Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Alternative mRNA splicing in cancer immunotherapy

Subjects

Abstract

Immunotherapies are yielding effective treatments for several previously untreatable cancers. Still, the identification of suitable antigens specific to the tumour that can be targets for cancer vaccines and T cell therapies is a challenge. Alternative processing of mRNA, a phenomenon that has been shown to alter the proteomic diversity of many cancers, may offer the potential of a broadened target space. Here, we discuss the promise of analysing mRNA processing events in cancer cells, with an emphasis on mRNA splicing, for the identification of potential new targets for cancer immunotherapy. Further, we highlight the challenges that must be overcome for this new avenue to have clinical applicability.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Alternative splicing and immunotherapy.
Fig. 2: Schematic illustration of the development of potential immunotherapies targeting mRNA processing-derived neoantigens.
Fig. 3: Potential mechanism of tumour escape from immunotherapy.

Similar content being viewed by others

References

  1. Paucek, R. D., Baltimore, D. & Li, G. The cellular immunotherapy revolution: arming the immune system for precision therapy. Trends Immunol. 40, 292–309 (2019).

    CAS  PubMed  Google Scholar 

  2. Hackl, H., Charoentong, P., Finotello, F. & Trajanoski, Z. Computational genomics tools for dissecting tumour–immune cell interactions. Nat. Rev. Genet. 17, 441–458 (2016).

    CAS  PubMed  Google Scholar 

  3. Rapoport, A. P. et al. NY-ESO-1-specific TCR-engineered T cells mediate sustained antigen-specific antitumor effects in myeloma. Nat. Med. 21, 914–921 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Robbins, P. F. et al. Tumor regression in patients with metastatic synovial cell sarcoma and melanoma using genetically engineered lymphocytes reactive with NY-ESO-1. J. Clin. Oncol. 29, 917–924 (2011).

    PubMed  PubMed Central  Google Scholar 

  5. Robbins, P. F. et al. A pilot trial using lymphocytes genetically engineered with an NY-ESO-1-reactive T cell receptor: long-term follow-up and correlates with response. Clin. Cancer Res. 21, 1019–1027 (2015).

    CAS  PubMed  Google Scholar 

  6. Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017). This work, along with the studies by Carreno et al. (2015) and Sahin et al. (2017), provides in-human evidence that vaccines against tumour neoantigens could be safe and effective in treating patients with advanced-stage melanoma.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Carreno, B. M. et al. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science 348, 803–808 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).

    CAS  PubMed  Google Scholar 

  9. Hilf, N. et al. Actively personalized vaccination trial for newly diagnosed glioblastoma. Nature 565, 240–245 (2019).

    CAS  PubMed  Google Scholar 

  10. Keskin, D. B. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239 (2019).

    CAS  PubMed  Google Scholar 

  11. Van den Berg, J. H. et al. Case report of a fatal serious adverse event upon administration of T cells transduced with a MART-1-specific T cell receptor. Mol. Ther. 23, 1541–1550 (2015).

    PubMed  PubMed Central  Google Scholar 

  12. Linette, G. P. et al. Cardiovascular toxicity and titin cross-reactivity of affinity-enhanced T cells in myeloma and melanoma. Blood 122, 863–871 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Cameron, B. J. et al. Identification of a titin-derived HLA-A1-presented peptide as a cross-reactive target for engineered MAGE A3-directed T cells. Sci. Transl Med. 5, 197ra103 (2013).

    PubMed  PubMed Central  Google Scholar 

  14. Johnson, L. A. et al. Gene therapy with human and mouse T cell receptors mediates cancer regression and targets normal tissues expressing cognate antigen. Blood 114, 535–546 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Bethune, M. T. & Joglekar, A. V. Personalized T cell-mediated cancer immunotherapy: progress and challenges. Curr. Opin. Biotechnol. 48, 142–152 (2017).

    CAS  PubMed  Google Scholar 

  16. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).

    CAS  PubMed  Google Scholar 

  18. Black, K. L. et al. Aberrant splicing in B cell acute lymphoblastic leukemia. Nucleic Acids Res. 46, 11357–11369 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014). This study, in conjunction with Van Allen et al. (2015) and Rizvi et al. (2015), provides evidence of the correlation between the response to CPI and the TMB.

    PubMed  PubMed Central  Google Scholar 

  20. Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).

    PubMed  PubMed Central  Google Scholar 

  21. Rosenberg, J. E. et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet 387, 1909–1920 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Seiwert, T. Y. et al. Biomarkers predictive of response to pembrolizumab in head and neck cancer (HNSCC). Cancer Res. 78 (Suppl.), LB–339 (2018).

    Google Scholar 

  23. Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Liu, Y. et al. Impact of alternative splicing on the human proteome. Cell Rep. 20, 1229–1241 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Weatheritt, R. J., Sterne-Weiler, T. & Blencowe, B. J. The ribosome-engaged landscape of alternative splicing. Nat. Struct. Mol. Biol. 23, 1117–1123 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Nilsen, T. W. & Graveley, B. R. Expansion of the eukaryotic proteome by alternative splicing. Nature 463, 457–463 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Kalsotra, A. et al. A postnatal switch of CELF and MBNL proteins reprograms alternative splicing in the developing heart. Proc. Natl Acad. Sci. USA 105, 20333–20338 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Yap, K., Lim, Z. Q., Khandelia, P., Friedman, B. & Makeyev, E. V. Coordinated regulation of neuronal mRNA steady-state levels through developmentally controlled intron retention. Genes Dev. 26, 1209–1223 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Wong, J. J.-L. et al. Orchestrated intron retention regulates normal granulocyte differentiation. Cell 154, 583–595 (2013).

