CTR-DB, an omnibus for patient-derived gene expression signatures correlated with cancer drug response. Issue Volume 50:Issue D1(2022) (27th September 2021)

• Record Type: Journal Article
• Authors: Liu, Zhongyang; Liu, Jiale; Liu, Xinyue; Wang, Xun; Xie, Qiaosheng; Zhang, Xinlei; Kong, Xiangya; He, Mengqi; Yang, Yuting; Deng, Xinru; Yang, Lele; Qi, Yaning; Li, Jiajun; Liu, Yuan; Yuan, Liying; Diao, Lihong; He, Fuchu; Li, Dong
• Is Part Of: Nucleic acids research. Volume 50:Issue D1(2022)
• Page Start: D1184
• Page End: D1199
• Publication Date: 2021-09-27
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkab860
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: To date, only some cancer patients can benefit from chemotherapy and targeted therapy. Drug resistance continues to be a major and challenging problem facing current cancer research. Rapidly accumulated patient-derived clinical transcriptomic data with cancer drug response bring opportunities for exploring molecular determinants of drug response, but meanwhile pose challenges for data management, integration, and reuse. Here we present the Cancer Treatment Response gene signature DataBase (CTR-DB, http://ctrdb.ncpsb.org.cn/ ), a unique database for basic and clinical researchers to access, integrate, and reuse clinical transcriptomes with cancer drug response. CTR-DB has collected and uniformly reprocessed 83 patient-derived pre-treatment transcriptomic source datasets with manually curated cancer drug response information, involving 28 histological cancer types, 123 drugs, and 5139 patient samples. These data are browsable, searchable, and downloadable. Moreover, CTR-DB supports single-dataset exploration (including differential gene expression, receiver operating characteristic curve, functional enrichment, sensitizing drug search, and tumor microenvironment analyses), and multiple-dataset combination and comparison, as well as biomarker validation function, which provide insights into the drug resistance mechanism, predictive biomarker discovery and validation, drug combination, and resistance mechanism heterogeneity.