Molecular Systems Biology (Mar 2019)
Genome‐wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy
- Avinash Das Sahu,
- Joo S Lee,
- Zhiyong Wang,
- Gao Zhang,
- Ramiro Iglesias‐Bartolome,
- Tian Tian,
- Zhi Wei,
- Benchun Miao,
- Nishanth Ulhas Nair,
- Olga Ponomarova,
- Adam A Friedman,
- Arnaud Amzallag,
- Tabea Moll,
- Gyulnara Kasumova,
- Patricia Greninger,
- Regina K Egan,
- Leah J Damon,
- Dennie T Frederick,
- Livnat Jerby‐Arnon,
- Allon Wagner,
- Kuoyuan Cheng,
- Seung Gu Park,
- Welles Robinson,
- Kevin Gardner,
- Genevieve Boland,
- Sridhar Hannenhalli,
- Meenhard Herlyn,
- Cyril Benes,
- Keith Flaherty,
- Ji Luo,
- J Silvio Gutkind,
- Eytan Ruppin
Affiliations
- Avinash Das Sahu
- Department of Biostatistics and Computational Biology, Harvard School of Public Health
- Joo S Lee
- University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland
- Zhiyong Wang
- Department of Pharmacology & Moores Cancer Center, University of California
- Gao Zhang
- Molecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute
- Ramiro Iglesias‐Bartolome
- National Cancer Institute, National Institutes of Health
- Tian Tian
- New Jersey Institute of Technology
- Zhi Wei
- New Jersey Institute of Technology
- Benchun Miao
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center
- Nishanth Ulhas Nair
- University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland
- Olga Ponomarova
- University of Massachusetts Medical School
- Adam A Friedman
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center
- Arnaud Amzallag
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center
- Tabea Moll
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center
- Gyulnara Kasumova
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center
- Patricia Greninger
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center
- Regina K Egan
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center
- Leah J Damon
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center
- Dennie T Frederick
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center
- Livnat Jerby‐Arnon
- Schools of Computer Science & Medicine, Tel‐Aviv University
- Allon Wagner
- Department of Electrical Engineering and Computer Science, the Center for Computational Biology, University of California
- Kuoyuan Cheng
- University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland
- Seung Gu Park
- Department of Biostatistics and Computational Biology, Harvard School of Public Health
- Welles Robinson
- University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland
- Kevin Gardner
- Cancer Data Science Lab, National Cancer Institute, National Institutes of Health
- Genevieve Boland
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center
- Sridhar Hannenhalli
- University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland
- Meenhard Herlyn
- Molecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute
- Cyril Benes
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center
- Keith Flaherty
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center
- Ji Luo
- National Cancer Institute, National Institutes of Health
- J Silvio Gutkind
- Department of Pharmacology & Moores Cancer Center, University of California
- Eytan Ruppin
- University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland
- DOI
- https://doi.org/10.15252/msb.20188323
- Journal volume & issue
-
Vol. 15,
no. 3
pp. 1 – 21
Abstract
Abstract Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here, we perform a genome‐wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients’ response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.
Keywords