    CAS  PubMed  Google Scholar 

  31. Pimentel, H. et al. A dynamic alternative splicing program regulates gene expression during terminal erythropoiesis. Nucleic Acids Res. 42, 4031–4042 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Frankiw, L. et al. Bud13 promotes a type I interferon response by countering intron retention in Irf7. Mol. Cell 73, 803–814 (2019).

    CAS  PubMed  Google Scholar 

  33. Baralle, F. E. & Giudice, J. Alternative splicing as a regulator of development and tissue identity. Nat. Rev. Mol. Cell. Biol. 18, 437–451 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Venables, J. P. Aberrant and alternative splicing in cancer. Cancer Res. 64, 7647–7654 (2004).

    CAS  PubMed  Google Scholar 

  35. Braun, C. J. et al. Coordinated splicing of regulatory detained introns within oncogenic transcripts creates an exploitable vulnerability in malignant glioma. Cancer Cell 32, 411–426 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Coltri, P. P., dos Santos, M. G. & da Silva, G. H. Splicing and cancer: challenges and opportunities. Wiley Interdiscip. Rev. RNA 10, e1527 (2019).

    PubMed  Google Scholar 

  37. Jayasinghe, R. G. et al. Systematic analysis of splice-site-creating mutations in cancer. Cell Rep. 23, 270–281 (2018). This study focuses on cancer mutations that had evidence of creating specific splicing junctions. These SCMs generated ~2 times as many neoepitopes per event compared with non-synonymous mutations.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Kahles, A. et al. Comprehensive analysis of alternative splicing across tumors from 8,705 patients. Cancer Cell 34, 211–224 (2018). This study focuses on cancer-specific neojunctions and shows that peptides derived from such events could significantly increase the target space for immunotherapy.

    CAS  PubMed  Google Scholar 

  39. Climente-Gonzalez, H., Porta-Pardo, E., Godzik, A. & Eyras, E. The functional impact of alternative splicing in cancer. Cell Rep. 20, 2215–2226 (2017).

    CAS  PubMed  Google Scholar 

  40. Dvinge, H., Kim, E., Abdel-Wahab, O. & Bradley, R. K. RNA splicing factors as oncoproteins and tumour suppressors. Nat. Rev. Cancer 16, 413–430 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Will, C. L. & Lührmann, R. Spliceosome structure and function. Cold Spring Harb. Perspect. Biol. 3, a003707 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Yoshida, K. et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature 478, 64–69 (2011).

    CAS  PubMed  Google Scholar 

  43. Papaemmanuil, E. et al. Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N. Engl. J. Med. 365, 1384–1395 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Graubert, T. A. et al. Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes. Nat. Genet. 44, 53–57 (2012).

    CAS  Google Scholar 

  45. Wang, L. et al. SF3B1 and other novel cancer genes in chronic lymphocytic leukemia. N. Engl. J. Med. 365, 2497–2506 (2011). This study reveals widespread spliceosomal mutations to the U2 component SF3B1 in chronic lymphocytic leukaemia. It is among the first works showing that such splicing-related mutations are ubiquitous in cancer.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Quesada, V. et al. Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia. Nat. Genet. 44, 47–52 (2012).

    CAS  Google Scholar 

  47. Ellis, M. J. et al. Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature 486, 353–360 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Stephens, P. J. et al. The landscape of cancer genes and mutational processes in breast cancer. Nature 486, 400–404 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Maguire, S. L. et al. SF3B1 mutations constitute a novel therapeutic target in breast cancer. J. Pathol. 235, 571–580 (2015).

    CAS  PubMed  Google Scholar 

  50. Biankin, A. V. et al. Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 491, 399–405 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Harbour, J. W. et al. Recurrent mutations at codon 625 of the splicing factor SF3B1 in uveal melanoma. Nat. Genet. 45, 133–135 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Martin, M. et al. Exome sequencing identifies recurrent somatic mutations in EIF1AX and SF3B1 in uveal melanoma with disomy 3. Nat. Genet. 45, 933–936 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Imielinski, M. et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 150, 1107–1120 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Alsafadi, S. et al. Cancer-associated SF3B1 mutations affect alternative splicing by promoting alternative branchpoint usage. Nat. Commun. 7, 10615 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Seiler, M. et al. Somatic mutational landscape of splicing factor genes and their functional consequences across 33 cancer types. Cell Rep. 23, 282–296 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Sebestyén, E. et al. Large-scale analysis of genome and transcriptome alterations in multiple tumors unveils novel cancer-relevant splicing networks. Genome Res. 26, 732–744 (2016).

    PubMed  PubMed Central  Google Scholar 

  57. Sveen, A., Kilpinen, S., Ruusulehto, A., Lothe, R. A. & Skotheim, R. I. Aberrant RNA splicing in cancer; expression changes and driver mutations of splicing factor genes. Oncogene 35, 2413–2427 (2016).

