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shinyDepMap, a tool to identify targetable cancer genes and their functional connections from Cancer Dependency Map data.
Shimada, Kenichi; Bachman, John A; Muhlich, Jeremy L; Mitchison, Timothy J.
Afiliação
  • Shimada K; Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, United States.
  • Bachman JA; Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, United States.
  • Muhlich JL; Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, United States.
  • Mitchison TJ; Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, United States.
Elife ; 102021 02 08.
Article em En | MEDLINE | ID: mdl-33554860
ABSTRACT
Individual cancers rely on distinct essential genes for their survival. The Cancer Dependency Map (DepMap) is an ongoing project to uncover these gene dependencies in hundreds of cancer cell lines. To make this drug discovery resource more accessible to the scientific community, we built an easy-to-use browser, shinyDepMap (https//labsyspharm.shinyapps.io/depmap). shinyDepMap combines CRISPR and shRNA data to determine, for each gene, the growth reduction caused by knockout/knockdown and the selectivity of this effect across cell lines. The tool also clusters genes with similar dependencies, revealing functional relationships. shinyDepMap can be used to (1) predict the efficacy and selectivity of drugs targeting particular genes; (2) identify maximally sensitive cell lines for testing a drug; (3) target hop, that is, navigate from an undruggable protein with the desired selectivity profile, such as an activated oncogene, to more druggable targets with a similar profile; and (4) identify novel pathways driving cancer cell growth and survival.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biologia Computacional / Neoplasias Tipo de estudo: Evaluation_studies Limite: Humans Idioma: En Revista: Elife Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biologia Computacional / Neoplasias Tipo de estudo: Evaluation_studies Limite: Humans Idioma: En Revista: Elife Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos