions across alleles. Lect Notes Comput Sci. 5251:210–221. 2008. View Article : Google Scholar

220 

Bordner AJ and Mittelmann HD: MultiRTA: A simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes. BMC Bioinformatics. 11:4822010. View Article : Google Scholar : PubMed/NCBI

221 

Zhang L, Chen Y, Wong HS, Zhou S, Mamitsuka H and Zhu S: TEPITOPEpan: Extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules. PLoS One. 7:e304832012. View Article : Google Scholar

222 

Chen B, Khodadoust MS, Olsson N, Wagar LE, Fast E, Liu CL, Muftuoglu Y, Sworder BJ, Diehn M, Levy R, et al: Predicting HLA class II antigen presentation through integrated deep learning. Nat Biotechnol. 37:1332–1343. 2019. View Article : Google Scholar : PubMed/NCBI

223 

Racle J, Michaux J, Rockinger GA, Arnaud M, Bobisse S, Chong C, Guillaume P, Coukos G, Harari A, Jandus C, et al: Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes. Nat Biotechnol. 37:1283–1286. 2019. View Article : Google Scholar

224 

Racle J, Guillaume P, Schmidt J, Michaux J, Larabi A, Lau K, Perez MAS, Croce G, Genolet R, Coukos G, et al: Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes. Immunity. 56:1359–1375.e13. 2023. View Article : Google Scholar : PubMed/NCBI

225 

Abelin JG, Harjanto D, Malloy M, Suri P, Colson T, Goulding SP, Creech AL, Serrano LR, Nasir G, Nasrullah Y, et al: Defining HLA-II ligand processing and binding rules with mass spectrometry enhances cancer epitope prediction. Immunity. 51:766–779.e17. 2019. View Article : Google Scholar : PubMed/NCBI

226 

Liu Z, Jin J, Cui Y, Xiong Z, Nasiri A, Zhao Y and Hu J: DeepSeqPanII: An interpretable recurrent neural network model with attention mechanism for peptide-HLA class II binding prediction. IEEE/ACM Trans Comput Biol Bioinform. 19:2188–2196. 2022. View Article : Google Scholar

227 

Rammensee HG, Friede T and Stevanoviíc S: MHC ligands and peptide motifs: First listing. Immunogenetics. 41:178–228. 1995. View Article : Google Scholar

228 

Chicz RM, Urban RG, Lane WS, Gorga JC, Stern LJ, Vignali DA and Strominger JL: Predominant naturally processed peptides bound to HLA-DR1 are derived from MHC-related molecules and are heterogeneous in size. Nature. 358:764–768. 1992. View Article : Google Scholar

229 

Wang P, Sidney J, Dow C, Mothé B, Sette A and Peters B: A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput Biol. 4:e10000482008. View Article : Google Scholar : PubMed/NCBI

230 

Yu K, Petrovsky N, Schönbach C, Koh JYL and Brusic V: Methods for prediction of peptide binding to MHC molecules: A comparative study. Mol Med. 8:137–148. 2002. View Article : Google Scholar

231 

Peters B, Bui HH, Frankild S, Nielson M, Lundegaard C, Kostem E, Basch D, Lamberth K, Harndahl M, Fleri W, et al: A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLoS Comput Biol. 2:e652006. View Article : Google Scholar : PubMed/NCBI

232 

Gowthaman U, Chodisetti SB, Parihar P and Agrewala JN: Evaluation of different generic in silico methods for predicting HLA class I binding peptide vaccine candidates using a reverse approach. Amino Acids. 39:1333–1342. 2010. View Article : Google Scholar : PubMed/NCBI

233 

Bonsack M, Hoppe S, Winter J, Tichy D, Zeller C, Küpper MD, Schitter EC, Blatnik R and Riemer AB: Performance evaluation of MHC class-I binding prediction tools based on an experimentally validated MHC-peptide binding data set. Cancer Immunol Res. 7:719–736. 2019. View Article : Google Scholar

234 

Trolle T, Metushi IG, Greenbaum JA, Kim Y, Sidney J, Lund O, Sette A, Peters B and Nielsen M: Automated benchmarking of peptide-MHC class I binding predictions. Bioinformatics. 31:2174–2181. 2015. View Article : Google Scholar :

