Abstract 3577: Splice expression variation analysis (SEVA) for differential gene isoform usage in cancer
Authors: Afsari Bahman , Guo Theresa , Considine Michael , Kelley Dylan , Flam Emily , Florea Liliana , Ha Patrick , Geman Donald , Ochs Michael F. , Califano Joseph A. , Gaykalova Daria A. , Favorov Alexander V. , Fertig Elana J. 
Published: July 2017

Alternative splicing events (ASE) are a significant component of expression alterations in cancer, and have been demonstrated to be critically important in the development of malignant phenotypes in a variety of tumors. These alternative gene isoforms alter cell-signaling networks and serve as a hidden source of tumor-driving alterations not identified in multi-omics analyses. Recent studies have demonstrated that reads from RNA-seq data can infer gene isoforms expressed in a single sample. Therefore, RNA-seq data of tumors offers the opportunity to systematically evaluate expressed gene isoforms and identify splicing events in cancer samples.

To characterize a cancer specific ASEs landscape, it is essential to perform differential splice variant expression analysis to identify isoform variants that are unique to tumor samples compared to normal tissue. In spite of the breadth of ASE algorithms, few have been validated in primary tumor samples. Current methods for differential splice variant analysis compare mean expression of gene isoforms in sample groups. Because these variants are tumor-specific, ASEs are expected to have more variable exon junction expression than normal samples. Therefore, current differential ASE analysis algorithms from RNA-seq may not account for heterogeneous gene isoform usage in tumors. To address this, we introduce Splice Expression Variability Analysis (SEVA) to detect differential splice variation usage in tumor and normal samples and accounts for tumor heterogeneity. This algorithm compares the degree of variability of junction expression profiles within a population of normal samples relative to that in tumor samples.

The performance of SEVA was compared with two existing algorithms, EBSeq and DiffSplice, in simulated and real RNA-seq data. Simulated data suggest that SEVA is robust and computationally efficient relative to EBSeq and DiffSplice. In contrast to EBSeq and DiffSplice, SEVA was able to identify alternative splicing events independent of overall gene expression differences. Finally, additional validation was performed using RNA-seq data for primary tumor data from HPV-positive oropharynx squamous cell carcinoma (OPSCC) tumors and normal samples from both TCGA and an independent tumor cohort of 46 OPSCC tumors and 25 normal samples. In these tumor samples, SEVA finds cancer-specific ASEs in genes that are independent of their differential expression status. Moreover, SEVA finds approximately hundreds of splice variant candidates, manageable for experimental validation in contrast to the thousands of candidates found with EBSeq or DiffSplice. These candidates include experimentally validated splice variants in HNSCC from a previous microarray study. Based on performance in both simulated and real data, SEVA represents a robust algorithm that is well suited for differential ASE analysis, particularly in RNA-sequencing data from heterogeneous primary tumor samples.

Citation Format: Bahman Afsari, Theresa Guo, Michael Considine, Dylan Kelley, Emily Flam, Liliana Florea, Patrick Ha, Donald Geman, Michael F. Ochs, Joseph A. Califano, Daria A. Gaykalova, Alexander V. Favorov, Elana J. Fertig. Splice expression variation analysis (SEVA) for differential gene isoform usage in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3577. doi:10.1158/1538-7445.AM2017-3577