mRNA-based personalized cancer vaccines: opportunities, challenges and outcomes

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mRNA-based cancer vaccines represent an innovative approach to cancer treatment. Cancer mRNA vaccines are structurally based on specific tumor antigens, a technique which enables the patient’s immune system to become activated against cancer cells. Clinical trials of mRNA vaccines against various types of tumors, including melanoma, lung cancer, pancreatic carcinoma, breast cancer and others, are currently underway. Because of their favorable safety profile and adaptability, these therapeutics hold considerable promise in efforts to enhance cancer treatment efficacy and prolong patient life. This review outlines steps in the development of manufacturing technologies for mRNA-based therapeutics, describes the algorithm used to design personalized anti-tumor mRNA vaccines, discusses their practical implementation, and summarizes current clinical trials in cancer immunotherapy.

Sobre autores

Arina Ibragimova

Tomsk National Research Medical Center, Russian Academy of Sciences

Email: arina.budnitskaya@gmail.com
ORCID ID: 0009-0003-1728-0723
Código SPIN: 3726-8132
Researcher ID: NPJ-0555-2025

Cancer Research Institute

Rússia, Tomsk, 634009

Anton Fedorov

Tomsk National Research Medical Center, Russian Academy of Sciences

Email: anton.fedorov.2014@mail.ru
ORCID ID: 0000-0002-5121-2535
Scopus Author ID: 57211136209

Cancer Research Institute

Rússia, Tomsk, 634009

Kirill Kirilenko

Tomsk National Research Medical Center, Russian Academy of Sciences

Email: kirillkirilenko.tomsk@gmail.com
ORCID ID: 0009-0005-5208-2495

Center for Systems Bioinformatics

Rússia, Tomsk, 634050

Evgeny Choynzonov

Tomsk National Research Medical Center, Russian Academy of Sciences

Email: choinzonov.el@ssmu.ru
ORCID ID: 0000-0002-3651-0665
Código SPIN: 2240-8730
Scopus Author ID: 6603352329
Researcher ID: C-9892-2012

Cancer Research Institute

Rússia, Tomsk, 634009

Evgeny Denisov

Tomsk National Research Medical Center, Russian Academy of Sciences

Email: d_evgeniy@oncology.tomsk.ru
ORCID ID: 0000-0003-2923-9755
Scopus Author ID: 26653961800
Researcher ID: C-8662-2012

Cancer Research Institute

Rússia, Tomsk, 634009

Marina Patysheva

Tomsk National Research Medical Center, Russian Academy of Sciences

Autor responsável pela correspondência
Email: patysheva_mr@onco.tnimc.ru
ORCID ID: 0000-0003-2865-7576
Scopus Author ID: 57200569624
Researcher ID: Q-9364-2017