    CAS  PubMed  Google Scholar 

  58. Fu, X.-D. & Ares Jr, M. Context-dependent control of alternative splicing by RNA-binding proteins. Nat. Rev. Genet. 15, 689–701 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Karni, R. et al. The gene encoding the splicing factor SF2/ASF is a proto-oncogene. Nat. Struct. Mol. Biol. 14, 185–193 (2007). This study shows that slight overexpression of the SF2/ASF splicing factor was pro-tumorigenic.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Karni, R., Hippo, Y., Lowe, S. W. & Krainer, A. R. The splicing-factor oncoprotein SF2/ASF activates mTORC1. Proc. Natl Acad. Sci. USA 105, 15323–15327 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. David, C. J., Chen, M., Assanah, M., Canoll, P. & Manley, J. L. hnRNP proteins controlled by c-Myc deregulate pyruvate kinase mRNA splicing in cancer. Nature 463, 364–368 (2010).

    CAS  PubMed  Google Scholar 

  62. Clower, C. V. et al. The alternative splicing repressors hnRNP A1/A2 and PTB influence pyruvate kinase isoform expression and cell metabolism. Proc. Natl Acad. Sci. USA 107, 1894–1899 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Anczuków, O. et al. The splicing factor SRSF1 regulates apoptosis and proliferation to promote mammary epithelial cell transformation. Nat. Struct. Mol. Biol. 19, 220–228 (2012).

    PubMed  PubMed Central  Google Scholar 

  64. Cohen-Eliav, M. et al. The splicing factor SRSF6 is amplified and is an oncoprotein in lung and colon cancers. J. Pathol. 229, 630–639 (2013).

    CAS  PubMed  Google Scholar 

  65. Jensen, M. A., Wilkinson, J. E. & Krainer, A. R. Splicing factor SRSF6 promotes hyperplasia of sensitized skin. Nat. Struct. Mol. Biol. 21, 189–197 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Gallardo, M. et al. hnRNP K is a haploinsufficient tumor suppressor that regulates proliferation and differentiation programs in hematologic malignancies. Cancer Cell 28, 486–499 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Wang, Y. et al. The splicing factor RBM4 controls apoptosis, proliferation, and migration to suppress tumor progression. Cancer Cell 26, 374–389 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Zong, F.-Y. et al. The RNA-binding protein QKI suppresses cancer-associated aberrant splicing. PLOS Genet. 10, e1004289 (2014).

    PubMed  PubMed Central  Google Scholar 

  69. Spinelli, R. et al. Identification of novel point mutations in splicing sites integrating whole-exome and RNA-seq data in myeloproliferative diseases. Mol. Genet. Genomic Med. 1, 246–259 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Liu, J. et al. Genome and transcriptome sequencing of lung cancers reveal diverse mutational and splicing events. Genome Res. 22, 2315–2327 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Jung, H. et al. Intron retention is a widespread mechanism of tumor-suppressor inactivation. Nat. Genet. 47, 1242–1248 (2015). This study shows that single-nucleotide variants causing intron retention were enriched in tumour suppressors.

    CAS  PubMed  Google Scholar 

  72. Supek, F., Miñana, B., Valcárcel, J., Gabaldón, T. & Lehner, B. Synonymous mutations frequently act as driver mutations in human cancers. Cell 156, 1324–1335 (2014).

    CAS  PubMed  Google Scholar 

  73. Lee, Y. & Rio, D. C. Mechanisms and regulation of alternative pre-mRNA splicing. Annu. Rev. Biochem. 84, 291–323 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Parada, G. E., Munita, R., Cerda, C. A. & Gysling, K. A comprehensive survey of non-canonical splice sites in the human transcriptome. Nucleic Acids Res. 42, 10564–10578 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Matera, A. G. & Wang, Z. A day in the life of the spliceosome. Nat. Rev. Mol. Cell. Biol. 15, 108–121 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Wang, Z. & Burge, C. B. Splicing regulation: from a parts list of regulatory elements to an integrated splicing code. RNA 14, 802–813 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Lim, S., Mullins, J. J., Chen, C. M., Gross, K. W. & Maquat, L. E. Novel metabolism of several beta zero-thalassemic beta-globin mRNAs in the erythroid tissues of transgenic mice. EMBO J. 8, 2613–2619 (1989).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Popp, M. W. & Maquat, L. E. Nonsense-mediated mRNA decay and cancer. Curr. Opin. Genet. Dev. 48, 44–50 (2018).

    CAS  PubMed  Google Scholar 

  79. Singh, B., Trincado, J. L., Tatlow, P. J., Piccolo, S. R. & Eyras, E. Genome sequencing and RNA-motif analysis reveal novel damaging noncoding mutations in human tumors. Mol. Cancer Res. 16, 1112–1124 (2018).

    CAS  PubMed  Google Scholar 

  80. Zatkova, A. et al. Disruption of exonic splicing enhancer elements is the principal cause of exon skipping associated with seven nonsense or missense alleles of NF1. Hum. Mutat. 24, 491–501 (2004).

