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Background: Ovarian cancer (OC) remains one of the most lethal cancers among women due to most cases going undiagnosed until later stages. The early detection and treatment of this malignancy provides the best prognosis, but the lack of an accurate and sensitive screening tool combined with ambiguous symptoms hinders these diagnoses. In contrast, screening for cervical cancer via Papanicolaou (Pap) tests is a widespread practice that greatly reduces the cancer's mortality rates. Interestingly, previous studies show evidence of OC cells in Pap tests, suggesting that proteins, and potentially lipids, shed from ovarian tumors end up in the cervix. The goal of this study is to evaluate the practicality of using Pap tests as biospecimens for OC-screening-related metabolomics. Methods: To evaluate the effectiveness of using residual Pap test samples as biospecimens for potential metabolomics work, 29 Pap test samples, collected from women over the age of 50 with normal cytology and no visible blood contamination, were first obtained from the University of Minnesota, with IRB approval. These samples were centrifuged to recover the cell pellets from the supernatants. The cell pellets underwent a biphasic extraction, followed by an RP-LC-MS analysis, while the supernatants underwent two separate extractions and analyses, including RP-LC-MS and HILIC-LC-MS. Non-targeted features were detected in the range of 220–1000 m/z to determine the sensitivity and scope of the various extraction and analytical workflows, as well as evaluating residual Pap test samples as viable metabolomics biospecimens. Results: The biphasic extraction and subsequent RP-LC-MS analysis of the isolated cell pellets from all 29 samples yielded informative, exploratory data, highlighting the potential of using residual Pap test samples as biospecimens for metabolomics, specifically lipidomics, studies. Each sample was analyzed in both the positive and negative ion mode, yielding the detection of 7318 in the positive ion mode and 3733 in the negative ion mode. Using multiple reference libraries, 22.85% and 36.19% of these features were annotated in the positive and negative ion mode, respectively. Among these detected features, 453 unique lipids, representative of 20 different lipid subclasses, were annotated in all 29 samples. Of the various lipid subclasses represented from the detected lipids, ceramides, triacylglycerols, hexosylceramides, and phosphatidylcholines contributed to over half (53.3%) of the detected lipids at 16.2%, 13.0%, 12.8%, and 11.3%, respectively. Conclusions: The detection of these 453 common lipids across all patients establishes a relative lipidome baseline for women over the age of 50 with normal cervical cytology. This exploratory study is the first investigation to utilize residual Pap test samples as biospecimens in a metabolomics/lipidomics workflow. [ABSTRACT FROM AUTHOR]

Background The biologic heterogeneity of soft tissue sarcomas (STS), even within histological subtypes, complicates treatment. In earlier studies, gene expression patterns that distinguish two subsets of clear cell renal carcinoma (RCC), serous ovarian carcinoma (OVCA), and aggressive fibromatosis (AF) were used to separate 73 STS into two or four groups with different probabilities of developing metastatic disease (PrMet). This study was designed to confirm our earlier observations in a larger independent data set. Methods We utilized these gene sets, hierarchical clustering (HC), and Kaplan-Meier analysis, to examine 309 STS, using Affymetrix chip expression profiling. Results HC using the combined AF-, RCC-, and OVCA-gene sets identified subsets of the STS samples. Analysis revealed differences in PrMet between the clusters defined by the first branch point of the clustering dendrogram (p = 0.048), and also among the four different clusters defined by the second branch points (p < 0.0001). Analysis also revealed differences in PrMet between the leiomyosarcomas (LMS), dedifferentiated liposarcomas (LipoD), and undifferentiated pleomorphic sarcomas (UPS) (p = 0.0004). HC of both the LipoD and UPS sample sets divided the samples into two groups with different PrMet (p = 0.0128, and 0.0002, respectively). HC of the UPS samples also showed four groups with different PrMet (p = 0.0007). HC found no subgroups of the LMS samples. Conclusions These data confirm our earlier studies, and suggest that this approach may allow the identification of more than two subsets of STS, each with distinct clinical behavior, and may be useful to stratify STS in clinical trials and in patient management. [ABSTRACT FROM AUTHOR]

