The development of novel therapies for pediatric cancers, the second leading cause of death in children, is challenging due to the lack of comprehensive pharmacogenomics resources, unlike the well-established ones in adult cancers. However, breakthroughs in deep learning methods allow learning of intricate pharmacogenomics patterns with unprecedented performance. With a uniquely cross-disciplinary background, the candidate for this proposed K99/R00 has already, as a postdoctoral fellow, (i) developed and published several deep learning models that accurately predicted adult cancer cells? drug sensitivity and genetic dependency using high- throughput genomics profiles, and (ii) demonstrated the feasibility of transferring the model to predict tumors by a ?transfer learning? design. The candidate will extend this research to study pediatric cancers and test the central hypothesis that deep learning extracts genomics signatures to predict the responses of pediatric cancer cells to chemical and genetic perturbations. The proposed study will develop novel deep learning models for predicting drug sensitivity and/or genetic dependency for (Aim 1) currently un-screened pediatric cancer cell lines by learning from screens of adult cells, and (Aim 2) pediatric tumors by learning from adult and/or pediatric cells. Prediction results will be validated by in vitro experiments and data collected from patient-derived xenografts. The proposed study is the first attempt to employ modern computational methods to advance pharmacogenomics studies of pediatric cancer, which would be difficult and costly to pursue via biological assays. Findings will shed light on the optimal drugs and novel therapeutic targets for pediatric malignancies, leading to an optimal and efficient design of preclinical tests. The candidate has a remarkable track record of bioinformatics studies of adult cancer genomics. The focus of this K99 training plan is to develop in-depth understanding of pediatric cancer and preclinical treatment models, and strengthen multifaceted components needed for a successful research career in cancer bioinformatics. The primary mentor, Dr. Peter Houghton, is a renowned leader in pediatric cancer research and preclinical drug testing programs. The candidate also has assembled an outstanding mentor team: Dr. Yidong Chen (co-mentor), a cancer genomics expert and pioneer in bioinformatics analysis of high-throughput technologies; Dr. Jinghui Zhang (collaborator), a computational biologist and leader in integrative genomics studies of major pediatric cancer genome consortiums; Dr. Yufei Huang (collaborator), an expert in state-of-the-art deep learning methods; and two highly knowledgeable consultants with relevant expertise. With this team?s guidance and structured training activities in an ideal training environment, the candidate will strengthen his skills in grant writing and lab management, teaching and mentoring, and broad connections. Overall, the K99/R00 award will be an indispensable support for a timely transition of the candidate to a successful career as a multifaceted, cross-disciplinary investigator in cancer bioinformatics.

Public Health Relevance

Pediatric cancer is the second leading cause of death in children, and genetic studies to inform new drug therapies have been challenging. The proposal will utilize deep learning bioinformatics methods to transfer well-established pharmacogenomics knowledge for adult cancers to the study of pediatric cancers. The findings will facilitate the optimal usage of existing drugs and the development of novel therapies for pediatric cancer, and will facilitate the candidate?s transition to an independent research career as an expert in this area.

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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University of Texas Health Science Center
Internal Medicine/Medicine
Schools of Medicine
San Antonio
United States
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