    CAS  PubMed  Google Scholar 

  81. Jaganathan, K. et al. Predicting splicing from primary sequence with deep learning. Cell 176, 535–548 (2019).

    CAS  PubMed  Google Scholar 

  82. Smart, A. C. et al. Intron retention is a source of neoepitopes in cancer. Nat. Biotechnol. 36, 1056–1058 (2018). This study is the first to show that cancer-specific intron retention events could be a source of neoepitopes.

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Wang, X. et al. Detection of proteome diversity resulted from alternative splicing is limited by trypsin cleavage specificity. Mol. Cell. Proteomics 17, 422–430 (2018).

    CAS  PubMed  Google Scholar 

  84. Wong, J. J.-L., Au, A. Y., Ritchie, W. & Rasko, J. E. Intron retention in mRNA: no longer nonsense: known and putative roles of intron retention in normal and disease biology. Bioessays 38, 41–49 (2016).

    PubMed  Google Scholar 

  85. Apcher, S. et al. Major source of antigenic peptides for the MHC class I pathway is produced during the pioneer round of mRNA translation. Proc. Natl Acad. Sci. USA 108, 11572–11577 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Laumont, C. M. et al. Noncoding regions are the main source of targetable tumor-specific antigens. Sci. Transl Med. 10, eaau5516 (2018).

    CAS  PubMed  Google Scholar 

  87. Turajlic, S. et al. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis. Lancet Oncol. 18, 1009–1021 (2017).

    CAS  PubMed  Google Scholar 

  88. Ishida, Y., Agata, Y., Shibahara, K. & Honjo, T. Induced expression of PD-1, a novel member of the immunoglobulin gene superfamily, upon programmed cell death. EMBO J. 11, 3887–3895 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Freeman, G. J. et al. Engagement of the PD-1 immunoinhibitory receptor by a novel B7 family member leads to negative regulation of lymphocyte activation. J. Exp. Med. 192, 1027–1034 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Baumeister, S. H., Freeman, G. J., Dranoff, G. & Sharpe, A. H. Coinhibitory pathways in immunotherapy for cancer. Annu. Rev. Immunol. 34, 539–573 (2016).

    CAS  PubMed  Google Scholar 

  91. Chambers, C. A., Kuhns, M. S., Egen, J. G. & Allison, J. P. CTLA-4-mediated inhibition in regulation of T cell responses: mechanisms and manipulation in tumor immunotherapy. Annu. Rev. Immunol. 19, 565–594 (2001).

    CAS  PubMed  Google Scholar 

  92. Walunas, T. L. et al. CTLA-4 can function as a negative regulator of T cell activation. Immunity 1, 405–413 (1994).

    CAS  PubMed  Google Scholar 

  93. Leach, D. R., Krummel, M. F. & Allison, J. P. Enhancement of antitumor immunity by CTLA-4 blockade. Science 271, 1734–1736 (1996).

    CAS  PubMed  Google Scholar 

  94. Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350–1355 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Peggs, K. S., Segal, N. H. & Allison, J. P. Targeting immunosupportive cancer therapies: accentuate the positive, eliminate the negative. Cancer Cell 12, 192–199 (2007).

    CAS  PubMed  Google Scholar 

  96. Segal, N. H. et al. Epitope landscape in breast and colorectal cancer. Cancer Res. 68, 889–892 (2008).

    CAS  PubMed  Google Scholar 

  97. Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014). This work provides evidence of intratumoural splicing heterogeneity in glioblastoma.

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Arzalluz-Luque, Á. & Conesa, A. Single-cell RNAseq for the study of isoforms—how is that possible? Genome Biol. 19, 110 (2018).

    PubMed  PubMed Central  Google Scholar 

  100. Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

    CAS  PubMed  Google Scholar 

  101. Zhang, Z. et al. Deep-learning augmented RNA-seq analysis of transcript splicing. Nat. Methods 16, 307–310 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Song, Y. et al. Single-cell alternative splicing analysis with expedition reveals splicing dynamics during neuron differentiation. Mol. Cell 67, 148–161 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Huang, Y. & Sanguinetti, G. BRIE: transcriptome-wide splicing quantification in single cells. Genome Biol. 18, (123 (2017).

    Google Scholar 

  104. Welch, J. D., Hu, Y. & Prins, J. F. Robust detection of alternative splicing in a population of single cells. Nucleic Acids Res. 44, e73 (2016).

    PubMed  PubMed Central  Google Scholar 

  105. Tress, M. L., Abascal, F. & Valencia, A. Alternative splicing may not be the key to proteome complexity. Trends Biochem. Sci. 42, 98–110 (2017).

    CAS  PubMed  Google Scholar 

  106. Pickrell, J. K., Pai, A. A., Gilad, Y. & Pritchard, J. K. Noisy splicing drives mRNA isoform diversity in human cells. PLOS Genet. 6, e1001236 (2010).

    PubMed  PubMed Central  Google Scholar 

  107. Caron, E. et al. Analysis of major histocompatibility complex (MHC) immunopeptidomes using mass spectrometry. Mol. Cell. Proteomics 14, 3105–3117 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Gfeller, D. & Bassani-Sternberg, M. Predicting antigen presentation—what could we learn from a million peptides? Front. Immunol. 9, 1716 (2018).

