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Activity Number: 322 - Data-Driven Patient Management in the Era of Precision Medicine - from Discovery, Diagnostics, to Therapeutics
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Council of Chapters
Abstract #326589
Title: Big Data Analysis Points Toward New Cancer Therapeutic Discovery Approach
Author(s): Bin Chen*
Companies: UCSF
Keywords: Big data; translational bioinformatics; drug repositioning; cancer therapy

Rapidly decreasing costs of molecular measurement technologies not only enable profiling of disease sample molecular features (e.g., transcriptome, proteome, metabolome) at different levels (e.g., tissues, single cells), but also enable measuring of molecular signatures of individual drugs in clinically relevant models. Exploring methods to relate diseases to potentially efficacious drugs through various molecular features is critical in the discovery of new therapeutics. The target-based drug discovery approach that focuses on interfering with individual targets is challenged by the lack of drug efficacy, drug resistance, and off-target effects. We propose to employ a systems-based approach that identifies drugs that reverse the molecular state of a disease. Using this system-based approach, we have successfully identified drug candidates for three cancers: Ewing's sarcoma, liver cancer and basal cell carcinoma.  In the Ewing's sarcoma work, this systems-approach achieved a hit rate of >50% in predicting effective drugs. In our recent pan-cancer analysis, we uncovered the principle that the drug's ability to reverse cancer gene expression correlates to its efficacy.

Authors who are presenting talks have a * after their name.

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