Abstract PR15: CanSplice: A user-friendly web application for analysis of genomic datasets by race and ethnicity

Abstract

Abstract The rate of cancer incidence and mortality differ in populations of different ancestry. Comparative analyses of cancer specimens have identified race- and ethnicity-related differential mutation spectra and gene expression, RNA splicing, and epigenetic patterns. These findings suggest that race- and ethnicity-related molecular mechanisms have the potential to contribute to cancer aggressiveness in individuals of different racial and ethnic groups. Advances in technology have enabled researchers to perform high-throughput genome-wide analyses of cancer specimens, including of the exome, transcriptome, proteome, and methylome. Such analyses have generated large amounts of data, which can be challenging for many cancer researchers to analyze. In addition, although many user-friendly tools have been developed for mining big data, there is an urgent need for tools that enable the mining of big data by patient race and ethnicity. Here, we developed a user-friendly online application, CanSplice, that enables the cancer disparity research community to interactively navigate and visualize genomic datasets and perform comparative and correlative analyses. Additionally, researchers using CanSplice can evaluate the association between genomics and patient survival probability in the context of patient race and ethnicity. Uniquely, CanSplice enables exploration of RNA splicing datasets by race and ethnicity and analysis of the association between a given RNA splicing event and patient survival probability. Currently, CanSplice includes data from The Cancer Genome Atlas (TCGA) and TCGASpliceSeq; however, new data from additional cohorts will be added continuously. In conclusion, CanSplice is a user-friendly online application that enables innovative exploration of publicly available cancer genomic datasets by patient race and ethnicity for hypothesis generation. Identified targets can then be validated and investigated for functionality in the laboratory. Thus, data from CanSplice will further understanding of molecular mechanisms underlying cancer disparities and has the potential to aid in the development of novel precision medicine interventions to mitigate cancer disparities. Citation Format: Muthana Al Abo, Steven R. Patierno, Jennifer A. Freedman. CanSplice: A user-friendly web application for analysis of genomic datasets by race and ethnicity [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PR15.