    PubMed  PubMed Central  Google Scholar 

  109. Schmidt, J. et al. In silico and cell-based analyses reveal strong divergence between prediction and observation of T cell–recognized tumor antigen T cell epitopes. J. Biol. Chem. 292, 11840–11849 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Vita, R. et al. The Immune Epitope Database (IEDB) 3.0. Nucleic Acids Res. 43, D405–D412 (2014).

    PubMed  PubMed Central  Google Scholar 

  111. Abelin, J. G. et al. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 46, 315–326 (2017). This study uses MS to identify MHC class I-binding peptides from single-HLA-expressing cell lines. Corresponding data were used to train epitope prediction models, which outperform the standard by 2-fold.

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Bassani-Sternberg, M. et al. Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity. PLOS Comput. Biol. 13, e1005725 (2017).

    PubMed  PubMed Central  Google Scholar 

  113. Jurtz, V. et al. NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 3360–3368 (2017).

    CAS  PubMed  Google Scholar 

  114. Guillaume, P. et al. The C-terminal extension landscape of naturally presented HLA-I ligands. Proc. Natl Acad. Sci. USA 115, 5083–5088 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. The problem with neoantigen prediction [Editorial]. Nat. Biotechnol. 35, 97 (2017).

  116. Backert, L. & Kohlbacher, O. Immunoinformatics and epitope prediction in the age of genomic medicine. Genome Med. 7, 119 (2015).

    PubMed  PubMed Central  Google Scholar 

  117. Wan, Y. & Larson, D. R. Splicing heterogeneity: separating signal from noise. Genome Biol. 19, 86 (2018).

    PubMed  PubMed Central  Google Scholar 

  118. Matsuda, T. et al. Induction of neoantigen-specific cytotoxic T cells and construction of T cell receptor–engineered T cells for ovarian cancer. Clin. Cancer Res. 24, 5357–5367 (2018).

    PubMed  Google Scholar 

  119. Li, G. et al. T cell antigen discovery via trogocytosis. Nat. Methods 16, 183–190 (2019). This work and that by Joglekar et al. (2019) are the first two studies to develop cell-based TCR ligand screening platforms.

    CAS  PubMed  PubMed Central  Google Scholar 

  120. Joglekar, A. V. et al. T cell antigen discovery via signaling and antigen-presenting bifunctional receptors. Nat. Methods 16, 191–198 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Gee, M. H. et al. Antigen identification for orphan T cell receptors expressed on tumor-infiltrating lymphocytes. Cell 172, 549–563 (2018).

    CAS  PubMed  Google Scholar 

  122. Bentzen, A. K. et al. Large-scale detection of antigen-specific T cells using peptide–MHC-I multimers labeled with DNA barcodes. Nat. Biotechnol. 34, 1037–1045 (2016).

    CAS  PubMed  Google Scholar 

  123. Zhang, S.-Q. et al. High-throughput determination of the antigen specificities of T cell receptors in single cells. Nat. Biotechnol. 36, 1156–1159 (2018).

    CAS  Google Scholar 

  124. Dijkstra, K. K. et al. Generation of tumor-reactive T cells by co-culture of peripheral blood lymphocytes and tumor organoids. Cell 174, 1586–1598 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Vitiello, A. & Zanetti, M. Neoantigen prediction and the need for validation. Nat. Biotechnol. 35, 815–817 (2017).

    CAS  PubMed  Google Scholar 

  126. Jensen, P. E. Recent advances in antigen processing and presentation. Nat. Immunol. 8, 1041–1048 (2007).

    CAS  PubMed  Google Scholar 

  127. Andersen, R. S. et al. High frequency of T cells specific for cryptic epitopes in melanoma patients. Oncoimmunology 2, e25374 (2013).

    PubMed  PubMed Central  Google Scholar 

  128. Robbins, P. F. et al. The intronic region of an incompletely spliced gp100 gene transcript encodes an epitope recognized by melanoma-reactive tumor-infiltrating lymphocytes. J. Immunol. 159, 303–308 (1997).

    CAS  PubMed  Google Scholar 

  129. Lupetti, R. et al. Translation of a retained intron in tyrosinase-related protein (TRP) 2 mRNA generates a new cytotoxic T lymphocyte (CTL)-defined and shared human melanoma antigen not expressed in normal cells of the melanocytic lineage. J. Exp. Med. 188, 1005–1016 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. Aarnoudse, C. A., Doel, P. B. van den, Heemskerk, B. & Schrier, P. I. Interleukin-2-induced, melanoma-specific T cells recognize camel, an unexpected translation product of LAGE-1. Int. J. Cancer 82, 442–448 (1999).

    CAS  PubMed  Google Scholar 

  131. Slager, E. H. et al. CD4+ Th2 cell recognition of HLA-DR-restricted epitopes derived from CAMEL: a tumor antigen translated in an alternative open reading frame. J. Immunol. 170, 1490–1497 (2003).

    CAS  PubMed  Google Scholar 

  132. Slager, E. H. et al. Identification of multiple HLA-DR-restricted epitopes of the tumor-associated antigen CAMEL by CD4+ Th1/Th2 lymphocytes. J. Immunol. 172, 5095–5102 (2004).

    CAS  PubMed  Google Scholar 

  133. Vauchy, C. et al. CD20 alternative splicing isoform generates immunogenic CD4 helper T epitopes. Int. J. Cancer 137, 116–126 (2015). This study shows that an alternative splice variant of CD20 could give rise to HLA-DR1 binding epitopes and that vaccination with CD20-derived peptide was able to elicit epitope-specific CD4 + and CD8 + responses.

    CAS  PubMed  Google Scholar 

  134. Volpe, G. et al. Alternative BCR/ABL splice variants in philadelphia chromosome-positive leukemias result in novel tumor-specific fusion proteins that may represent potential targets for immunotherapy approaches. Cancer Res. 67, 5300–5307 (2007).

    CAS  PubMed  Google Scholar 

  135. Kobayashi, J. et al. Comparative study on the immunogenicity between an HLA-A24-restricted cytotoxic T cell epitope derived from survivin and that from its splice variant survivin-2B in oral cancer patients. J. Transl Med. 7, 1 (2009).

    PubMed  PubMed Central  Google Scholar 

  136. Wang, R.-F. et al. A breast and melanoma-shared tumor antigen: T cell responses to antigenic peptides translated from different open reading frames. J. Immunol. 161, 3596–3606 (1998).

    CAS  Google Scholar 

  137. Strønen, E. et al. Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science 352, 1337–1341 (2016).

    PubMed  Google Scholar 

  138. Bräunlein, E. & Krackhardt, A. M. Identification and characterization of neoantigens as well as respective immune responses in cancer patients. Front. Immunol. 8, 1702 (2017).

    PubMed  PubMed Central  Google Scholar 

  139. McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168, 613–628 (2017).

    CAS  PubMed  Google Scholar 

  140. Watanabe, K., Kuramitsu, S., Posey, A. D. J. & June, C. H. Expanding the therapeutic window for CAR T cell therapy in solid tumors: the knowns and unknowns of CAR T cell biology. Front. Immunol. 9, 2486 (2018).

    PubMed  PubMed Central  Google Scholar 

  141. Marinov, G. K. et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 24, 496–510 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Shalek, A. K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Yap, K. & Makeyev, E. V. Functional impact of splice isoform diversity in individual cells. Biochem. Soc. Trans. 44, 1079–1085 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  144. Fry, T. J. et al. CD22-targeted CAR T cells induce remission in B-ALL that is naive or resistant to CD19-targeted CAR immunotherapy. Nat. Med. 24, 20–28 (2018).

    CAS  PubMed  Google Scholar 

  145. O’Rourke, D. M. et al. A single dose of peripherally infused EGFRvIII-directed CAR T cells mediates antigen loss and induces adaptive resistance in patients with recurrent glioblastoma. Sci. Transl Med. 9, eaaa0984 (2017).

    PubMed  PubMed Central  Google Scholar 

  146. Sotillo, E. et al. Convergence of acquired mutations and alternative splicing of CD19 enables resistance to CART-19 immunotherapy. Cancer Discov. 5, 1282–1295 (2015). This study shows that resistance to CART-19 immunotherapy could be mediated by alternative splicing of CD19 compromising expression of the CART-19 epitope.

    CAS  PubMed  PubMed Central  Google Scholar 

  147. Hegde, M. et al. Combinational targeting offsets antigen escape and enhances effector functions of adoptively transferred T cells in glioblastoma. Mol. Ther. 21, 2087–2101 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  148. Bethune, M. T. et al. Isolation and characterization of NY-ESO-1-specific T cell receptors restricted on various MHC molecules. Proc. Natl Acad. Sci. USA 115, E10702–E10711 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. d’Urso, C. M. et al. Lack of HLA class I antigen expression by cultured melanoma cells FO-1 due to a defect in B2m gene expression. J. Clin. Invest. 87, 284–292 (1991).

    PubMed  PubMed Central  Google Scholar 

  150. Restifo, N. P. et al. Loss of functional beta2-microglobulin in metastatic melanomas from five patients receiving immunotherapy. J. Natl Cancer Inst. 88, 100–108 (1996).

    CAS  PubMed  Google Scholar 

  151. Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Sade-Feldman, M. et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136 (2017).

    PubMed  PubMed Central  Google Scholar 

  153. Gettinger, S. et al. Impaired HLA class I antigen processing and presentation as a mechanism of acquired resistance to immune checkpoint inhibitors in lung cancer. Cancer Discov. 7, 1420–1435 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Chang, C.-C., Campoli, M., Restifo, N. P., Wang, X. & Ferrone, S. Immune selection of hot-spot β2-microglobulin gene mutations, HLA-A2 allospecificity loss, and antigen-processing machinery component down-regulation in melanoma cells derived from recurrent metastases following immunotherapy. J. Immunol. 174, 1462–1471 (2005).

    CAS  PubMed  Google Scholar 

  156. Elkon, R., Ugalde, A. P. & Agami, R. Alternative cleavage and polyadenylation: extent, regulation and function. Nat. Rev. Genet. 14, 496–506 (2013).

    CAS  PubMed  Google Scholar 

  157. Tian, B. & Manley, J. L. Alternative polyadenylation of mRNA precursors. Nat. Rev. Mol. Cell. Biol. 18, 18–30 (2017).

    CAS  PubMed  Google Scholar 

  158. Singh, I. et al. Widespread intronic polyadenylation diversifies immune cell transcriptomes. Nat. Commun. 9, 1716 (2018).

