Dataset Information

Mutational Analysis of Gene Fusions Predicts Novel MHC Class I-Restricted T-Cell Epitopes and Immune Signatures in a Subset of Prostate Cancer.

ABSTRACT: Purpose: Gene fusions are frequently found in prostate cancer and may result in the formation of unique chimeric amino acid sequences (CASQ) that span the breakpoint of two fused gene products. This study evaluated the potential for fusion-derived CASQs to be a source of tumor neoepitopes, and determined their relationship to patterns of immune signatures in prostate cancer patients.Experimental Design: A computational strategy was used to identify CASQs and their corresponding predicted MHC class I epitopes using RNA-Seq data from The Cancer Genome Atlas of prostate tumors. In vitro peptide-specific T-cell expansion was performed to identify CASQ-reactive T cells. A multivariate analysis was used to relate patterns of in silico-predicted tumor-infiltrating immune cells with prostate tumors harboring these mutational events.Results: Eighty-seven percent of tumors contained gene fusions with a mean of 12 per tumor. In total, 41% of fusion-positive tumors were found to encode CASQs. Within these tumors, 87% gave rise to predicted MHC class I-binding epitopes. This observation was more prominent when patients were stratified into low- and intermediate/high-risk categories. One of the identified CASQ from the recurrent TMPRSS2:ERG type VI fusion contained several high-affinity HLA-restricted epitopes. These peptides bound HLA-A*02:01 in vitro and were recognized by CD8+ T cells. Finally, the presence of fusions and CASQs were associated with expression of immune cell infiltration.Conclusions: Mutanome analysis of gene fusion-derived CASQs can give rise to patient-specific predicted neoepitopes. Moreover, these fusions predicted patterns of immune cell infiltration within a subgroup of prostate cancer patients. Clin Cancer Res; 23(24); 7596-607. ©2017 AACR.

SUBMITTER: Kalina JL

PROVIDER: S-EPMC5831673 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Mutational Analysis of Gene Fusions Predicts Novel MHC Class I-Restricted T-Cell Epitopes and Immune Signatures in a Subset of Prostate Cancer.

Kalina Jennifer L JL Neilson David S DS Lin Yen-Yi YY Hamilton Phineas T PT Comber Alexandra P AP Loy Emma M H EMH Sahinalp S Cenk SC Collins Colin C CC Hach Faraz F Lum Julian J JJ

Clinical cancer research : an official journal of the American Association for Cancer Research 20170927 24

<b>Purpose:</b> Gene fusions are frequently found in prostate cancer and may result in the formation of unique chimeric amino acid sequences (CASQ) that span the breakpoint of two fused gene products. This study evaluated the potential for fusion-derived CASQs to be a source of tumor neoepitopes, and determined their relationship to patterns of immune signatures in prostate cancer patients.<b>Experimental Design:</b> A computational strategy was used to identify CASQs and their corresponding pre ...[more]

PMID: 28954787

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Project description:T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected C57BL/6 mice (expressing H-2Db and H-2Kb), considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.

Dataset Information

Mutational Analysis of Gene Fusions Predicts Novel MHC Class I-Restricted T-Cell Epitopes and Immune Signatures in a Subset of Prostate Cancer.

Publications

Mutational Analysis of Gene Fusions Predicts Novel MHC Class I-Restricted T-Cell Epitopes and Immune Signatures in a Subset of Prostate Cancer.

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