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

doi:10.15252/msb.20188323

Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy

Mol Syst Biol. 2019 Mar 11;15(3):e8323. doi: 10.15252/msb.20188323.

Authors

Avinash Das Sahu^{1

2

3}, Joo S Lee^{3

4}, Zhiyong Wang⁵, Gao Zhang^{6

7}, Ramiro Iglesias-Bartolome⁸, Tian Tian⁹, Zhi Wei⁹, Benchun Miao², Nishanth Ulhas Nair^{3

4}, Olga Ponomarova¹⁰, Adam A Friedman², Arnaud Amzallag², Tabea Moll², Gyulnara Kasumova², Patricia Greninger², Regina K Egan², Leah J Damon², Dennie T Frederick², Livnat Jerby-Arnon¹¹, Allon Wagner¹², Kuoyuan Cheng³, Seung Gu Park¹³, Welles Robinson³, Kevin Gardner⁴, Genevieve Boland², Sridhar Hannenhalli³, Meenhard Herlyn⁶, Cyril Benes², Keith Flaherty², Ji Luo⁸, J Silvio Gutkind⁵, Eytan Ruppin^{14

4

11}

Affiliations

¹ Department of Biostatistics and Computational Biology, Harvard School of Public Health, Boston, MA, USA asahu@jimmy.harvard.edu eyruppin@gmail.com.
² Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
³ University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA.
⁴ Cancer Data Science Lab, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁵ Department of Pharmacology & Moores Cancer Center, University of California, San Diego La Jolla, CA, USA.
⁶ Molecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute, Philadelphia, PA, USA.
⁷ Department of Neurosurgery and The Preston Robert Tisch Brain Tumor Center, Duke University, Durham, NC, USA.
⁸ National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁹ New Jersey Institute of Technology, Newark, NJ, USA.
¹⁰ University of Massachusetts Medical School, Worcester, MA, USA.
¹¹ Schools of Computer Science & Medicine, Tel-Aviv University, Tel-Aviv, Israel.
¹² Department of Electrical Engineering and Computer Science, the Center for Computational Biology, University of California, Berkeley, CA, USA.
¹³ Department of Biostatistics and Computational Biology, Harvard School of Public Health, Boston, MA, USA.
¹⁴ University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA asahu@jimmy.harvard.edu eyruppin@gmail.com.

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: drug combination; drug resistance; immunotherapy; synergy.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Computational Biology*
Drug Resistance, Neoplasm / genetics*
Drug Synergism*
Female
Gene Expression Profiling
Humans
Immunotherapy
Male
Melanoma / drug therapy
Melanoma / genetics*
Molecular Targeted Therapy
Synthetic Lethal Mutations

Abstract

Publication types

MeSH terms

Grants and funding