Bioinformatics Methods for Prediction of Splice Variant Neoantigens

Abstract

Tumor-specific peptide epitopes that are generated from mutated genes and presented on cell surface MHC molecules, known as neoantigens, are attractive targets for therapeutic vaccination given the lack of central tolerance and corresponding presence of endogenous T cells that recognize them. Currently most available neoantigen prediction methods focus on predicting neoantigens derived from missense mutations or indels. In acute myeloid leukemia (AML), there are markedly fewer mutations and predicted neoantigens in the cancer genome compared to other cancers, so it is less feasible to target neoantigens derived from missense mutations and indels in AML. However, mutations in spliceosomal genes and genome-wide aberrant splicing events are common in patients with AML. In work contributed to by our group, a small number of splice variant neoantigens have been found to exist in cancer. Herein, we report the development of robust method, NeoSplice, to predict splice variant neoantigens from massively parallel RNA sequencing (RNA-Seq) data. One of the computational challenges for predicting splice variant neoantigens is to infer the novel transcript isoforms derived from tumor-specific splicing events. We utilized a Burrows Wheeler Transform (BWT) based algorithm to identify tumor specific k-mers and used a splice graph to determine whether such a k-mer represents a tumor-specific splice junction in a coding region and its corresponding amino-acid sequence. A frame-shift relative to the normal can easily lead to a novel peptide sequence that may be an actionable neoantigen. Most current neoantigen calling algorithms primarily rely on epitope/MHC binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity using approaches modeled from tumor-specific antigen data. We developed an algorithm based on peptide-intrinsic biochemical features associated with neoantigen and minor histocompatibility mismatch antigen (mHA) immunogenicity and present a gradient–boosting algorithm for predicting tumor antigen immunogenicity. In addition, as part of PhD training in bioinformatics analysis to complement training in methods development, we performed comprehensive genomic and immune characterizations of bladder tumors and triple-negative breast cancer brain metastases to gain novel insight about biomarkers that can be used with potential immunotherapies.Doctor of Philosoph