Paul E. Blower

MicroRNAs modulate the chemosensitivity of tumor cells

MicroRNAs are strongly implicated in such processes as development, carcinogenesis, cell survival, and apoptosis. It is likely, therefore, that they can also modulate sensitivity and resistance to anticancer drugs in substantial ways. To test this hypothesis, we studied the pharmacologic roles of three microRNAs previously implicated in cancer biology (let-7i, mir-16, and mir-21) and also used in silico methods to test pharmacologic microRNA effects more broadly. In the experimental system, we increased the expression of individual microRNAs by transfecting their precursors (which are active) or suppressed the expression by transfection of antisense oligomers. In three NCI-60 human cancer cell lines, a panel of 60 lines used for anticancer drug discovery, we assessed the growth-inhibitory potencies of 14 structurally diverse compounds with known anticancer activities. Changing the cellular levels of let-7i, mir-16, and mir-21 affected the potencies of a number of the anticancer agents by up to 4-fold. The effect was most prominent with mir-21, with 10 of 28 cell-compound pairs showing significant shifts in growth-inhibitory activity. Varying mir-21 levels changed potencies in opposite directions depending on compound class; indicating that different mechanisms determine toxic and protective effects. In silico comparison of drug potencies with microRNA expression profiles across the entire NCI-60 panel revealed that ∼30 microRNAs, including mir-21, show highly significant correlations with numerous anticancer agents. Ten of those microRNAs have already been implicated in cancer biology. Our results support a substantial role for microRNAs in anticancer drug response, suggesting novel potential approaches to the improvement of chemotherapy. [Mol Cancer Ther 2008;7(1):1–9]

MicroRNA expression profiles for the NCI-60 cancer cell panel

Advances in the understanding of cancer cell biology and response to drug treatment have benefited from new molecular technologies and methods for integrating information from multiple sources. The NCI-60, a panel of 60 diverse human cancer cell lines, has been used by the National Cancer Institute to screen >100,000 chemical compounds and natural product extracts for anticancer activity. The NCI-60 has also been profiled for mRNA and protein expression, mutational status, chromosomal aberrations, and DNA copy number, generating an unparalleled public resource for integrated chemogenomic studies. Recently, microRNAs have been shown to target particular sets of mRNAs, thereby preventing translation or accelerating mRNA turnover. To complement the existing NCI-60 data sets, we have measured expression levels of microRNAs in the NCI-60 and incorporated the resulting data into the CellMiner program package for integrative analysis. Cell line groupings based on microRNA expression were generally consistent with tissue type and with cell line clustering based on mRNA expression. However, mRNA expression seemed to be somewhat more informative for discriminating among tissue types than was microRNA expression. In addition, we found that there does not seem to be a significant correlation between microRNA expression patterns and those of known target transcripts. Comparison of microRNA expression patterns and compound potency patterns showed significant correlations, suggesting that microRNAs may play a role in chemoresistance. Combined with gene expression and other biological data using multivariate analysis, microRNA expression profiles may provide a critical link for understanding mechanisms involved in chemosensitivity and chemoresistance. [Mol Cancer Ther 2007;6(5):1483–91]

Pharmacogenomic analysis: correlating molecular substructure classes with microarray gene expression data

Genomic studies are producing large databases of molecular information on cancers and other cell and tissue types. Hence, we have the opportunity to link these accumulating data to the drug discovery processes. Our previous efforts at ‘information–intensive’ molecular pharmacology have focused on the relationship between patterns of gene expression and patterns of drug activity. In the present study, we take the process a step further—relating gene expression patterns, not just to the drugs as entities, but to ∼27 000 substructures and other chemical features within the drugs. This coupling of genomic information with structure-based data mining can be used to identify classes of compounds for which detailed experimental structure–activity studies may be fruitful. Using a systematic substructure analysis coupled with statistical correlations of compound activity with differential gene expression, we have identified two subclasses of quinones whose patterns of activity in the National Cancer Institutes 60-cell line screening panel (NCI-60) correlate strongly with the expression patterns of particular genes: (i) The growth inhibitory patterns of an electron-withdrawing subclass of benzodithiophenedione-containing compounds over the NCI-60 are highly correlated with the expression patterns of Rab7 and other melanoma-specific genes; (ii) the inhibitory patterns of indolonaphthoquinone-containing compounds are highly correlated with the expression patterns of the hematopoietic lineage-specific gene HS1 and other leukemia genes. As illustrated by these proof-of-principle examples, we introduce here a set of conceptual tools and fluent computational methods for projecting directly from gene expression patterns to drug substructures and vice versa. The analysis is presented in terms of the NCI-60 cell lines and microarray-based gene expression patterns, but the concept and methods are broadly applicable to other large-scale pharmacogenomic database sets as well. The approach (SAT for Structure-Activity-Target) provides a systematic way to mine databases for the design of further structure–activity studies, particularly to aid in target and lead identification.

