An integrated clinical-genomics approach identifies a candidate multi-analyte blood test for serous ovarian carcinoma

Utility of Matrix Metalloproteinases in the Diagnosis, Monitoring and Prognosis of Ovarian Cancer Patients

Ovarian cancer is one of the most common gynecologic malignancies. It is characterized by a high mortality rate, which is mainly due to the asymptomatic course of the disease. In light of the high mortality rate and increasing morbidity, new diagnostic methods are being explored to enable earlier detection, better monitoring, and improved prognosis. Such diagnostic methods include the assessment of tumor markers in various biological samples. Among the markers currently being investigated, extracellular matrix metalloproteinases (MMPs) are of particular interest. The objective of this article was to compile the existing knowledge of MMPs in ovarian cancer patients and to describe their potential diagnostic utility. Additionally, this article provides an overview of the symptoms, complications, and risk factors associated with ovarian cancer and the role of MMPs in physiology and pathology. Preliminary results indicate that tissue expression and blood and body fluid levels of MMPs may be different in ovarian cancer patients than in healthy women. The expression and concentration of individual MMPs have been shown to be correlated with cancer stage and disease severity. In addition, the preliminary value of some of these enzymes in predicting prognosis is discussed. However, as the amount of data is limited, more studies are needed to fully evaluate the potential function of individual MMPs in ovarian cancer patients. Based on the knowledge gathered for this article, it seems that MMP-2, MMP-3, MMP-7, MMP-8, MMP-9, MMP-13, are tentatively the most useful. A thorough evaluation of their utility as modern biomarkers in ovarian cancer requires further investigation.

Osteopontin as a Tumor Marker in Ovarian Cancer

Introduction

Ovarian cancer is associated with high morbidity and mortality. This is due to the nonspecific symptoms and no effective screening methods. Currently, carbohydrate antigen-125 (CA125) is used as a tumor biomarker for the diagnosis of ovarian cancer, but it has its own limitations. Hence, there is a need for other tumor biomarkers for the diagnosis of ovarian cancer. Objective of the study was to evaluate the diagnostic test characteristics of plasma osteopontin (OPN) in detecting ovarian malignancy and comparing its performance with CA125.

Materials and Methods

This is a prospective cross-sectional diagnostic test evaluation. Women with adnexal mass detected by clinical or radiological examination were enrolled as suspected cases. Women who presented with other gynecological conditions were enrolled as controls. OPN and CA125 levels were measured in all enrolled subjects.

Results

Among 106 women enrolled, 26 were ovarian cancer, 31 had benign ovarian masses, and 49 were controls. Median plasma CA125 levels were higher in subjects with ovarian cancer (298 U/ml; interquartile range [IQR]: 84-1082 U/ml vs. 37.5U/ml; IQR: 17.6-82.9U/ml; P < 0.001). CA125 sensitivity, specificity, positive, and negative likelihood ratios were 88.5%, 61.3%, 2.10, and 0.19, respectively. Median plasma OPN levels were higher in subjects with ovarian cancer (63.1 ng/ml; IQR: 39.3-137 ng/ml vs. 27 ng/ml; IQR: 20-52 ng/ml; P = 0.001). Sensitivity, specificity, positive, and negative likelihood ratios of OPN were 50%, 87%, 2.58, and 0.62, respectively.

Conclusion

OPN levels were higher in ovarian cancer than in the benign ovarian mass and had better specificity than CA125. OPN can better differentiate between benign and malignant ovarian mass as compared to CA125.

Applications of Proteomics in Ovarian Cancer: Dawn of a New Era

The ability to identify ovarian cancer (OC) at its earliest stages remains a challenge. The patients present an advanced stage at diagnosis. This heterogeneous disease has distinguishable etiology and molecular biology. Next-generation sequencing changed clinical diagnostic testing, allowing assessment of multiple genes, simultaneously, in a faster and cheaper manner than sequential single gene analysis. Technologies of proteomics, such as mass spectrometry (MS) and protein array analysis, have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of OC. Proteomics analysis of OC, as well as their adaptive responses to therapy, can uncover new therapeutic choices, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is an urgent need to better understand how the genomic and epigenomic heterogeneity intrinsic to OC is reflected at the protein level, and how this information could potentially lead to prolonged survival.

