Abstract A05: Biological factors associated with cancer health disparity - a high-throughput approach using big data

Abstract

Abstract The accumulation of public available high-throughput experimental data has made analyzing the data a bottleneck in scientific discovery. In this study, we explore a computational, high-throughput approach for identifying biological factors associated with cancer health disparity using the data available at the Cancer Genome Atlas (TCGA). We developed an integrative genomic analysis (IGA) tool, which performs multiple analysis tasks, including gene/protein level comparison, gene set level comparison and network/pathway level comparison. Several data types can be analyzed, including RNAseq, DNA methylation, miRNAseq, and protein expressions. By varying cancer types, race groups, and data types, we are able to generate a large set of novel findings, which may shed light on the biological factors associated with cancer health disparity for various cancer types and race groups. Interactive reports are generated to facilitate further exploration of the biological significance of the findings. With this high-throughput approach, the bottleneck for us now shifts to publishing the results. We invite researchers in cancer health disparity community to collaborate with us to publish the findings. Citation Format: Kaixian Yu, Yun Xu, Ke Tang, Albert Steppi, Jinfeng Zhang. Biological factors associated with cancer health disparity - a high-throughput approach using big data. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr A05.