RNA interference (RNAi) mediated by small interfering RNAs (siRNAs) is a powerful new tool for analyzing gene knockdown phenotypes in living mammalian cells. To facilitate large-scale, high-throughput functional genomics studies using RNAi, we have developed a microarray-based technology for highly parallel analysis. Specifically, siRNAs in a transfection matrix were first arrayed on glass slides, overlaid with a monolayer of adherent cells, incubated to allow reverse transfection, and assessed for the effects of gene silencing by digital image analysis at a single cell level. Validation experiments with HeLa cells stably expressing GFP showed spatially confined, sequence-specific, time-and dose-dependent inhibition of green fluorescence for those cells growing directly on microspots containing siRNA targeting the GFP sequence. Microarray-based siRNA transfections analyzed with a custom-made quantitative image analysis system produced results that were identical to those from traditional well-based transfection, quantified by flow cytometry. Finally, to integrate experimental details, image analysis, data display, and data archiving, we developed a prototype information management system for high-throughput cell-based analyses. In summary, this RNAi microarray platform, together with ongoing efforts to develop large-scale human siRNA libraries, should facilitate genomic-scale cell-based analyses of gene function.

Ovarian cancer is the fifth leading cause of cancer death among women and the most lethal gynecologic malignancy. One of the leading causes of death in high-grade serous ovarian cancer (HGSOC) is chemoresistant disease, which may present as intrinsic or acquired resistance to therapies. Here we discuss some of the known molecular mechanisms of chemoresistance that have been exhaustively investigated in chemoresistant ovarian cancer, including drug efflux pump multidrug resistance protein 1 (MDR1), the epithelial–mesenchymal transition, DNA damage and repair capacity. We also discuss novel therapeutics that may address some of the challenges in bringing approaches that target chemoresistant processes from bench to bedside. Some of these new therapies include novel drug delivery systems, targets that may halt adaptive changes in the tumor, exploitation of tumor mutations that leave cancer cells vulnerable to irreversible damage, and novel drugs that target ribosomal biogenesis, a process that may be uniquely different in cancer versus non-cancerous cells. Each of these approaches, or a combination of them, may provide a greater number of positive outcomes for a broader population of HGSOC patients.

Overexpression of protein tyrosine phosphatase PTP4A oncoproteins is common in many human cancers and is associated with poor patient prognosis and survival. We observed elevated levels of PTP4A3 phosphatase in 79% of human ovarian tumor samples, with significant overexpression in tumor endothelium and pericytes. Furthermore, PTP4A phosphatases appear to regulate several key malignant processes, such as invasion, migration, and angiogenesis, suggesting a pivotal regulatory role in cancer and endothelial signaling pathways. While phosphatases are attractive therapeutic targets, they have been poorly investigated because of a lack of potent and selective chemical probes. In this study, we disclose that a potent, selective, reversible, and noncompetitive PTP4A inhibitor, JMS-053, markedly enhanced microvascular barrier function after exposure of endothelial cells to vascular endothelial growth factor or lipopolysaccharide. JMS-053 also blocked the concomitant increase in RhoA activation and loss of Rac1. In human ovarian cancer cells, JMS-053 impeded migration, disrupted spheroid growth, and decreased RhoA activity. Importantly, JMS-053 displayed anticancer activity in a murine xenograft model of drug resistant human ovarian cancer. These data demonstrate that PTP4A phosphatases can be targeted in both endothelial and ovarian cancer cells, and confirm that RhoA signaling cascades are regulated by the PTP4A family.

A major challenge in studies of etiologic heterogeneity in breast cancer has been the limited throughput, accuracy and reproducibility of measuring tissue markers. Computerized image analysis systems may help address these concerns but published reports of their use are limited. We assessed agreement between automated and pathologist scores of a diverse set of immunohistochemical (IHC) assays performed on breast cancer TMAs. TMAs of 440 breast cancers previously stained for ER-α, PR, HER-2, ER-β and aromatase were independently scored by two pathologists and three automated systems (TMALabII, TMAx, Ariol). Agreement between automated and pathologist scores of negative/positive was measured using the area under the receiver operator characteristics curve (AUC) and weighted kappa statistics (κ) for categorical scores. We also investigated the correlation between IHC scores and mRNA expression levels. Agreement between pathologist and automated negative/positive and categorical scores was excellent for ER-α and PR (AUC range =0.98-0.99; κ range =0.86-0.91). Lower levels of agreement were seen for ER-β categorical scores (AUC=0.99-1.0; κ=0.80-0.86) and both negative/positive and categorical scores for aromatase (AUC=0.85-0.96; κ=0.41-0.67) and HER2 (AUC=0.94-0.97; κ=0.53-0.72). For ER-α and PR, there was strong correlation between mRNA levels and automated (ρ=0.67-0.74) and pathologist IHC scores (ρ=0.67-0.77). HER2 mRNA levels were more strongly correlated with pathologist (ρ=0.63) than automated IHC scores (ρ=0.41-0.49). Automated analysis of IHC markers is a promising approach for scoring large numbers of breast cancer tissues in epidemiologic investigations. This would facilitate studies of etiologic heterogeneity which ultimately may allow improved risk prediction and better prevention approaches.