Abstract
Novel strategies are proposed to quantitatively analyze and relate biological pathways to drug responses using gene expression and small-molecule growth inhibition data (GI50) derived from the National Cancer Institute's 60 cancer cells (NCI60). We have annotated groups of drug GI50 responses with pathways defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCarta, and functional categories defined by Gene Ontology (GO), through correlations between pathway gene expression patterns and drug GI50 profiles. Drug–gene-pathway relationships may then be utilized to find drug targets or target-specific drugs. Significantly correlated pathways and the gene products involved represent interesting targets for further exploration, whereas drugs that are significantly correlated with only certain pathways are more likely to be target specific. Separate pathway clustering finds that pathways engaged in the same biological process tend to have similar drug correlation patterns. The biological and statistical significances of our method are established by comparison to known small-molecule inhibitor–gene target relationships reported in the literature and by standard randomization procedures. The results of our pathway, gene expression and drug-induced growth inhibition associations, can serve as a basis for proposing testable hypotheses about potential anticancer drugs, their targets, and mechanisms of action.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 6 print issues and online access
$259.00 per year
only $43.17 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Capranico G . A rational selection of drug targets needs deeper insights into general regulation mechanisms. Curr Med Chem Anti-Cancer Agents 2004; 4: 393–394.
Butte AJ, Tamayo P, Slonim D, Golub TR, Kohane IS . Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci USA 2000; 97: 12182–12186.
Szakacs G, Annereau J-P, Lababidi S, Shankavaram U, Arciello A, Bussey KJ et al. Predicting drug sensitivity and resistance: profiling ABC transporter genes in cancer cells. Cancer Cell 2004; 6: 129–137.
Huang Y, Anderle P, Bussey KJ, Barbacioru C, Shankavaram U, Dai Z et al. Membrane transporters and channels: role of the transportome in cancer chemosensitivity and chemoresistance. Cancer Res 2004; 64: 4294–4301.
Blower PE, Yang C, Fligner MA, Verducci JS, Yu L, Richman S et al. Pharmacogenomic analysis: correlating molecular substructure classes with microarray gene expression data. Pharmacogenomics J 2002; 2: 259–271.
Zhou Y, Gwadry FG, Reinhold WC, Miller LD, Smith LH, Scherf U et al. Transcriptional regulation of mitotic genes by camptothecin-induced DNA damage: microarray analysis of dose- and time-dependent effects. Cancer Res 2002; 62: 1688–1695.
Lee JK, Scherf U, Smith LH, Tanabe L, Weinstein JN . Analysis of gene expression data of the NCI 60 cancer cell lines using Bayesian hierarchical effects model. Proc Int Soc Opt Eng 2001; 4266: 228–235.
Scherf U, Ross DT, Waltham M, Smith LH, Lee JK, Tanabe L et al. A gene expression database for the molecular pharmacology of cancer. Nat Genet 2000; 24: 236–244.
Wosikowski K, Schuurhuis D, Johnson K, Paull KD, Myers TG, Weinstein JN et al. Identification of epidermal growth factor receptor and c-erbB2 pathway inhibitors by correlation with gene expression patterns. J Natl Cancer Inst 1997; 89: 1505–1515.
O’Connor PM, Jackman J, Bae I, Myers TG, Fan S, Mutoh M et al. Characterization of the p53 tumor suppressor pathway in cell lines of the National Cancer Institute anticancer drug screen and correlations with the growth-inhibitory potency of 123 anticancer agents. Cancer Res 1997; 57: 4285–4300.
Alvarez M, Paull K, Monks A, Hose C, Lee JS, Weinstein J et al. Generation of a drug resistance profile by quantitation of mdr-1/P-glycoprotein in the cell lines of the National Cancer Institute Anticancer Drug Screen. J Clin Invest 1995; 95: 2205–2214.
Li KC, Yuan S . A functional genomic study on NCI's anticancer drug screen. Pharmacogenomics J 2004; 4: 127–135.
Wallqvist A, Rabow AA, Shoemaker RH, Sausville EA, Covell DG . Linking the growth inhibition response from the National Cancer Institute's anticancer screen to gene expression levels and other molecular target data. Bioinformatics 2003; 19: 2212–2224.
Freije JMP, Lawrence JA, Hollingshead MG, de la Rosa A, Narayanan V, Grever M et al. Identification of compounds with preferential inhibitory activity against low-NM23-expressing human breast carcinoma and melanoma cell lines. Nat Med 1997; 3: 395–401.
Ficenec D, Osborne M, Pradines J, Richards D, Felciano R, Cho Raymond J et al. Computational knowledge integration in biopharmaceutical research. Brief Bioinform 2003; 4: 260–278.
Covell DG, Wallqvist A, Huang R, Thanki N, Rabow AA, Lu XJ . Linking tumor cell cytotoxicity to mechanism of drug action: an integrated analysis of gene expression, small-molecule screening and structural databases. Proteins 2005; 59: 403–433.
