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Sequencing-based computational methods for identifying impactful genomic alterations in cancers

Abstract

Recent advances in collecting sequencing data from tumors is promising for both immediate individual patient treatment and investigation of cancer mechanisms. A resultant central goal is identifying changes in tumors that are impactful towards these ends. Here, we develop two tools to identify impactful changes at different levels. We develop both methods in the context of gliomas, a common form of brain cancer. Firstly, we develop and assess a tool for assessing the impact of fusion genes, a type of common mutation using RNA-sequencing data. We validate the tool by working with collaborators in The Cancer Genome Atlas. Secondly, we develop a tool for an overall assessment of patient outcome by integrating data from diverse sequencing platforms. We validate this tool using simulation, data from consortiums, and collaborators at UCSF.

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