Inviting an author to review:
Find an author and click ‘Invite to review selected article’ near their name.
Search for authorsSearch for similar articles
18
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Systematic Analysis of Splice-Site-Creating Mutations in Cancer

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          SUMMARY

          For the past decade, cancer genomic studies have focused on mutations leading to splice-site disruption, overlooking those having splice-creating potential. Here, we applied a bioinformatic tool, MiSplice, for the large-scale discovery of splice-site-creating mutations (SCMs) across 8,656 TCGA tumors. We report 1,964 originally mis-annotated mutations having clear evidence of creating alternative splice junctions. TP53 and GATA3 have 26 and 18 SCMs, respectively, and ATRX has 5 from lower-grade gliomas. Mutations in 11 genes, including PARP1, BRCA1, and BAP1, were experimentally validated for splice-site-creating function. Notably, we found that neoantigens induced by SCMs are likely several folds more immunogenic compared to missense mutations, exemplified by the recurrent GATA3 SCM. Further, high expression of PD-1 and PD-L1 was observed in tumors with SCMs, suggesting candidates for immune blockade therapy. Our work highlights the importance of integrating DNA and RNA data for understanding the functional and the clinical implications of mutations in human diseases.

          In Brief

          Jayasinghe et al. identify nearly 2,000 splice-site-creating mutations (SCMs) from over 8,000 tumor samples across 33 cancer types. They provide a more accurate interpretation of previously misannotated mutations, highlighting the importance of integrating data types to understand the functional and the clinical implications of splicing mutations in human disease.

          Related collections

          Most cited references41

          • Record: found
          • Abstract: found
          • Article: not found

          Cancer immunotherapy. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells.

          T cell immunity directed against tumor-encoded amino acid substitutions occurs in some melanoma patients. This implicates missense mutations as a source of patient-specific neoantigens. However, a systematic evaluation of these putative neoantigens as targets of antitumor immunity is lacking. Moreover, it remains unknown whether vaccination can augment such responses. We found that a dendritic cell vaccine led to an increase in naturally occurring neoantigen-specific immunity and revealed previously undetected human leukocyte antigen (HLA) class I-restricted neoantigens in patients with advanced melanoma. The presentation of neoantigens by HLA-A*02:01 in human melanoma was confirmed by mass spectrometry. Vaccination promoted a diverse neoantigen-specific T cell receptor (TCR) repertoire in terms of both TCR-β usage and clonal composition. Our results demonstrate that vaccination directed at tumor-encoded amino acid substitutions broadens the antigenic breadth and clonal diversity of antitumor immunity.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Gapped sequence alignment using artificial neural networks: application to the MHC class I system.

            Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8-11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis

              The focus of tumour-specific antigen analyses has been on single nucleotide variants (SNVs), with the contribution of small insertions and deletions (indels) less well characterised. We investigated whether the frameshift nature of indel mutations, which create novel open reading frames and a large quantity of mutagenic peptides highly distinct from self, might contribute to the immunogenic phenotype.
                Bookmark

                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                13 June 2018
                03 April 2018
                23 July 2018
                : 23
                : 1
                : 270-281.e3
                Affiliations
                [1 ]Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA
                [2 ]McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
                [3 ]Division of Oncology, Washington University in St. Louis, St. Louis, MO 63110, USA
                [4 ]Department of Genetics, Washington University in St. Louis, St. Louis, MO 63110, USA
                [5 ]Department of Mathematics, Washington University in St. Louis, St. Louis, MO 63130, USA
                [6 ]Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
                [7 ]Institute for Systems Biology, Seattle, WA 98109, USA
                [8 ]Institut Curie, 75248 Paris Cedex, France
                [9 ]MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, 77300 Fontainebleau, France
                [10 ]INSERM U900, 75248 Paris Cedex, France
                [11 ]Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
                [12 ]Curriculum in Bioinformatics and Computational Biology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
                [13 ]Division of Nephrology, Washington University in St. Louis, St. Louis, MO 63110, USA
                [14 ]Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY 10016, USA
                [15 ]Institute for Systems Genetics, New York University School of Medicine, New York, NY 10016, USA
                [16 ]Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
                [17 ]Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA
                [18 ]Catalan Institution of Research and Advanced Studies (ICREA), 08010 Barcelona, Spain
                [19 ]Computational RNA Biology Group, Pompeu Fabra University (UPF), 08003 Barcelona, Spain
                Author notes
                [20]

                These authors contributed equally

                [21]

                Senior author

                [22]

                Lead Contact

                Article
                NIHMS958979
                10.1016/j.celrep.2018.03.052
                6055527
                29617666
                3af75dc5-127c-442a-b26c-32248335395b

                This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Categories
                Article

                Cell biology
                Cell biology

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content538

                Cited by112

                Most referenced authors1,160