Balazs Gyorffy

Gene expression profiling of 30 cancer cell lines predicts resistance towards 11 anticancer drugs at clinically achieved concentrations

Cancer patients with tumors of similar grading, staging and histogenesis can have markedly different treatment responses to different chemotherapy agents. So far, individual markers have failed to correctly predict resistance against anticancer agents. We tested 30 cancer cell lines for sensitivity to 5‐fluorouracil, cisplatin, cyclophosphamide, doxorubicin, etoposide, methotrexate, mitomycin C, mitoxantrone, paclitaxel, topotecan and vinblastine at drug concentrations that can be systemically achieved in patients. The resistance index was determined to designate the cell lines as sensitive or resistant, and then, the subset of resistant vs. sensitive cell lines for each drug was compared. Gene expression signatures for all cell lines were obtained by interrogating Affymetrix U133A arrays. Prediction Analysis of Microarrays was applied for feature selection. An individual prediction profile for the resistance against each chemotherapy agent was constructed, containing 42–297 genes. The overall accuracy of the predictions in a leave‐one‐out cross validation was 86%. A list of the top 67 multidrug resistance candidate genes that were associated with the resistance against at least 4 anticancer agents was identified. Moreover, the differential expressions of 46 selected genes were also measured by quantitative RT‐PCR using a TaqMan micro fluidic card system. As a single gene can be correlated with resistance against several agents, associations with resistance were detected all together for 76 genes and resistance phenotypes, respectively. This study focuses on the resistance at the in vivo concentrations, making future clinical cancer response prediction feasible. The TaqMan‐validated gene expression patterns provide new gene candidates for multidrug resistance. Supplementary material for this article can be found on the International Journal of Cancer website at http://www.interscience.wiley.com/jpages/0020‐7136/suppmat.

Jetset: selecting the optimal microarray probe set to represent a gene

BackgroundInterpretation of gene expression microarrays requires a mapping from probe set to gene. On many Affymetrix gene expression microarrays, a given gene may be detected by multiple probe sets, which may deliver inconsistent or even contradictory measurements. Therefore, obtaining an unambiguous expression estimate of a pre-specified gene can be a nontrivial but essential task.ResultsWe developed scoring methods to assess each probe set for specificity, splice isoform coverage, and robustness against transcript degradation. We used these scores to select a single representative probe set for each gene, thus creating a simple one-to-one mapping between gene and probe set. To test this method, we evaluated concordance between protein measurements and gene expression values, and between sets of genes whose expression is known to be correlated. For both test cases, we identified genes that were nominally detected by multiple probe sets, and we found that the probe set chosen by our method showed stronger concordance.ConclusionsThis method provides a simple, unambiguous mapping to allow assessment of the expression levels of specific genes of interest.

Systematic evaluation of the miRNA-ome and its downstream effects on mRNA expression identifies gastric cancer progression.

We investigated the differential expression of Dicer and Drosha, as well as that of microRNA (miRNA), in adjacent normal and tumour samples of patients with gastric cancer. The expression of Dicer and Drosha was studied by immunohistochemistry in 332 gastric cancers and correlated with clinico‐pathological patient characteristics. Differential expression of miRNAs was studied using the Invitrogen NCode™ Multi‐Species miRNA Microarray Probe Set containing 857 mammalian probes in a test set of six primary gastric cancers (three with and three without lymph node metastases). Differential expression was validated by RT‐PCR on an independent validation set of 20 patients with gastric cancer. Dicer and Drosha were differentially expressed in non‐neoplastic and neoplastic gastric tissue. The expression of Drosha correlated with local tumour growth and was a significant independent prognosticator of patient survival. Twenty miRNAs were up‐ and two down‐regulated in gastric carcinoma compared with non‐neoplastic tissue. Six of these miRNAs separated node‐positive from node‐negative gastric cancers, ie miR‐103, miR‐21, miR‐145, miR‐106b, miR‐146a, and miR‐148a. Five miRNAs expressed differentially in node‐positive cancers had conserved binding sites for mRNAs differentially expressed in the same set of tumour samples. Gastric cancer shows a complex derangement of the miRNA‐ome, including Dicer and Drosha. These changes correlate independently with patient prognosis and probably influence local tumour growth and nodal spread. Copyright

The PI3K inhibitor LY294002 blocks drug export from resistant colon carcinoma cells overexpressing MRP1