235 

Engels B, Engelhard VH, Sidney J, Sette A, Binder DC, Liu RB, Kranz DM, Meredith SC, Rowley DA and Schreiber H: Relapse or eradication of cancer is predicted by peptide-major histocompatibility complex affinity. Cancer Cell. 23:516–526. 2013. View Article : Google Scholar :

236 

Nogueira C, Kaufmann JK, Lam H and Flechtner JB: Improving cancer immunotherapies through empirical neoantigen selection. Trends Cancer. 4:97–100. 2018. View Article : Google Scholar

237 

Liao WWP and Arthur JW: Predicting peptide binding to major histocompatibility complex molecules. Autoimmun Rev. 10:469–473. 2011. View Article : Google Scholar

238 

Backert L and Kohlbacher O: Immunoinformatics and epitope prediction in the age of genomic medicine. Genome Med. 7:1192015. View Article : Google Scholar : PubMed/NCBI

239 

Türeci Ö, Löwer M, Schrörs B, Lang M, Tadmor A and Sahin U: Challenges towards the realization of individualized cancer vaccines. Nat Biomed Eng. 2:566–569. 2018. View Article : Google Scholar

240 

Schumacher TN, Scheper W and Kvistborg P: Cancer neoantigens. Annu Rev Immunol. 37:173–200. 2019. View Article : Google Scholar

241 

Xie N, Shen G, Gao W, Huang Z, Huang C and Fu L: Neoantigens: Promising targets for cancer therapy. Signal Transduct Target Ther. 8:92023. View Article : Google Scholar :

242 

Bassani-Sternberg M, Bräunlein E, Klar R, Engleitner T, Sinitcyn P, Audehm S, Straub M, Weber J, Slotta-Huspenina J, Specht K, et al: Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry. Nat Commun. 7:134042016. View Article : Google Scholar : PubMed/NCBI

243 

Sette A, Vitiello A, Reherman B, Fowler P, Nayersina R, Kast WM, Melief CJ, Oseroff C, Yuan L, Ruppert J, et al: The relationship between class I binding affinity and immunogenicity of potential cytotoxic T cell epitopes. J Immunol. 153:5586–5592. 1994. View Article : Google Scholar : PubMed/NCBI

244 

Bjerregaard AM, Nielsen M, Jurtz V, Barra CM, Hadrup SR, Szallasi Z and Eklund AC: An analysis of natural T cell responses to predicted tumor neoepitopes. Front Immunol. 8:15662017. View Article : Google Scholar : PubMed/NCBI

245 

Shah K, Al-Haidari A, Sun J and Kazi JU: T cell receptor (TCR) signaling in health and disease. Signal Transduct Target Ther. 6:4122021. View Article : Google Scholar

246 

Larsen MV, Lundegaard C, Lamberth K, Buus S, Brunak S, Lund O and Nielsen M: An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions. Eur J Immunol. 35:2295–2303. 2005. View Article : Google Scholar : PubMed/NCBI

247 

Stranzl T, Larsen MV, Lundegaard C and Nielsen M: NetCTLpan: Pan-specific MHC class I pathway epitope predictions. Immunogenetics. 62:357–368. 2010. View Article : Google Scholar : PubMed/NCBI

248 

Zhou C, Zhu C and Liu Q: Toward in silico identification of tumor neoantigens in immunotherapy. Trends Mol Med. 25:980–992. 2019. View Article : Google Scholar : PubMed/NCBI

249 

Chen P, Chen D, Bu D, Gao J, Qin W, Deng K, Ren L, She S, Xu W, Yang Y, et al: Dominant neoantigen verification in hepatocellular carcinoma by a single-plasmid system coexpressing patient HLA and antigen. J Immunother Cancer. 11:e0063342023. View Article : Google Scholar : PubMed/NCBI

250 

Chudley L, McCann KJ, Coleman A, Cazaly AM, Bidmon N, Britten CM, van der Burg SH, Gouttefangeas C, Jandus C, Laske K, et al: Harmonisation of short-term in vitro culture for the expansion of antigen-specific CD8(+) T cells with detection by ELISPOT and HLA-multimer staining. Cancer Immunol Immunother. 63:1199–1211. 2014. View Article : Google Scholar :

