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Activity Number: 581 - Advanced Cross-Disciplinary Statistical Methods in Statistical Genomics
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #312493
Title: Utilizing Patient Information to Identify Subtype Heterogeneity of Cancer Driver Genes
Author(s): Bin Zhu* and Ho-Hsiang Wu and Xing Hua and Jianxin Shi and Nilanjan Chatterjee
Companies: NCI and Food and Drug Administration and Fred Hutchinson Cancer Research Center and National Cancer Institute and Johns Hopkins University
Keywords: cancer ; driver gene; selection pressure; subtype heterogeneity

Identifying cancer driver genes is essential for understanding mechanisms of carcinogenesis and designing therapeutic strategies. A number of driver genes have been identified for many cancer types. However, whether selection pressure of driver genes is homogeneous across cancer subtypes is less known. We propose a statistical framework MutScot to improve the identification of driver genes and to investigate the heterogeneity of driver genes across cancer subtypes. Through simulation studies, we show that MutScot properly controls the type I error and is more powerful for detecting driver genes. In addition, we demonstrate that MutScot is capable of identifying subtype heterogeneity of driver genes, which is infeasible by other methods. Applications to three studies in The Cancer Genome Atlas (TCGA) project showcase that MutScot has a better sensitivity for detecting driver genes and that MutScot identifies subtype heterogeneity of driver genes in breast cancer with regards to the status of hormone receptor.

Authors who are presenting talks have a * after their name.

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