Genome‐wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy
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...
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| Format: | Article |
| Language: | English |
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Springer Nature
2019-03-01
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| Series: | Molecular Systems Biology |
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| Online Access: | https://doi.org/10.15252/msb.20188323 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Avinash Das Sahu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3e0d9e37ccd24f31aaa5e8abf6557875 |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2019-03-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-3e0d9e37ccd24f31aaa5e8abf65578752025-08-20T04:02:45ZengSpringer NatureMolecular Systems Biology1744-42922019-03-0115312110.15252/msb.20188323Genome‐wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapyAvinash Das Sahu0Joo S Lee1Zhiyong Wang2Gao Zhang3Ramiro Iglesias‐Bartolome4Tian Tian5Zhi Wei6Benchun Miao7Nishanth Ulhas Nair8Olga Ponomarova9Adam A Friedman10Arnaud Amzallag11Tabea Moll12Gyulnara Kasumova13Patricia Greninger14Regina K Egan15Leah J Damon16Dennie T Frederick17Livnat Jerby‐Arnon18Allon Wagner19Kuoyuan Cheng20Seung Gu Park21Welles Robinson22Kevin Gardner23Genevieve Boland24Sridhar Hannenhalli25Meenhard Herlyn26Cyril Benes27Keith Flaherty28Ji Luo29J Silvio Gutkind30Eytan Ruppin31Department of Biostatistics and Computational Biology, Harvard School of Public HealthUniversity of Maryland Institute of Advanced Computer Science (UMIACS), University of MarylandDepartment of Pharmacology & Moores Cancer Center, University of CaliforniaMolecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar InstituteNational Cancer Institute, National Institutes of HealthNew Jersey Institute of TechnologyNew Jersey Institute of TechnologyDepartment of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer CenterUniversity of Maryland Institute of Advanced Computer Science (UMIACS), University of MarylandUniversity of Massachusetts Medical SchoolDepartment of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer CenterDepartment of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer CenterDepartment of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer CenterDepartment of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer CenterDepartment of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer CenterDepartment of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer CenterDepartment of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer CenterDepartment of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer CenterSchools of Computer Science & Medicine, Tel‐Aviv UniversityDepartment of Electrical Engineering and Computer Science, the Center for Computational Biology, University of CaliforniaUniversity of Maryland Institute of Advanced Computer Science (UMIACS), University of MarylandDepartment of Biostatistics and Computational Biology, Harvard School of Public HealthUniversity of Maryland Institute of Advanced Computer Science (UMIACS), University of MarylandCancer Data Science Lab, National Cancer Institute, National Institutes of HealthDepartment of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer CenterUniversity of Maryland Institute of Advanced Computer Science (UMIACS), University of MarylandMolecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar InstituteDepartment of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer CenterDepartment of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer CenterNational Cancer Institute, National Institutes of HealthDepartment of Pharmacology & Moores Cancer Center, University of CaliforniaUniversity of Maryland Institute of Advanced Computer Science (UMIACS), University of MarylandAbstract 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.https://doi.org/10.15252/msb.20188323drug combinationdrug resistanceimmunotherapysynergy |
| spellingShingle | 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 Genome‐wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy Molecular Systems Biology drug combination drug resistance immunotherapy synergy |
| title | Genome‐wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy |
| title_full | Genome‐wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy |
| title_fullStr | Genome‐wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy |
| title_full_unstemmed | Genome‐wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy |
| title_short | Genome‐wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy |
| title_sort | genome wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy |
| topic | drug combination drug resistance immunotherapy synergy |
| url | https://doi.org/10.15252/msb.20188323 |
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