Cancer Research Institute

Rússia, Tomsk, 634009

Bibliografia

  1. Kaprin AD, Starinsky VV, Shakhzadova AO. The state of oncological care for the population of Russia in 2022. P. Herzen Moscow Oncology Research Institute – the branch of the FSBI «National Medical Research Radiology Center» of the Ministry of Health of the Russian Federation; 2023.
  2. Shah A, Apple J, Belli AJ, et al. Real-world study of disease-free survival & patient characteristics associated with disease-free survival in early-stage non-small cell lung cancer: a retrospective observational study. Cancer Treat Res Commun. 2023;36:100742. doi: 10.1016/j.ctarc.2023.100742
  3. Garg P, Pareek S, Kulkarni P, Horne D, Salgia R, Singhal SS. Next-Generation Immunotherapy: Advancing Clinical Applications in Cancer Treatment. J Clin Med. 2024;13(21):6537. doi: 10.3390/jcm13216537
  4. Parvez A, Choudhary F, Mudgal P, et al. PD-1 and PD-L1: architects of immune symphony and immunotherapy breakthroughs in cancer treatment. Front Immunol. 2023;14:1296341. doi: 10.3389/fimmu.2023.1296341
  5. Yuan Y, Gao F, Chang Y, Zhao Q, He X. Advances of mRNA vaccine in tumor: a maze of opportunities and challenges. Biomark Res. 2023;11(1):6. doi: 10.1186/s40364-023-00449-w
  6. Fang E, Liu X, Li M, et al. Advances in COVID-19 mRNA vaccine development. Signal Transduct Target Ther. 2022;7(1):94. doi: 10.1038/s41392-022-00950-y
  7. Mu X, Hur S. Immunogenicity of in vitro-transcribed RNA. Acc Chem Res. 2021;54(21):4012–4023. doi: 10.1021/acs.accounts.1c00521
  8. Gao M, Zhang Q, Feng XH, Liu J. Synthetic modified messenger RNA for therapeutic applications. Acta Biomater. 2021;131:1−15. doi: 10.1016/j.actbio.2021.06.020
  9. Melton DA, Krieg PA, Rebagliati MR, Maniatis T, Zinn K, Green MR. Efficient in vitro synthesis of biologically active RNA and RNA hybridization probes from plasmids containing a bacteriophage SP6 promoter. Nucleic Acids Res. 1984;12(18):7035−7056. doi: 10.1093/nar/12.18.7035
  10. Gómez-Aguado I, Rodríguez-Castejón J, Vicente-Pascual M, Rodríguez-Gascón A, Solinís MÁ, Del Pozo-Rodríguez A. Nanomedicines to deliver mRNA: State of the Art and Future Perspectives. Nanomaterials (Basel). 2020;10(2):364 doi: 10.3390/nano10020364
  11. Malone RW, Felgner PL, Verma IM. Cationic liposome-mediated RNA transfection. Proc Natl Acad Sci USA. 1989;86(16):6077–6081. doi: 10.1073/pnas.86.16.6077
  12. Wolff JA, Malone RW, Williams P, et al. Direct gene transfer into mouse muscle in vivo. Science. 1990;247(4949 Pt 1):1465–1468. doi: 10.1126/science.1690918
  13. Jirikowski GF, Sanna PP, Maciejewski-Lenoir D, Bloom FE. Reversal of diabetes insipidus in Brattleboro rats: intrahypothalamic injection of vasopressin mRNA. Science. 1992;255(5047):996–998. doi: 10.1126/science.1546298
  14. Martinon F, Krishnan S, Lenzen G, et al. Induction of virus-specific cytotoxic T lymphocytes in vivo by liposome-entrapped mRNA. Eur J Immunol. 1993;23(7):1719–1722. doi: 10.1002/eji.1830230749
  15. Conry RM, LoBuglio AF, Wright M, et al. Characterization of a messenger RNA polynucleotide vaccine vector. Cancer Res. 1995;55(7):1397–1400.
  16. Zhou WZ, Hoon DS, Huang SK, et al. RNA melanoma vaccine: induction of antitumor immunity by human glycoprotein 100 mRNA immunization. Hum Gene Ther. 1999;10(16):2719–2724. doi: 10.1089/10430349950016762
  17. Heiser A, Coleman D, Dannull J, et al. Autologous dendritic cells transfected with prostate-specific antigen RNA stimulate CTL responses against metastatic prostate tumors. J Clin Invest. 2002;109(3):409–417. doi: 10.1172/jci14364
  18. Hoerr I, Obst R, Rammensee HG, Jung G. In vivo application of RNA leads to induction of specific cytotoxic T lymphocytes and antibodies. Eur J Immunol. 2000;30(1):1–7. doi: 10.1002/1521-4141(200001)30:1<1::AID-IMMU1>3.0.CO;2-#
  19. Probst J, Weide B, Scheel B, et al. Spontaneous cellular uptake of exogenous messenger RNA in vivo is nucleic acid-specific, saturable and ion dependent. Gene Ther. 2007;14(15):1175–1180. doi: 10.1038/sj.gt.3302964
  20. Karikó K, Kuo A, Barnathan E. Overexpression of urokinase receptor in mammalian cells following administration of the in vitro transcribed encoding mRNA. Gene Ther. 1999;6(6):1092−1100. doi: 10.1038/sj.gt.3300930
  21. Karikó K, Ni H, Capodici J, Lamphier M, Weissman D. mRNA is an endogenous ligand for Toll-like receptor 3. J Biol Chem. 2004;279(13):12542–12550. doi: 10.1074/jbc.M310175200
  22. Karikó K, Buckstein M, Ni H, Weissman D. Suppression of RNA recognition by Toll-like receptors: the impact of nucleoside modification and the evolutionary origin of RNA. Immunity. 2005;23(2):165–175. doi: 10.1016/j.immuni.2005.06.008
  23. Krammer F, Palese P. Profile of Katalin Karikó and Drew Weissman: 2023 Nobel laureates in Physiology or Medicine. Proc Natl Acad Sci USA. 2024;121(9):e2400423121. doi: 10.1073/pnas.2400423121
  24. Szabó GT, Mahiny AJ, Vlatkovic I. COVID-19 mRNA vaccines: Platforms and current developments. Mol Ther. 2022;30(5):1850–1868. doi: 10.1016/j.ymthe.2022.02.016
  25. Sahin U, Derhovanessian E, Miller M, et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature. 2017;547(7662):222–226. doi: 10.1038/nature23003
  26. Sahin U, Türeci Ö. Personalized vaccines for cancer immunotherapy. Science. 2018;359(6382):1355–1360. doi: 10.1126/science.aar7112
  27. Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science. 2015;348(6230):69–74. doi: 10.1126/science.aaa4971
  28. Zhao W, Wu J, Chen S, Zhou Z. Shared neoantigens: ideal targets for off-the-shelf cancer immunotherapy. Pharmacogenomics. 2020;21(9):637–645. doi: 10.2217/pgs-2019-0184
  29. Klebanoff CA, Wolchok JD. Shared cancer neoantigens: Making private matters public. J Exp Med. 2018;215(1):5–7. doi: 10.1084/jem.20172188
  30. Rojas LA, Sethna Z, Soares KC, et al. Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer. Nature. 2023;618(7963):144–150. doi: 10.1038/s41586-023-06063-y
  31. Weber JS, Luke JJ, Carlino MS, et al. INTerpath-001: Pembrolizumab with V940 (mRNA-4157) versus pembrolizumab with placebo for adjuvant treatment of high-risk stage II-IV melanoma. J Clin Oncol. 2024;42(16S):TPS9616. doi: 10.1200/JCO.2024.42.16_suppl.TPS9616
  32. Sethna Z, Guasp P, Reiche C, et al. RNA neoantigen vaccines prime long-lived CD8+ T cells in pancreatic cancer. Nature. 2025;639(8056):1042–1051. doi: 10.1038/s41586-024-08508-4
  33. Chan TA, Yarchoan M, Jaffee E, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol. 2019;30(1):44–56. doi: 10.1093/annonc/mdy495
  34. Richters MM, Xia H, Campbell KM, Gillanders WE, Griffith OL, Griffith M. Best practices for bioinformatic characterization of neoantigens for clinical utility. Genome Med. 2019;11(1):56. doi: 10.1186/s13073-019-0666-2
  35. Nguyen BQT, Tran TPD, Nguyen HT, et al. Improvement in neoantigen prediction via integration of RNA sequencing data for variant calling. Front Immunol. 2023;14:1251603. doi: 10.3389/fimmu.2023.1251603
  36. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–2120. doi: 10.1093/bioinformatics/btu170
  37. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17(1):10−12. doi: 10.14806/ej.17.1.200
  38. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357−359. doi: 10.1038/nmeth.1923
  39. Benjamin D, Sato T, Cibulskis K, et al. Calling Somatic SNVs and Indels with Mutect2. bioRxiv. 2019. doi: 10.1101/861054
  40. Saunders CT, Wong WSW, Swamy S, Becq J, Murray LJ, Cheetham RK. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics. 2012;28(14):1811−1817. doi: 10.1093/bioinformatics/bts271
  41. Koboldt DC, Zhang Q, Larson DE, et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 2012;22(3):568−576. doi: 10.1101/gr.129684.111
  42. Wood DE, White JR, Georgiadis A, et al. A machine learning approach for somatic mutation discovery. Sci Transl Med. 2018;10(457):eaar7939. doi: 10.1126/scitranslmed.aar7939
  43. Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15−21. doi: 10.1093/bioinformatics/bts635
  44. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323. doi: 10.1186/1471-2105-12-323
  45. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14(4):417−419. doi: 10.1038/nmeth.4197
  46. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34(5):525−527. doi: 10.1038/nbt.3519
  47. Szolek A, Schubert B, Mohr C, Sturm M, Feldhahn M, Kohlbacher O. OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics. 2014;30(23):3310−3316. doi: 10.1093/bioinformatics/btu548
  48. Andreatta M, Nielsen M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics. 2016;32(4):511−517. doi: 10.1093/bioinformatics/btv639
  49. O’Donnell TJ, Rubinsteyn A, Bonsack M, Riemer AB, Laserson U, Hammerbacher J. MHCflurry: Open-Source Class I MHC Binding Affinity Prediction. Cell Syst. 2018;7(1):129−132.e4. doi: 10.1016/j.cels.2018.05.014
  50. 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. doi: 10.3389/fimmu.2017.00278
  51. Abelin JG, Keskin DB, Sarkizova S, et al. Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction. Immunity. 2017;46(2):315−326. doi: 10.1016/j.immuni.2017.02.007
  52. Yu W, Yu H, Zhao J, et al. NeoDesign: a computational tool for optimal selection of polyvalent neoantigen combinations. Bioinformatics. 2024;40(10):btae585. doi: 10.1093/bioinformatics/btae585
  53. Lu RM, Hsu HE, Perez SJLP, et al. Current landscape of mRNA technologies and delivery systems for new modality therapeutics. J Biomed Sci. 2024;31(1):89. doi: 10.1186/s12929-024-01080-z
  54. Lorenz R, Bernhart SH, Höner Zu Siederdissen C, et al. ViennaRNA Package 2.0. Algorithms Mol Biol. 2011;6:26. doi: 10.1186/1748-7188-6-26
  55. Zadeh JN, Steenberg CD, Bois JS, et al. NUPACK: Analysis and design of nucleic acid systems. J Comput Chem. 2011;32(1):170−173. doi: 10.1002/jcc.21596
  56. Zuker M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003;31(13):3406−3415. doi: 10.1093/nar/gkg595
  57. Ni L. Advances in mRNA-Based Cancer Vaccines. Vaccines (Basel). 2023;11(10):1599. doi: 10.3390/vaccines11101599
  58. Fu Q, Zhao X, Hu J, et al. mRNA vaccines in the context of cancer treatment: from concept to application. J Transl Med. 2025;23(1):12. doi: 10.1186/s12967-024-06033-6
  59. Kraft JC, Freeling JP, Wang Z, Ho RJY. Emerging research and clinical development trends of liposome and lipid nanoparticle drug delivery systems. J Pharm Sci. 2014;103(1):29−52. doi: 10.1002/jps.23773
  60. Tenchov R, Bird R, Curtze AE, Zhou Q. Lipid Nanoparticles – From Liposomes to mRNA Vaccine Delivery, a Landscape of Research Diversity and Advancement. ACS Nano. 2021;15(11):16982−17015. doi: 10.1021/acsnano.1c04996
  61. Bartlett S, Skwarczynski M, Toth I. Lipids as activators of innate immunity in peptide vaccine delivery. Curr Med Chem. 2020;27(17):2887–2901. doi: 10.2174/0929867325666181026100849
  62. Brewer JM, Pollock KGJ, Tetley L, Russell DG. Vesicle size influences the trafficking, processing, and presentation of antigens in lipid vesicles. J Immunol. 2004;173(10):6143–6150. doi: 10.4049/jimmunol.173.10.6143
  63. Ghaffar KA, Giddam AK, Zaman M, Skwarczynski M, Toth I. Liposomes as nanovaccine delivery systems. Curr Top Med Chem. 2014;14(9):1194–1208. doi: 10.2174/1568026614666140329232757
  64. Henriksen-Lacey M, Christensen D, Bramwell VW, et al. Liposomal cationic charge and antigen adsorption are important properties for the efficient deposition of antigen at the injection site and ability of the vaccine to induce a CMI response. J Control Release. 2010;145(2):102–108. doi: 10.1016/j.jconrel.2010.03.027
  65. Miller CR, Bondurant B, McLean SD, McGovern KA, O’Brien DF. Liposome-cell interactions in vitro: effect of liposome surface charge on the binding and endocytosis of conventional and sterically stabilized liposomes. Biochemistry. 1998;37(37):12875–12883. doi: 10.1021/bi980096y
  66. Swetha K, Kotla NG, Tunki L, et al. Recent advances in the lipid nanoparticle-mediated delivery of mRNA vaccines. Vaccines (Basel). 2023;11(3):658. doi: 10.3390/vaccines11030658
  67. Christensen D, Henriksen-Lacey M, Kamath AT, et al. A cationic vaccine adjuvant based on a saturated quaternary ammonium lipid have different in vivo distribution kinetics and display a distinct CD4 T cell-inducing capacity compared to its unsaturated analog. J Control Release. 2012;160(3):468–476. doi: 10.1016/j.jconrel.2012.03.016
  68. Tanaka Y, Taneichi M, Kasai M, Kakiuchi T, Uchida T. Liposome-coupled antigens are internalized by antigen-presenting cells via pinocytosis and cross-presented to CD8 T cells. PLoS One. 2010;5(12):e15225. doi: 10.1371/journal.pone.0015225
  69. Szebeni J, Baranyi L, Savay S, et al. The interaction of liposomes with the complement system: in vitro and in vivo assays. Methods Enzymol. 2003;373:136–154. doi: 10.1016/S0076-6879(03)73010-9
  70. Hemmi H, Takeuchi O, Kawai T, et al. A Toll-like receptor recognizes bacterial DNA. Nature. 2000;408(6813):740–745. doi: 10.1038/35047123
  71. Henriksen-Lacey M, Devitt A, Perrie Y. The vesicle size of DDA:TDB liposomal adjuvants plays a role in the cell-mediated immune response but has no significant effect on antibody production. J Control Release. 2011;154(2):131–137. doi: 10.1016/j.jconrel.2011.05.019
  72. Lee Y, Lee YS, Cho SY, Kwon HJ. Perspective of peptide vaccine composed of epitope peptide, CpG-DNA, and liposome complex without carriers. Adv Protein Chem Struct Biol. 2015;99:75–97. doi: 10.1016/bs.apcsb.2015.03.004
  73. Vabulas RM, Pircher H, Lipford GB, Häcker H, Wagner H. CpG-DNA activates in vivo T cell epitope presenting dendritic cells to trigger protective antiviral cytotoxic T cell responses. J Immunol. 2000;164(5):2372–2378. doi: 10.4049/jimmunol.164.5.2372
  74. Schulz O, Diebold SS, Chen M, et al. Toll-like receptor 3 promotes cross-priming to virus-infected cells. Nature. 2005;433(7028):887–892. doi: 10.1038/nature03326
  75. Zaks K, Jordan M, Guth A, et al. Efficient immunization and cross-priming by vaccine adjuvants containing TLR3 or TLR9 agonists complexed to cationic liposomes. J Immunol. 2006;176(12):7335–7345. doi: 10.4049/jimmunol.176.12.7335
  76. Jin B, Sun T, Yu XH, et al. Immunomodulatory effects of dsRNA and its potential as vaccine adjuvant. J Biomed Biotechnol. 2010;2010:690438. doi: 10.1155/2010/690438
  77. Liu Y, Janeway CA Jr. Microbial induction of co-stimulatory activity for CD4 T-cell growth. Int Immunol. 1991;3(4):323–332. doi: 10.1093/intimm/3.4.323
  78. Werninghaus K, Babiak A, Gross O, et al. Adjuvanticity of a synthetic cord factor analogue for subunit Mycobacterium tuberculosis vaccination requires FcRγ-Syk-Card9-dependent innate immune activation. J Exp Med. 2009;206(1):89–97. doi: 10.1084/jem.20081445
