Quantification of putative ovarian cancer serum protein biomarkers using a multiplexed targeted mass spectrometry assay.

BACKGROUND: Ovarian cancer is the most lethal gynecologic malignancy in women, and high-grade serous ovarian cancer (HGSOC) is the most common subtype. Currently, no clinical test has been approved by the FDA to screen the general population for ovarian cancer. This underscores the critical need for the development of a robust methodology combined with novel technology to detect diagnostic biomarkers for HGSOC in the sera of women. Targeted mass spectrometry (MS) can be used to identify and quantify specific peptides/proteins in complex biological samples with high accuracy, sensitivity, and reproducibility. In this study, we sought to develop and conduct analytical validation of a multiplexed Tier 2 targeted MS parallel reaction monitoring (PRM) assay for the relative quantification of 23 putative ovarian cancer protein biomarkers in sera. METHODS: To develop a PRM method for our target peptides in sera, we followed nationally recognized consensus guidelines for validating fit-for-purpose Tier 2 targeted MS assays. The endogenous target peptide concentrations were calculated using the calibration curves in serum for each target peptide. Receiver operating characteristic (ROC) curves were analyzed to evaluate the diagnostic performance of the biomarker candidates. RESULTS: We describe an effort to develop and analytically validate a multiplexed Tier 2 targeted PRM MS assay to quantify candidate ovarian cancer protein biomarkers in sera. Among the 64 peptides corresponding to 23 proteins in our PRM assay, 24 peptides corresponding to 16 proteins passed the assay validation acceptability criteria. A total of 6 of these peptides from insulin-like growth factor-binding protein 2 (IBP2), sex hormone-binding globulin (SHBG), and TIMP metalloproteinase inhibitor 1 (TIMP1) were quantified in sera from a cohort of 69 patients with early-stage HGSOC, late-stage HGSOC, benign ovarian conditions, and healthy (non-cancer) controls. Confirming the results from previously published studies using orthogonal analytical approaches, IBP2 was identified as a diagnostic biomarker candidate based on its significantly increased abundance in the late-stage HGSOC patient sera compared to the healthy controls and patients with benign ovarian conditions. CONCLUSIONS: A multiplexed targeted PRM MS assay was applied to detect candidate diagnostic biomarkers in HGSOC sera. To evaluate the clinical utility of the IBP2 PRM assay for HGSOC detection, further studies need to be performed using a larger patient cohort.

Evaluation of the potential of Pap test fluid and cervical swabs to serve as clinical diagnostic biospecimens for the detection of ovarian cancer by mass spectrometry-based proteomics.

BACKGROUND: The purpose of this study was to determine whether the residual fixative from a liquid-based Pap test or a swab of the cervix contained proteins that were also found in the primary tumor of a woman with high grade serous ovarian cancer. This study is the first step in determining the feasibility of using the liquid-based Pap test or a cervical swab for the detection of ovarian cancer protein biomarkers. METHODS: Proteins were concentrated by acetone precipitation from the cell-free supernatant of the liquid-based Pap test fixative or eluted from the cervical swab. Protein was also extracted from the patient's tumor tissue. The protein samples were digested into peptides with trypsin, then the peptides were run on 2D-liquid chromatography mass spectrometry (2D-LCMS). The data was searched against a human protein database for the identification of peptides and proteins in each biospecimen. The proteins that were identified were classified for cellular localization and molecular function by bioinformatics integration. RESULTS: We identified almost 5000 proteins total in the three matched biospecimens. More than 2000 proteins were expressed in each of the three biospecimens, including several known ovarian cancer biomarkers such as CA125, HE4, and mesothelin. By Scaffold analysis of the protein Gene Ontology categories and functional analysis using PANTHER, the proteins were classified by cellular localization and molecular function, demonstrating that the Pap test fluid and cervical swab proteins are similar to each other, and also to the tumor extract. CONCLUSIONS: Our results suggest that Pap test fixatives and cervical swabs are a rich source of tumor-specific biomarkers for ovarian cancer, which could be developed as a test for ovarian cancer detection.

