Predicting and affecting response to cancer therapy based on pathway-level biomarkers

Rotem Ben-Hamo, Adi Jacob Berger, Nancy Gavert, Mendy Miller, Guy Pines, Roni Oren, Eli Pikarsky, Cyril H. Benes, Tzahi Neuman, Yaara Zwang, Sol Efroni, Gad Getz () and Ravid Straussman ()
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Rotem Ben-Hamo: Weizmann Institute of Science
Adi Jacob Berger: Weizmann Institute of Science
Nancy Gavert: Weizmann Institute of Science
Mendy Miller: Broad Institute of MIT and Harvard
Guy Pines: Affiliated to the Hebrew University School of Medicine
Roni Oren: Weizmann Institute of Science
Eli Pikarsky: Hebrew University of Jerusalem
Cyril H. Benes: Bar Ilan University
Tzahi Neuman: Hebrew University of Jerusalem
Yaara Zwang: Weizmann Institute of Science
Sol Efroni: Bar Ilan University
Gad Getz: Broad Institute of MIT and Harvard
Ravid Straussman: Weizmann Institute of Science

Nature Communications, 2020, vol. 11, issue 1, 1-16

Abstract: Abstract Identifying robust, patient-specific, and predictive biomarkers presents a major obstacle in precision oncology. To optimize patient-specific therapeutic strategies, here we couple pathway knowledge with large-scale drug sensitivity, RNAi, and CRISPR-Cas9 screening data from 460 cell lines. Pathway activity levels are found to be strong predictive biomarkers for the essentiality of 15 proteins, including the essentiality of MAD2L1 in breast cancer patients with high BRCA-pathway activity. We also find strong predictive biomarkers for the sensitivity to 31 compounds, including BCL2 and microtubule inhibitors (MTIs). Lastly, we show that Bcl-xL inhibition can modulate the activity of a predictive biomarker pathway and re-sensitize lung cancer cells and tumors to MTI therapy. Overall, our results support the use of pathways in helping to achieve the goal of precision medicine by uncovering dozens of predictive biomarkers.

Date: 2020
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DOI: 10.1038/s41467-020-17090-y

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