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Your Environment. Your Health.

INTEGRATING TRANSCRIPTOMIC, PROTEOMIC AND PHARMACOGENOMIC DATA TO INFORM INDIVIDUALIZED THERAPY IN CANCERS

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Principal Investigator: Chen, Bin
Institute Receiving Award Michigan State University
Location East Lansing, MI
Grant Number K01ES028047
Funding Organization National Institute of Environmental Health Sciences
Award Funding Period 01 May 2018 to 30 Apr 2022
DESCRIPTION (provided by applicant): PROJECT SUMMARY As a computational biologist, my long-term goal is to develop methods and tools to discover new or better therapeutics for cancers. In the past few years, I have identified drug-repositioning candidates for a number of primary cancers using Big Data approaches. These candidates have been validated successfully in preclinical mouse models. To maximize the utility of Big Data, I plan to translate the findings into therapeutics; therefore, I propose to develop methods to utilize transcriptomic, proteomic and pharmacogenomic data to inform individualized therapy in cancers. Current preclinical and clinical approaches including the NCI MATCH trial select therapies primarily based on actionable mutations, yet patients may have no actionable mutations or multiple actionable mutations that are hard to prioritize, suggesting the need for other different types of molecular biomarkers. The recent efforts have enabled the large-scale identification of various types of molecular biomarkers through correlating drug sensitivity with molecular profiles of pre-treatment cancer cell lines. Computational methods to match these biomarkers to individual patients to inform therapy in the clinic are thus in high demand. The objective of this award is therefore to develop computational approaches to identify therapeutics for individual patients by leveraging large-scale biomarkers identified from cancer cell lines. Through conducing this research, I expect to expand my knowledge in cancer clinical trials, cancer genomics, cancer biology, and statistics. To achieve the goal, I have gathered seven renowned experts from different fields related to Big Data Science as mentors/advisors/collaborators: Primary Mentor Dr. Atul Butte in translational bioinformatics from UCSF, Co-mentor Dr. Samuel So in cancer biology from Stanford University, Co-mentor Dr. Mark Segal in statistics from UCSF, Advisor Dr. Andrei Goga in cancer biology from UCSF, Advisor Dr. Laura Esserman in breast cancer trials from UCSF, Collaborator Dr. John Gordan in liver cancer trials from UCSF and Collaborator Dr. Xin Chen in cancer biology from UCSF. With the support from my world- class mentors, advisors and collaborators, this award will prepare me to be a leader in developing big data methods that are broadly impactful.
Science Code(s)/Area of Science(s) Primary: 75 - Computational Biology/Computational Methods for Exposure Assessment
Publications See publications associated with this Grant.
Program Officer Carol Shreffler
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