Modeling cancer evolution for prediction with neural networks: methods and applications

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PROJECT SUMMARY/ABSTRACT The study of tumor evolution can uncover events and interactions that drive tumor development through alternative routes, reveal differences in therapeutic vulnerabilities and improve clinical decision making. Yet, studying tumor evolution is challenging, hindered by the difficulty to interpret noisy genomic data and the lack of temporal ordering of major genetic events. There is therefore a critical need for the development of computational approaches that can facilitate efficient investigation of cancer data under an evolutionary, temporal perspective. The long-term goal of this project is to develop computational tools combining advanced machine learning with molecular evolution techniques and provide novel strategies to investigate tumor evolution. The overall objective is to establish a deep-learning framework to study tumor development that will be used to distinguish early and late genetic events that are associated with tumor characteristics, survival and therapeutic vulnerabilities. The rationale of the proposed research is that the study of tumor evolution through integration of machine learning with molecular evolution techniques could enhance the performance of otherwise difficult clinical classification tasks. The specific aims of this project are to (1) Characterize the interplay between driver mutations and aneuploidy in tumor evolution and identify determinants of clinical outcome and therapeutic vulnerabilities (2) Introduce computational approaches to represent snapshot genomic data through temporal and functional ordering of genetic events (3) Develop a recurrent neural network approach to learn different dynamics in tumor evolution from ordered genomic data, and predict phenotypic features and clinical outcome. The proposed research is innovative because it will combine recent advances in machine learning with evolutionary techniques into a single framework, establishing novel computational tools that will facilitate a comprehensive investigation of cancer development. The proposed framework is significant because it will enable application of temporal modeling with machine learning to cancer data, for prediction of clinical features. To achieve the proposed goals the candidate, Dr. Noam Auslander, requires additional training and mentoring in evolutionary research, comparative genomics and mathematical modeling. During the K99 phase, Dr. Auslander will conduct this research as a postdoctoral fellow at the National Center for Biotechnology Information. She will be mentored by Dr. Eugene Koonin, a recognized expert in the fields of molecular evolution and computational biology, and additional mentoring from senior members from the Koonin lab. Together with her previous training in machine learning and cancer data science, this application for the NIH Pathway to Independence Award (K99/R00) describes a career development plan that will allow Dr. Auslander to achieve her career goals and become an independent investigator and leader in computational research of cancer evolution.