251 

Slota M, Lim JB, Dang Y and Disis ML: ELISpot for measuring human immune responses to vaccines. Expert Rev Vaccines. 10:299–306. 2011. View Article : Google Scholar : PubMed/NCBI

252 

Czerkinsky CC, Nilsson LA, Nygren H, Ouchterlony O and Tarkowski A: A solid-phase enzyme-linked immunospot (ELISPOT) assay for enumeration of specific antibody-secreting cells. J Immunol Methods. 65:109–121. 1983. View Article : Google Scholar

253 

Taguchi T, McGhee JR, Coffman RL, Beagley KW, Eldridge JH, Takatsu K and Kiyono H: Detection of individual mouse splenic T cells producing IFN-gamma and IL-5 using the enzyme-linked immunospot (ELISPOT) assay. J Immunol Methods. 128:65–73. 1990. View Article : Google Scholar

254 

Miyahira Y, Murata K, Rodriguez D, Rodriguez JR, Esteban M, Rodrigues MM and Zavala F: Quantification of antigen specific CD8+ T cells using an ELISPOT assay. J Immunol Methods. 181:45–54. 1995. View Article : Google Scholar : PubMed/NCBI

255 

Verneris MR, Karimi M, Baker J, Jayaswal A and Negrin RS: Role of NKG2D signaling in the cytotoxicity of activated and expanded CD8+ T cells. Blood. 103:3065–3072. 2004. View Article : Google Scholar : PubMed/NCBI

256 

Wonderlich J, Shearer G, Livingstone A, Brooks A, Soloski MJ and Presby MM: Induction and measurement of cytotoxic T lymphocyte activity. Curr Protoc Immunol. 120:3.11.1–3.11.29. 2018.

257 

Chan JK, Hamilton CA, Cheung MK, Karimi M, Baker J, Gall JM, Schulz S, Thorne SH, Teng NN, Contag CH, et al: Enhanced killing of primary ovarian cancer by retargeting autologous cytokine-induced killer cells with bispecific antibodies: A preclinical study. Clin Cancer Res. 12:1859–1867. 2006. View Article : Google Scholar : PubMed/NCBI

258 

Clevers H and Tuveson DA: Organoid models for cancer research. Annu Rev Cancer Biol. 3:223–234. 2019. View Article : Google Scholar

259 

Jacob F, Salinas RD, Zhang DY, Nguyen PTT, Schnoll JG, Wong SZH, Thokala R, Sheikh S, Saxena D, Prokop S, et al: A patient-derived glioblastoma organoid model and biobank recapitulates inter- and intra-tumoral heterogeneity. Cell. 180:188–204.e22. 2020. View Article : Google Scholar

260 

Lee SH, Hu W, Matulay JT, Silva MV, Owczarek TB, Kim K, Chua CW, Barlow LJ, Kandoth C, Williams AB, et al: Tumor evolution and drug response in patient-derived organoid models of bladder cancer. Cell. 173:515–528.e17. 2018. View Article : Google Scholar

261 

Cattaneo CM, Dijkstra KK, Fanchi LF, Kelderman S, Kaing S, van Rooij N, van den Brink S, Schumacher TN and Voest EE: Tumor organoid-T-cell coculture systems. Nat Protoc. 15:15–39. 2020. View Article : Google Scholar

262 

Weng G, Tao J, Liu Y, Qiu J, Su D, Wang R, Luo W and Zhang T: Organoid: Bridging the gap between basic research and clinical practice. Cancer Lett. 572:2163532023. View Article : Google Scholar : PubMed/NCBI

263 

Shultz LD, Ishikawa F and Greiner DL: Humanized mice in translational biomedical research. Nat Rev Immunol. 7:118–130. 2007. View Article : Google Scholar : PubMed/NCBI

264 

Rongvaux A, Takizawa H, Strowig T, Willinger T, Eynon EE, Flavell RA and Manz MG: Human hemato-lymphoid system mice: Current use and future potential for medicine. Annu Rev Immunol. 31:635–674. 2013. View Article : Google Scholar : PubMed/NCBI