Fluid biospecimens (blood, serum, urine, saliva, cerebrospinal fluid and bronchial lavage fluid) contain not only cells and subcellular components, but also proteins, enzymes, lipids, metabolites, and peptides, which are utilized as biomarkers. Availability of high-quality biospecimens is a requirement for biomarker discovery. The quality of the biospecimens depends upon preanalytical variables (ie, collection and processing techniques, freeze/thaw stability, and storage stability), which account for > 60%-90% of the diagnostic errors. Currently, millions of fluid biospecimens are stored in hundreds of biorepositories across the nation, and tens of thousands of new biospecimens are added to the pool daily. Specimen stabilization is imperative, because fluid biospecimens degrade quickly when kept untreated at room temperature. Achieving a high-quality fluid biospecimen requires understanding the effects of storage processing parameters (eg, freezing and thawing as well as cryo-/ lyoprotectant additives) and storage conditions on biomarkers contained within the biospecimens. In this article, we will discuss the main issues related to the stabilization of specific biofluids by reviewing (a) the current preservation and storage practices applied in biobanks/biorepositories and (b) the sensitivity of certain biomarkers to current storage techniques. [ABSTRACT FROM AUTHOR]

Following initial standard chemotherapy (platinum/taxol), more than 75% of those patients with advanced stage epithelial ovarian cancer (EOC) experience a recurrence. There are currently no accurate prognostic tests that, at the time of the diagnosis/surgery, can identify those patients with advanced stage EOC who will respond to chemotherapy. Using a novel mathematical theory, we have developed three prognostic biomarker models (complex mathematical functions) that-based on a global gene expression analysis of tumor tissue collected during surgery and prior to the commencement of chemotherapy-can identify with a high accuracy those patients with advanced stage EOC who will respond to the standard chemotherapy [long-term survivors (.7 yrs)] and those who will not do so [short-term survivors (,3 yrs)]. Our three prognostic biomarker models were developed with 34 subjects and validated with 20 unknown (new and different) subjects. Both the overall biomarker model sensitivity and specificity ranged from 95.83% to 100.00%. The 12 most significant genes identified, which are also the input variables to the three mathematical functions, constitute three distinct gene networks with the following functions: 1) production of cytoskeletal components, 2) cell proliferation, and 3) cell energy production. The first gene network is directly associated with the mechanism of action of anti-tubulin chemotherapeutic agents, such as taxanes and epothilones. This could have a significant impact in the discovery of new, more effective pharmacological treatments that may significantly extend the survival of patients with advanced stage EOC. [ABSTRACT FROM AUTHOR]

Claudin 4 is a cellular adhesion molecule that is frequently overexpressed in ovarian cancer and other epithelial cancers. In this study, we sought to determine whether the expression of claudin 4 is associated with outcome in ovarian cancer patients and may be involved in tumor progression. We examined claudin 4 expression in ovarian cancer tissues and cell lines, as well as by immunohistochemical staining of tissue microarrays (TMAs; n = 500), spheroids present in patients' ascites, and spheroids formed in vitro. Claudin 4 was expressed in nearly 70% of the ovarian cancer tissues examined and was differentially expressed across ovarian cancer subtypes, with the lowest expression in clear cell subtype. No association was found between claudin 4 expression and disease-specific survival in any subtype. Claudin 4 expression was also observed in multicellular spheroids obtained from patients' ascites. Using an in vitro spheroid formation assay, we found that NIH:OVCAR5 cells treated with shRNA against claudin 4 required a longer time to form compact spheroids compared to control NIH:OVCAR5 cells that expressed high levels of claudin 4. The inability of the NIH:OVCAR5 cells treated with claudin 4 shRNA to form compact spheroids was verified by FITC-dextran exclusion. These results demonstrate a role for claudin 4 and tight junctions in spheroid formation and integrity. [ABSTRACT FROM AUTHOR]

