Background: Drug resistance is a major limit of anti-cancer therapy effectiveness. To identify biomarkers and regulators of drug resistance, many genomics technologies have been applied to identify alterations in patient samples and tumor models. Recent years have seen the rapid growth of cancer genomics data; however, a major gap in the field is a lack of computational frameworks that integrate diverse types of data related with cancer drug resistance to develop response biomarkers and understand resistance mechanisms. To overcome this problem, I have developed prototype computational methods to predict response biomarkers for both targeted therapies and immune checkpoint blockade (ICB), and conducted cell line experiments to validate regulator genes in targeted therapy resistance. In this proposal, I aim to develop novel data-driven methods to overcome the data-to-knowledge challenge in drug resistance research and facilitate precision cancer medicine using a combination of computational and experimental approaches, with a melanoma disease focus. Research:
In aim 1 (K99 phase), I will develop a network inference framework to identify regulators of targeted therapy resistance through integrating the molecular profiles of drug resistant cells with gene interaction and regulation networks. The performance of my algorithm will be systematically validated through both public and in-house CRISPR or shRNA screens.
In aim 2 (R00 phase), I will create gene biomarkers to predict ICB response and decompose ICB resistance causes, through modeling the gene expression signatures of T cell dysfunction and exclusion in tumor.
In aim 3 (R00 phase), I will identify the regulators of ICB resistance through a combination of network-based gene prioritization and mini-pool CRISPR screen on tumor models in syngeneic recipient mice. My extensive background in computational biology and training in experimental biology puts me in a unique position to accomplish this proposal, which requires a seamless integration between data science and functional genomics approaches. Career and Training: I received my PhD in Computer Science at Princeton University, and started postdoc research in the Lab of Prof. X. Shirley Liu at Dana-Farber Cancer Institute and Harvard School of Public Health. During the K99 phase, I will continue to be mentored by Prof. Liu to conduct research. Under the supervision of co-mentors Prof. Kai Wucherpfennig and Myles Brown, I will acquire trainings on CRISPR screening, customized screen library construction, and tumor models in syngeneic recipient mice. This proposed plan would prepare myself as an independent scientist in translational cancer research.

Public Health Relevance

Drug resistance is a major limit of anti-cancer therapy effectiveness. This proposal will leverage the large number of datasets from public domains to identify biomarkers and regulators of anti-cancer drug resistance. Synergistic target genes whose pharmacological inhibition could overcome the resistance to primary drugs will be identified through the proposed works and validated in pre-clinical models for further clinical exploration.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Career Transition Award (K99)
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Subcommittee I - Transistion to Independence (NCI)
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Radaev, Sergey
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Dana-Farber Cancer Institute
United States
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