Cytomegalovirus and systemic inflammation at time of surgery is associated with worse outcomes in serous ovarian cancer.

OBJECTIVES: Cytomegalovirus (CMV) is a common infection that establishes latency in healthy people. CMV has been associated with alterations of the immune compartment leading to improved responses, while inflammation has been shown to adversely impact outcomes. We investigated whether CMV serostatus predicts outcomes in ovarian cancer in the presence or absence of inflammation. METHODS: A total of 106 patients with serous ovarian cancer from 2006 to 2009 were analyzed. CMV and systemic inflammation was measured using CMV immunoglobulin G (IgG) and C-reactive protein (CRP), respectively, in serum collected prior to cytoreduction. Patients were stratified by CMV IgG (non-reactive, reactive/borderline) and CRP (≤10, >10 mg/L) status. Overall survival (OS) and recurrence-free survival (RFS) were compared by group using log-rank tests and Cox proportional hazards regression models adjusting for age at surgery. RESULTS: Of 106 eligible patients, 40 (37.7%) were CMV+/CRP+, 24 (22.6%) CMV+/CRP-, 19 (17.9%) CMV-/CRP+, and 23 (21.7%) CMV-/CRP-. CRP+ had higher CA-125 levels (P = 0.05) and higher rates of suboptimal debulking (P = 0.03). There were no other significant differences in demographic, surgical, or pathologic factors between groups. CMV+/CRP+ patients median RFS and OS were 16.9 months (95% CI: 9.0-21.1) and 31.7 months (95% CI: 25.0-48.7), respectively, with a significantly worse RFS (aHR: 1.85, 95% CI: 1.05-3.24, P = 0.03) and OS (aHR: 2.12, 95% CI: 1.17-3.82, P = 0.01) compared to CMV-/CRP- (RFS = 31.2 months (95% CI: 16.0-56.4) and OS = 63.8 months (95% CI: 50.7-87.0)). CMV+/CRP- group displayed the longest OS (89.3 months). CONCLUSIONS: Previous exposure to CMV and high CRP at surgery portended worse RFS and OS compared to women who tested negative. The CMV+/CRP- group had the longest OS, indicating that CMV status alone, in the absence of inflammation, may be protective.

Inhibition of Ovarian Cancer Cell Spheroid Formation by Synthetic Peptides Derived from Nectin-4.

The formation of 3D multicellular spheroids in the ascites fluid of ovarian cancer patients is an understudied component of the disease progression. Spheroids are less sensitive to chemotherapy, in part due to the protection afforded by their structure, but also due to their slower proliferation rate. Previous studies suggest that the cell adhesion molecule Nectin-4 plays a key role in the formation of ovarian cancer spheroids. In this study, we further examined the role of Nectin-4 at early time points in spheroid formation using real-time digital photography. Human NIH:OVCAR5 ovarian cancer cells formed aggregates within 8 h, which further contracted into compact spheroids over 24 h. In contrast, Nectin-4 knockdown cells did not form tightly compacted spheroids. Synthetic peptides derived from Nectin-4 were tested for their ability to alter spheroid formation in two ovarian cancer cell lines. Nectin-4 peptide 10 (N4-P10) had an immediate effect on disrupting ovarian cancer spheroid formation, which continued for over 24 h, while a scrambled version of the peptide had no effect. N4-P10 inhibited spheroid formation in a concentration-dependent manner and was not cytotoxic; suggesting that N4-P10 treatment could maintain the cancer cells as single cells which may be more sensitive to chemotherapy.

Effect of chemotherapy on cancer stem cells and tumor-associated macrophages in a prospective study of preoperative chemotherapy in soft tissue sarcoma.