The prognostic and diagnostic potential of kallikrein-related peptidases in ovarian cancer

Introduction: Ovarian cancer is one of the deadliest malignancies among women worldwide. The lack of early diagnostic markers fuels an unfavorable prognosis as most patients are at an advanced stage when the disease is diagnosed for the first time. The role of the kallikrein-related peptidase (KLK) family in ovarian cancer progression and prognosis has been thoroughly investigated in various studies. Most of these peptidases are upregulated in ovarian cancer tissue compared to normal ovarian tissue and their expression is linked to overall and progression-free survival (OS/PFS). In this review, we address the clinical relevance of KLKs in ovarian cancer and their diagnostic potential.Areas covered: This review covers the expression and regulation of KLKs in ovarian cancer with focus on the prognostic and diagnostic potential, especially in liquid biopsies.Expert opinion: In ovarian cancer, several kallikrein-related peptidases are markedly expressed in a tissue-specific manner. Tumor-derived protease secretion results in elevated levels in serum and ascites. KLKs may thus serve as potential biomarkers alone or in combination with other serum tumor markers, such as Cancer Antigen 125 (CA125), for early detection and assessment of the prognosis of ovarian cancer patients.

Systematic module approach identifies altered genes and pathways in four types of ovarian cancer

The present study aimed to identify altered genes and pathways associated with four histotypes of ovarian cancer, according to the systematic tracking of dysregulated modules of reweighted protein-protein interaction (PPI) networks. Firstly, the PPI network and gene expression data were initially integrated to infer and reweight normal ovarian and four types of ovarian cancer (endometrioid, serous, mucinous and clear cell carcinoma) PPI networks based on Spearman's correlation coefficient. Secondly, modules in the PPI network were mined using a clique-merging algorithm and the differential modules were identified through maximum weight bipartite matching. Finally, the gene compositions in the altered modules were analyzed, and pathway functional enrichment analyses for disrupted module genes were performed. In five conditional-specific networks, universal alterations in gene correlations were revealed, which leads to the differential correlation density among disrupted module pairs. The analyses revealed 28, 133, 139 and 33 altered modules in endometrioid, serous, mucinous and clear cell carcinoma, respectively. Gene composition analyses of the disrupted modules revealed five common genes (mitogen-activated protein kinase 1, phosphoinositide 3-kinase-encoding catalytic 110-KDα, AKT serine/threonine kinase 1, cyclin D1 and tumor protein P53) across the four subtypes of ovarian cancer. In addition, pathway enrichment analysis confirmed one common pathway (pathways in cancer), in the four histotypes. This systematic module approach successfully identified altered genes and pathways in the four types of ovarian cancer. The extensive differences of gene correlations result in dysfunctional modules, and the coordinated disruption of these modules contributes to the development and progression of ovarian cancer.

Diagnostic values of osteopontin combined with CA125 for ovarian cancer: a meta-analysis

