An integrative computational framework for personalized detection of tumor epitopes in melanoma immunotherapy

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1 Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Bavaria, Germany
2 Department of Systems Biology and Bioinformatics, Universität Rostock, Rostock, Germany
3 Department of Biology, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Bavaria, Germany
DOI
10.7287/peerj.preprints.2385v1
Published
Accepted
Subject Areas
Bioinformatics, Dermatology, Immunology
Keywords
Personalized anti-cancer immunotherapy, epitope prediction, docking studies, NGS data, dendritic cells
Copyright
© 2016 Jaitly et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Jaitly T, Schaft N, Doerrie J, Gross S, Schuler-Thurner B, Wolkenhauer O, Schuler G, Taher L, Gupta S, Vera J. 2016. An integrative computational framework for personalized detection of tumor epitopes in melanoma immunotherapy. PeerJ Preprints 4:e2385v1

Abstract

In aggressive solid tumors like melanoma, a strategy for therapy personalization can be achieved by combining high-throughput data on the patient’s specific tumor mutation and expression profiles. A remarkable case is dendritic cell-based immunotherapy, where tumor epitopes identified from the patient’s specific mutation profiles are loaded on patient-derived mature dendritic cells to stimulate cytotoxic T cell mediated anticancer immunity. Here we present a personalized computational pipeline for the selection of tumor-specific epitopes based on 1) patient specific haplotype; 2) cancer associated mutations; and 3) expression profiles of mutation carrying genes. We applied our workflow to one melanoma patient. Specifically, we analyzed tumor whole exome sequencing and RNA sequencing data to first detect tumor-specific mutations followed by epitope prediction based on the patient’s HLA haplotype and filtering of epitopes using expression profile and binding affinity. We performed docking studies to predict the best set of epitopes targeting the patient’s alleles. The proposed workflow enables us to find personalized tumor-specific epitopes for stimulating cytotoxic T-cell responses.

Author Comment

This is an article which has been accepted for the "GCB 2016 Conference".