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

Author

Listed:
  • Rotem Ben-Hamo

    (Weizmann Institute of Science
    Broad Institute of MIT and Harvard)

  • 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
    Massachusetts General Hospital)

  • 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
    Massachusetts General Hospital
    Harvard Medical School
    Massachusetts General Hospital)

  • Ravid Straussman

    (Weizmann Institute of Science)

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.

Suggested Citation

  • 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 & Ravid Straussman, 2020. "Predicting and affecting response to cancer therapy based on pathway-level biomarkers," Nature Communications, Nature, vol. 11(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17090-y
    DOI: 10.1038/s41467-020-17090-y
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    Cited by:

    1. Smriti Chawla & Anja Rockstroh & Melanie Lehman & Ellca Ratther & Atishay Jain & Anuneet Anand & Apoorva Gupta & Namrata Bhattacharya & Sarita Poonia & Priyadarshini Rai & Nirjhar Das & Angshul Majumd, 2022. "Gene expression based inference of cancer drug sensitivity," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Kuang Du & Shiyou Wei & Zhi Wei & Dennie T. Frederick & Benchun Miao & Tabea Moll & Tian Tian & Eric Sugarman & Dmitry I. Gabrilovich & Ryan J. Sullivan & Lunxu Liu & Keith T. Flaherty & Genevieve M. , 2021. "Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    3. Tom Kaufman & Erez Nitzan & Nir Firestein & Miriam Bracha Ginzberg & Seshu Iyengar & Nish Patel & Rotem Ben-Hamo & Ziv Porat & Jaryd Hunter & Andreas Hilfinger & Varda Rotter & Ran Kafri & Ravid Strau, 2022. "Visual barcodes for clonal-multiplexing of live microscopy-based assays," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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