Literature DB >> 15541957

Transcription profiling of gene expression in drug discovery and development: the NCI experience.

Edward A Sausville1, Susan L Holbeck.   

Abstract

Transcript profiling, using microarray or other analogous technologies, to query on a large-scale the expression of genes in tumours or their derivative cell lines has numerous potential uses in oncology drug discovery and development. Characterisation of genes expressed in tumours may allow tumours to be separated into subsets defining subtypes that have a distinctive pathway utilisation. The molecular entities comprising the pathways which distinguish one disease subset from another then become potential candidate drug targets. Alternatively, gene expression patterns may be correlated with the degree of antiproliferative effect of candidate drug leads. This can reveal aspects of the drug's action that could serve to provide a further basis for benchmarking the generation of analogues or provide important information about pathways potentially modulated by the drug in achieving cytotoxicity. New information is emerging that the expression of drug transport-related molecules is a major variable that can be usefully explored using gene expression data, and the features promoting successful drug handling by the tumour cell may be an additional variable which can be illuminated by gene expression studies.

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Year:  2004        PMID: 15541957     DOI: 10.1016/j.ejca.2004.08.006

Source DB:  PubMed          Journal:  Eur J Cancer        ISSN: 0959-8049            Impact factor:   9.162


  6 in total

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4.  Somatic mutation patterns and compound response in cancers.

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6.  Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages--strategies for data mining in support of therapeutic studies.

Authors:  Melinda G Hollingshead; Luke H Stockwin; Sergio Y Alcoser; Dianne L Newton; Benjamin C Orsburn; Carrie A Bonomi; Suzanne D Borgel; Raymond Divelbiss; Kelly M Dougherty; Elizabeth J Hager; Susan L Holbeck; Gurmeet Kaur; David J Kimmel; Mark W Kunkel; Angelena Millione; Michael E Mullendore; Howard Stotler; Jerry Collins
Journal:  BMC Genomics       Date:  2014-05-22       Impact factor: 3.969

  6 in total

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