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

Authors

Avinash Das Sahu, Harvard T.H. Chan School of Public Health
Joo S Lee, University of Maryland, College Park
Zhiyong Wang, University of California, San Diego
Gao Zhang, The Wistar Institute
Ramiro Iglesias-Bartolome, National Cancer Institute (NCI)
Tian Tian, New Jersey Institute of Technology
Zhi Wei, New Jersey Institute of Technology
Benchun Miao, Massachusetts General Hospital Cancer Center
Nishanth Ulhas Nair, University of Maryland, College Park
Olga Ponomarova, University of Massachusetts Chan Medical School
Adam A. Friedman, Massachusetts General Hospital Cancer Center
Arnaud Amzallag, Massachusetts General Hospital Cancer Center
Tabea Moll, Massachusetts General Hospital Cancer Center
Gyulnara Kasumova, Massachusetts General Hospital Cancer Center
Patricia Greninger, Massachusetts General Hospital Cancer Center
Regina K. Egan, Massachusetts General Hospital Cancer Center
Leah J. Damon, Massachusetts General Hospital Cancer Center
Dennie T. Frederick, Massachusetts General Hospital Cancer Center
Livnat Jerby-Arnon, Tel Aviv University

Document Type

Article

Publication Date

3-1-2019

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.

Identifier

85062892216 (Scopus)

Publication Title

Molecular Systems Biology

External Full Text Location

https://doi.org/10.15252/msb.20188323

e-ISSN

17444292

PubMed ID

30858180

Issue

3

Volume

15

Grant

1U54CA224070

Fund Ref

National Institutes of Health

Recommended Citation

Sahu, Avinash Das; S Lee, Joo; Wang, Zhiyong; Zhang, Gao; Iglesias-Bartolome, Ramiro; Tian, Tian; Wei, Zhi; Miao, Benchun; Nair, Nishanth Ulhas; Ponomarova, Olga; Friedman, Adam A.; Amzallag, Arnaud; Moll, Tabea; Kasumova, Gyulnara; Greninger, Patricia; Egan, Regina K.; Damon, Leah J.; Frederick, Dennie T.; and Jerby-Arnon, Livnat, "Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy" (2019). Faculty Publications. 7775.
https://digitalcommons.njit.edu/fac_pubs/7775