Huang Y, Blower PE, Yang C, Barbacioru C, Dai Z, Zhang Y et al. Correlating gene expression with chemical scaffolds of cytotoxic agents: ellipticines as substrates and inhibitors of MDR1. Pharmacogenomics J 2005; 5: 112–125.
Nakatsu N, Yoshida Y, Yamazaki K, Nakamura T, Dan S, Fukui Y et al. Chemosensitivity profile of cancer cell lines and identification of genes determining chemosensitivity by an integrated bioinformatical approach using cDNA arrays. Mol Cancer Therap 2005; 4: 399–412.
Stegmaier K, Ross KN, Colavito SA, O'Malley S, Stockwell BR, Golub TR . Gene expression-based high-throughput screening (GE-HTS) and application to leukemia differentiation. Nat Genet 2004; 36: 257–263.
Huang R, Wallqvist A, Covell DG . Comprehensive analysis of pathway or functionally related gene expression in the National Cancer Institute's anticancer screen. Genomics 2005, submitted.
Kohonen T . Self-Organizing Maps. Springer Verlag: Berlin, Germany, 1995.
Paull KD, Shoemaker RH, Hodes L, Monks A, Scudiero DA, Rubinstein L et al. Display and analysis of patterns of differential activity of drugs against human tumor cell lines: development of mean graph and COMPARE algorithm. J Natl Cancer Inst 1989; 81: 1088–1092.
Rabow AA, Shoemaker RH, Sausville EA, Covell DG . Mining the National Cancer Institute's tumor-screening database: identification of compounds with similar cellular activities. J Med Chem 2002; 45: 818–840.
Huang R, Wallqvist A, Covell DG . Anticancer metal compounds in NCI's tumor-screening database: putative mode of action. Biochem Pharmacol 2005; 69: 1009–1039.
Tormo JR, Gallardo T, Peris E, Bermejo A, Cabedo N, Estornell E et al. Inhibitory effects on mitochondrial complex I of semisynthetic mono-tetrahydrofuran acetogenin derivatives. Bioorg Med Chem Lett 2003; 13: 4101–4105.
Tormo JR, Royo I, Gallardo T, Zafra-Polo MC, Hernandez P, Cortes D et al. In vitro antitumor structure–activity relationships of threo/trans/threo mono-tetrahydrofuranic acetogenins: correlations with their inhibition of mitochondrial complex I. Oncol Res 2003; 14: 147–154.
Lannuzel A, Michel PP, Hoglinger GU, Champy P, Jousset A, Medja F et al. The mitochondrial complex I inhibitor annonacin is toxic to mesencephalic dopaminergic neurons by impairment of energy metabolism. Neuroscience 2003; 121: 287–296.
Randic M . On characterization of chemical structure. J Chem Inf Comput Sci 1997; 37: 672–687.
Rosen R . An approach to molecular similarity. In: Johnson MAM, Gerald M (eds). Concepts and Applications of Molecular Similarity. Wiley: New York, NY, 1990, pp 369–382.
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al. The Protein Data Bank. Nucleic Acids Res 2000; 28: 235–242.
Vinatier D, Monnier JC . Receiver operating curve, an aid in decision making. Principles and applications illustrated with some examples. J Gynecol Obstet Biol Reprod (Paris) 1988; 17: 981–989.
Westwell AD, Stevens MF . Hitting the chemotherapy jackpot: strategy, productivity and chemistry. Drug Discov Today 2004; 9: 625–627.
Acknowledgements
We thank the members of the STB staff, especially Drs Robert Shoemaker and Susan Mertins, for valuable contributions during the preparation of this manuscript. This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. NO1-CO-12400. The content of this publication does not necessarily reflect the view or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government.
Author information
Authors and Affiliations
Corresponding author
Additional information
Our reference to ‘drug’ in this manuscript is not a clinical ‘drug’ per se, but generally a small-molecule compound that has been screened in the NCI60 for anticancer acticity.
DUALITY OF INTEREST
None declared.
Supplementary Information
Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website (http://www.nature.com/tpj).
Supplementary information
Rights and permissions
About this article
Cite this article
Huang, R., Wallqvist, A., Thanki, N. et al. Linking pathway gene expressions to the growth inhibition response from the National Cancer Institute's anticancer screen and drug mechanism of action. Pharmacogenomics J 5, 381–399 (2005). https://doi.org/10.1038/sj.tpj.6500331
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/sj.tpj.6500331
Keywords
This article is cited by
-
Computational discovery of transcription factors associated with drug response
The Pharmacogenomics Journal (2016)
-
Integrating Systems Biology Sources Illuminates Drug Action
Clinical Pharmacology & Therapeutics (2014)
-
Evaluation of molecular descriptors for antitumor drugs with respect to noncovalent binding to DNA and antiproliferative activity
BMC Pharmacology (2009)
-
Conditional drug screening shows that mitotic inhibitors induce AKT/PKB-insensitive apoptosis
Journal of Chemical Biology (2009)
-
Identifying differential correlation in gene/pathway combinations
BMC Bioinformatics (2008)