Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells
By Wanjuan Yang, Jorge Soares, Patricia Greninger, E. Edelman, H. Lightfoot, S. Forbes, Nidhi Bindal, D. Beare, James A. Smith, I. Thompson and 7 more. Nucleic Acids Res., 2012
3,577 citations recorded by the source corpus.
Alterations in cancer genomes strongly influence clinical responses to treatment and in many instances are potent biomarkers for response to drugs. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) is the largest public resource for information on drug sensitivity in cancer cells and molecular markers of drug response. Data are freely available without restriction. GDSC currently contains drug sensitivity data for almost 75 000 experiments, describing response to 138 anticancer drugs across almost 700 cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from the Catalogue of Somatic Mutations in Cancer database, including information on somatic mutations in cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal focused on identifying molecular biomarkers of drug sensitivity based on queries of specific anticancer drugs or cancer genes. Graphical representations of the data are used throughout with links to related resources and all datasets are fully downloadable. GDSC provides a unique resource incorporating large drug sensitivity and genomic datasets to facilitate the discovery of new therapeutic biomarkers for cancer therapies.
Subfields: Cancer Research, Computational Theory and Mathematics, Pulmonary and Respiratory Medicine
View Paper: https://www.semanticscholar.org/paper/d7763cf32f1fc0e023d03bcf680c22f24eeda23d
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Citations in the corpus
- Large-scale integration of small molecule-induced genome-wide transcriptional responses, Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action (2014, background): “…responses in diseased and healthy cell states, involving various molecular entities such as drugs, proteins, genes, transcripts, cellular, and molecular processes, characteristics (e.g., genetic) of the cell model systems, etc. (Barretina et al., 2012; Heiser et al., 2012; Yang et al., 2013).”
- Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, background): “GDSC: Genomics in Drug Sensitivity in Cancer (GDSC) is a public database that curates experimental values (IC50) of drug response in various cancer cell lines (Yang et al. 2012).”
- Network as a Biomarker: A Novel Network-Based Sparse Bayesian Machine for Pathway-Driven Drug Response Prediction (2019, background): “The Genomics of Drug Sensitivity in Cancer (GDSC) database [21,22] contains the gene expression data of 789 cancer cell lines and provides sensitivity data of various cancer cell lines to drugs from in-vitro drug screening experiments.”
- Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells (2020, methodology): “To train the model, we harmonized data from two large cancer drug screening resources: the Cancer Therapeutics Response Portal (CTRP) v2 and the Genomics of Drug Sensitivity in Cancer (GDSC) database (Seashore-Ludlow et al., 2015; Yang et al., 2013).”
- Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles (2020, methodology): “Since we also required an external test set of at least 10 cell lines, only cancer types with at least 45 cell lines in the GDSC [3] were analyzed.”
- Artificial Intelligence in Cancer Research and Precision Medicine (2021, background): “This work has gained importance due to availability of large cancer drug efficacy datasets, obtained from experiments done in cell lines (87, 89, 111, 112).”
- Next-Generation Machine Learning for Biological Networks. (2018, neutral): “Extensive multi-omics data from drug treatments (Barretina et al., 2012; Basu et al., 2013; Garnett et al., 2012; Goodspeed et al., 2016; Musa et al., 2017; Rees et al., 2016; Seashore-Ludlow et al., 2015; Shoemaker, 2006; Yang et al., 2013), together with large amounts of genotypic data collected a”