Research Area:  Machine Learning

Abstract:

Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on mono- therapies. We address these challenges by developing DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lines to 684 drugs. Tumor genotypes induce states in cellular subsystems that are integrated with drug structure to predict response to therapy and, simultaneously, learn biological mechanisms underlying the drug response. DrugCell predictions are accu- rate in cell lines and also stratify clinical outcomes. Analysis of DrugCell mechanisms leads directly to the design of synergistic drug combinations, which we validate systematically by combinatorial CRISPR, drug-drug screening in vitro, and patient-derived xenografts. DrugCell provides a blueprint for constructing interpretable models for predictive medicine.

Keywords:  
Predicting Drug Response
Synergy
Deep Learning Model
Human Cancer Cells

Author(s) Name:  Brent M. Kuenzi, Jisoo Park, Samson H. Fong, ..., Jason F. Kreisberg, Jianzhu Ma, Trey Ideker

Publisher name:  Cell Press

DOI:  10.1016/j.ccell.2020.09.014

Volume Information:  

Paper Link:   https://www.cell.com/cancer-cell/pdf/S1535-6108(20)30488-8.pdf