Genome-wide mapping and characterization of exitrons in human cancer

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Project Summary & Abstract Advance in sequencing technology and computational algorithms revealed various alternative splicing variations in cancer transcriptome. Although several common classes of splicing events, such as exon skipping, intron retention and alternative splice sites, have been linked to tumor progression and therapy resistance, the roles of many non-canonical splicing events in cancer remain unknown due to the lack of dedicated approaches to detect and characterize these events. This proposal will focus on exitron splicing events because emerging evidence revealed they are dysregulated in cancer and occurred frequently in cancer-related genes. An exitron is an internal region within a coding exon that has splicing potential to create a cryptic intron. Splicing of exitron results in protein isoforms with altered sequences that may affect functional domains and post-translational modification sites. The observations of exitron splicing occurred in cancer genes suggest that exitron-spliced isoforms may contribute to cancer development. Furthermore, tumor- specific exitron splicing junctions resulting internal deletions or frameshifts may generate immunogenic peptides (i.e., neoantigens) that could form a basis for developing cancer vaccines or T-cell therapeutic targets. In this proposal, we will develop customized computational methods and conduct integrative multi- omics analysis with the goal to uncover the regulation of exitron splicing, driver exitron splicing events and neoantigens derived from tumor-specific exitrons in cancers. (Aim 1) We will develop an integrated framework to detect and validate exitrons with joint analysis of multi-omics data generated by multiple sequencing platforms. We will identify splicing factors that preferentially affect exitron splicing in cancers. (Aim 2) We will develop novel statistical approaches to identify genes and pathways enriched with exitron splicing alterations. We will implement a semi-supervised machine learning model to predict exitron splicing-associated cancer driver genes based on transcriptomic features. (Aim 3) We will develop a computational tool to identify splicing- derived neoantigens and validate them through mass spectrometry-based immunopeptidome data. We will assess the association of exitron splicing-derived neoantigens with clinical outcomes in patients receiving immune checkpoint inhibitor therapy. This project will provide a unique computational platform for dedicated exitron splicing analyses. The knowledge gained from this proposed study will help to understand the underlying mechanisms by which exitron alterations promote cancer progression. We expect that these analyses will be rapidly translated into clinical utility by providing new approaches to predict patient response in immune checkpoint inhibition therapies.