A personalized probabilistic approach to ovarian cancer diagnostics.

OBJECTIVE: The identification/development of a machine learning-based classifier that utilizes metabolic profiles of serum samples to accurately identify individuals with ovarian cancer. METHODS: Serum samples collected from 431 ovarian cancer patients and 133 normal women at four geographic locations were analyzed by mass spectrometry. Reliable metabolites were identified using recursive feature elimination coupled with repeated cross-validation and used to develop a consensus classifier able to distinguish cancer from non-cancer. The probabilities assigned to individuals by the model were used to create a clinical tool that assigns a likelihood that an individual patient sample is cancer or normal. RESULTS: Our consensus classification model is able to distinguish cancer from control samples with 93% accuracy. The frequency distribution of individual patient scores was used to develop a clinical tool that assigns a likelihood that an individual patient does or does not have cancer. CONCLUSIONS: An integrative approach using metabolomic profiles and machine learning-based classifiers has been employed to develop a clinical tool that assigns a probability that an individual patient does or does not have ovarian cancer. This personalized/probabilistic approach to cancer diagnostics is more clinically informative and accurate than traditional binary (yes/no) tests and represents a promising new direction in the early detection of ovarian cancer.

High throughput, label-free isolation of circulating tumor cell clusters in meshed microwells.

Extremely rare circulating tumor cell (CTC) clusters are both increasingly appreciated as highly metastatic precursors and virtually unexplored. Technologies are primarily designed to detect single CTCs and often fail to account for the fragility of clusters or to leverage cluster-specific markers for higher sensitivity. Meanwhile, the few technologies targeting CTC clusters lack scalability. Here, we introduce the Cluster-Wells, which combines the speed and practicality of membrane filtration with the sensitive and deterministic screening afforded by microfluidic chips. The >100,000 microwells in the Cluster-Wells physically arrest CTC clusters in unprocessed whole blood, gently isolating virtually all clusters at a throughput of >25 mL/h, and allow viable clusters to be retrieved from the device. Using the Cluster-Wells, we isolated CTC clusters ranging from 2 to 100+ cells from prostate and ovarian cancer patients and analyzed a subset using RNA sequencing. Routine isolation of CTC clusters will democratize research on their utility in managing cancer.

Sequence diverse miRNAs converge to induce mesenchymal-to-epithelial transition in ovarian cancer cells through direct and indirect regulatory controls.

Epithelial-to-mesenchymal transition (EMT) has been shown to be similarly regulated by multiple miRNAs, some displaying little or no sequence identity. While alternate models have been proposed to explain the functional convergence of sequence divergent miRNAs, little experimental evidence exists to elucidate the underlying mechanisms involved. Representative members of the miR-200 family of miRNAs and the sequence divergent miR-205 miRNA were independently over expressed in mesenchymal-like ovarian cancer (OC) cells resulting in mesenchymal-to-epithelial transition (MET). The miR-205 and the miR-200 family of miRNAs were found to coordinately induce MET in mesenchymal-like OC cells by affecting both direct and indirect changes in the expression of genes previously associated with EMT/MET. Only two direct targets of these miRNAs (ZEB 1 and WNT5A) are commonly down regulated in response to over-expression of miR-205 and/or the miR-200 family of miRNAs. Down-regulation of these genes, alone or in combination, only partially recapitulates the changes induced by the miRNAs indicating an additional contribution of indirect changes regulated by the miRNAs. Combined gene expression analyses and phylogenetic comparisons suggest an evolutionarily more recent involvement of miR-205 in the EMT/MET process.

Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy.

Precision or personalized cancer medicine is a clinical approach that strives to customize therapies based upon the genomic profiles of individual patient tumors. Machine learning (ML) is a computational method particularly suited to the establishment of predictive models of drug response based on genomic profiles of targeted cells. We report here on the application of our previously established open-source support vector machine (SVM)-based algorithm to predict the responses of 175 individual cancer patients to a variety of standard-of-care chemotherapeutic drugs from the gene-expression profiles (RNA-seq or microarray) of individual patient tumors. The models were found to predict patient responses with >80% accuracy. The high PPV of our algorithms across multiple drugs suggests a potential clinical utility of our approach, particularly with respect to the identification of promising second-line treatments for patients failing standard-of-care first-line therapies.

Time-course analysis of microRNA-induced mesenchymal-to-epithelial transition underscores the complexity of the underlying molecular processes.

Expression levels of the miR-200 family of miRNAs are significantly reduced during the epithelial-to-mesenchymal transition (EMT) and consequent metastasis of ovarian and other cancers. Consistently, ectopic over-expression of miR-200 family miRNAs in mesenchymal-like cells reverses the process by converting treated cells to an epithelial phenotype, thereby reducing invasiveness and increasing sensitivity to chemotherapeutic drugs. To better understand the dynamics and molecular processes underlying miRNA-induced mesenchymal-to mesenchymal transition (MET), a time-course study was conducted where miRNA-induced morphological and molecular changes associated with MET were monitored over a period of 144 h. Morphological transition from an elongated mesenchymal-like to a cuboidal epithelial-like phenotype is maximized at 48 h with cells returning to the elongated phenotype by 144 h. Changes in the expression of >3000 genes, including many previously associated with epithelial-to-mesenchymal transition (EMT), are most pronounced at 48 h, and approach starting levels of expression by 144 h. The majority of these genes are not direct targets of miR-429. Targeted (siRNA) inhibition of key miR-429 regulated genes previously implicated as drivers of EMT/MET, do not recapitulate miR-429 induced MET indicating that the underlying molecular processes are complex.