265 

Morillon YM II, Sabzevari A, Schlom J and Greiner JW: The development of next-generation PBMC humanized mice for preclinical investigation of cancer immunotherapeutic agents. Anticancer Res. 40:5329–5341. 2020. View Article : Google Scholar

266 

Ehx G, Ritacco C and Baron F: Pathophysiology and preclinical relevance of experimental graft-versus-host disease in humanized mice. Biomark Res. 12:1392024. View Article : Google Scholar : PubMed/NCBI

267 

Chuprin J, Buettner H, Seedhom MO, Greiner DL, Keck JG, Ishikawa F, Shultz LD and Brehm MA: Humanized mouse models for immuno-oncology research. Nat Rev Clin Oncol. 20:192–206. 2023. View Article : Google Scholar : PubMed/NCBI

268 

Guil-Luna S, Sedlik C and Piaggio E: Humanized mouse models to evaluate cancer immunotherapeutics. Annu Rev Cancer Biol. 5:119–136. 2021. View Article : Google Scholar

269 

De La Rochere P, Guil-Luna S, Decaudin D, Azar G, Sidhu SS and Piaggio E: Humanized mice for the study of immuno-oncology. Trends Immunol. 39:748–763. 2018. View Article : Google Scholar : PubMed/NCBI

270 

Camacho RE, Wnek R, Fischer P, Shah K, Zaller DM, Woods A, La Monica N, Aurisicchio L, Fitzgerald-Bocarsly P and Koo GC: Characterization of the NOD/scid-[Tg]DR1 mouse expressing HLA-DRB1*01 transgene: A model of SCID-hu mouse for vaccine development. Exp Hematol. 35:1219–1230. 2007. View Article : Google Scholar

271 

Spranger S, Frankenberger B and Schendel DJ: NOD/scid IL-2Rg(null) mice: A preclinical model system to evaluate human dendritic cell-based vaccine strategies in vivo. J Transl Med. 10:302012. View Article : Google Scholar

272 

Li R, Han DS, Shi JP, Han YX, Tan P, Zhang R and Li JM: Choosing tumor mutational burden wisely for immunotherapy: A hard road to explore. Biochim Biophys Acta Rev Cancer. 1874:1884202020. View Article : Google Scholar

273 

Ahmed J, Das B, Shin S and Chen A: Challenges and future directions in the management of tumor mutational burden-high (TMB-H) advanced solid malignancies. Cancers (Basel). 15:58412023. View Article : Google Scholar : PubMed/NCBI

274 

Teixido C, Castillo P, Martinez-Vila C, Arance A and Alos L: Molecular markers and targets in melanoma. Cells. 10:23202021. View Article : Google Scholar : PubMed/NCBI

275 

Yarchoan M, Hopkins A and Jaffee EM: Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med. 377:2500–2501. 2017. View Article : Google Scholar : PubMed/NCBI

276 

Ott PA, Hu-Lieskovan S, Chmielowski B, Govindan R, Naing A, Bhardwaj N, Margolin K, Awad MM, Hellmann MD, Lin JJ, et al: A phase Ib trial of personalized neoantigen therapy plus anti-PD-1 in patients with advanced melanoma, non-small cell lung cancer, or bladder cancer. Cell. 183:347–362.e24. 2020. View Article : Google Scholar : PubMed/NCBI

277 

Mørk SK, Kadivar M, Bol KF, Draghi A, Wulff Westergaard MC, Skadborg SK, Overgaard N, Sørensen AB, Rasmussen IS, Andreasen LV, et al: Personalized therapy with peptide-based neoantigen vaccine (EVX-01) including a novel adjuvant, CAF®09b, in patients with metastatic melanoma. Oncoimmunology. 11:20232552022. View Article : Google Scholar

278 

Weber JS, Carlino MS, Khattak A, Meniawy T, Ansstas G, Taylor MH, Kim KB, McKean M, Long GV, Sullivan RJ, et al: Individualised neoantigen therapy mRNA-4157 (V940) plus pembrolizumab versus pembrolizumab monotherapy in resected melanoma (KEYNOTE-942): A randomised, phase 2b study. Lancet. 403:632–644. 2024. View Article : Google Scholar : PubMed/NCBI

