A visible machine learning system to discover targeted treatment solutions in cancer

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Project Summary/Abstract Understanding of genetic interactions can lead to therapeutic design for individual cancer patients by targeting the specific genetic vulnerability in the cancer genome. For example, by identifying gene pairs that pose severe fitness defects when knocked out simultaneously (compared to separate knockouts), one can selectively kill cancer cells that harbor loss-of-function mutation in one protein by inhibiting its synthetic-lethal partner. Despite generation of large-scale data delineating the tumor transcriptome, proteome, metabolome, imaging, and so on, little is known regarding how different genes interact with each other and it is unclear how one can design targeted treatments based on the ‘omics data available. To address these challenges, the proposed research will develop a “visible” machine learning framework to systematically understand the higher-order genetic interactions (i.e. di-genic and tri-genic interactions) in cancer and design targeted treatments. The first step for the proposed framework is to gain a holistic view of cancer pathways through combining the ‘omics data available. Multiple approaches have been applied to integrate data of similar forms, but there yet lacks an effective solution for integrating data of vastly different qualities and formats. To address this challenge, Yue Qin has developed a method to infer a hierarchical cancer cell map capturing cancer pathways at multi- scale resolution by fusing immunofluorescence (IF) imaging data and affinity purification-mass spectrometry (AP- MS). During the F99 phase of the proposed research, by tying the architecture of a deep neural network to the hierarchical cancer cell map, Yue will develop a “visible” neural network (VNN) that can predict the cancer cell fitness from genetic perturbation (i.e. knockouts) and genomic backgrounds (i.e. mutations) while providing mechanistic insights in cancer pathways critical for genotype-phenotype prediction. During the K00 phase of the award, Yue will develop genetic engineering approaches to experimentally map higher-order genetic interactions in cancer cells based on the mechanistic insights obtained from VNN during genotype-phenotype prediction. The data generated experimentally can directly inspire targeted treatment designs. In addition, the new data can be integrated into the hierarchical cancer cell map to improve accuracy and resolution of the inferred pathways, thus further improving the “visibility” of VNN in genotype-phenotype prediction. The combination of a computational focused training during F99 phase and experimental focused training during K00 phase will fully prepare Yue leading her own interdisciplinary research in cancer biology. In addition, the personalized training plan covering aspects including mentoring and teaching, scientific writing, and oral presentation will ensure Yue acquiring skills necessary for her future establishment as an independent investigator.