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.

A phase II randomized, double-blind trial of a polyvalent Vaccine-KLH conjugate (NSC 748933 IND# 14384) + OPT-821 versus OPT-821 in patients with epithelial ovarian, fallopian tube, or peritoneal cancer who are in second or third complete remission: An NRG Oncology/GOG study.

OBJECTIVE: Early-phase data have demonstrated induction of antibody responses to a polyvalent vaccine conjugate (Globo-H, GM2, MUC1-TN, TF) with adjuvant OPT-821. We sought to determine if this combination decreases the hazard of progression or death compared to OPT-821 alone in patients with ovarian cancer in second/third clinical complete remission following chemotherapy. Secondary and translational objectives were overall survival (OS), safety, and immunogenicity. METHODS: From 2010-2013, patients were randomized (1:1) to receive OPT-821±vaccine-KLH conjugate subcutaneously at weeks 1, 2, 3, 7, 11, and then every 12 weeks (total 11). Dose delay or reduction was not permitted. Patients were removed for pre-defined dose-limiting toxicity. RESULTS: Of 171 patients randomized, 170 were treated. Most had disease of serous histology (85%), stage 3 disease at diagnosis (77%), and had received 2 prior regimens (68%). 32% received >6 treatment cycles [median 6, each arm (p = 0.33)]. 77% discontinued due to progression, 4% due to toxicity, and 1 due to myeloid dysplastic syndrome (MDS). Maximum toxicities included grade 4 MDS and depression/personality change (1 each, unlikely related), as well as grade 3 gastrointestinal disorders and others (n = 21, 4 related). Lesser adverse events were injection site reactions (82%) and fever (11%). Estimated HR for progression-free survival (PFS) of the vaccine + OPT-821 to OPT-821 arm was 0.98 (95% CI: 0.71-1.36). At a median follow-up of 60 months, median OS was 47 and 46 months, respectively. CONCLUSIONS: Vaccine + OPT-821 compared to OPT-821 alone was modestly immunogenic and did not prolong PFS or OS. Multi-remission patients are a viable, well-defined population for exploring innovative consolidation and maintenance approaches. TRIAL REGISTRATION: NCT00857545.

Glycomic analysis of membrane glycoproteins with bisecting glycosylation from ovarian cancer tissues reveals novel structures and functions.

Biomarkers capable of detecting and targeting epithelial ovarian cancer cells for diagnostics and therapeutics would be extremely valuable. Ovarian cancer is the deadliest reproductive malignancy among women in the U.S., killing over 14 000 women each year. Both the lack of presenting symptoms and high mortality rates illustrate the need for earlier diagnosis and improved treatment of this disease. The glycosyltransferase enzyme GnT-III encoded by the Mgat3 gene is responsible for the addition of GlcNAc (N-acetylglucosamine) to form bisecting N-linked glycan structures. GnT-III mRNA expression is amplified in ovarian cancer tissues compared with normal ovarian tissue. We use a lectin capture strategy coupled to nano-ESI-RPLC-MS/MS to isolate and identify the membrane glycoproteins and unique glycan structures associated with GnT-III amplification in human ovarian cancer tissues. Our data illustrate that the majority of membrane glycoproteins with bisecting glycosylation are common to both serous and endometrioid histological subtypes of ovarian cancer, and several have been reported to participate in signaling pathways such as Notch, Wnt, and TGFß.

p66Shc longevity protein regulates the proliferation of human ovarian cancer cells.

p66Shc functions as a longevity protein in murine and exhibits oxidase activity in regulating diverse biological activities. In this study, we investigated the role of p66Shc protein in regulating ovarian cancer (OCa) cell proliferation. Among three cell lines examined, the slowest growing OVCAR-3 cells have the lowest level of p66Shc protein. Transient transfection with p66Shc cDNA expression vector in OVCAR-3 cells increases cell proliferation. Conversely, knock-down of p66Shc by shRNA in rapidly growing SKOV-3 cells results in decreased cell growth. In estrogen (E2)-treated CaOV-3 cells, elevated p66Shc protein level correlates with ROS level, ErbB-2 and ERK/MAPK activation, and cell proliferation. Further, the E2-stimulated proliferation of CaOV-3 cells was blocked by antioxidants and ErbB-2 inhibitor. Additionally, in E2-stimulated cells, the tartrate-sensitive, but not the tartrate-resistant, phosphatase activity decreases; concurrently, the tyrosine phosphorylation of ErbB-2 increases. Conversely, inhibition of phosphatase activity by L(+)-tartrate treatment increases p66Shc protein level, ErbB-2 tyrosine phosphorylation, ERK/MAPK activation, and cell growth. Further, inhibition of the ERK/MAPK pathway by PD98059 blocks E2-induced ERK/MAPK activation and cell proliferation in CaOV-3 cells. Moreover, immunohistochemical analyses showed that the p66Shc protein level was significantly higher in cancerous cells than in noncancerous cells in archival OCa tissues (n = 76; P = 0.00037). These data collectively indicate that p66Shc protein plays a critical role in up-regulating OCa progression.

