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Paul E. Blower, Ph.D.Research Professor
Department of Pharmacology
5072B Graves Hall
333 W. 10th Avenue
Columbus, OH 43210
Phone: (614) 688-5565
Fax: (614) 292-7232
Email: blower.7@osu.edu
Research Interests
Chemoresistance is a main obstacle to successful cancer therapy, and my research focuses on applying chemogenomic techniques to study chemoresistance in tumor cell lines. Chemogenomics combines high-throughput genomics or proteomic profiling with chemoinformatic and statistical analysis to study the response of a biological system to chemical compounds. It also investigates the consequences of differential gene/protein expression on cellular response to compound treatment.
My current research objectives are two-fold: 1. Develop a software platform that allows facile exploration, visualization, and data mining of integrated genomic datasets related to the NCI-60 panel of cancer cell lines (NCI60), used at the NCI for drug discovery. 2. Apply these new software tools, in combination with experimental validation of relevant results, to assess the role of microRNAs in chemoresistance and -sensitivity. Our intention is to use these new tools in search of biomarkers predictive of drug efficacy for optimizing cancer chemotherapy.
We have previously developed data mining techniques for discovering associations between compound classes and molecular targets for cancer chemotherapy, relying on mRNA profliles in the NCI60. This has led to discovery and experimental validation of novel gene-drug correlations relevant to chemoresistance and -sensitivity. Cytotoxic chemicals can serve as probes for characterizing miRNA/mRNA function, using a chemogenomics approach molded by access to the suite of datasets related to NCI-60 cell lines. This is an unparalleled public resource for elucidating molecular targets and mechanisms of chemosensitivity/resistanc, with cytotoxic potencies for >50,000 compounds, and mRNA proflies and proteomes, enabling correlations between drug potencies and mRNA/protein expression.
MicroRNAs (miRNAs) are small noncoding RNAs thought to regulate translation of mRNA into protein, or mRNA stability, affecting cell differentiation, proliferation, and apoptosis. miRNA expression is strongly implicated in cancer genesis and progression. We hypothesize that miRNAs affect response to cytotoxic compounds and can serve as predictive biomarkers/targets for chemotherapy. Recently we have obtained microarray expression data for the full set of NCI-60 cell lines. Relevant array results have been partially validated with RT-PCR.
Representative Publications
Ikediobi O, Reimers M, Durinck S, Blower P, Futreal PA, Stratton M, Weinstein J. (2008) In vitro differential sensitivity of melanomas to phenothiazines is based on the presence of Codon 600 BRAF mutation. Mol Cancer 7:1337-1346.
Blower PE, Chung J-H, Verducci JS, Lin S, Park J-K, Dai Z, Liu C-G, Schmittgen TD, Reinhold W, Croce CM, Weinstein JN, Sadee W. (2008) MicroRNAs modulate the chemosensitivity of tumor cells. Mol Cancer Ther 7:1-9.
Dai Z, Sadee W, Blower P. (2007) Chemogenomics of sensitivity and resistance to anticancer drugs. Curr Pharmacogen 5:11-19.
Daiz Z, Huang Y, Sadee W, Blower P. (2007) Chemoinformatics analysis identifies cytotoxic compounds susceptible to chemoresistance mediated by glutathione and cystine/glutamate transport system xc-. J Med Chem 50:1896-1906.
Huang Y, Blower PE, Liu R, Dai Z, Pham A-N, Moon H, Fang J, Sadee W. (2007) Chemogenomic analysis identifies geldanamycins as substrates and inhibitors of ABCB1. Pharm Res 24:1702-1712.
Blower PE, Verducci JS, Lin S, Zhou J, Chung J-H, Dai Z, Liu C-G, Reinhold W, Lorenzi PL, Kaldijian EP, Croce CM, Weinstein JN, Sadee W. (2007) MicroRNA expression profiles for the NCI-60 cancer cell panel. Mol Cancer Ther 6:1483-1491.
Liu R, Blower P, Pham A-N, Fang J, Dai Z, Wise C, Green B, Teitel CH, Ning B, Ling W, Lyn-Cook BD, Kadlubar FF, Sadee W, Huang Y. Cystine-glutamate transporter SLC7A11 mediates resistance to geldanamycin but not to 17-AAG. Mol Pharm 72:1637-1646.
Blower PE, Cross KC. (2006) Decision tree methods in pharmaceutical research. Curr Top Med Chem 6:31-39.
Blower PE, Cross KC, Eichler GS, Myatt GJ, Weinstein JN, Yang C. (2006) Comparison of methods for sequential screening of large compound sets. Comb Chem High Throughput Screen 9:115-122.
Huang Y, Blower PE, Yang C, Barbacioru C, Dai Z, Zhang Y, Xiao JJ, Chan KK, Sadee W. (2005) Correlating gene expression with chemical scaffolds of cytotoxic agents: Ellipticines as substrates and inhibitors of MDR1 (ABCB1). Pharmacogen 5:112-125.
Yang C, Cross K, Myatt G, Blower P, Rathman J. (2004) Building predictive models for PTP1B inhibitors based on discriminating structural features by reassembling medicinal chemistry building blocks. J Med Chem 47:5984-5994.
Blower P, Cross K, Fligner M, Myatt G, Verducci J, Yang C. (2004) Systematic analysis of large screening sets. Curr Drug Disc Technol 37-47.
Cross KP, Myatt G, Yang C, Fligner MF, Verducci JS, Blower PE. (2003) Finding discriminating structural features by reassembling common building blocks. J Med Chem 46:4770-4775.
Blower P, Yang C, Fligner MA, Verducci JS, Yu L, Richman S, Weinstein JN. (2002)Pharmacogenomic analysis: Correlating molecular substructure classes with microarray gene expression data. Pharmacogen 2:259-271.
Fligner MA, Verducci JS, Blower PE. (2004) Modification of the Jaccard/Tanimoto similarity index for diverse selection of chemical compounds using binary strings. Technometrics 44:110-119.
Blower P, Fligner M, Verducci J, Bjoraker J. (2002) On combining recursive partitioning and simulated annealing to detect groups of biologically active compounds. J Chem Inf Comput Sci 42:393-404.
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