A Modification of the Jaccard–Tanimoto Similarity Index for Diverse Selection of Chemical Compounds Using Binary Strings

Determination of molecular similarity plays an important role in analyzing large compound databases in chemical and pharmaceutical research. When molecules are described by binary vectors with bits corresponding to the presence or absence of structural features, the Tanimoto association coefficient is the most commonly used measure of similarity or chemical distance between two compounds. However, when used to select compounds for an optimal spread design, the Tanimoto coefficient produces an intrinsic bias toward smaller compounds. We have developed a new association coefficient that overcomes this bias. This article gives details of the new coefficient and contrasts the two coefficients for selecting diverse sets of compounds from a large collection. When the Tanimoto coefficient is modified as suggested to select a diverse set in the National Cancer Institute and Registry of Toxic Effects of Chemical Substances databases, the average number of features among the selected compounds increases by more than 50%.

Correlating gene expression with chemical scaffolds of cytotoxic agents: ellipticines as substrates and inhibitors of MDR1.

To facilitate a systematic study of chemoresistance across diverse classes of anticancer drug candidates, we performed correlation analyses between cytotoxic drug potency and gene expression in 60 tumor cell lines (NCI-60; NCI—National Cancer Institute). Ellipticine analogs displayed a range of correlation coefficients (r) with MDR1 (ABCB1, encoding multidrug resistance (MDR) protein MDR1 or P-glycoprotein). To determine MDR1 interactions of five ellipticines with diverse MDR1-r values, we employed MDR1-transport and cytotoxicity assays, using MDR1 inhibitors and siRNA-mediated MDR1 downregulation, in MDR1-overexpressing cells. Ellipticines with negative correlations—indicative of MDR1-mediated resistance—were shown to be MDR1 substrates, whereas those with neutral or positive correlations served as MDR1 inhibitors, which escape MDR1-mediated chemoresistance. Correlation with additional genes in the NCI-60 confirmed topoisomerases as ellipticine targets, but suggested distinct mechanisms of action and chemoresistance among them, providing a guide for selecting optimal drug candidates.

Pharmacogenomic approach reveals a role for the xc- cystine/glutamate antiporter in growth and celastrol resistance of glioma cell lines.

The xc− cystine/glutamate antiporter has been implicated in GSH-based chemoresistance because it mediates cellular uptake of cystine/cysteine for sustenance of intracellular GSH levels. Celastrol, isolated from a Chinese medicinal herb, is a novel heat shock protein 90 (Hsp90) inhibitor with potent anticancer activity against glioma in vitro and in vivo. In search of correlations between growth-inhibitory potency of celastrol in NCI-60 cell lines and microarray expression profiles of most known transporters, we found that expression of SLC7A11, the gene encoding the light chain subunit of xc−, showed a strong negative correlation with celastrol activity. This novel gene-drug correlation was validated. In celastrol-resistant glioma cells that highly expressed SLC7A11, sensitivity to celastrol was consistently increased via treatment with xc− inhibitors, including glutamate, (S)-4-carboxyphenylglycine, sulfasalazine, and SLC7A11 small interfering RNA. The GSH synthesis inhibitor, buthionine sulfoximine, also increased celastrol sensitivity, whereas the GSH booster, N-acetylcysteine, suppressed its cytotoxicity. Furthermore, the glioma cell lines were dependent on xc−-mediated cystine uptake for viability, because cystine omission from the culture medium resulted in cell death and treatment with sulfasalazine depleted GSH levels and inhibited their growth. Combined treatment of glioma cells with sulfasalazine and celastrol led to chemosensitization, as suggested by increased celastrol-induced cell cycle arrest, apoptosis, and down-regulation of the Hsp90 client protein, epidermal growth factor receptor. These results indicate that the xc− transporter provides a useful target for glioma therapy. xc− inhibitors such as sulfasalazine, a Food and Drug Administration-approved drug, may be effective both as an anticancer drug and as an agent for sensitizing gliomas to celastrol.