Ectopic over-expression of miR-429 induces mesenchymal-to-epithelial transition (MET) and increased drug sensitivity in metastasizing ovarian cancer cells.

OBJECTIVE: We recently determined that the ectopic over-expression of miR-429 and other members of the miR-200 family of microRNAs in ovarian cancer (OC) mesenchymal-like cell lines induces mesenchymal-to-epithelial transition (MET) with a concomitant increase in sensitivity to platinum drugs. We sought to determine if metastasizing OC cells isolated from an OC patient could also be induced by miR-429 to undergo MET and become sensitized to established first-line platinum-based therapies. METHODS: We established and characterized a new primary cell line (OCI-984) from free-floating OC cells isolated from the ascites fluid of an advanced stage OC patient. miR-429 was ectopically over-expressed in these cells. RESULTS: The over-expression of miR-429 in OCI-984 cells induced morphological, functional and molecular changes consistent with MET and a concomitant significant increase in the sensitivity of the converted cells to cisplatin. CONCLUSIONS: Our findings indicate that the miR-200 family of microRNAs, and miR-429 in particular, play a critical role in the functioning of OC metastasizing cells and that targeted delivery of miR-429, and perhaps other miR-200 family members, in combination with platinum-based chemotherapies may be an effective strategy in reducing OC metastasis and tumor recurrence.

High-dose chemotherapy with autologous stem cell support as salvage therapy in recurrent gestational trophoblastic disease.

BACKGROUND: 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 suffice. 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. CASE: 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. CONCLUSION: 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.

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.

Label-free microfluidic enrichment of cancer cells from non-cancer cells in ascites.

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.

Identification of candidate biomarkers with cancer-specific glycosylation in the tissue and serum of endometrioid ovarian cancer patients by glycoproteomic analysis.

Epithelial ovarian cancer is diagnosed less than 25% of the time when the cancer is confined to the ovary, leading to 5-year survival rates of less than 30%. Therefore, there is an urgent need for early diagnostics for ovarian cancer. Our study using glycotranscriptome comparative analysis of endometrioid ovarian cancer tissue and normal ovarian tissue led to the identification of distinct differences in the transcripts of a restricted set of glycosyltransferases involved in N-linked glycosylation. Utilizing lectins that bind to glycan structures predicted to show changes, we observed differences in lectin-bound glycoproteins consistent with some of the transcript differences. In this study, we have extended our observations by the use of selected lectins to perform a targeted glycoproteomic analysis of ovarian cancer and normal ovarian tissues. Our results have identified several glycoproteins that display tumor-specific glycosylation changes. We have verified these glycosylation changes on glycoproteins from tissue using immunoprecipitation followed by lectin blot detection. The glycoproteins that were verified were then analyzed further using existing microarray data obtained from benign ovarian adenomas, borderline ovarian adenocarcinomas, and malignant ovarian adenocarcinomas. The verified glycoproteins found to be expressed above control levels in the microarray data sets were then screened for tumor-specific glycan modifications in serum from ovarian cancer patients. Results obtained from two of these glycoprotein markers, periostin and thrombospondin, have confirmed that tumor-specific glycan changes can be used to distinguish ovarian cancer patient serum from normal serum.

Selective removal of ovarian cancer cells from human ascites fluid using magnetic nanoparticles.

A majority of ovarian cancer metastases result from the shedding of malignant cells from the primary tumor into the abdominal cavity. Free-floating cancer cells in serous effusions of late-stage ovarian cancer patients may spread to internal organs, making effective treatment extremely difficult. Selective removal of ovarian cancer cells from serous fluids may abate metastasis and improve long-term prognoses. We have already shown that superparamagnetic nanoparticles conjugated to an ephrin-A1 mimetic peptide with a high affinity for the EphA2 receptor can be used to capture and remove cultured human ovarian cancer cells from the peritoneal of experimental mice. Here we demonstrate the potential clinical utility of the methodology by in vitro capture and isolation of cancer cells from the ascites fluid of ovarian cancer patients. FROM THE CLINICAL EDITOR: Ovarian cancer metastases usually are the result of shedding of malignant cells from the primary tumor into the abdominal cavity. In this paper, a novel nanotechnology-based method is demonstrated for the in vitro capture and isolation of cancer cells from the ascites fluid of ovarian cancer patients.

Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines.

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.

Epigenetic changes within the promoter region of the HLA-G gene in ovarian tumors.