Background: New biomarkers that replace or are used in conjunction with the current ovarian cancer diagnostic antigen, CA125, are needed for detection of ovarian cancer in the presurgical setting, as well as for detection of disease recurrence. We previously demonstrated the upregulation of leucine-rich alpha-2-glycoprotein-1 (LRG1) in the sera of ovarian cancer patients compared to healthy women using quantitative mass spectrometry. Methods: LRG1 was quantified by ELISA in serum from two relatively large cohorts of women with ovarian cancer and benign gynecological disease. The expression of LRG1 in ovarian cancer tissues and cell lines was examined by gene microarray, reverse-transcriptase polymerase chain reaction (RT-PCR), Western blot, immunocytochemistry and mass spectrometry. Results: Mean serum LRG1 was higher in 58 ovarian cancer patients than in 56 healthy women (89.33 ± 77.90 vs. 42.99 ± 9.88 ug/ml; p = 0.0008) and was highest among stage III/IV patients. In a separate set of 193 pre-surgical samples, LRG1 was higher in patients with serous or clear cell ovarian cancer (145.82 ± 65.99 ug/ml) compared to patients with benign gynecological diseases (82.53 ± 76.67 ug/ml, p < 0.0001). CA125 and LRG1 levels were moderately correlated (r = 0.47, p < 0.0001). LRG1 mRNA levels were higher in ovarian cancer tissues and cell lines compared to their normal counterparts when analyzed by gene microarray and RT-PCR. LRG1 protein was detected in ovarian cancer tissue samples and cell lines by immunocytochemistry and Western blotting. Multiple iosforms of LRG1 were observed by Western blot and were shown to represent different glycosylation states by digestion with glycosidase. LRG1 protein was also detected in the conditioned media of ovarian cancer cell culture by ELISA, Western blotting, and mass spectrometry. Conclusions: Serum LRG1 was significantly elevated in women with ovarian cancer compared to healthy women and women with benign gynecological disease, and was only moderately correlated with CA125. Ovarian cancer cells secrete LRG1 and may contribute directly to the elevated levels of LRG1 observed in the serum of ovarian cancer patients. Future studies will determine whether LRG1 may serve as a biomarker for presurgical diagnosis, disease recurrence, and/or as a target for therapy. [ABSTRACT FROM AUTHOR]

Background: Ovarian cancer is the most lethal gynecologic malignancy, with the majority of cases diagnosed at an advanced stage when treatments are less successful. Novel serum protein markers are needed to detect ovarian cancer in its earliest stage; when detected early, survival rates are over 90%. The identification of new serum biomarkers is hindered by the presence of a small number of highly abundant proteins that comprise approximately 95% of serum total protein. In this study, we used pooled serum depleted of the most highly abundant proteins to reduce the dynamic range of proteins, and thereby enhance the identification of serum biomarkers using the quantitative proteomic method iTRAQ®. Results: Medium and low abundance proteins from 6 serum pools of 10 patients each from women with serous ovarian carcinoma, and 6 non-cancer control pools were labeled with isobaric tags using iTRAQ® to determine the relative abundance of serum proteins identified by MS. A total of 220 unique proteins were identified and fourteen proteins were elevated in ovarian cancer compared to control serum pools, including several novel candidate ovarian cancer biomarkers: extracellular matrix protein-1, leucine-rich alpha-2 glycoprotein-1, lipopolysaccharide binding protein-1, and proteoglycan-4. Western immunoblotting validated the relative increases in serum protein levels for several of the proteins identified. Conclusions: This study provides the first analysis of immunodepleted serum in combination with iTRAQ® to measure relative protein expression in ovarian cancer patients for the pursuit of serum biomarkers. Several candidate biomarkers were identified which warrant further development. [ABSTRACT FROM AUTHOR]

