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Tytuł pozycji:

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

Tytuł:
Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy.
Autorzy:
Sahu AD; Department of Biostatistics and Computational Biology, Harvard School of Public Health, Boston, MA, USA .; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.; University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA.
S Lee J; University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA.; Cancer Data Science Lab, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Wang Z; Department of Pharmacology & Moores Cancer Center, University of California, San Diego La Jolla, CA, USA.
Zhang G; Molecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute, Philadelphia, PA, USA.; Department of Neurosurgery and The Preston Robert Tisch Brain Tumor Center, Duke University, Durham, NC, USA.
Iglesias-Bartolome R; National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Tian T; New Jersey Institute of Technology, Newark, NJ, USA.
Wei Z; New Jersey Institute of Technology, Newark, NJ, USA.
Miao B; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
Nair NU; University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA.; Cancer Data Science Lab, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Ponomarova O; University of Massachusetts Medical School, Worcester, MA, USA.
Friedman AA; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
Amzallag A; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
Moll T; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
Kasumova G; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
Greninger P; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
Egan RK; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
Damon LJ; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
Frederick DT; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
Jerby-Arnon L; Schools of Computer Science & Medicine, Tel-Aviv University, Tel-Aviv, Israel.
Wagner A; Department of Electrical Engineering and Computer Science, the Center for Computational Biology, University of California, Berkeley, CA, USA.
Cheng K; University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA.
Park SG; Department of Biostatistics and Computational Biology, Harvard School of Public Health, Boston, MA, USA.
Robinson W; University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA.
Gardner K; Cancer Data Science Lab, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Boland G; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
Hannenhalli S; University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA.
Herlyn M; Molecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute, Philadelphia, PA, USA.
Benes C; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
Flaherty K; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
Luo J; National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Gutkind JS; Department of Pharmacology & Moores Cancer Center, University of California, San Diego La Jolla, CA, USA.
Ruppin E; University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA .; Cancer Data Science Lab, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.; Schools of Computer Science & Medicine, Tel-Aviv University, Tel-Aviv, Israel.
Źródło:
Molecular systems biology [Mol Syst Biol] 2019 Mar 11; Vol. 15 (3), pp. e8323. Date of Electronic Publication: 2019 Mar 11.
Typ publikacji:
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.
Język:
English
Imprint Name(s):
Publication: 2014- : London : Wiley Blackwell
Original Publication: London : Nature Pub. Group, c2005-
MeSH Terms:
Computational Biology*
Drug Synergism*
Drug Resistance, Neoplasm/*genetics
Melanoma/*genetics
Female ; Gene Expression Profiling ; Humans ; Immunotherapy ; Male ; Melanoma/drug therapy ; Molecular Targeted Therapy ; Synthetic Lethal Mutations
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Grant Information:
R33 CA225291 United States CA NCI NIH HHS; P30 CA010815 United States CA NCI NIH HHS; P01 CA114046 United States CA NCI NIH HHS; P50 CA174523 United States CA NCI NIH HHS; R01 DE026870 United States DE NIDCR NIH HHS; U54 CA224070 United States CA NCI NIH HHS
Contributed Indexing:
Keywords: drug combination; drug resistance; immunotherapy; synergy
Entry Date(s):
Date Created: 20190313 Date Completed: 20200413 Latest Revision: 20231129
Update Code:
20240105
PubMed Central ID:
PMC6413886
DOI:
10.15252/msb.20188323
PMID:
30858180
Czasopismo naukowe
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) interac tions, 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.
(© 2019 The Authors. Published under the terms of the CC BY 4.0 license.)
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