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Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy.
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; Jerby-Arnon, Livnat; Wagner, Allon; Cheng, Kuoyuan; Park, Seung Gu; Robinson, Welles; Gardner, Kevin; Boland, Genevieve; Hannenhalli, Sridhar; Herlyn, Meenhard; Benes, Cyril; Flaherty, Keith; Luo, Ji; Gutkind, J Silvio; Ruppin, Eytan.
Afiliação
  • Sahu AD; Department of Biostatistics and Computational Biology, Harvard School of Public Health, Boston, MA, USA asahu@jimmy.harvard.edu eyruppin@gmail.com.
  • S Lee J; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
  • Wang Z; University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA.
  • Zhang G; University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA.
  • Iglesias-Bartolome R; Cancer Data Science Lab, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • Tian T; Department of Pharmacology & Moores Cancer Center, University of California, San Diego La Jolla, CA, USA.
  • Wei Z; Molecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute, Philadelphia, PA, USA.
  • Miao B; Department of Neurosurgery and The Preston Robert Tisch Brain Tumor Center, Duke University, Durham, NC, USA.
  • Nair NU; National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • Ponomarova O; New Jersey Institute of Technology, Newark, NJ, USA.
  • Friedman AA; New Jersey Institute of Technology, Newark, NJ, USA.
  • Amzallag A; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
  • Moll T; University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA.
  • Kasumova G; Cancer Data Science Lab, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • Greninger P; University of Massachusetts Medical School, Worcester, 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; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
  • Wagner A; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
  • Cheng K; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
  • Park SG; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
  • Robinson W; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
  • Gardner K; Schools of Computer Science & Medicine, Tel-Aviv University, Tel-Aviv, Israel.
  • Boland G; Department of Electrical Engineering and Computer Science, the Center for Computational Biology, University of California, Berkeley, CA, USA.
  • Hannenhalli S; University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA.
  • Herlyn M; Department of Biostatistics and Computational Biology, Harvard School of Public Health, Boston, MA, USA.
  • Benes C; University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA.
  • Flaherty K; Cancer Data Science Lab, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • Luo J; Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
  • Gutkind JS; University of Maryland Institute of Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA.
  • Ruppin E; Molecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute, Philadelphia, PA, USA.
Mol Syst Biol ; 15(3): e8323, 2019 03 11.
Article em En | MEDLINE | ID: mdl-30858180
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Resistencia a Medicamentos Antineoplásicos / Biologia Computacional / Sinergismo Farmacológico / Melanoma Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male Idioma: En Revista: Mol Syst Biol Assunto da revista: BIOLOGIA MOLECULAR / BIOTECNOLOGIA Ano de publicação: 2019 Tipo de documento: Article
Texto completo
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Resistencia a Medicamentos Antineoplásicos / Biologia Computacional / Sinergismo Farmacológico / Melanoma Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male Idioma: En Revista: Mol Syst Biol Assunto da revista: BIOLOGIA MOLECULAR / BIOTECNOLOGIA Ano de publicação: 2019 Tipo de documento: Article