The heterogeneity that soft tissue sarcomas (STS) exhibit in their clinical behavior, even within histological subtypes, complicates patient care. Histological appearance is determined by gene expression. Morphologic features are generally good predictors of biologic behavior, however, metastatic propensity, tumor growth, and response to chemotherapy may be determined by gene expression patterns that do not correlate well with morphology. One approach to identify heterogeneity is to search for genetic markers that correlate with differences in tumor behavior. Alternatively, subsets may be identified based on gene expression patterns alone, independent of knowledge of clinical outcome. We have reported gene expression patterns that distinguish two subgroups of clear cell renal carcinoma (ccRCC), and other gene expression patterns that distinguish heterogeneity of serous ovarian carcinoma (OVCA) and aggressive fibromatosis (AF). In this study, gene expression in 53 samples of STS and AF [including 16 malignant fibrous histiocytoma (MFH), 9 leiomyosarcoma, 12 liposarcoma, 4 synovial sarcoma, and 12 samples of AF] was determined at Gene Logic Inc. (Gaithersburg, MD) using Affymetrix GeneChip® U_133 arrays containing approximately 40,000 genes/ESTs. Gene expression analysis was performed with the Gene Logic Genesis Enterprise System® Software and Expressionist software. Hierarchical clustering of the STS using our three previously reported gene sets, each generated subgroups within the STS that for some subtypes correlated with histology, and also suggested the existence of subsets of MFH. All three gene sets also recognized the same two subsets of the fibromatosis samples that we had found in our earlier study of AF. These results suggest that these subgroups may have biological significance, and that these gene sets may be useful for sub-classification of STS. In addition, several genes that are targets of some anti-tumor drugs were found to be differentially expressed in particular subsets of STS. [ABSTRACT FROM AUTHOR]

Background: Malignant ascites often develops in advanced stages of ovarian carcinoma, consisting of single and aggregated tumor cells, or spheroids. Spheroids have commonly been used as tumor models to study drug efficacy, and have shown resistance to some chemotherapies and radiation. However, little is known about the adhesive or invasive capabilities of spheroids, and whether this particular cellular component of the ascites can contribute to dissemination of ovarian cancer. Here, we examined the invasive ability of ascites spheroids recovered from seven ovarian carcinoma patients and one primary peritoneal carcinoma (PPC) patient. Methods: Ascites spheroids were isolated from patients, purified, and immunohistochemical analyses were performed by a pathologist to confirm diagnosis. In vitro assays were designed to quantify spheroid disaggregation on a variety of extracellular matrices and dissemination on and invasion into normal human mesothelial cell monolayers. Cell proliferation and viability were determined in each assay, and statistical significance demonstrated by the student's t-test. Results: Spheroids from all of the patients' ascites samples disaggregated on extracellular matrix components, with the PPC spheroids capable of complete disaggregation on type I collagen. Additionally, all of the ascites spheroid samples adhered to and disaggregated on live human mesothelial cell monolayers, typically without invading them. However, the PPC ascites spheroids and one ovarian carcinoma ascites spheroid sample occasionally formed invasive foci in the mesothelial cell monolayers, suggestive of a more invasive phenotype. Conclusion: We present here in vitro assays using ascites spheroids that imitate the spread of ovarian cancer in vivo. Our results suggest that systematic studies of the ascites cellular content are necessary to understand the biology of ovarian carcinoma. [ABSTRACT FROM AUTHOR]

Background: The tetraspan protein CD63, originally described as a stage-specific melanoma antigen but also present in a number of normal cells, regulates melanoma cell growth in nude mice, motility in serum containing media, and adhesion to several extracellular matrix proteins. CD63 has been reported to associate with β1 and β2 integrins, but the mechanism of signal transduction by CD63 is not clear. This study examined whether CD63 is associated with protein kinase and can transmit signals in melanoma cells. Methods: Immunoprecipitation and radiolabeling were used to test for association of protein kinase activity with CD63. Adhesion of cells to monoclonal antibodies immobilized to microtiter plates was used to examine the ability of CD63 to transmit signals. Results: CD63 was capable of transmitting a signal in melanoma cells that required extracellular calcium. In the absence of extracellular calcium at the time of binding to the CD63 mAb, the cell was no longer responsive to stimulation by CD63. Immunoprecipitation studies demonstrated protein kinase activity associated with CD63, and phosphoamino acid analysis revealed that most of this protein kinase activity was due to serine kinase activity. Conclusion: The current study suggests that serine protein kinase activity associated with CD63 may play a role in signaling by CD63 in melanoma cells. [ABSTRACT FROM AUTHOR]