BACKGROUND: Previous findings have suggested that epigenetic-mediated HLA-G expression in tumor cells may be associated with resistance to host immunosurveillance. To explore the potential role of DNA methylation on HLA-G expression in ovarian cancer, we correlated differences in HLA-G expression with methylation changes within the HLA-G regulatory region in an ovarian cancer cell line treated with 5-aza-deoxycytidine (5-aza-dC) and in malignant and benign ovarian tumor samples and ovarian surface epithelial cells (OSE) isolated from patients with normal ovaries. RESULTS: A region containing an intact hypoxia response element (HRE) remained completely methylated in the cell line after treatment with 5-aza-dC and was completely methylated in all of the ovarian tumor (malignant and benign) samples examined, but only variably methylated in normal OSE samples. HLA-G expression was significantly increased in the 5-aza-dC treated cell line but no significant difference was detected between the tumor and OSE samples examined. CONCLUSION: Since HRE is the binding site of a known repressor of HLA-G expression (HIF-1), we hypothesize that methylation of the region surrounding the HRE may help maintain the potential for expression of HLA-G in ovarian tumors. The fact that no correlation exists between methylation and HLA-G gene expression between ovarian tumor samples and OSE, suggests that changes in methylation may be necessary but not sufficient for HLA-G expression in ovarian cancer.

Aurora kinase inhibitors synergize with paclitaxel to induce apoptosis in ovarian cancer cells.

BACKGROUND: A large percentage of patients with recurrent ovarian cancer develop resistance to the taxane class of chemotherapeutics. While mechanisms of resistance are being discovered, novel treatment options and a better understanding of disease resistance are sorely needed. The mitotic kinase Aurora-A directly regulates cellular processes targeted by the taxanes and is overexpressed in several malignancies, including ovarian cancer. Recent data has shown that overexpression of Aurora-A can confer resistance to the taxane paclitaxel. METHODS: We used expression profiling of ovarian tumor samples to determine the most significantly overexpressed genes. In this study we sought to determine if chemical inhibition of the Aurora kinase family using VE-465 could synergize with paclitaxel to induce apoptosis in paclitaxel-resistant and sensitive ovarian cancer cells. RESULTS: Aurora-A kinase and TPX2, an activator of Aurora-A, are two of the most significantly overexpressed genes in ovarian carcinomas. We show that inhibition of the Aurora kinases prevents phosphorylation of a mitotic marker and demonstrate a dose-dependent increase of apoptosis in treated ovarian cancer cells. We demonstrate at low doses that are specific to Aurora-A, VE-465 synergizes with paclitaxel to induce 4.5-fold greater apoptosis than paclitaxel alone in 1A9 cells. Higher doses are needed to induce apoptosis in paclitaxel-resistant PTX10 cells. CONCLUSION: Our results show that VE-465 is a potent killer of taxane resistant ovarian cancer cells and can synergize with paclitaxel at low doses. These data suggest patients whose tumors exhibit high Aurora-A expression may benefit from a combination therapy of taxanes and Aurora-A inhibition.

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.

Identification of candidate methylation-responsive genes in ovarian cancer.

BACKGROUND: Aberrant methylation of gene promoter regions has been linked to changes in gene expression in cancer development and progression. Genes associated with CpG islands (CGIs) are especially prone to methylation, but not all CGI-associated genes display changes in methylation patterns in cancers. RESULTS: In order to identify genes subject to regulation by methylation, we conducted gene expression profile analyses of an ovarian cancer cell line (OVCAR-3) before and after treatment with the demethylating agent 5-aza-deoxycytidine (5-aza-dC). An overlapping subset of these genes was found to display significant differences in gene expression between normal ovarian surface epithelial cells and malignant cells isolated from ovarian carcinomas. While 40% of all human genes are associated with CGIs, > 94% of the overlapping subset of genes is associated with CGIs. The predicted change in methylation status of genes randomly selected from the overlapping subset was experimentally verified. CONCLUSION: We conclude that correlating genes that are upregulated in response to 5-aza-dC treatment of cancer cell lines with genes that are down-regulated in cancer cells may be a useful method to identify genes experiencing epigenetic-mediated changes in expression over cancer development.

Evidence for the importance of post-transcriptional regulatory changes in ovarian cancer progression and the contribution of miRNAs.

High-throughput technologies have identified significant changes in patterns of mRNA expression over cancer development but the functional significance of these changes often rests upon the assumption that observed changes in levels of mRNA accurately reflect changes in levels of their encoded proteins. We systematically compared the expression of 4436 genes on the RNA and protein levels between discrete tumor samples collected from the ovary and from the omentum of the same OC patient. The overall correlation between global changes in levels of mRNA and their encoding proteins is low (r = 0.38). The majority of differences are on the protein level with no corresponding change on the mRNA level. Indirect and direct evidence indicates that a significant fraction of the differences may be mediated by microRNAs.

Highly-accurate metabolomic detection of early-stage 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.

L1 and HERV-W retrotransposons are hypomethylated in human ovarian carcinomas.

Wide-spread hypomethylation of CpG dinucleotides is characteristic of many cancers. Retrotransposons have been identified as potential targets of hypomethylation during cellular transformation. We report the results of an preliminary examination of the methylation status of CpG dinucleotides associated with the L1 and HERV-W retrotransposons in benign and malignant human ovarian tumors. We find a reduction in the methylation of CpG dinucleotides within the promoter regions of these retroelements in malignant relative to non-malignant ovarian tissues. Consistent with these results, we find that relative L1 and HERV-W expression levels are elevated in representative samples of malignant vs. non-malignant ovarian tissues.