Catalin Barbacioru

Membrane Transporters and Channels: Role of the Transportome in Cancer Chemosensitivity and Chemoresistance

Membrane transporters and channels (collectively the transportome) govern cellular influx and efflux of ions, nutrients, and drugs. We used oligonucleotide arrays to analyze gene expression of the transportome in 60 human cancer cell lines used by the National Cancer Institute for drug screening. Correlating gene expression with the potencies of 119 standard anticancer drugs identified known drug-transporter interactions and suggested novel ones. Folate, nucleoside, and amino acid transporters positively correlated with chemosensitivity to their respective drug substrates. We validated the positive correlation between SLC29A1 (nucleoside transporter ENT1) expression and potency of nucleoside analogues, azacytidine and inosine-glycodialdehyde. Application of an inhibitor of SLC29A1, nitrobenzylmercaptopurine ribonucleoside, significantly reduced the potency of these two drugs, indicating that SLC29A1 plays a role in cellular uptake. Three ABC efflux transporters (ABCB1, ABCC3, and ABCB5) showed significant negative correlations with multiple drugs, suggesting a mechanism of drug resistance. ABCB1 expression correlated negatively with potencies of 19 known ABCB1 substrates and with Baker’s antifol and geldanamycin. Use of RNA interference reduced ABCB1 mRNA levels and concomitantly increased sensitivity to these two drugs, as expected for ABCB1 substrates. Similarly, specific silencing of ABCB5 by small interfering RNA increased sensitivity to several drugs in melanoma cells, implicating ABCB5 as a novel chemoresistance factor. Ion exchangers, ion channels, and subunits of proton and sodium pumps variably correlated with drug potency. This study identifies numerous potential drug-transporter relationships and supports a prominent role for membrane transport in determining chemosensitivity. Measurement of transporter gene expression may prove useful in predicting anticancer drug response.

Cystine-Glutamate Transporter SLC7A11 in Cancer Chemosensitivity and Chemoresistance

SLC7A11 (xCT), together with SLC3A2 (4F2hc), encodes the heterodimeric amino acid transport system x(c)-, which mediates cystine-glutamate exchange and thereby regulates intracellular glutathione levels. We used microarrays to analyze gene expression of transporters in 60 human cancer cell lines used by the National Cancer Institute for drug screening (NCI-60). The expression of SLC7A11 showed significant correlation with that of SLC3A2 (r = 0.66), which in turn correlated with SLC7A5 (r = 0.68), another known partner for SLC3A2, and with T1A-2 (r = 0.60; all P < 0.0001). Linking expression of SLC7A11 with potency of 1,400 candidate anticancer drugs identified 39 showing positive correlations, e.g., amino acid analogue, L-alanosine, and 296 with negative correlations, e.g., geldanamycin. However, no significant correlation was observed with the geldanamycin analogue 17-allylamino, 17-demethoxygeldanamycin (17-AAG). Inhibition of transport system x(c)- with glutamate or (S)-4-carboxyphenylglycine in lung A549 and HOP-62, and ovarian SK-OV-3 cells, reduced the potency of L-alanosine and lowered intracellular glutathione levels. This further resulted in increased potency of geldanamycin, with no effect on 17-AAG. Down-regulation of SLC7A11 by small interfering RNA affected drug potencies similarly to transport inhibitors. The inhibitor of gamma-glutamylcysteine synthetase, buthionine sulfoximine, also decreased intracellular glutathione levels and enhanced potency of geldanamycin, but did not affect L-alanosine. These results indicate that SLC7A11 mediates cellular uptake of L-alanosine but confers resistance to geldanamycin by supplying cystine for glutathione maintenance. SLC7A11 expression could serve as a predictor of cellular response to L-alanosine and glutathione-mediated resistance to geldanamycin, yielding a potential target for increasing chemosensitivity to multiple drugs.

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.

Prediction of Anticancer Drug Potency from Expression of Genes Involved in Growth Factor Signaling

PurposeThis study develops and evaluates a systematic approach to finding biomarker genes for predicting potency of anticancer drugs against tumor cells, focusing on gene families related to growth factor signaling.MethodsCytotoxic potencies of 119 drugs against 60 neoplastic cell lines (NCI-60) were correlated with expression of 343 genes, including 90 growth factors and receptors, 63 metalloproteinases, and 92 ras-like GTPases as downstream signaling factors. Progressively more stringent criteria and predictive models aim at identifying the smallest subset of genes predictive of cytotoxic potency.ResultsComparing gene expression with drug potency across the NCI-60 yielded genes with negative and positive correlations (p < 0.001), indicative of a role in chemoresistance and chemosensitivity, respectively. Of 17 genes with multiple negative correlations, 8 are known chemoresistance factors, validating the approach. Negatively correlated genes clustered into two main groups with distinct expression profiles and drug correlations, represented by EGFR and ERBB2 (Her-2/Neu). Accordingly, no synergism was observed between EGFR and ERBB2 inhibitors. However, combinations with classical anticacer drugs were not correlated with EGFR and ERBB2 expression in four cell lines tested, suggesting complex interactions in combination treatments. Finally, a subset of only 13 genes was found to be sufficient for near optimal prediction of drug potency against the NCI-60.ConclusionsOur approach using a small subset of genes reveals known and potential biomarkers in cancer chemotherapy, providing a strategy for genome-wide analysis.

An algorithm for reconstruction of Markov blankets in Bayesian networks of gene expression datasets

This work presents an efficient algorithm, of polynomial complexity for learning Bayesian belief networks over a dataset of gene expression levels. Given a dataset that is large enough, the algorithm generates a belief network close to the underlying model by recovering the Markov blanket of every node. The time complexity is dependent on the connectivity of the generating graph and not on the size of it, and therefore yields to exponential savings in computational time relative to some previously known algorithms. We use bootstrap and permutation techniques in order to measure confidence in our finding. To evaluate this algorithm, we present experimental results on S.cerevisiae cell-cycle measurements of Spettman et al. (1998).