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The metastatic potential of cells is an important parameter in the design of optimal strategies for the personalized treatment of cancer. Using atomic force microscopy (AFM), we show, consistent with previous studies conducted in other types of epithelial cancer, that ovarian cancer cells are generally softer and display lower intrinsic variability in cell stiffness than non-malignant ovarian epithelial cells. A detailed examination of highly invasive ovarian cancer cells (HEY A8) relative to their less invasive parental cells (HEY), demonstrates that deformability is also an accurate biomarker of metastatic potential. Comparative gene expression analyses indicate that the reduced stiffness of highly metastatic HEY A8 cells is associated with actin cytoskeleton remodeling and microscopic examination of actin fiber structure in these cell lines is consistent with this prediction. Our results indicate that cell stiffness may be a useful biomarker to evaluate the relative metastatic potential of ovarian and perhaps other types of cancer cells.

Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be \"drivers\" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm \"open source\", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

Cell mechanical properties, such as cell stiffness and viscous behavior, have been proposed as biomarkers of cell disease states. Moreover, the molecular pathways that modify cell mechanics may also be potential novel targets for managing lethal diseases, such as cancer, by specifically changing the mechanical phenotype of cells along with the associated functional phenotype. This study explores the relationship between the viscosity and stiffness of cells and the underlying molecular mechanisms. We used a large linked molecular dataset to explore the correlations between gene expression, cell migration, and cell mechanical properties, which were quantified by two viscous rate constants from a standard linear solid viscoelasticity model and apparent Young’s modulus from a Hertzian contact mechanics model. Using a causal network analysis built on known relationships curated from literature in Qiagen’s Ingenuity Pathway Analysis package, we identified potential molecular control nodes that could modify the expression of multiple genes correlated with cell mechanics. We investigated the up- and down-regulation of expression by two predicted potential small molecule regulators (lacidipine and AG879) and four predicted potential gene regulators ( AKT2 , ITGB6, mir-183, and CD82 ) through small molecule inhibition, RNA interference, and introduction of microRNAs. The effects of modulation of these regulators were measured on both cell mechanical properties and gene expression in three ovarian cancer cell types. We identified several regulators that change the viscosity and stiffness of the cell with a corresponding change to the functional migratory ability in a cell-type specific manner.

Whole-genome expression data generated by microarray studies have shown promise for quantitative human health risk assessment. While numerous approaches have been developed to determine benchmark doses (BMDs) from probeset-level dose responses, sensitivity of the results to methods used for normalization of the data has not yet been systematically investigated. Normalization of microarray data converts raw hybridization signals to expression estimates that are expected to be proportional to the amounts of transcripts in the profiled specimens. Different approaches to normalization have been shown to greatly influence the results of some downstream analyses, including biological interpretation. In this study we evaluate the influence of microarray normalization methods on the transcriptomic BMDs. We demonstrate using in vivo data that the use of alternative pipelines for normalization of Affymetrix microarray data can have a considerable impact on the number of detected differentially expressed genes and pathways (processes) determined to be treatment responsive, which may lead to alternative interpretations of the data. In addition, we found that normalization can have a considerable effect (as much as ~30-fold in this study) on estimation of the minimum biological potency (transcriptomic point of departure). We argue for consideration of alternative normalization methods and their data-informed selection to most effectively interpret microarray data for use in human health risk assessment.

Tick-borne encephalitis virus (TBEV) is transmitted mainly by tick bites, but humans can acquire infection through consuming unpasteurized milk from infected animals. Interhuman transmission of TBEV by breast milk has not been confirmed or ruled out. We report a case of probable transmission of TBEV from an unvaccinated mother to an infant through breast-feeding.

Cancers consist of a heterogeneous populations of cells that may respond differently to treatment through drug-resistant sub-populations. The scarcity of these resistant sub-populations makes it challenging to understand how to counter their resistance. We report a label-free microfluidic approach to separate cancer cells treated with chemotherapy into sub-populations enriched in chemoresistant and chemosensitive cells based on the differences in cellular stiffness. The sorting approach enabled analysis of the molecular distinctions between resistant and sensitive cells. Consequently, the role of multiple mechanisms of drug resistance was identified, including decreased sensitivity to apoptosis, enhanced metabolism, and extrusion of drugs, and, for the first time, the role of estrogen receptor in drug resistance of leukemia cells. To validate these findings, several inhibitors for the identified resistance pathways were tested with chemotherapy to increase cytotoxicity sevenfold. Thus, microfluidic sorting can identify molecular mechanisms of drug resistance to examine heterogeneous responses of cancers to therapies.

