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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. Metastatic CTC clusters remain relatively unexplored due to the lack of optimized and practical technologies for their detection. Here the authors report Cluster-Wells to isolate CTC clusters in whole blood; they show this allows viable cluster retrieval for further molecular and functional analysis.

Among patients with platinum-sensitive, recurrent ovarian cancer, the use of niraparib, a PARP inhibitor, was associated with a significantly longer duration of progression-free survival than placebo, with moderate bone marrow toxicity. Ovarian cancer is a leading cause of death from gynecologic cancers worldwide. 1 , 2 Despite a high initial response rate to platinum and taxane treatment in patients with advanced cancer, the effectiveness of the treatments diminishes over time, and most patients have a relapse. 3 Platinum retreatment is used in patients in whom there is an assumed platinum sensitivity, with diminishing effectiveness and a cumulative increase in toxicity. 3 Niraparib is a highly selective inhibitor of poly(adenosine diphosphate [ADP]–ribose) polymerase (PARP) 1/2, 4 nuclear proteins that detect DNA damage and promote its repair. Clinical studies have evaluated PARP inhibitors in patients with recurrent ovarian . . .

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.

Quality of life (QOL) has become an important complementary endpoint in cancer clinical studies alongside more traditional assessments (eg, tumour response, progression-free survival, overall survival). Niraparib maintenance treatment has been shown to significantly improve progression-free survival in patients with recurrent ovarian cancer. We aimed to assess whether the benefits of extending progression-free survival are offset by treatment-associated toxic effects that affect QOL. The ENGOT-OV16/NOVA trial was a multicentre, double-blind, phase 3, randomised controlled trial done in 107 study sites in the USA, Canada, Europe, and Israel. Patients with recurrent ovarian cancer who were in response to their last platinum-based chemotherapy were randomly assigned (2:1) to receive either niraparib (300 mg once daily) as a maintenance treatment or placebo. Randomisation was stratified based on time to progression after the penultimate platinum-based regimen, previous use of bevacizumab, and best response (complete or partial) to the last platinum-based regimen with permuted-block randomisation (six in each block) using an interactive web response system. The trial enrolled two independent cohorts on the basis of germline BRCA (gBRCA) mutation status (determined by BRACAnalysis Testing, Myriad Genetics, Salt Lake City, UT, USA). The primary endpoint of the trial was progression-free survival, and has already been reported. In this study, we assessed patient-reported outcomes (PROs) in the intention-to-treat population using the Functional Assessment of Cancer Therapy–Ovarian Symptoms Index (FOSI) and European QOL five-dimension five-level questionnaire (EQ-5D-5L). We collected PROs from trial entry every 8 weeks for the first 14 cycles and every 12 weeks thereafter. If a patient discontinued, we collected PROs at discontinuation and during a postprogression visit 8 weeks (plus or minus 2 weeks) later. We assessed the effect of haematological toxic effects on QOL with disutility analyses of the most common grade 3–4 adverse events (thrombocytopenia, anaemia, and neutropenia) using a mixed model with histology, region, previous treatment, age, planned treatment, and baseline score as covariates. This study is registered with ClinicalTrials.gov, number NCT01847274. Between Aug 28, 2013, and June 1, 2015, 553 patients were enrolled and randomly assigned to receive niraparib (n=138 in the gBRCAmut cohort, n=234 in the non-gBRCAmut cohort) or placebo (n=65 in the gBRCAmut cohort, n=116 in the non-gBRCAmut cohort). The mean FOSI score at baseline was similar between the two groups (range between 25·0–25·6 in the two groups). Overall QOL scores remained stable during the treatment and preprogression period in the niraparib group; no significant differences were observed between the niraparib and placebo group, and preprogression EQ-5D-5L scores were similar between the two groups in both cohorts (0·838 [0·0097] in the niraparib group vs 0·834 [0·0173] in the placebo group in the gBRCAmut cohort; and 0·833 [0·0077] in the niraparib group vs 0·815 [0·0122] in the placebo group in the non-gBRCAmut cohort). The most common adverse events reported at screening (baseline) were lack of energy (425 [79%]; 97 [18%] reporting severe lack of energy), pain (236 [44%]), and nausea (118 [22%]). All symptoms, except nausea, either remained stable or improved over time in the niraparib group. The most common grade 3 or 4 toxicities observed in the niraparib group were haematological in nature: thrombocytopenia (124 [34%] of 367 patients), anaemia (93 [25%]), and neutropenia (72 [20%]); disutility analyses showed no significant QOL impairment associated with these toxic effects. These PRO data suggest that women who receive niraparib as maintenance treatment for recurrent ovarian cancer after responding to platinum treatment are able to maintain QOL during their treatment when compared with placebo. TESARO.