Multidrug resistance may be achieved by the activation of membrane transporters, detoxification, alterations in DNA repair or failure in apoptotic pathways. Recent data have suggested an involvement of mitogenic signalling pathways mediated by Ras and phosphoinositol-3-kinase (PI3K/Akt) in controlling multidrug resistance. Since these pathways are important targets for therapeutic interference, we sought to investigate whether blocking effectors kinases by specific inhibitors would result in a sensitization toward cytotoxic drugs. We found that cotreatment of drug-resistant HT29RDB colon cancer cells with the topoisomerase inhibitor doxorubicin and the PI3K-inhibitor LY294002 resulted in massive apoptosis, while cotreatment with the Mek inhibitors PD98059 or U0126 had no effect. This suggested that the PI3K-pathways controls cell survival and drug resistance in these cells. Besides blocking Akt phosphorylation, the PI3K-inibitor increased the intracellular doxorubicin concentration threefold. LY294002 inhibits drug export in a competitive manner as revealed by measuring drug efflux in the presence and the absence of inhibitor. The efficacy of drug efflux inhibition by LY294002 was similar to that achieved by the MRP1 inhibitors MK571 and genistein. We conclude that the PI3K inhibitor LY294002 may have therapeutic potential when combined with doxorubicin in the treatment of MRP1-mediated drug resistance.

Genome-wide reprogramming of the chromatin landscape underlies endocrine therapy resistance in breast cancer.

Significance Resistance to treatment with endocrine therapy occurs in ∼50% of all breast cancer patients. The pathway(s) leading to drug resistance is ill-defined. We show that accessibility to the genome is altered in drug-resistant compared with responsive breast cancer cells. This coincides with the overactivation of the NOTCH pathway in drug-resistant compared with responsive cancer cells. The transcription factor PBX1, a known NOTCH target gene, is required for the growth of endocrine therapy-resistant breast cancer cells. Accordingly, a gene expression signature based on NOTCH-PBX1 activity can discriminate a priori breast cancer patients that are responsive or not to endocrine therapy. The estrogen receptor (ER)α drives growth in two-thirds of all breast cancers. Several targeted therapies, collectively termed endocrine therapy, impinge on estrogen-induced ERα activation to block tumor growth. However, half of ERα-positive breast cancers are tolerant or acquire resistance to endocrine therapy. We demonstrate that genome-wide reprogramming of the chromatin landscape, defined by epigenomic maps for regulatory elements or transcriptional activation and chromatin openness, underlies resistance to endocrine therapy. This annotation reveals endocrine therapy-response specific regulatory networks where NOTCH pathway is overactivated in resistant breast cancer cells, whereas classical ERα signaling is epigenetically disengaged. Blocking NOTCH signaling abrogates growth of resistant breast cancer cells. Its activation state in primary breast tumors is a prognostic factor of resistance in endocrine treated patients. Overall, our work demonstrates that chromatin landscape reprogramming underlies changes in regulatory networks driving endocrine therapy resistance in breast cancer.

Remodeling of central metabolism in invasive breast cancer compared to normal breast tissue – a GC-TOFMS based metabolomics study

BackgroundChanges in energy metabolism of the cells are common to many kinds of tumors and are considered a hallmark of cancer. Gas chromatography followed by time-of-flight mass spectrometry (GC-TOFMS) is a well-suited technique to investigate the small molecules in the central metabolic pathways. However, the metabolic changes between invasive carcinoma and normal breast tissues were not investigated in a large cohort of breast cancer samples so far.ResultsA cohort of 271 breast cancer and 98 normal tissue samples was investigated using GC-TOFMS-based metabolomics. A total number of 468 metabolite peaks could be detected; out of these 368 (79%) were significantly changed between cancer and normal tissues (p<0.05 in training and validation set). Furthermore, 13 tumor and 7 normal tissue markers were identified that separated cancer from normal tissues with a sensitivity and a specificity of >80%. Two-metabolite classifiers, constructed as ratios of the tumor and normal tissues markers, separated cancer from normal tissues with high sensitivity and specificity. Specifically, the cytidine-5-monophosphate / pentadecanoic acid metabolic ratio was the most significant discriminator between cancer and normal tissues and allowed detection of cancer with a sensitivity of 94.8% and a specificity of 93.9%.ConclusionsFor the first time, a comprehensive metabolic map of breast cancer was constructed by GC-TOF analysis of a large cohort of breast cancer and normal tissues. Furthermore, our results demonstrate that spectrometry-based approaches have the potential to contribute to the analysis of biopsies or clinical tissue samples complementary to histopathology.

Evaluation of Microarray Preprocessing Algorithms Based on Concordance with RT-PCR in Clinical Samples

Background Several preprocessing algorithms for Affymetrix gene expression microarrays have been developed, and their performance on spike-in data sets has been evaluated previously. However, a comprehensive comparison of preprocessing algorithms on samples taken under research conditions has not been performed. Methodology/Principal Findings We used TaqMan RT-PCR arrays as a reference to evaluate the accuracy of expression values from Affymetrix microarrays in two experimental data sets: one comprising 84 genes in 36 colon biopsies, and the other comprising 75 genes in 29 cancer cell lines. We evaluated consistency using the Pearson correlation between measurements obtained on the two platforms. Also, we introduce the log-ratio discrepancy as a more relevant measure of discordance between gene expression platforms. Of nine preprocessing algorithms tested, PLIER+16 produced expression values that were most consistent with RT-PCR measurements, although the difference in performance between most of the algorithms was not statistically significant. Conclusions/Significance Our results support the choice of PLIER+16 for the preprocessing of clinical Affymetrix microarray data. However, other algorithms performed similarly and are probably also good choices.