    PubMed  PubMed Central  Google Scholar 

  159. Alt, F. W. et al. Synthesis of secreted and membrane-bound immunoglobulin mu heavy chains is directed by mRNAs that differ at their 3′ ends. Cell 20, 293–301 (1980).

    CAS  PubMed  Google Scholar 

  160. Mayr, C. & Bartel, D. P. Widespread shortening of 3′ UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells. Cell 138, 673–684 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  161. Ni, T. K. & Kuperwasser, C. Premature polyadenylation of MAGI3 produces a dominantly-acting oncogene in human breast cancer. eLife 5, e14730 (2016).

    PubMed  PubMed Central  Google Scholar 

  162. Lee, S.-H. et al. Widespread intronic polyadenylation inactivates tumour suppressor genes in leukaemia. Nature 561, 127–131 (2018). This work reveals that intronic polyadenylation is widespread in leukaemia and is a common mechanism of tumour-suppressor inactivation.

    CAS  PubMed  PubMed Central  Google Scholar 

  163. Dubbury, S. J., Boutz, P. L. & Sharp, P. A. CDK12 regulates DNA repair genes by suppressing intronic polyadenylation. Nature 564, 141–145 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. Lianoglou, S., Garg, V., Yang, J. L., Leslie, C. S. & Mayr, C. Ubiquitously transcribed genes use alternative polyadenylation to achieve tissue-specific expression. Genes Dev. 27, 2380–2396 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  165. Bass, B. L. RNA editing by adenosine deaminases that act on RNA. Annu. Rev. Biochem. 71, 817–846 (2002).

    CAS  PubMed  Google Scholar 

  166. Nishikura, K. Functions and regulation of RNA editing by ADAR deaminases. Annu. Rev. Biochem. 79, 321–349 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  167. Speyer, J. F., Lengyel, P., Basilio, C. & Ochoa, S. Synthetic polynucleotides and the amino acid code. II. Proc. Natl Acad. Sci. USA 48, 63–68 (1962).

    CAS  PubMed  Google Scholar 

  168. Han, L. et al. The genomic landscape and clinical relevance of A-to-I RNA editing in human cancers. Cancer Cell 28, 515–528 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  169. Fumagalli, D. et al. Principles governing A-to-I RNA editing in the breast cancer transcriptome. Cell Rep. 13, 277–289 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  170. Paz-Yaacov, N. et al. Elevated RNA editing activity is a major contributor to transcriptomic diversity in tumors. Cell Rep. 13, 267–276 (2015).

    CAS  PubMed  Google Scholar 

  171. Chen, Y., Wang, H., Lin, W. & Shuai, P. ADAR1 overexpression is associated with cervical cancer progression and angiogenesis. Diagn. Pathol. 12, 12 (2017).

    PubMed  PubMed Central  Google Scholar 

  172. Zhang, M. et al. RNA editing derived epitopes function as cancer antigens to elicit immune responses. Nat. Commun. 9, 3919 (2018). This study shows that RNA editing-derived epitopes are immunogenic and can broaden the immunotherapy target space.

    PubMed  PubMed Central  Google Scholar 

  173. Yang, W. et al. Immunogenic neoantigens derived from gene fusions stimulate T cell responses. Nat. Med. 25, 767–775 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  174. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    CAS  PubMed  Google Scholar 

  175. Pimentel, H., Bray, N. L., Puente, S., Melsted, P. & Pachter, L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat. Methods 14, 687–690 (2017).

    CAS  PubMed  Google Scholar 

  176. Froussios, K., Mourão, K., Simpson, G. G., Barton, G. J. & Schurch, N. J. Identifying differential isoform abundance with RATs: a universal tool and a warning. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/132761v2 (2017).

  177. Trincado, J. L. et al. SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions. Genome Biol. 19, 40 (2018).

    PubMed  PubMed Central  Google Scholar 

  178. Nowicka, M. & Robinson, M. D. DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics. F1000Res. 5, 1356 (2016).

    PubMed  PubMed Central  Google Scholar 

  179. Katz, Y., Wang, E. T., Airoldi, E. M. & Burge, C. B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009–1015 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  180. Shen, S. et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc. Natl Acad. Sci. USA 111, E5593–E5601 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  181. Vaquero-Garcia, J. et al. A new view of transcriptome complexity and regulation through the lens of local splicing variations. eLife 5, e11752 (2016).

    PubMed  PubMed Central  Google Scholar 

  182. Li, Y. I. et al. Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50, 151–158 (2018).

    CAS  PubMed  Google Scholar 

  183. Kahles, A., Ong, C. S., Zhong, Y. & Rätsch, G. SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data. Bioinformatics 32, 1840–1847 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  184. Wang, Q. & Rio, D. C. JUM is a computational method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patterns. Proc. Natl Acad. Sci. USA 115, E8181–E8190 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  185. Sterne-Weiler, T., Weatheritt, R. J., Best, A. J., Ha, K. C. & Blencowe, B. J. Efficient and accurate quantitative profiling of alternative splicing patterns of any complexity on a laptop. Mol. Cell 72, 187–200 (2018).

    CAS  PubMed  Google Scholar 

  186. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  187. O’Donnell, T. J. et al. MHCflurry: open-source class I MHC binding affinity prediction. Cell Syst. 7, 129–132 (2018).