Osteopontin (OPN) is currently one of the most studied serum biomarkers of ovarian cancer (OC). This meta-analysis aims to ascertain whether OPN is a useful diagnostic biomarker for OC and determine the overall diagnostic accuracy of OPN measurement when combined with cancer antigen 125 (CA125). A systematic literature search was conducted in Cochrane Library, PubMed, EMBASE, CBM, and China National Knowledge Infrastructure databases. Information was independently extracted by two investigators. The Quality Assessment for Studies of Diagnostic Accuracy tool was applied to examine the quality of eligible studies, and a bivariate model was used to calculate the pooled estimates. We identified 17 and 10 studies that evaluated the role of OPN alone and OPN combined with CA125 in diagnosing ovarian tumor, respectively. The overall diagnostic sensitivity and specificity of OPN in OC were 0.766 (95% CI 0.685-0.831) and 0.897 (95% CI 0.849-0.931), correspondingly. When OPN was combined with CA125, the sensitivity and specificity were 0.871 (95% CI 0.788-0.924) and 0.881 (95% CI 0.837-0.914), respectively. OPN is a useful tumor biomarker in future screening tests of OC and can be a promising adjunct to CA125. Additional studies with multicenter trials and carefully selected controls are needed to further verify the results.

Diagnostic Accuracy of Serum Kallikrein-Related Peptidases for Ovarian Cancer: A Systematic Review and Meta-Analysis

OBJECTIVE

At present, considerable efforts have been made to identify new cancer-specific markers for ovarian cancer (OC) diagnosis and the kallikrein-related peptidases (KLKs) family is one of the most studied candidates. This meta-analysis aims to evaluate the pooled diagnostic value of serum KLK measurement for diagnosing OC.

METHODS

The Cochrane Library, PubMed, Excerpt Medica Database were searched for all relevant literature. The Quality Assessment for Studies of Diagnostic Accuracy tool was applied to assess the quality of enrolled studies. Statistical analysis was conducted by using Stata 13.0 software and Meta-Disc.

RESULTS

A total of 15 studies from 13 articles were considered eligible for inclusion in the present analysis. The following pooled parameters were calculated by using the bivariate model: sensitivity of 0.582 (95% confidence interval [CI], 0.517-0.644), specificity of 0.909 (95% CI, 0.833-0.952), positive likelihood ratios of 6.367 (95% CI, 3.330-12.172), negative likelihood ratios of 0.460 (95% CI, 0.388-0.546), diagnostic odds ratio of 13.831 (95% CI, 6.460-29.614), respectively.

CONCLUSIONS

Kallikrein-related peptidase seems to be a promising candidate biomarker in diagnosing OC, but the associated poor sensitivity of KLK individually may limit its value in clinical application. To resolve this problem, the combination of KLK and other markers may offer improved performance than a single marker.

A Pilot Study Evaluating Serum MMP7 as a Preoperative Prognostic Marker for Pancreatic Ductal Adenocarcinoma Patients

BACKGROUND

Current preoperative risk stratification modalities for pancreatic ductal adenocarcinoma (PDA) patients are inadequate. Elevated serum matrix metalloproteinase 7 (MMP7) is associated with metastatic PDA. We evaluated preoperative MMP7 level as a prognostic marker in patients with resectable PDA.

METHODS

From a prospectively maintained database, we identified PDA patients who underwent operation with curative intent from 2004 to 2008 and had serum collected preoperatively. MMP7 was measured by enzyme-linked immunosorbent assay. Patients were defined as having advanced disease if they were found to be unresectable at the time of operation or had nodal involvement on final pathology.

RESULTS

Preoperative serum samples were available for 134 patients. Using a cutoff of 13.5 ng/mL, MMP7 was highly predictive for advanced disease. For patients who underwent R0 resection, MMP7 > 13.5 ng/mL was strongly associated with N1 status, T3/T4 stage, moderate/poor differentiation, and perineural invasion. The median recurrence-free survival was 5.0 months in patients with MMP7 > 13.5 ng/mL versus 9.9 months for patients with lower values (P = 0.004).

CONCLUSIONS

Very elevated serum MMP7 was highly predictive of unresectable disease and nodal involvement despite favorable preoperative cross-sectional imaging. MMP7 should be further evaluated as a biomarker to risk-stratify PDA patients prior to operation.