  79. Hou X, Zaks T, Langer R, Dong Y. Lipid nanoparticles for mRNA delivery. Nat Rev Mater. 2021;6(12):1078–1094. doi: 10.1038/s41578-021-00358-0
  80. Fan T, Xu C, Wu J, et al. Lipopolyplex-formulated mRNA cancer vaccine elicits strong neoantigen-specific T cell responses and antitumor activity. Sci Adv. 2024;10(41):eadn9961. doi: 10.1126/sciadv.adn9961
  81. Kisakov DN, Karpenko LI, Kisakova LA, et al. Jet injection of naked mRNA encoding the RBD of the SARS-CoV-2 spike protein induces a high level of a specific immune response in mice. Vaccines (Basel). 2025;13(1):65. doi: 10.3390/vaccines13010065
  82. Ramos da Silva J, Bitencourt Rodrigues K, Formoso Pelegrin G, et al. Single immunizations of self-amplifying or non-replicating mRNA-LNP vaccines control HPV-associated tumors in mice. Sci Transl Med. 2023;15(686):eabn3464. doi: 10.1126/scitranslmed.abn3464
  83. Cao Y, Gao GF. mRNA vaccines: a matter of delivery. EClinicalMedicine. 2021;32:100746. doi: 10.1016/j.eclinm.2021.100746
  84. Pambudi NA, Sarifudin A, Gandidi IM, Romadhon R. Vaccine cold chain management and cold storage technology to address the challenges of vaccination programs. Energy Rep. 2022;8:955–972. doi: 10.1016/j.egyr.2021.12.039
  85. Schmidt M, Vogler I, Derhovanessian E, et al. 88MO T-cell responses induced by an individualized neoantigen specific immune therapy in post (neo)adjuvant patients with triple negative breast cancer. Ann Oncol. 2020;31(S4):S276. doi: 10.1016/j.annonc.2020.08.209
  86. Chen J, Ye Z, Huang C, et al. Lipid nanoparticle-mediated lymph node-targeting delivery of mRNA cancer vaccine elicits robust CD8+ T cell response. Proc Natl Acad Sci USA. 2022;119(34):e2207841119. doi: 10.1073/pnas.2207841119
  87. Ols S, Yang L, Thompson EA, et al. Route of vaccine administration alters antigen trafficking but not innate or adaptive immunity. Cell Rep. 2020;30(12):3964–3971.e7. doi: 10.1016/j.celrep.2020.02.111
  88. Weber JS, Carlino MS, Khattak A, et al. Individualised neoantigen therapy mRNA-4157 (V940) plus pembrolizumab versus pembrolizumab monotherapy in resected melanoma (KEYNOTE-942): a randomised, phase 2b study. Lancet. 2024;403(10427):632–644. doi: 10.1016/S0140-6736(23)02268-7
  89. Gainor JF, Patel MR, Weber JS, 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. 2024;14(11):2209–2223. doi: 10.1158/2159-8290.CD-24-0158
  90. Cafri G, Gartner JJ, Zaks T, et al. mRNA vaccine-induced neoantigen-specific T cell immunity in patients with gastrointestinal cancer. J Clin Invest. 2020;130(11):5976–5988. doi: 10.1172/JCI134915
  91. Islam MA, Rice J, Reesor E, et al. Adjuvant-pulsed mRNA vaccine nanoparticle for immunoprophylactic and therapeutic tumor suppression in mice. Biomaterials. 2021;266:120431. doi: 10.1016/j.biomaterials.2020.120431
  92. Wang QT, Nie Y, Sun SN, et al. Tumor-associated antigen-based personalized dendritic cell vaccine in solid tumor patients. Cancer Immunol Immunother. 2020;69(7):1375–1387. doi: 10.1007/s00262-020-02496-w
  93. Thomas KS. Intramuscular injections for COVID-19 vaccinations. J Nucl Med Technol. 2021;49(1):11–12. doi: 10.2967/jnmt.121.262049
  94. Persano S, Guevara ML, Li Z, et al. Lipopolyplex potentiates anti-tumor immunity of mRNA-based vaccination. Biomaterials. 2017;125:81–89. doi: 10.1016/j.biomaterials.2017.02.019
  95. Rini BI, Stenzl A, Zdrojowy R, et al. IMA901, a multipeptide cancer vaccine, plus sunitinib versus sunitinib alone, as first-line therapy for advanced or metastatic renal cell carcinoma (IMPRINT): a multicentre, open-label, randomised, controlled, phase 3 trial. Lancet Oncol. 2016;17(11):1599–1611. doi: 10.1016/S1470-2045(16)30408-9
