Bridging “Big Data” and Mechanistic Insight To Enable Precision Medicine

Abstract

Realizing the promise of precision medicine will require breaking down communication barriers between genomic, screening and literature "big data." We discuss the opportunities and challenges of reconciling data science approaches with the scientific method and the critical role of chemical biologists as a bridge between data and mechanism. Finally we propose next steps on the road to precision medicine and give examples of new tools designed to catalyze these steps. Clinical Need Better understanding drug mechanisms to match drugs to individual patients Small-molecule chemotherapies have been the therapeutic modality of choice for metastatic cancer since the 1960s. [1] Although drug-mechanism research has often focused on identification of a drug's protein targets, there is growing literature that additional factors affect clinical efficacy including (Figure 1 left): [2]

Translating Data Science Results into Precision Oncology Decisions: A Mini Review

“…Big data makes hypothesis-driven approaches less viable and instead suggests a more agnostic strategy by avoiding assumptions and prioritizing insights from data patterns and correlations. A potential problem of this approach is ignoring the mechanism behind data generation, with the risk of underestimating or not considering key physiological aspects [ 18 , 19 ].…”