MMP-7 is a highly specific negative marker for benign and malignant mesothelial cells in serous effusions

The aim of this study was to analyze the diagnostic role of MMP-7 in effusion cytology. Effusions (n = 356), consisting of 307 carcinomas (184 ovarian, 55 breast, 32 lung, 36 carcinomas of other origin) and 49 malignant mesotheliomas, were analyzed for MMP-7 expression using immunohistochemistry. MMP-7 was expressed in 124/307 (40%) carcinomas and was uniformly absent in malignant mesotheliomas (0/49; 0%; P < .001). Reactive mesothelial cells were similarly MMP-7 negative in all carcinoma specimens. In carcinomas, expression was most frequent in tumors of ovarian and other female genital (cervical and endometrial) origin (P < .001). The sensitivity and specificity of this marker in the differential diagnosis between high-grade serous carcinoma and malignant mesothelioma were 46% and 100%, respectively. In conclusion, MMP-7 expression is highly specific, though only of moderate sensitivity, for the diagnosis of carcinoma in the differential diagnosis from both benign and malignant mesothelial cells.

Meta-analysis of incomplete microarray studies

Meta-analysis of microarray studies to produce an overall gene list is relatively straightforward when complete data are available. When some studies lack information-providing only a ranked list of genes, for example-it is common to reduce all studies to ranked lists prior to combining them. Since this entails a loss of information, we consider a hierarchical Bayes approach to meta-analysis using different types of information from different studies: the full data matrix, summary statistics, or ranks. The model uses an informative prior for the parameter of interest to aid the detection of differentially expressed genes. Simulations show that the new approach can give substantial power gains compared with classical meta-analysis and list aggregation methods. A meta-analysis of 11 published studies with different data types identifies genes known to be involved in ovarian cancer and shows significant enrichment.

Ovarian cancer: involvement of the matrix metalloproteinases

Ovarian cancer is the leading cause of death from gynecologic malignancies. One of the reasons for the high mortality rate associated with ovarian cancer is its late diagnosis, which often occurs after the cancer has metastasized throughout the peritoneal cavity. Cancer metastasis is facilitated by the remodeling of the extracellular tumor matrix by a family of proteolytic enzymes known as the matrix metalloproteinases (MMPs). There are 23 members of the MMP family, many of which have been reported to be associated with ovarian cancer. In the current paradigm, ovarian tumor cells and the surrounding stromal cells stimulate the synthesis and/or activation of various MMPs to aid in tumor growth, invasion, and eventual metastasis. The present review sheds light on the different MMPs in the various types of ovarian cancer and on their impact on the progression of this gynecologic malignancy.

Diagnostic value of osteopontin in ovarian cancer: a meta-analysis and systematic review

AIMS

Osteopontin (OPN) plays an important role in many physiological and pathological processes (wound healing, inflammation, immune response, and tumorigenesis). This meta-analysis assessed the diagnostic value of osteopontin in ovarian cancer.

METHODS AND RESULTS

Searches in Embase and PubMed were conducted, in order to identify eligible studies on osteopontin expression and its diagnostic value in ovarian cancer. The revised Quality Assessment for Studies of Diagnostic Accuracy (QUADAS-2) tool was applied to examine the quality of these studies and the overall osteopontin diagnostic accuracy in ovarian cancer was pooled using the bivariate model. The publication bias was assessed using funnel plots and Deek's test. This search methodology resulted in 13 studies with a total of 839 ovarian cancer patients and 1439 controls in this meta-analysis. The overall osteopontin diagnostic sensitivity and specificity of ovarian cancer were 0.66 (95% CI, 0.51-0.78) and 0.88 (95% CI, 0.78-0.93), respectively. The area under summary receiver operating characteristic (sROC) curves (AUC) was 0.85 (95%CI, 0.81-0.88). There was no significant publication bias observed across the eligible studies. However, a major design deficiency of the eligible studies is the issue of subject selection bias.

CONCLUSIONS

Osteopontin could be a useful biomarker in diagnosis of ovarian cancer. Due to the design deficits of the eligible studies, a future study with a larger sample size and better design is needed to rigorously confirm the diagnostic potential of osteopontin in ovarian cancer.

Ovarian cancer biomarker discovery based on genomic approaches

Ovarian cancer presents at an advanced stage in more than 75% of patients. Early detection has great promise to improve clinical outcomes. Although the advancing proteomic technologies led to the discovery of numerous ovarian cancer biomarkers, no screening method has been recommended for early detection of ovarian cancer. Complexity and heterogeneity of ovarian carcinogenesis is a major obstacle to discover biomarkers. As cancer arises due to accumulation of genetic change, understanding the close connection between genetic changes and ovarian carcinogenesis would provide the opportunity to find novel gene-level ovarian cancer biomarkers. In this review, we summarize the various gene-based biomarkers by genomic technologies, including inherited gene mutations, epigenetic changes, and differential gene expression. In addition, we suggest the strategy to discover novel gene-based biomarkers with recently introduced next generation sequencing.

Systematic analysis and validation of differential gene expression in ovarian serous adenocarcinomas and normal ovary

PURPOSE

Cancer of the ovary confers the worst prognosis among women with gynecological malignancies, primarily because most ovarian cancers are diagnosed at late stage. Hence, there is a substantial need to develop new diagnostic biomarkers to enable detection of ovarian cancer at earlier stages, which would confer better prognosis. In addition, the identification of druggable targets is of substantial interest to find new therapeutic strategies for ovarian cancer.

METHODS

The expression of 22,500 genes in a series of 67 serous papillary carcinomas was compared with 9 crudely enriched normal ovarian tissue samples by RNA hybridization on oligonucleotide microarrays. Multiple genes with near-uniformly expression were elevated in carcinomas of varying grade and malignant potential, including several previously described genes (e.g., MUC-1, CD9, CD24, claudin 3, and mesothelin). We performed immunohistochemical staining with antibodies against several of the proteins encoded by differentially expressed genes in an independent cohort of 71 cases of paraffin-embedded ovarian cancer samples.

RESULTS

We found striking differences in EpCAM (p < 0.005), CD9 (p < 0.001), MUC-1 (p < 0.001), and claudin 3 proteins (p < 0.001) but not for mesothelin (p > 0.05) using the Mann-Whitney U test.

CONCLUSIONS

Protein expression of a majority of the differentially expressed genes tested was found to be elevated in ovarian carcinomas and, as such, define potential new biomarkers or targets.

From bench to bedside: discovery of ovarian cancer biomarkers using high-throughput technologies in the past decade

Ovarian cancer is the most lethal gynecological malignancy and survival of this disease has remained relatively unchanged over the past 30 years. A contributing factor to this has been the lack of reliable biomarkers for the clinical management of ovarian cancer. Rapid advances in high-throughput technologies over the past decade has allowed for new and exciting opportunities for biomarker discovery in the field of ovarian cancer, especially with respect to serum biomarkers that can be used for various clinical applications. This review highlights the major genomic and proteomic studies dedicated to ovarian cancer biomarker discovery over the past decade. An emphasis will be placed on the HE4, Risk of Malignancy Algorithm (ROMA) and OVA1™ serum-based tests/algorithms that have recently been approved by the US FDA as ovarian cancer biomarkers.

Copy number variation analysis of matched ovarian primary tumors and peritoneal metastasis

Ovarian cancer is the most deadly gynecological cancer. The high rate of mortality is due to the large tumor burden with extensive metastatic lesion of the abdominal cavity. Despite initial chemosensitivity and improved surgical procedures, abdominal recurrence remains an issue and results in patients' poor prognosis. Transcriptomic and genetic studies have revealed significant genome pathologies in the primary tumors and yielded important information regarding carcinogenesis. There are, however, few studies on genetic alterations and their consequences in peritoneal metastatic tumors when compared to their matched ovarian primary tumors. We used high-density SNP arrays to investigate copy number variations in matched primary and metastatic ovarian cancer from 9 patients. Here we show that copy number variations acquired by ovarian tumors are significantly different between matched primary and metastatic tumors and these are likely due to different functional requirements. We show that these copy number variations clearly differentially affect specific pathways including the JAK/STAT and cytokine signaling pathways. While many have shown complex involvement of cytokines in the ovarian cancer environment we provide evidence that ovarian tumors have specific copy number variation differences in many of these genes.

