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

The x(c)(-) 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 x(c)(-), 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 x(c)(-) 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 x(c)(-)-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 x(c)(-) transporter provides a useful target for glioma therapy. x(c)(-) 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.

Pharmacogenomics approach reveals MRP1 (ABCC1)-mediated resistance to geldanamycins.

PURPOSE: Geldanamycin 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. METHODS: To 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. RESULTS: The 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. CONCLUSIONS: These 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.

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 approximately 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.

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.

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.

Chemogenomic analysis identifies geldanamycins as substrates and inhibitors of ABCB1.

PURPOSE: A prerequisite for geldanamycin (GA, NSC122750) to targeting heat shock protein 90 and inhibiting tumor growth is sufficient intracellular drug accumulation. We hypothesized that membrane transporters on tumor cells determine at least in part the response to GA analogues. MATERIALS AND METHODS: To facilitate a systematic study of chemosensitivity across a group of GA analogues with similar chemical structures, we correlated mRNA expression profiles of most known transporters with growth inhibitory potencies of compounds in 60 tumor cell lines (NCI-60). We subsequently validated the gene-drug correlations using cytotoxicity and transport assays. RESULTS: Geldanamycin analogues displayed a range of negative correlations coefficients with ABCB1 (MDR1, or P-glycoprotein) expression. Suppressing ABCB1 in multidrug resistant cells (NCI/ADR-RES and K562/DOX) and ABCB1-transfected cells (BC19) increased sensitivity to GA analogues, as expected for substrates. Moreover, ABCB1-mediated efflux of daunorubicin in K562/DOX cells could be blocked markedly by GA analogues in a dose-dependent fashion. The IC(50) values (half-maximum inhibition of daunorubicin efflux) were 5.5, 7.3 and 12 muM for macbecin II (NSC330500), 17-AAG (NSC330507) and GA, respectively. CONCLUSIONS: These observations demonstrate that GA analogues are substrates as well as inhibitors of ABCB1, suggesting that drug interactions between GA analogues and other agents that are ABCB1 substrates may occur via ABCB1 in normal or tumor cells.

Chemoinformatics analysis identifies cytotoxic compounds susceptible to chemoresistance mediated by glutathione and cystine/glutamate transport system xc-.

Glutathione detoxification has been broadly implicated in resistance to chemotherapy. This study explores the relationship between chemical structure and GSH-mediated chemoresistance. System xc-, the heterodimeric cystine/glutamate exchanger composed of SLC7A11 and SLC3A2, plays a role in maintaining cellular glutathione (GSH) levels. Previous results show that SLC7A11 expression negatively correlates with drug potency across the National Cancer Institute's 60 cell lines for compounds susceptible to GSH-mediated chemoresistance. The number of significant SLC7A11-drug correlations was much greater than those of other genes tested, suggesting that SLC7A11 plays a critical role. Approximately 15% of a curated set of 3045 compounds yielded significant negative SLC7A11 correlations. These compounds tend to contain structural features amenable to GSH reactivity, such as Mannich bases. In cell lines strongly expressing SLC7A11, the potency of selected compounds, was enhanced by inhibition of SLC7A11. This system provides a rapid screen for detecting susceptibility of anticancer drugs to GSH-mediated resistance.

Comparison of methods for sequential screening of large compound sets.

Sequential screening is an iterative procedure that can greatly increase hit rates over random screening or non-iterative procedures. We studied the effects of three factors on enrichment rates: the method used to rank compounds, the molecular descriptor set and the selection of initial training set. The primary factor influencing recovery rates was the method of selecting the initial training set. Rates for recovering active compounds were substantially lower with the diverse training sets than they were with training sets selected by other methods. Because structure-activity information is incrementally enhanced in intermediate training sets, sequential screening provides significant improvement in the average rate of recovery of active compounds when compared with non-iterative selection procedures.

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.

Building predictive models for protein tyrosine phosphatase 1B inhibitors based on discriminating structural features by reassembling medicinal chemistry building blocks.