A prognostic gene expression index in ovarian cancer - validation across different independent data sets.

Ovarian carcinoma has the highest mortality rate among gynaecological malignancies. In this project, we investigated the hypothesis that molecular markers are able to predict outcome of ovarian cancer independently of classical clinical predictors, and that these molecular markers can be validated using independent data sets. We applied a semi‐supervised method for prediction of patient survival. Microarrays from a cohort of 80 ovarian carcinomas (TOC cohort) were used for the development of a predictive model, which was then evaluated in an entirely independent cohort of 118 carcinomas (Duke cohort). A 300‐gene ovarian prognostic index (OPI) was generated and validated in a leave‐one‐out approach in the TOC cohort (Kaplan‐Meier analysis, p = 0.0087). In a second validation step, the prognostic power of the OPI was confirmed in an independent data set (Duke cohort, p = 0.0063). In multivariate analysis, the OPI was independent of the post‐operative residual tumour, the main clinico‐pathological prognostic parameter with an adjusted hazard ratio of 6.4 (TOC cohort, CI 1.8–23.5, p = 0.0049) and 1.9 (Duke cohort, CI 1.2–3.0, p = 0.0068). We constructed a combined score of molecular data (OPI) and clinical parameters (residual tumour), which was able to define patient groups with highly significant differences in survival. The integrated analysis of gene expression data as well as residual tumour can be used for optimized assessment of the prognosis of platinum‐taxol‐treated ovarian cancer. As traditional treatment options are limited, this analysis may be able to optimize clinical management and to identify those patients who would be candidates for new therapeutic strategies. Copyright

Inflammation, adenoma and cancer: objective classification of colon biopsy specimens with gene expression signature.

Gene expression analysis of colon biopsies using high-density oligonucleotide microarrays can contribute to the understanding of local pathophysiological alterations and to functional classification of adenoma (15 samples), colorectal carcinomas (CRC) (15) and inflammatory bowel diseases (IBD) (14). Total RNA was extracted, amplified and biotinylated from frozen colonic biopsies. Genome-wide gene expression profile was evaluated by HGU133plus2 microarrays and verified by RT-PCR. We applied two independent methods for data normalization and used PAM for feature selection. Leave one-out stepwise discriminant analysis was performed. Top validated genes included collagenIVα1, lipocalin-2, calumenin, aquaporin-8 genes in CRC; CD44, met proto-oncogene, chemokine ligand-12, ADAM-like decysin-1 and ATP-binding casette-A8 genes in adenoma; and lipocalin-2, ubiquitin D and IFITM2 genes in IBD. Best differentiating markers between Ulcerative colitis and Crohns disease were cyclin-G2; tripartite motif-containing-31; TNFR shedding aminopeptidase regulator-1 and AMICA. The discriminant analysis was able to classify the samples in overall 96.2% using 7 discriminatory genes (indoleamine-pyrrole-2,3-dioxygenase, ectodermal-neural cortex, TIMP3, fucosyltransferase-8, collectin sub-family member 12, carboxypeptidase D, and transglutaminase-2). Using routine biopsy samples we successfully performed whole genomic microarray analysis to identify discriminative signatures. Our results provide further insight into the pathophysiological background of colonic diseases. The results set up data warehouse which can be mined further.

Cyclin-dependent kinase 8 mediates chemotherapy-induced tumor-promoting paracrine activities

Conventional chemotherapy not only kills tumor cells but also changes gene expression in treatment-damaged tissues, inducing production of multiple tumor-supporting secreted factors. This secretory phenotype was found here to be mediated in part by a damage-inducible cell-cycle inhibitor p21 (CDKN1A). We developed small-molecule compounds that inhibit damage-induced transcription downstream of p21. These compounds were identified as selective inhibitors of a transcription-regulating kinase CDK8 and its isoform CDK19. Remarkably, p21 was found to bind to CDK8 and stimulate its kinase activity. p21 and CDK8 also cooperate in the formation of internucleolar bodies, where both proteins accumulate. A CDK8 inhibitor suppresses damage-induced tumor-promoting paracrine activities of tumor cells and normal fibroblasts and reverses the increase in tumor engraftment and serum mitogenic activity in mice pretreated with a chemotherapeutic drug. The inhibitor also increases the efficacy of chemotherapy against xenografts formed by tumor cell/fibroblast mixtures. Microarray data analysis revealed striking correlations between CDK8 expression and poor survival in breast and ovarian cancers. CDK8 inhibition offers a promising approach to increasing the efficacy of cancer chemotherapy.