Cancer testis antigens: novel biomarkers and targetable proteins for ovarian cancer

Ovarian cancer is the fifth leading cause of cancer death in women and the leading cause from gynecological malignancies. Despite the recently improved outcomes of new chemotherapeutical agents in the therapy of ovarian cancer and the increased 5-year survival rate, the mortality of this malignancy disease remains unchanged. Ovarian cancer therapy is often correlated to the stage of the tumor, but the first step is usually surgical treatment. Afterward, various courses of chemotherapy and radiation are suggested. Obviously, the higher the developmental stage of the tumor, the less the probability is in eradicating it surgically, especially in relation to metastasis. It is clear that an early diagnosis of ovarian cancer is important for the survival of these patients. In order to identify ovarian cancer patients in the early stages, a number of studies are focusing on a particular class of antigens called cancer testis antigens. These antigens display high expression in tumors of different histology, but are normally restricted to the testis and have low or no expression in normal tissues. The testes are an immunologically-privileged site due to the presence of tight junctions between adjacent Sertoli cells that constitute the blood-testis barrier, which prevents auto-immune reactions. In the past few years, some of these antigens were demonstrated to be very promising for the early diagnosis and development of vaccines for ovarian cancer. This review aims to underline the most reliable cancer testis antigens under investigation at this moment.

State of the art in tumor antigen and biomarker discovery

Our knowledge of tumor immunology has resulted in multiple approaches for the treatment of cancer. However, a gap between research of new tumors markers and development of immunotherapy has been established and very few markers exist that can be used for treatment. The challenge is now to discover new targets for active and passive immunotherapy. This review aims at describing recent advances in biomarkers and tumor antigen discovery in terms of antigen nature and localization, and is highlighting the most recent approaches used for their discovery including “omics” technology.

The role of p53 as a surrogate marker for chemotherapeutical responsiveness in ovarian cancer

INTRODUCTION

In advanced ovarian cancers (OCs), p53 mutations are frequently observed. The objective of this study was to explore the value of the p53 mutational status, using four different techniques, in advanced OC patients as a predictive marker for responsiveness to platinum-based chemotherapy.

METHODS

One hundred and four, mostly serous papillary OC specimens were analyzed, of which all received a platinum containing chemotherapy after optimal cyto-reductive surgery. To verify the p53 mutational status, immunohistochemical staining with monoclonal antibodies, functional yeast assay (FASAY), single-strand conformation polymorphism analysis (SSCP) and genomic sequencing was performed in parallel.

RESULTS

Out of ten OC patients [2 low malignant potential (LMP)/8 G1] only two had a mutant p53, whereas eight showed a wild-type p53. 40 out of 63 (G2/3) patients with G2/3 OC showed mutant p53 and 23 patients showed a wild-type pattern. p53 status was significantly different between these two groups (LMP/G1 vs. G2/3) (P = 0.015). A progressive disease after chemotherapy completion was noted in 35.6% of the patients (26 out of 73); in 69.2%, a mutated p53 and in 30.8%, a wild-type p53 was found. Nine (12.3%) patients showed a complete response at the end of the first-line chemotherapy. Out of these nine patients five had a mutated and four a wild-type p53. A partial response was observed in nine (12.3%) patients of whom four had a mutated p53. With respect to response to first-line chemotherapy (six cycles of platinum containing regimen), the p53 status was not predictive; no statistical significance regarding the p53 mutational status was observed when the two extreme groups PD versus PR/CR were compared (P > 0.05).

