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Open AccessJournal ArticleDOI

Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells

TLDR
The Genomics of Drug Sensitivity in Cancer (GDSC) provides a unique resource incorporating large drug sensitivity and genomic datasets to facilitate the discovery of new therapeutic biomarkers for cancer therapies.
Abstract
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.

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COSMIC: exploring the world's knowledge of somatic mutations in human cancer

TL;DR: COSMIC, the Catalogue Of Somatic Mutations In Cancer is the world's largest and most comprehensive resource for exploring the impact of somatic mutations in human cancer, describing 2 002 811 coding point mutations in over one million tumor samples and across most human genes.
Journal ArticleDOI

A community effort to assess and improve drug sensitivity prediction algorithms

TL;DR: This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods and discusses the innovations underlying the top-performing methodology, Bayesian multitask MKL.
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Next-Generation Machine Learning for Biological Networks

TL;DR: A primer on machine learning for life scientists is provided, including an introduction to deep learning, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.
Journal ArticleDOI

TP53 Variations in Human Cancers: New Lessons from the IARC TP53 Database and Genomics Data.

TL;DR: This analysis showed that IARC has more TP53 somatic mutation data than genomic repositories (29,000 vs. 4,000), but the more complete screening achieved by genomic studies highlighted some overlooked facts about TP53 mutations.
References
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Journal ArticleDOI

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

TL;DR: The results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents and the generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of ‘personalized’ therapeutic regimens.
Journal ArticleDOI

Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia

TL;DR: After 5 years of follow-up, continuous treatment of chronic-phase CML with imatinib as initial therapy was found to induce durable responses in a high proportion of patients.
Journal ArticleDOI

COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer.

TL;DR: With all genomic information recently updated to GRCh37, COSMIC integrates many diverse types of mutation information and is making much closer links with Ensembl and other data resources.
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