Acquisition of estrogen independence induces TOB1-related mechanisms supporting breast cancer cell proliferation.

Resistance to therapies targeting the estrogen pathway remains a challenge in the treatment of estrogen receptor-positive breast cancer. To address this challenge, a systems biology approach was used. A library of small interfering RNAs targeting an estrogen receptor (ER)- and aromatase-centered network identified 46 genes that are dispensable in estrogen-dependent MCF7 cells, but are selectively required for the survival of estrogen-independent MCF7-derived cells and multiple additional estrogen-independent breast cancer cell lines. Integration of this information identified a tumor suppressor gene TOB1 as a critical determinant of estrogen-independent ER-positive breast cell survival. Depletion of TOB1 selectively promoted G1 phase arrest and sensitivity to AKT and mammalian target of rapmycin (mTOR) inhibitors in estrogen-independent cells but not in estrogen-dependent cells. Phosphoproteomic profiles from reverse-phase protein array analysis supported by mRNA profiling identified a significant signaling network reprogramming by TOB1 that differed in estrogen-sensitive and estrogen-resistant cell lines. These data support a novel function for TOB1 in mediating survival of estrogen-independent breast cancers. These studies also provide evidence for combining TOB1 inhibition and AKT/mTOR inhibition as a therapeutic strategy, with potential translational significance for the management of patients with ER-positive breast cancers.

Identification of aberrant pathway and network activity from high-throughput data.

The workshop focused on approaches to deduce changes in biological activity in cellular pathways and networks that drive phenotype from high-throughput data. Work in cancer has demonstrated conclusively that cancer etiology is driven not by single gene mutation or expression change, but by coordinated changes in multiple signaling pathways. These pathway changes involve different genes in different individuals, leading to the failure of gene-focused analysis to identify the full range of mutations or expression changes driving cancer development. There is also evidence that metabolic pathways rather than individual genes play the critical role in a number of metabolic diseases. Tools to look at pathways and networks are needed to improve our understanding of disease and to improve our ability to target therapeutics at appropriate points in these pathways.

Chronic rosiglitazone therapy normalizes expression of ACE1, SCD1 and other genes in the kidney of obese Zucker rats as determined by microarray analysis.

Thiazolidinediones increase tissue insulin sensitivity and are protective against worsening of nephropathy and hypertension in diabetes. Mechanisms underlying protection at the renal level likely involve a variety of unknown changes in gene expression. We examined kidney gene expression in obese and lean Zucker rats in response to rosiglitazone (Avandia), a peroxisome proliferator activated receptor (gamma-subtype) agonist. Lean and obese Zucker rats were treated with either control chow or chow with added rosiglitazone (3 mg/kg x bw) for 12 weeks (n = 3/group). Total kidney mRNA expression was evaluated using the Affymetrix Rat Genome 230 2.0 GeneChip. 903 probe sets were significantly (P < 0.05) altered with at least 1.5-fold changes between groups. In untreated obese rats, 300 probe sets were increased and 244 decreased, relative to lean. Increased genes included the beta-subunit of the epithelial sodium channel (ENaC), the thiazide-sensitive Na-Cl cotransporter, and aquaporin 3. Decreased genes included angiotensin converting enzyme, type 1 (ACE1). FatiGO analysis showed that the highest number of altered genes between lean and obese belonged to the categories: ion binding, hydrolase activity, and protein binding. RGZ increased expression of uncoupling protein 1 (UCP1), CD36, and fatty acid binding protein 4 (FAbp4) in both lean and obese rats. In obese rats, 33 genes were normalized by RGZ (no longer different from lean) including ACE1, fatty acid synthase (Fasn), and stearoyl-coenzyme A desaturase (SCD1). Ingenuity Pathways System analysis of genes upregulated by RGZ in obese rats revealed two major nodes affected: PPAR-gamma and tumor necrosis factor alpha (TNF-alpha).

Peak selection from MALDI-TOF mass spectra using ant colony optimization.

MOTIVATION: Due to the large number of peaks in mass spectra of low-molecular-weight (LMW) enriched sera, a systematic method is needed to select a parsimonious set of peaks to facilitate biomarker identification. We present computational methods for matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) spectral data preprocessing and peak selection. In particular, we propose a novel method that combines ant colony optimization (ACO) with support vector machines (SVM) to select a small set of useful peaks. RESULTS: The proposed hybrid ACO-SVM algorithm selected a panel of eight peaks out of 228 candidate peaks from MALDI-TOF spectra of LMW enriched sera. An SVM classifier built with these peaks achieved 94% sensitivity and 100% specificity in distinguishing hepatocellular carcinoma from cirrhosis in a blind validation set of 69 samples. Area under the receiver operating characteristic (ROC) curve was 0.996. The classification capability of these peaks is compared with those selected by the SVM-recursive feature elimination method. AVAILABILITY: Supplementary material and MATLAB scripts to implement the methods described in this article are available at http://microarray.georgetown.edu/web/files/bioinf.htm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effect of Akt inhibition on scatter factor-regulated gene expression in DU-145 human prostate cancer cells.

The cytokine scatter factor (SF) (hepatocyte growth factor) transduces various biologic actions, including cell motility, invasion, angiogenesis and apoptosis inhibition. The latter is relevant to understanding the role of SF in promoting tumor cell survival in different contexts, for example, detachment from basement membrane, growth in metastatic sites and responses to chemo- and radiotherapy. Previously, we showed that SF protects cells against apoptosis owing to DNA damage, by a mechanism involving phosphoinositol-3-kinase/c-Akt signaling. Here, we used DNA microarray assays to identify c-Akt-regulated genes that might contribute to cell protection. DU-145 human prostate cancer cells were transfected+/-a dominant-negative mutant Akt, treated+/-SF and analysed for gene expression using Affymetrix arrays. These studies identified SF-regulated genes for which induction was c-Akt-dependent vs -independent. Selected microarray findings were confirmed by semiquantitative and quantitative reverse transcription-polymerase chain reaction. We tested the contribution of four SF-inducible/c-Akt-dependent genes (AMPD3, EPHB2, MX1 and WNT4) to protection against adriamycin (a DNA topoisomerase IIalpha inhibitor) using RNA interference. Knockdown of each gene except EPHB2 caused a small but significant reduction in the SF cell protection. The lack of effect of EPHB2 knockdown may be due to the fact that DU-145 cells contain a single-mutant EPHB2 allele. A combination of three small interfering RNAs blocked most of the protection by SF in both DU-145 and T47D cells. These findings identify novel c-Akt-regulated genes, some of which contribute to SF-mediated cytoprotection.

Adaptive double self-organizing maps for clustering gene expression profiles.

This paper introduces a new model of self-organizing map (SOM) known as adaptive double self-organizing map (ADSOM). ADSOM has a flexible topology and performs data partitioning and cluster visualization simultaneously without requiring a priori knowledge about the number of clusters. It combines features of the popular SOM with two-dimensional position vectors, which serve as a visualization tool to detect the number of clusters present in the data. ADSOM updates its free parameters and allows convergence of its position vectors to a fairly consistent number of clusters provided its initial number of nodes is greater than the expected number of clusters. A novel index is introduced based on hierarchical clustering of the final locations of position vectors. The index allows automated detection of the number of clusters, thereby reducing human error that could be incurred from counting clusters visually. To test ADSOM's consistency in data partitioning, we examine the number of common profiles found in the clusters that were obtained by varying the initial number of nodes. This provides a confidence measure for the clusters formed by ADSOM and illustrates the effect of different initial number of nodes on data partitioning. The reliance of ADSOM in identifying number of clusters is demonstrated by applying it to publicly available yeast gene expression data.