Increasing evidence supports the existence of a subpopulation of cancer cells capable of self-renewal and differentiation into diverse cell lineages. These cancer stem-like or cancer-initiating cells (CICs) also demonstrate resistance to chemo- and radiotherapy and may function as a primary source of cancer recurrence. We report here on the isolation and in vitro propagation of multicellular ovarian cancer spheroids from a well-established ovarian cancer cell line (OVCAR-3). The spheroid-derived cells (SDCs) display self-renewal potential, the ability to produce differentiated progeny, and increased expression of genes previously associated with CICs. SDCs also demonstrate higher invasiveness, migration potential, and enhanced resistance to standard anticancer agents relative to parental OVCAR-3 cells. Furthermore, SDCs display up-regulation of genes associated with epithelial-to-mesenchymal transition (EMT), anticancer drug resistance and/or decreased susceptibility to apoptosis, as well as, down-regulation of genes typically associated with the epithelial cell phenotype and pro-apoptotic genes. Pathway and biological process enrichment analyses indicate significant differences between the SDCs and precursor OVCAR-3 cells in TGF-beta-dependent induction of EMT, regulation of lipid metabolism, NOTCH and Hedgehog signaling. Collectively, our results indicate that these SDCs will be a useful model for the study of ovarian CICs and for the development of novel CIC-targeted therapies.

Recently, multicellular spheroids were isolated from a well-established epithelial ovarian cancer cell line, OVCAR-3, and were propagated in vitro. These spheroid-derived cells displayed numerous hallmarks of cancer stem cells, which are chemo- and radioresistant cells thought to be a significant cause of cancer recurrence and resultant mortality. Gene set enrichment analysis of expression data from the OVCAR-3 cells and the spheroid-derived putative cancer stem cells identified several metabolic pathways enriched in differentially expressed genes. Before this, there had been little previous knowledge or investigation of systems-scale metabolic differences between cancer cells and cancer stem cells, and no knowledge of such differences in ovarian cancer stem cells. To determine if there were substantial metabolic changes corresponding with these transcriptional differences, we used two-dimensional gas chromatography coupled to mass spectrometry to measure the metabolite profiles of the two cell lines. These two cell lines exhibited significant metabolic differences in both intracellular and extracellular metabolite measurements. Principal components analysis, an unsupervised dimensional reduction technique, showed complete separation between the two cell types based on their metabolite profiles. Pathway analysis of intracellular metabolomics data revealed close overlap with metabolic pathways identified from gene expression data, with four out of six pathways found enriched in gene-level analysis also enriched in metabolite-level analysis. Some of those pathways contained multiple metabolites that were individually statistically significantly different between the two cell lines, with one of the most broadly and consistently different pathways, arginine and proline metabolism, suggesting an interesting hypothesis about cancerous and stem-like metabolic phenotypes in this pair of cell lines. Overall, we demonstrate for the first time that metabolism in an ovarian cancer stem cell line is distinct from that of more differentiated isogenic cancer cells, supporting the potential importance of metabolism in the differences between cancer cells and cancer stem cells.

Camalexin, the phytoalexin produced in the model plant Arabidopsis thaliana, possesses antiproliferative and cancer chemopreventive effects. We have demonstrated that the cytostatic/cytotoxic effects of camalexin on several prostate cancer (PCa) cells are due to oxidative stress. Lysosomes are vulnerable organelles to Reactive Oxygen Species (ROS)-induced injuries, with the potential to initiate and or facilitate apoptosis subsequent to release of proteases such as cathepsin D (CD) into the cytosol. We therefore hypothesized that camalexin reduces cell viability in PCa cells via alterations in expression and activity of CD. Cell viability was evaluated by MTS cell proliferation assay in LNCaP and ARCaP Epithelial (E) cells, and their respective aggressive sublines C4-2 and ARCaP Mesenchymal (M) cells, whereby the more aggressive PCa cells (C4-2 and ARCaPM) displayed greater sensitivity to camalexin treatments than the lesser aggressive cells (LNCaP and ARCaPE). Immunocytochemical analysis revealed CD relocalization from the lysosome to the cytosol subsequent to camalexin treatments, which was associated with increased protein expression of mature CD; p53, a transcriptional activator of CD; BAX, a downstream effector of CD, and cleaved PARP, a hallmark for apoptosis. Therefore, camalexin reduces cell viability via CD and may present as a novel therapeutic agent for treatment of metastatic prostate cancer cells.

MicroRNAs (miRNAs) are short (∼22 nucleotides) regulatory RNAs that can modulate gene expression and are aberrantly expressed in many diseases including cancer. Previous studies have shown that miRNAs inhibit the translation and facilitate the degradation of their targeted messenger RNAs (mRNAs) making them attractive candidates for use in cancer therapy. However, the potential clinical utility of miRNAs in cancer therapy rests heavily upon our ability to understand and accurately predict the consequences of fluctuations in levels of miRNAs within the context of complex tumor cells. To evaluate the predictive power of current models, levels of miRNAs and their targeted mRNAs were measured in laser captured micro-dissected (LCM) ovarian cancer epithelial cells (CEPI) and compared with levels present in ovarian surface epithelial cells (OSE). We found that the predicted inverse correlation between changes in levels of miRNAs and levels of their mRNA targets held for only ∼11% of predicted target mRNAs. We demonstrate that this low inverse correlation between changes in levels of miRNAs and their target mRNAs in vivo is not merely an artifact of inaccurate miRNA target predictions but the likely consequence of indirect cellular processes that modulate the regulatory effects of miRNAs in vivo. Our findings underscore the complexities of miRNA-mediated regulation in vivo and the necessity of understanding the basis of these complexities in cancer cells before the therapeutic potential of miRNAs can be fully realized.