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Chen B, Li P, Liu M, Liu K, Zou M, Geng Y, Zhuang S, Xu H, Wang L, Chen T, Li Y, Zhao Z, Qi L, Gu Y. A genetic map of the chromatin regulators to drug response in cancer cells. J Transl Med 2022; 20:438. [PMID: 36180906 PMCID: PMC9523919 DOI: 10.1186/s12967-022-03651-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 09/18/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Diverse drug vulnerabilities owing to the Chromatin regulators (CRs) genetic interaction across various cancers, but the identification of CRs genetic interaction remains challenging. METHODS In order to provide a global view of the CRs genetic interaction in cancer cells, we developed a method to identify potential drug response-related CRs genetic interactions for specific cancer types by integrating the screen of CRISPR-Cas9 and pharmacogenomic response datasets. RESULTS Totally, 625 drug response-related CRs synthetic lethality (CSL) interactions and 288 CRs synthetic viability (CSV) interactions were detected. Systematically network analysis presented CRs genetic interactions have biological function relationship. Furthermore, we validated CRs genetic interactions induce multiple omics deregulation in The Cancer Genome Atlas. We revealed the colon adenocarcinoma patients (COAD) with mutations of a CRs set (EP300, MSH6, NSD2 and TRRAP) mediate a better survival with low expression of MAP2 and could benefit from taxnes. While the COAD patients carrying at least one of the CSV interactions in Vorinostat CSV module confer a poor prognosis and may be resistant to Vorinostat treatment. CONCLUSIONS The CRs genetic interaction map provides a rich resource to investigate cancer-associated CRs genetic interaction and proposes a powerful strategy of biomarker discovery to guide the rational use of agents in cancer therapy.
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Affiliation(s)
- Bo Chen
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Pengfei Li
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Mingyue Liu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Kaidong Liu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Min Zou
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yiding Geng
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuping Zhuang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Huanhuan Xu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Linzhu Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Tingting Chen
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yawei Li
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhangxiang Zhao
- The Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Lishuang Qi
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
| | - Yunyan Gu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
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