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.
One goal of the precision medicine initiative is to select optimal therapies for individual cancer patients based on their molecular and clinical profiles. Current preclinical and clinical approaches select therapies primarily based on actionable mutations. This work is expected to employ the protein/gene expression based biomarkers computed from public databases to inform individualized cancer therapy.
|Zhang, Shanshan; Wang, Jingxiao; Wang, Haichuan et al. (2018) Hippo Cascade Controls Lineage Commitment of Liver Tumors in Mice and Humans. Am J Pathol 188:995-1006|