  96. Wang B, Pei J, Xu S, Liu J, Yu J. Recent advances in mRNA cancer vaccines: meeting challenges and embracing opportunities. Front Immunol. 2023;14:1246682. doi: 10.3389/fimmu.2023.1246682
  97. Gradel AKJ, Porsgaard T, Lykkesfeldt J, et al. Factors affecting the absorption of subcutaneously administered insulin: effect on variability. J Diabetes Res. 2018;2018(1):1205121. doi: 10.1155/2018/1205121
  98. Oberli MA, Reichmuth AM, Dorkin JR, et al. Lipid nanoparticle assisted mRNA delivery for potent cancer immunotherapy. Nano Lett. 2017;17(3):1326–1335. doi: 10.1021/acs.nanolett.6b03329
  99. Ott PA, Hu Z, Keskin DB, et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature. 2017;547(7662):217–221. doi: 10.1038/nature22991
  100. Lorentzen CL, Haanen JB, Met Ö, Svane IM. Clinical advances and ongoing trials on mRNA vaccines for cancer treatment. Lancet Oncol. 2022;23(10):e450–e458. doi: 10.1016/S1470-2045(22)00372-2
  101. Haabeth OAW, Blake TR, McKinlay CJ, et al. Local delivery of Ox40l, Cd80, and Cd86 mRNA kindles global anticancer immunity. Cancer Res. 2019;79(7):1624–1634. doi: 10.1158/0008-5472.CAN-18-2867
  102. Diken M, Kreiter S, Selmi A, et al. Selective uptake of naked vaccine RNA by dendritic cells is driven by macropinocytosis and abrogated upon DC maturation. Gene Ther. 2011;18(7):702–708. doi: 10.1038/gt.2011.17
  103. Pardi N, Hogan MJ, Porter FW, Weissman D. mRNA vaccines – a new era in vaccinology. Nat Rev Drug Discov. 2018;17(4):261–279. doi: 10.1038/nrd.2017.243
  104. Choueiri TK, Powles T, Braun D, et al. 45 INTerpath-004: a phase 2, randomized, double-blind study of pembrolizumab with V940 (mRNA-4157) or placebo in the adjuvant treatment of renal cell carcinoma. Oncologist. 2024;29(S1):S15. doi: 10.1093/oncolo/oyae181.022
  105. Sonpavde GP, Valderrama BP, Chamie K, et al. Phase 1/2 INTerpath-005 study: V940 (mRNA-4157) plus pembrolizumab with or without enfortumab vedotin (EV) for resected high-risk muscle-invasive urothelial carcinoma (MIUC). J Clin Oncol. 2025;43(5S):TPS893. doi: 10.1200/JCO.2025.43.5_suppl.TPS893
  106. Sadeghi Rad H, Monkman J, Warkiani ME, et al. Understanding the tumor microenvironment for effective immunotherapy. Med Res Rev. 2021;41(3):1474–1498. doi: 10.1002/med.21765
  107. Seidel JA, Otsuka A, Kabashima K. Anti-PD-1 and anti-CTLA-4 therapies in cancer: mechanisms of action, efficacy, and limitations. Front Oncol. 2018;8:86. doi: 10.3389/fonc.2018.00086
  108. Shiravand Y, Khodadadi F, Kashani SMA, et al. Immune checkpoint inhibitors in cancer therapy. Curr Oncol. 2022;29(5):3044–3060. doi: 10.3390/curroncol29050247
  109. Burris HA, Patel MR, Cho DC, et al. A phase I multicenter study to assess the safety, tolerability, and immunogenicity of mRNA-4157 alone in patients with resected solid tumors and in combination with pembrolizumab in patients with unresectable solid tumors. J Clin Oncol. 2019;37(15S):2523. doi: 10.1200/JCO.2019.37.15_suppl.2523
  110. Khattak A, Weber JS, Meniawy TM, et al. Distant metastasis-free survival results from the randomized, phase 2 mRNA-4157-P201/KEYNOTE-942 trial. J Clin Oncol. 2023;41(17S):LBA9503. doi: 10.1200/JCO.2023.41.17_suppl.LBA9503
  111. Lopez J, Powles T, Braiteh F, et al. Autogene cevumeran with or without atezolizumab in advanced solid tumors: a phase 1 trial. Nat Med. 2025;31(1):152–164. doi: 10.1038/s41591-024-03334-7
  112. Chen JK, Eisenberg E, Krutchkoff DJ, Katz RV. Changing trends in oral cancer in the United States, 1935 to 1985: a Connecticut study. J Oral Maxillofac Surg. 1991;49(11):1152–1158. doi: 10.1016/0278-2391(91)90406-C

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