279 

Ding Z, Li Q, Zhang R, Xie L, Shu Y, Gao S, Wang P, Su X, Qin Y, Wang Y, et al: Personalized neoantigen pulsed dendritic cell vaccine for advanced lung cancer. Signal Transduct Target Ther. 6:262021. View Article : Google Scholar :

280 

Gainor JF, Patel MR, Weber JS, Gutierrez M, Bauman JE, Clarke JM, Julian R, Scott AJ, Geiger JL, Kirtane K, et al: T-cell responses to individualized neoantigen therapy mRNA-4157 (V940) alone or in combination with pembrolizumab in the phase 1 KEYNOTE-603 study. Cancer Discov. 14:2209–2223. 2024. View Article : Google Scholar : PubMed/NCBI

281 

Lee JM, Spicer J, Nair S, Khattak A, Brown M, Meehan RS, Shariati NM, Deng X, Samkari A and Chaft JE: The phase 3 INTerpath-002 study design: Individualized neoantigen therapy (INT) V940 (mRNA-4157) plus pembrolizumab vs placebo plus pembrolizumab for resected early-stage non-small-cell lung cancer (NSCLC). J Clin Oncol. 42(16 Suppl): TPS81162024. View Article : Google Scholar

282 

Li F, Deng L, Jackson KR, Talukder AH, Katailiha AS, Bradley SD, Zou Q, Chen C, Huo C, Chiu Y, et al: Neoantigen vaccination induces clinical and immunologic responses in non-small cell lung cancer patients harboring EGFR mutations. J Immunother Cancer. 9:e0025312021. View Article : Google Scholar : PubMed/NCBI

283 

Gao S, Wang J, Zhu Z, Fang J, Zhao Y, Liu Z, Qin H, Wei Y, Xu H, Dan X, et al: Effective personalized neoantigen vaccine plus anti-PD-1 in a PD-1 blockade-resistant lung cancer patient. Immunotherapy. 15:57–69. 2023. View Article : Google Scholar : PubMed/NCBI

284 

Awad MM, Govindan R, Balogh KN, Spigel DR, Garon EB, Bushway ME, Poran A, Sheen JH, Kohler V, Esaulova E, et al: Personalized neoantigen vaccine NEO-PV-01 with chemotherapy and anti-PD-1 as first-line treatment for non-squamous non-small cell lung cancer. Cancer Cell. 40:1010–1026.e11. 2022. View Article : Google Scholar

285 

McCann K, von Witzleben A, Thomas J, Wang C, Wood O, Singh D, Boukas K, Bendjama K, Silvestre N, Nielsen FC, et al: Targeting the tumor mutanome for personalized vaccination in a TMB low non-small cell lung cancer. J Immunother Cancer. 10:e0038212022. View Article : Google Scholar :

286 

Moen MD: Bevacizumab: In previously treated glioblastoma. Drugs. 70:181–189. 2010. View Article : Google Scholar

287 

Engelhardt B: The blood-central nervous system barriers actively control immune cell entry into the central nervous system. Curr Pharm Des. 14:1555–1565. 2008. View Article : Google Scholar : PubMed/NCBI

288 

Johanns TM, Bowman-Kirigin JA, Liu C and Dunn GP: Targeting neoantigens in glioblastoma: An overview of cancer immunogenomics and translational implications. Neurosurgery. 64(CN Suppl 1): S165–S176. 2017. View Article : Google Scholar

289 

Dang L, Yen K and Attar EC: IDH mutations in cancer and progress toward development of targeted therapeutics. Ann Oncol. 27:599–608. 2016. View Article : Google Scholar

290 

Ni YQ, Shen PB, Wang XC, Liu HY, Luo HY and Han XZ: The roles of IDH1 in tumor metabolism and immunity. Future Oncol. 18:3941–3953. 2022. View Article : Google Scholar

291 

de la Fuente MI: Targeting IDH1/IDH2 mutations in gliomas. Curr Opin Neurol. 35:787–793. 2022. View Article : Google Scholar : PubMed/NCBI