    PubMed  Google Scholar 

  188. Andreatta, M. & Nielsen, M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32, 511–517 (2015).

    PubMed  PubMed Central  Google Scholar 

  189. Zhang, H., Lund, O. & 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  190. Karosiene, E., Lundegaard, C., Lund, O. & Nielsen, M. NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. Immunogenetics 64, 177–186 (2012).

    CAS  PubMed  Google Scholar 

  191. Bhattacharya, R. et al. Evaluation of machine learning methods to predict peptide binding to MHC class I proteins. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/154757v2 (2017).

  192. Han, Y. & Kim, D. Deep convolutional neural networks for pan-specific peptide–MHC class I binding prediction. BMC Bioinformatics 18, 585 (2017).

    PubMed  PubMed Central  Google Scholar 

  193. Vang, Y. S. & Xie, X. HLA class I binding prediction via convolutional neural networks. Bioinformatics 33, 2658–2665 (2017).

    CAS  PubMed  Google Scholar 

  194. Rasmussen, M. et al. Pan-specific prediction of peptide–MHC class I complex stability, a correlate of T cell immunogenicity. J. Immunol. 197, 1517–1524 (2016).

    CAS  PubMed  Google Scholar 

  195. Jørgensen, K. W., Rasmussen, M., Buus, S. & Nielsen, M. NetMHCstab—predicting stability of peptide–MHC-I complexes; impacts for cytotoxic T lymphocyte epitope discovery. Immunology 141, 18–26 (2014).

    PubMed  Google Scholar 

  196. Rammensee, H., Bachmann, J., Emmerich, N. P., Bachor, O. A. & Stevanovic, S. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50, 213–219 (1999).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Preparation of this review was supported by an endowment provided by the Raymond and Beverly Sackler Foundation, the Parker Institute for Cancer Immunotherapy and the National Cancer Institute (grant 1U54 CA199090-01).

Author information

Authors and Affiliations

Authors

Contributions

The authors contributed equally to all aspects of the article.

Corresponding authors

Correspondence to David Baltimore or Guideng Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Immunology thanks Zlatko Trajanoski and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Glossary

Major histocompatibility complex

(MHC). A set of genes that code for cell surface proteins (most notably the MHC class I and class II glycoproteins) that are responsible for presenting antigens to lymphocytes.

Adoptive cell therapy

A type of immunotherapy approach that uses antigen-specific T cells to treat patients with chronic viral infections or various malignancies.

Non-synonymous mutation

A nucleotide mutation that changes the amino acid sequence of a protein.

Neoantigens

Newly formed antigens that have not been previously recognized by the immune system.

Cancer germline antigens

Antigens that are normally exclusively expressed in germline cells but have aberrant expression in tumours, such as NY-ESO-1.

Tumour mutational burden

(TMB). Also referred to as the tumour mutational load, this is a measurement of mutations carried by tumour tissue taken from a patient.

Tumour-specific antigens

(TSAs). Antigens that are exclusively presented by tumour cells but not by any other cells.

RNA editing

A molecular process resulting in alteration of the RNA sequence before translating to protein.

Alternative polyadenylation

(APA). An RNA-processing event that generates distinct 3ʹ termini on mRNAs and other RNA polymerase II transcripts.

Alternative splicing

A regulated process during gene expression that results in a single gene coding for multiple proteins.

Intron retention

A form of alternative splicing that results in inclusion of introns in the final protein product.

Spliceosome

The multi-megadalton ribonucleoprotein complex responsible for removing introns from pre-mRNA sequences.

The Cancer Genome Atlas

(TCGA). The world’s largest and richest collection of genomic data.

Nonsense-mediated decay

A translation-coupled mechanism that degrades mRNAs harbouring premature translation-termination codons.

Splice-site-creating mutations

(SCMs). Genomic mutations that induce splice-site creation. Often mis-annotated as missense and silent mutations.

Clinical Proteomic Tumor Analysis Consortium

(CPTAC). The first large-scale project that produced proteomics data sets from the mass spectrometric interrogation of tumour samples previously studied by The Cancer Genome Atlas programme.

Neojunctions

Novel exon–exon junctions found in tumour samples that are not typically found in healthy tissue.

Indels

Insertion or deletion of nucleotides into genomic DNA, less than 1 kb in length.

Checkpoint inhibitors

(CPIs). Types of drug that block the inhibitory checkpoint molecules.

Crossreactivity

The recognition of two or more peptide–major histocompatibility complex complexes by a T cell receptor.

Immune Epitope Database

(IEDB). A database containing detailed information for more than 100,000 unique immune epitopes related to infectious and immune-mediated diseases.

Trogocytosis

A biological process where interacting cells share membrane and membrane-associated proteins.

Chimeric antigen receptor

(CAR). Recombinant receptor protein that has been engineered to direct T cells to target a specific protein on malignant cells.

Loss of heterozygosity

A common somatic genome event that results in loss of the entire gene and the surrounding chromosomal region.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Frankiw, L., Baltimore, D. & Li, G. Alternative mRNA splicing in cancer immunotherapy. Nat Rev Immunol 19, 675–687 (2019). https://doi.org/10.1038/s41577-019-0195-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41577-019-0195-7

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing