Abstract LB-96: Exploring personalized pathways enriched with putative cancer genes identified by next generation sequencing

Abstract

Abstract Keywords: pathway enrichment test; pathway crosstalk; co-mutation; lung adenocarcinomas; glioblastoma; gene length; cancer mutation genes Contact information of the corresponding author: Zhongming Zhao, Ph.D. Department of Biomedical Informatics Vanderbilt University School of Medicine 2525 West End Avenue, Suite 600 Nashville, TN 37203, USA Phone: (615) 343-9158 FAX: (615) 936-8545 Email: zhongming.zhao@vanderbilt.edu List of abbreviations: GBM, Glioblastoma multiforme; NGS, next generation sequencing. Financial support: None. Abstract Pathway analysis of a set of genes represents an important area in large-scale omic data analysis. However, the application of traditional pathway enrichment methods to next-generation sequencing (NGS) data is prone to several potential biases including genomic/genetic factors (e.g., the particular disease and gene length) and environmental factors (e.g., personal life-style and frequency and dosage of exposure to mutagens). We proposed a novel method for the pathway analysis of NGS mutation data by explicitly taking into account the gene-wise mutation rate. We estimated the gene-wise mutation rate based on the individual-specific background mutation rate, which makes different samples comparable, along with the gene length, which makes different genes comparable. The mutation rate was then utilized as the weight for each gene in our weighted resampling strategy, which builds the null distribution for each pathway while matching the gene length patterns. The empirical P value obtained then provides an adjusted statistical evaluation. We demonstrated our weighted resampling method to a lung adenocarcinomas dataset and a glioblastoma (GBM) dataset, and compared it to other widely applied methods. By explicitly adjusting gene-length, the weighted resampling method effectively reject many marginally significant pathways detected by standard methods, including several long gene based, cancer unrelated pathways. We further demonstrated that by reducing such biases, pathway crosstalk for each individual and pathway co-mutation map across multiple individuals can be objectively explored and evaluated. This method performs pathway analysis in a sample-centered fashion, providing an alternative way for accurate analysis of cancer personalized genomic data. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-96. doi:1538-7445.AM2012-LB-96