Nature Communications (Jun 2023)
A landscape of response to drug combinations in non-small cell lung cancer
- Nishanth Ulhas Nair,
- Patricia Greninger,
- Xiaohu Zhang,
- Adam A. Friedman,
- Arnaud Amzallag,
- Eliane Cortez,
- Avinash Das Sahu,
- Joo Sang Lee,
- Anahita Dastur,
- Regina K. Egan,
- Ellen Murchie,
- Michele Ceribelli,
- Giovanna S. Crowther,
- Erin Beck,
- Joseph McClanaghan,
- Carleen Klump-Thomas,
- Jessica L. Boisvert,
- Leah J. Damon,
- Kelli M. Wilson,
- Jeffrey Ho,
- Angela Tam,
- Crystal McKnight,
- Sam Michael,
- Zina Itkin,
- Mathew J. Garnett,
- Jeffrey A. Engelman,
- Daniel A. Haber,
- Craig J. Thomas,
- Eytan Ruppin,
- Cyril H. Benes
Affiliations
- Nishanth Ulhas Nair
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health
- Patricia Greninger
- Massachusetts General Hospital, Harvard Medical School
- Xiaohu Zhang
- Howard Hughes Medical Institute
- Adam A. Friedman
- Massachusetts General Hospital, Harvard Medical School
- Arnaud Amzallag
- Massachusetts General Hospital, Harvard Medical School
- Eliane Cortez
- Massachusetts General Hospital, Harvard Medical School
- Avinash Das Sahu
- University of New Mexico, Comprehensive Cancer Center
- Joo Sang Lee
- Samsung Medical Center, Sungkyunkwan University School of Medicine
- Anahita Dastur
- Massachusetts General Hospital, Harvard Medical School
- Regina K. Egan
- Massachusetts General Hospital, Harvard Medical School
- Ellen Murchie
- Massachusetts General Hospital, Harvard Medical School
- Michele Ceribelli
- Howard Hughes Medical Institute
- Giovanna S. Crowther
- Massachusetts General Hospital, Harvard Medical School
- Erin Beck
- Howard Hughes Medical Institute
- Joseph McClanaghan
- Massachusetts General Hospital, Harvard Medical School
- Carleen Klump-Thomas
- Howard Hughes Medical Institute
- Jessica L. Boisvert
- Massachusetts General Hospital, Harvard Medical School
- Leah J. Damon
- Massachusetts General Hospital, Harvard Medical School
- Kelli M. Wilson
- Howard Hughes Medical Institute
- Jeffrey Ho
- Massachusetts General Hospital, Harvard Medical School
- Angela Tam
- Massachusetts General Hospital, Harvard Medical School
- Crystal McKnight
- Howard Hughes Medical Institute
- Sam Michael
- Howard Hughes Medical Institute
- Zina Itkin
- Howard Hughes Medical Institute
- Mathew J. Garnett
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus
- Jeffrey A. Engelman
- Massachusetts General Hospital, Harvard Medical School
- Daniel A. Haber
- Massachusetts General Hospital, Harvard Medical School
- Craig J. Thomas
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institute of Health
- Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health
- Cyril H. Benes
- Massachusetts General Hospital, Harvard Medical School
- DOI
- https://doi.org/10.1038/s41467-023-39528-9
- Journal volume & issue
-
Vol. 14,
no. 1
pp. 1 – 19
Abstract
Abstract Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.