A new approach to predicting the biological activity of small molecule pharmaceutics is demonstrated. Structural features of medicinal chemistry building blocks are used as 2-D molecular descriptors. These descriptors include predefined structural features and macrostructures obtained from a supervised process in which features in the core library are reassembled to provide larger features that strongly differentiate the desired biological response variable. Chemical features derived in this manner can serve as predictor variables for diverse modeling algorithms, and application using partial least squares techniques is demonstrated here. Models are presented for inhibition by benzofuran and benzothiophene biphenyl analogues of protein tyrosine phosphatase 1B (PTP1B), a target for insulin-resistant disease states. Results are compared to models for PTP1B inhibitors available in the literature based on CoMFA-related techniques and 3-D molecular descriptors.

Systematic analysis of large screening sets in drug discovery.

Each year large pharmaceutical companies produce massive amounts of primary screening data for lead discovery. To make better use of the vast amount of information in pharmaceutical databases, companies have begun to scrutinize the lead generation stage to ensure that more and better qualified lead series enter the downstream optimization and development stages. This article describes computational techniques for end to end analysis of large drug discovery screening sets. The analysis proceeds in three stages: In stage 1 the initial screening set is filtered to remove compounds that are unsuitable as lead compounds. In stage 2 local structural neighborhoods around active compound classes are identified, including similar but inactive compounds. In stage 3 the structure-activity relationships within local structural neighborhoods are analyzed. These processes are illustrated by analyzing two large, publicly available databases.

Finding discriminating structural features by reassembling common building blocks.

We present a new method for constructing discriminating substructures by reassembling common medicinal chemistry building blocks. The algorithm can be parametrized to meet differing objectives: (1) to build features that discriminate for biological activity in a local structural neighborhood, (2) to build scaffolds for R-group analysis, (3) to construct cluster signatures that discriminate for membership in the cluster and provide a graphical representation for its members, and (4) to identify substructures that characterize major classes in a heterogeneous compound set. We illustrated the results of the algorithm on a literature dataset is of 118 compounds with in vitro inhibition data against recombinant human protein tyrosine phosphatase 1B (PTP-1B).

Multiscale and Bayesian approaches to data analysis in genomics high-throughput screening.

Tremendous amounts of data are produced by high-throughput screening methods currently employed in drug discovery and product development. A typical cDNA microarray or oligonucleotide-based gene chip experiment easily generates over 10,000 data points for each array or chip. The challenge of inferring meaningful information is formidable given the size and number of these datasets. This paper reviews the current status of statistical tools available for gene expression analysis, with emphasis on Bayesian approaches and multiscale wavelet filtering. Fundamental concepts of Bayesian and multiscale modeling are discussed from the perspective of their potential to address important issues related to the analysis of gene expression data, such as the fact that genomic data often have non-Gaussian distributions and feature localization and multiple scales in both frequency and measurement dimension. Recent publications in these areas are reviewed. Wavelet filtering and the advantages of multiscale methods are demonstrated by application to publicly available gene expression data from the National Cancer Institute (NCI). Multiscale methods, including multiscale principal component analysis (MSPCA), are applied to extract gene subsets and to visualize data in multidimensions for comparisons. Similarity in cell lines and gene selection are effectively visualized and quantitatively compared.

On combining recursive partitioning and simulated annealing to detect groups of biologically active compounds.

Statistical data mining methods have proven to be powerful tools for investigating correlations between molecular structure and biological activity. Recursive partitioning (RP), in particular, offers several advantages in mining large, diverse data sets resulting from high throughput screening. When used with binary molecular descriptors, the standard implementation of RP splits on single descriptors. We use simulated annealing (SA) to find combinations of molecular descriptors whose simultaneous presence best separates off the most active, chemically similar group of compounds. The search is incorporated into a recursive partitioning design to produce a regression tree for biological activity on the space of structural fingerprints. Each node is characterized by a specific combination of structural features, and the terminal nodes with high average activities correspond, roughly, to different classes of compounds. Using LeadScope structural features as descriptors to mine a database from the National Cancer Institute, the merging of RP and SA consistently identifies structurally homogeneous classes of highly potent anticancer agents.