Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery
Summary: Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, b...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019-05-01
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| Series: | iScience |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004219301361 |
| _version_ | 1811282091506139136 |
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| author | Chen-Tsung Huang Chiao-Hui Hsieh Yun-Hsien Chung Yen-Jen Oyang Hsuan-Cheng Huang Hsueh-Fen Juan |
| author_facet | Chen-Tsung Huang Chiao-Hui Hsieh Yun-Hsien Chung Yen-Jen Oyang Hsuan-Cheng Huang Hsueh-Fen Juan |
| author_sort | Chen-Tsung Huang |
| collection | DOAJ |
| description | Summary: Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a gene-expression-based approach, which uses the recurrent perturbation-transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound-protein target counterparts, were reproducible in external datasets, and correlated with small-molecule sensitivity. We applied these recurrent relationships to predict synergistic drug pairs for cancer and experimentally confirmed two unexpected drug combinations in vitro. Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases. : Bioinformatics; Cancer Systems Biology; Pharmacoinformatics Subject Areas: Bioinformatics, Cancer Systems Biology, Pharmacoinformatics |
| first_indexed | 2024-04-13T01:45:23Z |
| format | Article |
| id | doaj.art-991cc04a108a4e6283b4cfcf650d8f1a |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-04-13T01:45:23Z |
| publishDate | 2019-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-991cc04a108a4e6283b4cfcf650d8f1a2022-12-22T03:08:02ZengElsevieriScience2589-00422019-05-0115291306Perturbational Gene-Expression Signatures for Combinatorial Drug DiscoveryChen-Tsung Huang0Chiao-Hui Hsieh1Yun-Hsien Chung2Yen-Jen Oyang3Hsuan-Cheng Huang4Hsueh-Fen Juan5Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, TaiwanInstitute of Molecular and Cellular Biology, National Taiwan University, Taipei 10617, TaiwanDepartment of Life Science, National Taiwan University, Taipei 10617, TaiwanGraduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, TaiwanInstitute of Biomedical Informatics, Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei 11221, Taiwan; Corresponding authorGraduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan; Institute of Molecular and Cellular Biology, National Taiwan University, Taipei 10617, Taiwan; Department of Life Science, National Taiwan University, Taipei 10617, Taiwan; Corresponding authorSummary: Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a gene-expression-based approach, which uses the recurrent perturbation-transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound-protein target counterparts, were reproducible in external datasets, and correlated with small-molecule sensitivity. We applied these recurrent relationships to predict synergistic drug pairs for cancer and experimentally confirmed two unexpected drug combinations in vitro. Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases. : Bioinformatics; Cancer Systems Biology; Pharmacoinformatics Subject Areas: Bioinformatics, Cancer Systems Biology, Pharmacoinformaticshttp://www.sciencedirect.com/science/article/pii/S2589004219301361 |
| spellingShingle | Chen-Tsung Huang Chiao-Hui Hsieh Yun-Hsien Chung Yen-Jen Oyang Hsuan-Cheng Huang Hsueh-Fen Juan Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery iScience |
| title | Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery |
| title_full | Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery |
| title_fullStr | Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery |
| title_full_unstemmed | Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery |
| title_short | Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery |
| title_sort | perturbational gene expression signatures for combinatorial drug discovery |
| url | http://www.sciencedirect.com/science/article/pii/S2589004219301361 |
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