Prediction of tumor-specific splicing from somatic mutations as a source of neoantigen candidates

Abstract

Splicing is dysregulated in many tumors and may result in tumor-specific transcripts that can encode neoantigens, which are promising targets for cancer immunotherapy. Detecting tumor-specific splicing is challenging because many non-canonical splice junctions identified in tumor transcriptomes also appear in healthy tissues. However, somatic mutations can disrupt canonical splicing motifs or create novel ones leading to true tumor-specific targets. Here, we developed splice2neo to integrate the predicted splice effects from somatic mutations with splice junctions detected in tumor RNA-seq for individual cancer patients. Using splice2neo, we excluded canonical splice junctions and splice junctions from healthy tissue samples, annotated resulting transcript and peptide sequences, and integrated targeted re-quantification of supporting RNA-seq reads. By analyzing melanoma patient cohorts, we established a stringent detection rule to predict splice junctions as mutation-derived and tumor-specific targets. In an independent melanoma cohort, we identified 1.7 target splice junctions per tumor with an estimated false discovery rate of less than 5% and established tumor-specificity using additional healthy tissue samples. For individual examples of exon skipping events, we confirmed the expression in tumor-derived RNA by quantitative real-time PCR experiments. Most target splice junctions encoded at least one neoepitope candidate with predicted MHC-I or MHC-II binding. Compared to neoepitope candidates derived from non-synonymous point mutations, the splicing-derived neoepitope candidates had a lower self-similarity to corresponding wild-type peptides. In conclusion, identifying mutation-derived and tumor-specific splice junctions can lead to additional neoantigen candidates to expand the target repertoire for cancer immunotherapies.