292 

Pellegatta S, Valletta L, Corbetta C, Patanè M, Zucca I, Riccardi Sirtori F, Bruzzone MG, Fogliatto G, Isacchi A, Pollo B and Finocchiaro G: Effective immuno-targeting of the IDH1 mutation R132H in a murine model of intracranial glioma. Acta Neuropathol Commun. 3:42015. View Article : Google Scholar : PubMed/NCBI

293 

Platten M, Bunse L, Wick A, Bunse T, Le Cornet L, Harting I, Sahm F, Sanghvi K, Tan CL, Poschke I, et al: A vaccine targeting mutant IDH1 in newly diagnosed glioma. Nature. 592:463–468. 2021. View Article : Google Scholar :

294 

Platten M, Schilling D, Bunse L, Wick A, Bunse T, Riehl D, Green E, Sanghvi K, Karapanagiotou-Schenkel I, Harting I, et al: Atim-33. Noa-16: A first-in-man multicenter phase I clinical trial of the German neurooncology working group evaluating a mutation-specific peptide vaccine targeting idh1r132h in patients with newly diagnosed malignant astrocytomas. Neuro Oncol. 20(Suppl 6): vi8–vi9. 2018. View Article : Google Scholar

295 

Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, Kos I, Batinic-Haberle I, Jones S, Riggins GJ, et al: IDH1 and IDH2 mutations in gliomas. N Engl J Med. 360:765–773. 2009. View Article : Google Scholar

296 

Hilf N, Kuttruff-Coqui S, Frenzel K, Bukur V, Stevanović S, Gouttefangeas C, Platten M, Tabatabai G, Dutoit V, van der Burg SH, et al: Actively personalized vaccination trial for newly diagnosed glioblastoma. Nature. 565:240–245. 2019. View Article : Google Scholar

297 

Narita Y, Arakawa Y, Yamasaki F, Nishikawa R, Aoki T, Kanamori M, Nagane M, Kumabe T, Hirose Y, Ichikawa T, et al: A randomized, double-blind, phase III trial of personalized peptide vaccination for recurrent glioblastoma. Neuro Oncol. 21:348–359. 2019. View Article : Google Scholar :

298 

Bruix J and Sherman M; Practice Guidelines Committee American Association for the Study of Liver Diseases: Management of hepatocellular carcinoma. Hepatology. 42:1208–1236. 2005. View Article : Google Scholar

299 

Tampaki M, Papatheodoridis GV and Cholongitas E: Intrahepatic recurrence of hepatocellular carcinoma after resection: An update. Clin J Gastroenterol. 14:699–713. 2021. View Article : Google Scholar : PubMed/NCBI

300 

European Association for the Study of the Liver: EASL clinical practice guidelines: Management of hepatocellular carcinoma. J Hepatol. 69:182–236. 2018. View Article : Google Scholar

301 

Charneau J, Suzuki T, Shimomura M, Fujinami N and Nakatsura T: Peptide-based vaccines for hepatocellular carcinoma: A review of recent advances. J Hepatocell Carcinoma. 8:1035–1054. 2021. View Article : Google Scholar

302 

Liu C, Shao J, Dong Y, Xu Q, Zou Z, Chen F, Yan J, Liu J, Li S, Liu B and Shen J: Advanced HCC patient benefit from neoantigen reactive T cells based immunotherapy: A case report. Front Immunol. 12:6851262021. View Article : Google Scholar :

303 

Cai Z, Su X, Qiu L, Li Z, Li X, Dong X, Wei F, Zhou Y, Luo L, Chen G, et al: Personalized neoantigen vaccine prevents postoperative recurrence in hepatocellular carcinoma patients with vascular invasion. Mol Cancer. 20:1642021. View Article : Google Scholar : PubMed/NCBI

304 

Peng S, Chen S, Hu W, Mei J, Zeng X, Su T, Wang W, Chen Z, Xiao H, Zhou Q, et al: Combination neoantigen-based dendritic cell vaccination and adoptive T-cell transfer induces antitumor responses against recurrence of hepatocellular carcinoma. Cancer Immunol Res. 10:728–744. 2022. View Article : Google Scholar : PubMed/NCBI

305 

Shen J, Wang LF, Zou ZY, Kong WW, Yan J, Meng FY, Chen FJ, Du J, Shao J, Xu QP, et al: Phase I clinical study of personalized peptide vaccination combined with radiotherapy for advanced hepatocellular carcinoma. World J Gastroenterol. 23:5395–5404. 2017. View Article : Google Scholar

306 

Guo Z, Yuan Y, Chen C, Lin J, Ma Q, Liu G, Gao Y, Huang Y, Chen L, Chen LZ, et al: Durable complete response to neoantigen-loaded dendritic-cell vaccine following anti-PD-1 therapy in metastatic gastric cancer. NPJ Precis Oncol. 6:342022. View Article : Google Scholar :

307 

Yu YJ, Shan N, Li LY, Zhu YS, Lin LM, Mao CC, Hu TT, Xue XY, Su XP, Shen X and Cai ZZ: Preliminary clinical study of personalized neoantigen vaccine therapy for microsatellite stability (MSS)-advanced colorectal cancer. Cancer Immunol Immunother. 72:2045–2056. 2023. View Article : Google Scholar

308 

Sultan H, Salazar AM and Celis E: Poly-ICLC, a multi-functional immune modulator for treating cancer. Semin Immunol. 49:1014142020. View Article : Google Scholar : PubMed/NCBI

309 

Zeng Y, Zhang W, Li Z, Zheng Y, Wang Y, Chen G, Qiu L, Ke K, Su X, Cai Z, et al: Personalized neoantigen-based immunotherapy for advanced collecting duct carcinoma: Case report. J Immunother Cancer. 8:e0002172020. View Article : Google Scholar :

310 

Chen Z, Zhang S, Han N, Jiang J, Xu Y, Ma D, Lu L, Guo X, Qiu M, Huang Q, et al: A neoantigen-based peptide vaccine for patients with advanced pancreatic cancer refractory to standard treatment. Front Immunol. 12:6916052021. View Article : Google Scholar

311 

Kwok DW, Stevers NO, Etxeberria I, Nejo T, Colton Cove M, Chen LH, Jung J, Okada K, Lakshmanachetty S, Gallus M, et al: Tumour-wide RNA splicing aberrations generate actionable public neoantigens. Nature. 639:463–473. 2025. View Article : Google Scholar : PubMed/NCBI

312 

Rosenberg-Mogilevsky A, Siegfried Z and Karni R: Generation of tumor neoantigens by RNA splicing perturbation. Trends Cancer. 11:12–24. 2025. View Article : Google Scholar

313 

Huang P, Wen F, Tuerhong N, Yang Y and Li Q: Neoantigens in cancer immunotherapy: Focusing on alternative splicing. Front Immunol. 15:14377742024. View Article : Google Scholar :

314 

Jayasinghe RG, Cao S, Gao Q, Wendl MC, Vo NS, Reynolds SM, Zhao Y, Climente-González H, Chai S, Wang F, et al: Systematic analysis of splice-site-creating mutations in cancer. Cell Rep. 23:270–281.e3. 2018. View Article : Google Scholar

315 

Smart AC, Margolis CA, Pimentel H, He MX, Miao D, Adeegbe D, Fugmann T, Wong KK and Van Allen EM: Intron retention is a source of neoepitopes in cancer. Nat Biotechnol. 36:1056–1058. 2018. View Article : Google Scholar : PubMed/NCBI

316 

Wang F, Cai G, Wang Y, Zhuang Q, Cai Z, Li Y, Gao S, Li F, Zhang C, Zhao B and Liu X: Circular RNA-based neoantigen vaccine for hepatocellular carcinoma immunotherapy. MedComm (2020). 5:e6672024. View Article : Google Scholar : PubMed/NCBI

317 

Hu ZX, Guo XY, Li ZT, Meng ZQ and Huang SL: The neoantigens derived from transposable elements-A hidden treasure for cancer immunotherapy. Biochim Biophys Acta Rev Cancer. 1879:1891262024. View Article : Google Scholar