Cystine-Glutamate Transporter SLC7A11 Mediates Resistance to Geldanamycin but Not to 17-(Allylamino)-17-demethoxygeldanamycin

The cystine-glutamate transporter SLC7A11 has been implicated in chemoresistance, by supplying cystine to the cell for glutathione maintenance. In the NCI-60 cell panel, SLC7A11 expression shows negative correlation with growth inhibitory potency of geldanamycin but not with its analog 17-(allylamino)-17-demethoxygeldanamycin (17-AAG), which differs in the C-17 substituent in that the the methoxy moiety of geldanamycin is replaced by an amino group. Structure and potency analysis classified 18 geldanamycin analogs into two subgroups, “17-O/H” (C-17 methoxy or unsubstituted) and “17-N” (C-17 amino), showing distinct SLC7A11 correlation. We used three 17-O/H analogs and four 17-N analogs to test the role of the 17-substituents in susceptibility to SLC7A11-mediated resistance. In A549 cells, which are resistant to geldanamycin and strongly express SLC7A11, inhibition of SLC7A11 by (S)-4-carboxyphenylglycine or small interfering RNA increased sensitivity to 17-O/H, but had no effect on 17-N analogs. Ectopic expression of SLC7A11 in HepG2 cells, which are sensitive to geldanamycin and express low SLC7A11, confers resistance to geldanamycin, but not to 17-AAG. Antioxidant N-acetylcysteine, a precursor for glutathione synthesis, completely suppressed cytotoxic effects of 17-O/H but had no effect on 17-N analogs, whereas the prooxidant ascorbic acid had the opposite effect. Compared with 17-AAG, geldanamycin led to significantly more intracellular reactive oxygen species (ROS) production, which was quenched by addition of N-acetylcysteine. We conclude that SLC7A11 confers resistance selectively to 17-O/H (e.g., geldanamycin) but not to 17-N (e.g., 17-AAG) analogs partly as a result of differential dependence on ROS for cytotoxicity. Distinct mechanisms could significantly affect antitumor response and organ toxicity of these compounds in vivo.

Decision Tree Methods in Pharmaceutical Research

Decision trees are among the most popular of the new statistical learning methods being used in the pharmaceutical industry for predicting quantitative structure-activity relationships. This article reviews applications of decision trees in drug discovery research and extensions to the basic algorithm using hybrid or ensemble methods that improve prediction accuracy.

Pharmacogenomics Approach Reveals MRP1 (ABCC1)-Mediated Resistance to Geldanamycins

PurposeGeldanamycin and its analogues belong to a new class of anticancer agents that inhibit the molecular chaperone heat shock protein 90. We hypothesized that membrane transporters expressed on tumor cells may contribute at least in part to cellular sensitivity to these agents. The purpose of this study is to identify novel transporters as determinant for sensitivity and resistance to geldanamycins.MethodsTo facilitate a systematic study of chemosensitivity across multiple geldanamycin analogues, we correlated mRNA expression profiles of majority of transporters with anticancer drug activities in 60 human tumor cell lines (NCI-60). We subsequently validated the gene–drug correlations using cytotoxicity and transport assays.ResultsThe GA analogues displayed negative correlations with mRNA expression levels of the multidrug resistance protein 1 (MRP1, ABCC1). Suppressing MRP1 efflux using the inhibitor MK-571 and small interfering RNA in cell lines with intrinsic and acquired MRP1 overexpression (A549 and HL-60/ADR) and in cell lines stably transduced with MRP1 (MCF7/MRP1) increased intracellular drug accumulation and increased tumor cell sensitivity to geldanamycin analogues.ConclusionsThese results suggest that elevated expression of MRP1, like the alternative efflux transporter MDR1 (ABCB1, P-glycoprotein), can significantly influence tumor cell sensitivity to geldanamycins as a potential chemoresistance factor.

In vitro differential sensitivity of melanomas to phenothiazines is based on the presence of codon 600 BRAF mutation

The panel of 60 human cancer cell lines (the NCI-60) assembled by the National Cancer Institute for anticancer drug discovery is a widely used resource. We previously sequenced 24 cancer genes in those cell lines. Eleven of the genes were found to be mutated in three or more of the lines. Using a pharmacogenomic approach, we analyzed the relationship between drug activity and mutations in those 11 genes (APC, RB1, KRAS, NRAS, BRAF, PIK3CA, PTEN, STK11, MADH4, TP53, and CDKN2A). That analysis identified an association between mutation in BRAF and the antiproliferative potential of phenothiazine compounds. Phenothiazines have been used as antipsychotics and as adjunct antiemetics during cancer chemotherapy and more recently have been reported to have anticancer properties. However, to date, the anticancer mechanism of action of phenothiazines has not been elucidated. To follow up on the initial pharmacologic observations in the NCI-60 screen, we did pharmacologic experiments on 11 of the NCI-60 cell lines and, prospectively, on an additional 24 lines. The studies provide evidence that BRAF mutation (codon 600) in melanoma as opposed to RAS mutation is predictive of an increase in sensitivity to phenothiazines as determined by 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt assay (Wilcoxon P = 0.007). That pattern of increased sensitivity to phenothiazines based on the presence of codon 600 BRAF mutation may be unique to melanomas, as we do not observe it in a panel of colorectal cancers. The findings reported here have potential implications for the use of phenothiazines in the treatment of V600E BRAF mutant melanoma. [Mol Cancer Ther 2008;7(6):1337–46]