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.

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.

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.

Although it has been established that cellular stiffness can change as a stem cell differentiates, the precise relationship between cell mechanics and other phenotypic properties remains unclear. Inherent cell heterogeneity and asynchronous differentiation complicate population analysis; therefore, single-cell analysis was employed to determine how changes in cell stiffness correlate with changes in molecular biomarkers during differentiation. Design of a custom gridded tissue culture dish facilitated single-cell comparisons between cell mechanics and other differentiation biomarkers by enabling sequential measurement of cell mechanics and protein biomarker expression at the single cell level. The Young’s modulus of mesenchymal stem cells was shown not only to decrease during chemically-induced osteoblast differentiation, but also to correlate more closely with the day of differentiation than did the relative expression of the traditional osteoblast differentiation markers, bone sialoprotein and osteocalcin. Therefore, cell stiffness, a measurable property of individual cells, may serve as an improved indicator of single-cell osteoblast differentiation compared to traditional biological markers. Revelation of additional osteoblast differentiation indicators, such as cell stiffness, can improve identification and collection of starting cell populations, with applications to mesenchymal stem cell therapies and stem cell-based tissue engineering.

Background: Ovarian cancer diagnosis is problematic because the disease is typically asymptomatic, especially at the early stages of progression and/or recurrence. We report here the integration of a new mass spectrometric technology with a novel support vector machine computational method for use in cancer diagnostics, and describe the application of the method to ovarian cancer.Methods: We coupled a high-throughput ambient ionization technique for mass spectrometry (direct analysis in real-time mass spectrometry) to profile relative metabolite levels in sera from 44 women diagnosed with serous papillary ovarian cancer (stages I-IV) and 50 healthy women or women with benign conditions. The profiles were input to a customized functional support vector machine-based machine-learning algorithm for diagnostic classification. Performance was evaluated through a 64-30 split validation test and with a stringent series of leave-one-out cross-validations.Results: The assay distinguished between the cancer and control groups with an unprecedented 99% to 100% accuracy (100% sensitivity and 100% specificity by the 64-30 split validation test; 100% sensitivity and 98% specificity by leave-one-out cross-validations).Conclusion: The method has significant clinical potential as a cancer diagnostic tool. Because of the extremely low prevalence of ovarian cancer in the general population (∼0.04%), extensive prospective testing will be required to evaluate the test's potential utility in general screening applications. However, more immediate applications might be as a diagnostic tool in higher-risk groups or to monitor cancer recurrence after therapeutic treatment.Impact: The ability to accurately and inexpensively diagnose ovarian cancer will have a significant positive effect on ovarian cancer treatment and outcome. Cancer Epidemiol Biomarkers Prev; 19(9); 2262-71. ©2010 AACR.

Prostate cancer (PCa) is the second leading cause of cancer-related mortality in men. The prevalent diagnosis method is based on the serum prostate-specific antigen (PSA) screening test, which suffers from low specificity, overdiagnosis, and overtreatment. In this work, untargeted metabolomic profiling of age-matched serum samples from prostate cancer patients and healthy individuals was performed using ultraperformance liquid chromatography coupled to high-resolution tandem mass spectrometry (UPLC-MS/MS) and machine learning methods. A metabolite-based in vitro diagnostic multivariate index assay (IVDMIA) was developed to predict the presence of PCa in serum samples with high classification sensitivity, specificity, and accuracy. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent PSA test. Within the discriminant panel, 31 metabolites were identified by MS and MS/MS, with 10 further confirmed chromatographically by standards. Numerous discriminant metabolites were mapped in the steroid hormone biosynthesis pathway. The identification of fatty acids, amino acids, lysophospholipids, and bile acids provided further insights into the metabolic alterations associated with the disease. With additional work, the results presented here show great potential toward implementation in clinical settings.

High performance mass spectrometry was employed to interrogate the serum metabolome of early-stage ovarian cancer (OC) patients and age-matched control women. The resulting spectral features were used to establish a linear support vector machine (SVM) model of sixteen diagnostic metabolites that are able to identify early-stage OC with 100% accuracy in our patient cohort. The results provide evidence for the importance of lipid and fatty acid metabolism in OC and serve as the foundation of a clinically significant diagnostic test.

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