318 

Li M, Wang Y, Wu P, Zhang S, Gong Z, Liao Q, Guo C, Wang F, Li Y, Zeng Z, et al: Application prospect of circular RNA-based neoantigen vaccine in tumor immunotherapy. Cancer Lett. 563:2161902023. View Article : Google Scholar : PubMed/NCBI

319 

Lang F, Schrörs B, Löwer M, Türeci Ö and Sahin U: Identification of neoantigens for individualized therapeutic cancer vaccines. Nat Rev Drug Discov. 21:261–282. 2022. View Article : Google Scholar

320 

Gurung HR, Heidersbach AJ, Darwish M, Chan PPF, Li J, Beresini M, Zill OA, Wallace A, Tong AJ, Hascall D, et al: Systematic discovery of neoepitope-HLA pairs for neoantigens shared among patients and tumor types. Nat Biotechnol. 42:1107–1117. 2024. View Article : Google Scholar

321 

Shen KY, Zhu Y, Xie SZ and Qin LX: Immunosuppressive tumor microenvironment and immunotherapy of hepatocellular carcinoma: Current status and prospectives. J Hematol Oncol. 17:252024. View Article : Google Scholar : PubMed/NCBI

322 

Cherepnev G, Volk HD and Kern F: Use of peptides and peptide libraries as T-cell stimulants in flow cytometric studies. Methods Cell Biol. 75:453–479. 2004. View Article : Google Scholar : PubMed/NCBI

323 

Gascoigne NR: Do T cells need endogenous peptides for activation? Nat Rev Immunol. 8:895–900. 2008. View Article : Google Scholar

324 

Shemesh CS, Hsu JC, Hosseini I, Shen BQ, Rotte A, Twomey P, Girish S and Wu B: Personalized cancer vaccines: Clinical landscape, challenges, and opportunities. Mol Ther. 29:555–570. 2021. View Article : Google Scholar

325 

Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, et al: Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 366:883–892. 2012. View Article : Google Scholar

326 

Prehn RT and Main JM: Immunity to methylcholanthrene-induced sarcomas. J Natl Cancer Inst. 18:769–778. 1957.PubMed/NCBI

327 

Hao Q, Long YH, Yang Y, Deng YQ, Ding ZY, Yang L, Shu Y and Xu H: Development and clinical applications of therapeutic cancer vaccines with individualized and shared neoantigens. Vaccines (Basel). 12:7172024. View Article : Google Scholar

328 

D'Alise AM, Brasu N, De Intinis C, Leoni G, Russo V, Langone F, Baev D, Micarelli E, Petiti L, Picelli S, et al: Adenoviral-based vaccine promotes neoantigen-specific CD8+ T cell stemness and tumor rejection. Sci Transl Med. 14:eabo76042022. View Article : Google Scholar

329 

Tsimberidou AM: Targeted therapy in cancer. Cancer Chemother Pharmacol. 76:1113–1132. 2015. View Article : Google Scholar

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Copy and paste a formatted citation
Spandidos Publications style
Feng H, Jin Y and Wu B: Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review). Int J Oncol 66: 43, 2025.
APA
Feng, H., Jin, Y., & Wu, B. (2025). Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review). International Journal of Oncology, 66, 43. https://doi.org/10.3892/ijo.2025.5749
MLA
Feng, H., Jin, Y., Wu, B."Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review)". International Journal of Oncology 66.6 (2025): 43.
Chicago
Feng, H., Jin, Y., Wu, B."Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review)". International Journal of Oncology 66, no. 6 (2025): 43. https://doi.org/10.3892/ijo.2025.5749
Copy and paste a formatted citation
x
Spandidos Publications style
Feng H, Jin Y and Wu B: Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review). Int J Oncol 66: 43, 2025.
APA
Feng, H., Jin, Y., & Wu, B. (2025). Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review). International Journal of Oncology, 66, 43. https://doi.org/10.3892/ijo.2025.5749
MLA
Feng, H., Jin, Y., Wu, B."Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review)". International Journal of Oncology 66.6 (2025): 43.
Chicago
Feng, H., Jin, Y., Wu, B."Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review)". International Journal of Oncology 66, no. 6 (2025): 43. https://doi.org/10.3892/ijo.2025.5749
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