The isolation of a patient's metastatic cancer cells is the first, enabling step toward treatment of that patient using modern personalized medicine techniques. Whereas traditional standard-of-care approaches select treatments for cancer patients based on the histological classification of cancerous tissue at the time of diagnosis, personalized medicine techniques leverage molecular and functional analysis of a patient's own cancer cells to select treatments with the highest likelihood of being effective. Unfortunately, the pure populations of cancer cells required for these analyses can be difficult to acquire, given that metastatic cancer cells typically reside in fluid containing many different cell populations. Detection and analyses of cancer cells therefore require separation from these contaminating cells. Conventional cell sorting approaches such as Fluorescence Activated Cell Sorting or Magnetic Activated Cell Sorting rely on the presence of distinct surface markers on cells of interest which may not be known nor exist for cancer applications. In this work, we present a microfluidic platform capable of label-free enrichment of tumor cells from the ascites fluid of ovarian cancer patients. This approach sorts cells based on differences in biomechanical properties, and therefore does not require any labeling or other pre-sort interference with the cells. The method is also useful in the cases when specific surface markers do not exist for cells of interest. In model ovarian cancer cell lines, the method was used to separate invasive subtypes from less invasive subtypes with an enrichment of ~ sixfold. In ascites specimens from ovarian cancer patients, we found the enrichment protocol resulted in an improved purity of P53 mutant cells indicative of the presence of ovarian cancer cells. We believe that this technology could enable the application of personalized medicine based on analysis of liquid biopsy patient specimens, such as ascites from ovarian cancer patients, for quick evaluation of metastatic disease progression and determination of patient-specific treatment.

Background Accumulating evidence suggests that somatic stem cells undergo mutagenic transformation into cancer initiating cells. The serous subtype of ovarian adenocarcinoma in humans has been hypothesized to arise from at least two possible classes of progenitor cells: the ovarian surface epithelia (OSE) and/or an as yet undefined class of progenitor cells residing in the distal end of the fallopian tube. Methods Comparative gene expression profiling analyses were carried out on OSE removed from the surface of normal human ovaries and ovarian cancer epithelial cells (CEPI) isolated by laser capture micro-dissection (LCM) from human serous papillary ovarian adenocarcinomas. The results of the gene expression analyses were randomly confirmed in paraffin embedded tissues from ovarian adenocarcinoma of serous subtype and non-neoplastic ovarian tissues using immunohistochemistry. Differentially expressed genes were analyzed using gene ontology, molecular pathway, and gene set enrichment analysis algorithms. Results Consistent with multipotent capacity, genes in pathways previously associated with adult stem cell maintenance are highly expressed in ovarian surface epithelia and are not expressed or expressed at very low levels in serous ovarian adenocarcinoma. Among the over 2000 genes that are significantly differentially expressed, a number of pathways and novel pathway interactions are identified that may contribute to ovarian adenocarcinoma development. Conclusions Our results are consistent with the hypothesis that human ovarian surface epithelia are multipotent and capable of serving as the origin of ovarian adenocarcinoma. While our findings do not rule out the possibility that ovarian cancers may also arise from other sources, they are inconsistent with claims that ovarian surface epithelia cannot serve as the origin of ovarian cancer initiating cells.

Background The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease. Results In this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or \"panels\" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM. Conclusion Three evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer.

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.

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.

Gestational trophoblastic disease usually follows a molar pregnancy but can occur also after an abortion or a term pregnancy. In only 10% of cases will treatment be required; and usually, single-agent chemotherapy will suff ice. In high-risk disease, the multiagent regimen EMA-CO is usually used; and if that fails, most oncologists will use the EMA-EP regimen. If this does not produce a remission, there is no unanimity of opinion as to how to proceed. Numerous salvage regimens are in current use, and some centers do not consider high-dose chemotherapy. A young woman presented 4 months after a normal spontaneous delivery with an elevated human chorionic gonadotropin level and multiple pulmonary metastases. She failed both the EMA-CO and EMA-EP regimens as well as additional standard chemotherapy. She was then treated with 4 separate courses of high-dose chemotherapy with autologous stem cell support, which produced a complete remission. Even patients with high-risk gestational trophoblastic disease are usually cured with standard chemotherapy. Patients who fail such treatment should be considered for high-dose chemotherapy.