BACKGROUND: Cancer stem cells (CSC) may respond to chemotherapy differently from other tumor cells. METHODS: This study examined the expression of the putative cancer stem cell markers ALDH1, CD44, and CD133; the angiogenesis marker CD31; and the macrophage marker CD68 in soft tissue sarcomas (STS) before and after 4 cycles of chemotherapy with doxorubicin and ifosfamide in 31 patients with high-grade soft tissue sarcoma in a prospective clinical trial. RESULTS: None of the markers clearly identified CSCs in STS samples. Macrophages represented a prominent component in viable tumor areas in pre-treatment STS biopsies, ranging from < 5 to > 50%. Furthermore, macrophages expressed CD44 and ALDH1. Macrophage density correlated with baseline maximum standardized uptake value (SUVmax) on fluoro-deoxyglucose positron emission tomography (PET) imaging. Pre-chemotherapy CD68 staining correlated positively with the baseline SUVmax, and negatively with the percent of viable tumor cells in post-chemotherapy resection samples. In particular, cases with more CD68-positive cells at biopsy had fewer viable tumor cells at resection, suggesting a better response to chemotherapy. CONCLUSIONS: In conclusion, ALDH1, CD44, and CD133 are not likely to be useful markers of CSCs in STS. However, our observation of infiltrating macrophages in STS specimens indicates that these immune cells may contribute significantly to STS biology and response to chemotherapy, and could provide a potential target of therapy. Future studies should investigate macrophage contribution to STS pathophysiology by cytokine signaling.

Cytokine-induced memory-like natural killer cells have enhanced function, proliferation, and in vivo expansion against ovarian cancer cells.

OBJECTIVE: Natural killer (NK) cells are lymphocytes well suited for adoptive immunotherapy. Attempts with adoptive NK cell immunotherapy against ovarian cancer have proven unsuccessful, with the main limitations including failure to expand and diminished effector function. We investigated if incubation of NK cells with interleukin (IL)-12, IL-15, and IL-18 for 16h could produce cytokine-induced memory-like (CIML) NK cells capable of enhanced function against ovarian cancer. METHODS: NK cells were preactivated briefly with IL-12, IL-15, and IL-18, rested, then placed against ovarian cancer targets to assess phenotype and function via flow cytometry. Real-time NK-cell-mediated tumor-killing was evaluated. Using ascites cells and cell-free ascites fluid, NK cell proliferation and function within the immunosuppressive microenvironment was evaluated in vitro. Finally, CIML NK cells were injected intraperitoneal (IP) into an in vivo xenogeneic mouse model of ovarian cancer. RESULTS: CIML NK cells demonstrate enhanced cytokine (IFN-γ) production and NK-cell-mediated killing of ovarian cancer. NK cells treated overnight with cytokines led to robust activation characterized by temporal shedding of CD16, induction of CD25, and enhanced proliferation. CIML NK cells proliferate more with enhanced effector function compared to controls in an immunosuppressive microenvironment. Finally, human CIML NK cells exhibited potent antitumor effects within a xenogeneic mouse model of ovarian cancer. CONCLUSIONS: CIML NK cells have enhanced functionality and persistence against ovarian cancer in vitro and in vivo, even when exposed to ascites fluid. These findings provide a strategy for NK cell-based immunotherapy to circumvent the immunosuppressive nature of ovarian cancer.

Ectodomain shedding of the cell adhesion molecule Nectin-4 in ovarian cancer is mediated by ADAM10 and ADAM17.

We previously showed that the cell adhesion molecule Nectin-4 is overexpressed in ovarian cancer tumors, and its cleaved extracellular domain can be detected in the serum of ovarian cancer patients. The ADAM (adisintegrin and metalloproteinase) proteases are involved in ectodomain cleavage of transmembrane proteins, and ADAM17 is known to cleave Nectin-4 in breast cancer. However, the mechanism of Nectin-4 cleavage in ovarian cancer has not yet been determined. Analysis of ovarian cancer gene microarray data showed that higher expression of Nectin-4, ADAM10, and ADAM17 is associated with significantly decreased progression-free survival. We quantified Nectin-4 shedding from the surface of ovarian cancer cells after stimulation with lysophosphatidic acid. We report that ADAM17 and ADAM10 cleave Nectin-4 and release soluble Nectin-4 (sN4). Small molecule inhibitors and siRNA knockdown of both ADAM proteases confirmed these results. In matched samples from 11 high-grade serous ovarian cancer patients, we detected 2-20-fold more sN4 in ascites fluid than serum. Co-incubation of ovarian cancer cells with ascites fluid significantly increased sN4 shedding, which could be blocked using a dual inhibitor of ADAM10 and ADAM17. Furthermore, we detected RNA for Nectin-4, ADAM10, and ADAM17 in primary ovarian carcinoma tumors, secondary omental metastases, and ascites cells isolated from serous ovarian cancer patients. In a signaling pathway screen, lysophosphatidic acid increased phosphorylation of AKT, EGF receptor, ERK1/2, JNK1/2/3, and c-Jun. Understanding the function of Nectin-4 shedding in ovarian cancer progression is critical to facilitate its development as both a serum biomarker and a therapeutic target for ovarian cancer.

A multiplex platform for the identification of ovarian cancer biomarkers.

BACKGROUND: Currently, there are no FDA approved screening tools for detecting early stage ovarian cancer in the general population. Development of a biomarker-based assay for early detection would significantly improve the survival of ovarian cancer patients. METHODS: We used a multiplex approach to identify protein biomarkers for detecting early stage ovarian cancer. This new technology (Proseek® Multiplex Oncology Plates) can simultaneously measure the expression of 92 proteins in serum based on a proximity extension assay. We analyzed serum samples from 81 women representing healthy, benign pathology, early, and advanced stage serous ovarian cancer patients. RESULTS: Principle component analysis and unsupervised hierarchical clustering separated patients into cancer versus non-cancer subgroups. Data from the Proseek® plate for CA125 levels exhibited a strong correlation with current clinical assays for CA125 (correlation coefficient of 0.89, 95% CI 0.83, 0.93). CA125 and HE4 were present at very low levels in healthy controls and benign cases, while higher levels were found in early stage cases, with highest levels found in the advanced stage cases. Overall, significant trends were observed for 38 of the 92 proteins (p < 0.001), many of which are novel candidate serum biomarkers for ovarian cancer. The area under the ROC curve (AUC) for CA125 was 0.98 and the AUC for HE4 was 0.85 when comparing early stage ovarian cancer versus healthy controls. In total, 23 proteins had an estimated AUC of 0.7 or greater. Using a naïve Bayes classifier that combined 12 proteins, we improved the sensitivity corresponding to 95% specificity from 93 to 95% when compared to CA125 alone. Although small, a 2% increase would have a significant effect on the number of women correctly identified when screening a large population. CONCLUSIONS: These data demonstrate that the Proseek® technology can replicate the results established by conventional clinical assays for known biomarkers, identify new candidate biomarkers, and improve the sensitivity and specificity of CA125 alone. Additional studies using a larger cohort of patients will allow for validation of these biomarkers and lead to the development of a screening tool for detecting early stage ovarian cancer in the general population.

Gene expression identifies heterogeneity of metastatic behavior among gastrointestinal stromal tumors.

BACKGROUND: Adjuvant imatinib is useful in patients with gastrointestinal stromal tumors (GIST) at high risk of recurrence. At present, the risk of recurrence is determined based on tumor size, mitotic rate, tumor site, and tumor rupture. Previous studies using various biochemical pathways identified gene expression patterns that distinguish two subsets of aggressive fibromatosis (AF), serous ovarian carcinoma (OVCA), and clear cell renal cell carcinoma (RCC). These gene sets separated soft tissue sarcomas into two groups with different probabilities of developing metastatic disease. The present study used these gene sets to identify GIST subgroups with different probabilities of developing metastatic disease. METHODS: We utilized these three gene sets, hierarchical clustering, and Kaplan-Meier analysis, to examine 60 primary resected GIST samples using Agilent chip expression profiling. RESULTS: Hierarchical clustering using both the combined and individual AF-, OVCA-, and RCC- gene sets identified differences in probabilities of developing metastatic disease between the clusters defined by the first branch point of the clustering dendrograms (p = 0.029 for the combined gene set, p = 0.003 for the AF-gene set, p < 0.001 for the OVCA-gene set, and p = 0.003 for the RCC-gene set). CONCLUSIONS: Hierarchical clustering using these gene sets identified at least two subsets of GIST with distinct clinical behavior and risk of metastatic disease. The use of gene expression analysis along with other known prognostic factors may better predict the long-term outcome following surgery, and thus restrict the use of adjuvant therapy to high-risk GIST, and reduce heterogeneity among groups in clinical trials of new drugs.

Gene expression identifies heterogeneity of metastatic behavior among high-grade non-translocation associated soft tissue sarcomas.

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.

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease.

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification, and prioritization of microbial proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant [to generate peptide-spectral matches (PSMs) and quantification], PepQuery2 (to verify the quality of PSMs), Unipept (for taxonomic and functional annotation), and MSstatsTMT (for statistical analysis). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies. IMPORTANCE: Clinical metaproteomics has immense potential to offer functional insights into the microbiome and its contributions to human disease. However, there are numerous challenges in the metaproteomic analysis of clinical samples, including handling of very large protein sequence databases for sensitive and accurate peptide and protein identification from mass spectrometry data, as well as taxonomic and functional annotation of quantified peptides and proteins to enable interpretation of results. To address these challenges, we have developed a novel clinical metaproteomics workflow that provides customized bioinformatic identification, verification, quantification, and taxonomic and functional annotation. This bioinformatic workflow is implemented in the Galaxy ecosystem and has been used to characterize diverse clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness and availability for use by the research community via analysis of residual fluid from cervical swabs.

Targeting CD133 in an in vivo ovarian cancer model reduces ovarian cancer progression.

OBJECTIVES: While most women with ovarian cancer will achieve complete remission after treatment, the majority will relapse within two years, highlighting the need for novel therapies. Cancer stem cells (CSC) have been identified in ovarian cancer and most other carcinomas as a small population of cells that can self-renew. CSC are more chemoresistant and radio-resistant than the bulk tumor cells; it is likely that CSC are responsible for relapse, the major problem in cancer treatment. CD133 has emerged as one of the most promising markers for CSC in ovarian cancer. The hypothesis driving this study is that despite their low numbers in ovarian cancer tumors, CSC can be eradicated using CD133 targeted therapy and tumor growth can be inhibited. METHODS: Ovarian cancer cell lines were evaluated using flow cytometry for expression of CD133. In vitro viability studies with an anti-CD133 targeted toxin were performed on one of the cell lines, NIH:OVCAR5. The drug was tested in vivo using a stably transfected luciferase-expressing NIH:OVCAR5 subline in nude mice, so that tumor growth could be monitored by digital imaging in real time. RESULTS: Ovarian cancer cell lines showed 5.6% to 16.0% CD133 expression. dCD133KDEL inhibited the in vitro growth of NIH:OVCAR5 cells. Despite low numbers of CD133-expressing cells in the tumor population, intraperitoneal drug therapy caused a selective decrease in tumor progression in intraperitoneal NIH:OVCAR5-luc tumors. CONCLUSIONS: Directly targeting CSC that are a major cause of drug resistant tumor relapse with an anti-CD133 targeted toxin shows promise for ovarian cancer therapy.

Isothermal vitrification methodology development for non-cryogenic storage of archival human sera.

Biorepositories worldwide collect human serum samples and store them for future research. Currently, hundreds of biorepositories across the world store human serum samples in refrigerators, freezers, or liquid nitrogen without following any specific cryopreservation protocol. This method of storage is both expensive and potentially detrimental to the biospecimens. To decrease both cost of storage and the freeze/thaw stresses, we explored the feasibility of storing archival human serum samples at non-cryogenic temperatures using isothermal vitrification. When biospecimens are vitrified, biochemical reactions can be stopped, the specimen ceases to degrade, and macromolecules can be stabilized without requiring cryogenic storage. In this study, 0.2, 0.4, or 0.8M trehalose; 0, 0.005 or 0.01M dextran; and 0 or 10% (v/v) glycerol was added to human serum samples. The samples were either dried diffusively as sessile droplets or desiccated under vacuum after they are adsorbed onto glass microfiber filters. The glass transition temperatures (Tg) of the desiccated samples were measured by temperature-ramp Fourier Transform Infrared (FTIR) spectroscopy. Sera samples vitrified at 4±2°C when 0.8M trehalose and 0.01M dextran were added and the samples were vacuum dried for two hours. Western immunoblotting showed that vitrified serum proteins were minimally degraded when stored for up to one month at 4°C. About 80% of all proteins were recovered after storage at 4°C on glass microfiber filters, and recovery did not decrease with storage time. These results demonstrated the feasibility of long-term storage of vitrified serum at hypothermic (and non-cryogenic) temperatures.

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease.

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, which are usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification and prioritization of microbial and host proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant (to generate peptide-spectral matches (PSMs) and quantification), PepQuery2 (to verify the quality of PSMs), and Unipept and MSstatsTMT (for taxonomy and functional annotation). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.

Gene expression identifies heterogeneity of metastatic propensity in high-grade soft tissue sarcomas.

BACKGROUND: Metastatic propensity of soft tissue sarcoma (STS) is heterogeneous and may be determined by gene expression patterns that do not correlate well with morphology. The authors have reported gene expression patterns that distinguish 2 broad classes of clear cell renal carcinoma (ccRCC-gene set), and other patterns that can distinguish heterogeneity of serous ovarian carcinoma (OVCA-gene set) and aggressive fibromatosis (AF-gene set); however, clinical follow-up data were not available for these samples. METHODS: In the current study, gene expression patterns in 73 samples of high-grade STS were examined using spotted cDNA microarray slides that contained ∼16,000 unique UniGene clusters. Approximately 50% of the genes present in the ccRCC-, OVCA-, and AF-gene sets were also represented in the data from this chip set, and these were combined to form a composite gene set of 278 probes. RESULTS: Hierarchical clustering using this composite gene set suggested the existence of subsets of the STS samples. Analysis revealed differences in the time to development of metastatic disease between the clusters defined by the first branch point of the clustering dendrogram (P = .005), and also among the 4 different clusters defined by the second branch points (P = .001). CONCLUSIONS: This approach suggests the existence of >2 subsets of high-grade pleomorphic STS, each with distinct clinical behavior. A composite gene set such as that described here may be useful to stratify STS in clinical trials, and may be of practical utility in patient management.

Development of a Multiprotein Classifier for the Detection of Early Stage Ovarian Cancer.

BACKGROUND: Individual serum biomarkers are neither adequately sensitive nor specific for use in screening the general population for ovarian cancer. The purpose of this study was to develop a multiprotein classifier to detect the early stages of ovarian cancer, when it is most treatable. METHODS: The Olink Proseek Multiplex Oncology II panel was used to simultaneously quantify the expression levels of 92 cancer-related proteins in sera. RESULTS: In the discovery phase, we generated a multiprotein classifier that included CA125, HE4, ITGAV, and SEZ6L, based on an analysis of sera from 116 women with early stage ovarian cancer and 336 age-matched healthy women. CA125 alone achieved a sensitivity of 87.9% at a specificity of 95%, while the multiprotein classifier resulted in an increased sensitivity of 91.4%, while holding the specificity fixed at 95%. The performance of the multiprotein classifier was validated in a second cohort comprised of 192 women with early stage ovarian cancer and 467 age-matched healthy women. The sensitivity at 95% specificity increased from 74.5% (CA125 alone) to 79.2% with the multiprotein classifier. In addition, the multiprotein classifier had a sensitivity of 95.1% at 98% specificity for late stage ovarian cancer samples and correctly classified 80.5% of the benign samples using the 98% specificity cutpoint. CONCLUSIONS: The inclusion of the proteins HE4, ITGAV, and SEZ6L improved the sensitivity and specificity of CA125 alone for the detection of early stages of ovarian cancer in serum samples. Furthermore, we identified several proteins that may be novel biomarkers of early stage ovarian cancer.

Claudin 4 Is differentially expressed between ovarian cancer subtypes and plays a role in spheroid formation.

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.

Identification of candidate biomarkers in ovarian cancer serum by depletion of highly abundant proteins and differential in-gel electrophoresis.

Ovarian cancer is the fifth leading cause of cancer death for women in the US, yet survival rates are over 90% when it is diagnosed at an early stage, highlighting the need for biomarkers for early detection. To enhance the discovery of tumor-specific proteins that could represent novel serum biomarkers for ovarian cancer, we depleted serum of highly abundant proteins which can mask the detection of proteins present in serum at low concentrations. Three commercial immunoaffinity columns were used in parallel to deplete the highly abundant proteins in serum from 60 patients with serous ovarian carcinoma and 60 non-cancer controls. Medium and low abundance serum proteins from each serum pool were then evaluated by the quantitative proteomic technique of differential in-gel electrophoresis. The number of protein spots that were elevated in ovarian cancer sera by at least twofold ranged from 36 to 248, depending upon the depletion and separation methods. From the 33 spots picked for MS analysis, nine different proteins were identified, including the novel candidate ovarian cancer biomarkers leucine-rich alpha2 glycoprotein-1 and ficolin 3. Western blotting validated the relative increases in serum protein levels for three of the proteins identified, demonstrating the utility of this approach for the identification of novel serum biomarkers for ovarian cancer.

Early detection of ovarian cancer using group biomarkers.

One reason that ovarian cancer is such a deadly disease is because it is not usually diagnosed until it has reached an advanced stage. In this study, we developed a novel algorithm for group biomarkers identification using gene expression data. Group biomarkers consist of coregulated genes across normal and different stage diseased tissues. Unlike prior sets of biomarkers identified by statistical methods, genes in group biomarkers are potentially involved in pathways related to different types of cancer development. They may serve as an alternative to the traditional single biomarkers or combination of biomarkers used for the diagnosis of early-stage and/or recurrent ovarian cancer. We extracted group biomarkers by applying biclustering algorithms that we recently developed on the gene expression data of over 400 normal, cancerous, and diseased tissues. We identified several groups of coregulated genes that encode for secreted proteins and exhibit expression levels in ovarian cancer that are at least 2-fold (in log2 scale) higher than in normal ovary and nonovarian tissues. In particular, three candidate group biomarkers exhibited a conserved biological pattern that may be used for early detection or recurrence of ovarian cancer with specificity greater than 99% and sensitivity equal to 100%. We validated these group biomarkers using publicly available gene expression data sets downloaded from a NIH Web site (http://www.ncbi.nlm.nih.gov/geo). Statistical analysis showed that our methodology identified an optimum combination of genes that have the highest effect on the diagnosis of the disease compared with several computational techniques that we tested. Our study also suggests that single or group biomarkers correlate with the stage of the disease.

Development of a multiplexed giant magnetoresistive biosensor array prototype to quantify ovarian cancer biomarkers.

In this work, we developed benchtop and handheld Giant Magnetoresistive (GMR) biosensing systems that serve as platforms for detecting a wide variety of protein biomarkers for human diseases. System development included spintronic and nanomagnetic materials, biomolecular chemistry, electronic circuitry, analog and digital signal processing, firmware programming, user interface programming on both PC and Android smartphone, communications over both USB and Bluetooth, and mechanical integration. In this work, we demonstrated the benchtop GMR biosensing system in the context of ovarian cancer assay development. The prototype system delivered the required performance in terms of high-sensitivity multiplex assays in a portable format with enough flexibility to serve as a platform for ovarian cancer and many other diseases. We achieved multiplex detection of cancer antigen 125 (CA125 II), human epididymis protein 4 (HE4), and interleukin 6 (IL6), with limits of detection (LOD) as low as 3.7 U/mL, 7.4 pg/mL, and 7.4 pg/mL, respectively.