Literature DB >> 20657799

Gene expression associations with the growth inhibitory effects of small molecules on live cells: specificity of effects and uniformity of mechanisms.

Kerby Shedden1, Yang Yang, Gus Rosania.   

Abstract

The NCI60 human tumor cell line screen is a public resource for studying selective and non-selective growth inhibition of small molecules against cancer cells. By coupling growth inhibition screening data with biological characterizations of the different cell lines, it becomes possible to infer mechanisms of action underlying some of the observable patterns of selective activity. Using these data, mechanistic relationships have been identified including specific associations between single genes and small families of closely related compounds, and less specific relationships between biological processes involving several cooperating genes and broader families of compounds. Here we aim to characterize the degree to which such specific and general relationships are present in these data. A related question is whether genes tend to act with a uniform mechanism for all associated compounds, or whether multiple mechanisms are commonly involved. We address these two issues in a statistical framework placing special emphasis on the effects of measurement error in the gene expression and chemical screening data. We find that as measurement accuracy increases, the pattern of apparent associations shifts from one dominated by isolated gene/compound pairs, to one in which families consisting of an average of 25 compounds are associated to the same gene. At the same time, the number of genes that appear to play a role in influencing compound activities decreases. For less than half of the genes, the presence of both positive and negative correlations indicates pleiotropic associations with molecules via different mechanisms of action.

Entities:  

Year:  2009        PMID: 20657799      PMCID: PMC2907114          DOI: 10.1002/sam.10049

Source DB:  PubMed          Journal:  Stat Anal Data Min        ISSN: 1932-1864            Impact factor:   1.051


  19 in total

1.  Chemosensitivity prediction by transcriptional profiling.

Authors:  J E Staunton; D K Slonim; H A Coller; P Tamayo; M J Angelo; J Park; U Scherf; J K Lee; W O Reinhold; J N Weinstein; J P Mesirov; E S Lander; T R Golub
Journal:  Proc Natl Acad Sci U S A       Date:  2001-09-11       Impact factor: 11.205

2.  A functional genomic study on NCI's anticancer drug screen.

Authors:  K-C Li; S Yuan
Journal:  Pharmacogenomics J       Date:  2004       Impact factor: 3.550

3.  Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance.

Authors:  Guo Wei; David Twomey; Justin Lamb; Krysta Schlis; Jyoti Agarwal; Ronald W Stam; Joseph T Opferman; Stephen E Sallan; Monique L den Boer; Rob Pieters; Todd R Golub; Scott A Armstrong
Journal:  Cancer Cell       Date:  2006-09-28       Impact factor: 31.743

4.  Feasibility of drug screening with panels of human tumor cell lines using a microculture tetrazolium assay.

Authors:  M C Alley; D A Scudiero; A Monks; M L Hursey; M J Czerwinski; D L Fine; B J Abbott; J G Mayo; R H Shoemaker; M R Boyd
Journal:  Cancer Res       Date:  1988-02-01       Impact factor: 12.701

5.  Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma.

Authors:  Levi A Garraway; Hans R Widlund; Mark A Rubin; Gad Getz; Aaron J Berger; Sridhar Ramaswamy; Rameen Beroukhim; Danny A Milner; Scott R Granter; Jinyan Du; Charles Lee; Stephan N Wagner; Cheng Li; Todd R Golub; David L Rimm; Matthew L Meyerson; David E Fisher; William R Sellers
Journal:  Nature       Date:  2005-07-07       Impact factor: 49.962

Review 6.  The NCI60 human tumour cell line anticancer drug screen.

Authors:  Robert H Shoemaker
Journal:  Nat Rev Cancer       Date:  2006-10       Impact factor: 60.716

7.  Linking pathway gene expressions to the growth inhibition response from the National Cancer Institute's anticancer screen and drug mechanism of action.

Authors:  R Huang; A Wallqvist; N Thanki; D G Covell
Journal:  Pharmacogenomics J       Date:  2005       Impact factor: 3.550

8.  Data mining the NCI cancer cell line compound GI(50) values: identifying quinone subtypes effective against melanoma and leukemia cell classes.

Authors:  Kenneth A Marx; Philip O'Neil; Patrick Hoffman; M L Ujwal
Journal:  J Chem Inf Comput Sci       Date:  2003 Sep-Oct

9.  A rational approach to personalized anticancer therapy: chemoinformatic analysis reveals mechanistic gene-drug associations.

Authors:  Kerby Shedden; Leroy B Townsend; John C Drach; Gustavo R Rosania
Journal:  Pharm Res       Date:  2003-06       Impact factor: 4.200

10.  Gene expression patterns within cell lines are predictive of chemosensitivity.

Authors:  Brian Z Ring; Stella Chang; L Winston Ring; Robert S Seitz; Douglas T Ross
Journal:  BMC Genomics       Date:  2008-02-08       Impact factor: 3.969

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.