CONCLUSION

In this study, the p53 mutational status was not predictive for responsiveness to platinum-based chemotherapy; but p53 was significantly more frequently mutated in poorly differentiated OCs.

Microarray-Based Gene Expression Studies in Ovarian Cancer

Background

DNA microarray technology is a powerful genomic tool that has the potential to elucidate the relationship between clinical features of cancers and their underlying biological alterations.

Methods

We performed a systemic search in PubMed and Medline databases for recently published articles. The search terms used included “genome-wide,” “microarrays,” “ovarian cancer,” “prognosis” “gene expression profiling,” “molecular marker,” and “molecular biomarker.”

Results

Genome-wide expression profiling using DNA microarray technology has enhanced our understanding of the genes that influence ovarian cancer development, histopathologic subtype, progression, response to therapy, and overall survival.

Conclusions

Gene expression profiling has demonstrated its utility in ovarian cancer research. It is hoped that with technologic, statistical, and bioinformatic advances, the reliability and reproducibility of this technique will increase, spawning clinical applications that may enhance our understanding of the disease and our ability to care for patients in the future.

Confounding effects of hormone replacement therapy in protein biomarker studies

BACKGROUND

We have recently investigated effects of hormone replacement therapy (HRT) on the serum proteome, and found a high proportion of proteins with altered levels associated with oral estrogen and/or estrogen plus progesterone treatment. Given this finding, we have investigated the extent to which exposure to HRT may have a confounding effect in the assessment of circulating proteins as cancer biomarkers.

METHODS

We utilize mass spectrometry data collected from the HRT serum proteome studies to estimate the overall effect of postmenopausal hormone therapy on candidate ovarian cancer biomarkers that have been previously reported.

RESULTS

Levels of approximately half of the proteins reported as potential ovarian cancer biomarkers were found to be affected by HRT. The impact of HRT on levels of insulin-like growth factor and inhibin protein families was found to be substantial.

CONCLUSIONS

We conclude that the potential confounding effect of HRT and other types of exposures should be taken into consideration in cancer biomarker study design.

IMPACT

HRT significantly affects the serum proteome and should be taken into account as part of biomarker study design and data analysis.

Integrating high-throughput technologies in the quest for effective biomarkers for ovarian cancer

Despite widespread interest, few serum biomarkers have been introduced to the clinic over the past 20 years. Each approach to ovarian cancer biomarker discovery has its own advantages and disadvantages and it seems likely that a global biomarker discovery platform that mines all possible sources for biomarkers might be more useful. Such data could be combined with information from relevant microarray data, bioinformatic analyses and literature searches. This proposed integrated systems biology approach has the potential to yield promising ovarian cancer markers for diagnosis, prognosis and monitoring of patients during therapy.

Clinical application of proteomics in ovarian cancer prevention and treatment

As recent scientific findings using whole-genome mutational scanning technologies have concluded, cancer is a protein pathway disease, which is often diagnosed too late, when the success of therapeutic modalities is very limited. Proteomics has been proposed as the field that can help overcome this limitation and usher in a new era of molecular investigation for early diagnosis and classification of tumors. Proteomics applications in cancer research encompass two general aspects: (i) the study and characterization of protein production; and (ii) the definition of protein function. The first aims to identify qualitative or quantitative differences in the proteome that can help differentiate between healthy and diseased states or achieve a better clinical classification of diseases. The second studies the complexity of protein interactions and their activation states, mapping the network of signaling pathways within and outside the cells. The challenges in translating the findings of proteomics research into clinical practice are numerous. Lack of reproducibility, variable availability of samples and the bias associated with their selection and handling, the need for large, prospective validation trials, and finally the strict requirement for a very high level of clinical sensitivity and specificity are some of the hurdles that need to be overcome to achieve early detection and treatment. Nevertheless, proteomics is a field in rapid progression that has already developed beyond initial criticism and is making its way toward important applications and discoveries. Specifically, there has been an increasing number of reports on the potential clinical application of proteomics for early detection as well as risk assessment and management of ovarian cancer. This disease is the leading cause of death from gynecologic malignancies in the US, with poor prognosis resulting from the lack of reliable, sensitive screening tests and the limited understanding of the mechanisms of chemoresistance and relapse. In the future, serum proteomics applications in the gynecologic oncology field could identify blood-based biomarkers that are predictors of disease presence or progression, and tissue proteomics could help define the optimal targeted agent and effective dose for each patient's disease. These advances will allow improved monitoring of therapy response and disease relapse, and aid in the engineering of new drugs and strategies to circumvent resistance mechanisms while avoiding the adverse effects of traditional chemotherapy.

Clinical utility of circulating matrix metalloproteinase-7 (MMP-7), CC chemokine ligand 18 (CCL18) and CC chemokine ligand 11 (CCL11) as markers for diagnosis of epithelial ovarian cancer

Ovarian cancer remains a highly lethal disease. The aim of the present study was to evaluate the usefulness of measuring serum matrix metalloproteinase-7 (MMP-7), CC chemokine ligand 18 (CCL18) and CC chemokine ligand 11 (CCL11) in comparison with serum cancer antigen 125 (CA 125) for diagnosis of epithelial ovarian cancer (EOC). This study included 51 patients with EOC, 27 patients with benign ovarian lesions and 29 healthy volunteers. Serum CA 125 was determined by microparticle enzyme immunoassay, while serum MMP-7, CCL18 and CCL11 were measured using enzyme-linked immunosorbent assay. The sensitivity and specificity were 86.3% and 92.9% for CA 125, 80.4% and 87.5% for MMP-7, 84.3% and 91.1% for CCL18 and, 68.6% and 62.5% for CCL11. Combination of CA 125, MMP-7, CCL18 and CCL11 gave a promising sensitivity of 100%, but specificity was decreased to 60.7%. The combined use of serum CA 125, MMP-7, CCL18 and CCL11 effectively detected early stages EOC with high sensitivity of 94.4%. Our data indicate that serum MMP-7, CCL18 and CCL11, in combination with CA 125 could be useful in diagnosis of EOC.

Biomarker discovery in ovarian cancer

Ovarian cancer is a leading cause of gynecologic cancer death among women. Tumors diagnosed early (in stage I) have a cure rate approaching 90%. However, because specific symptoms and screening tools are lacking, most ovarian cancers are very advanced when finally diagnosed. CA125 expression and pelvic ultrasonography are of limited efficacy in screening, and the search for new, complementary ovarian cancer biomarkers continues. New technology and research techniques have allowed the identification of over 100 possible tumor markers, many of which are still being evaluated for clinical relevance and several of which have entered clinical trials. Here, we review the methods of biomarker discovery, address the significance and functions of newly identified ovarian cancer tumor markers, and provide further insight into the future of ovarian cancer biomarkers.

Anti-idiotypic antibody abagovomab in advanced ovarian cancer

Ovarian cancer is the fifth most common malignancy with approximately 22,000 newly diagnosed cases each year in the USA. Standard of care after cytoreductive surgery is the application of carboplatin and paclitaxel. The newly developed anti-idiotypic monoclonal antibody abagovomab demonstrated promising results in Phase I/II trials. This new type of drug is currently being tested in a Phase II/III trial in ovarian cancer patients with a complete response after standard first-line chemotherapy. Activating the cancer hosts immune system is a new strategy that is worth being pursued in the fight against ovarian cancer.

Mining the ovarian cancer ascites proteome for potential ovarian cancer biomarkers

Current ovarian cancer biomarkers are inadequate because of their relatively low diagnostic sensitivity and specificity. There is a need to discover and validate novel ovarian cancer biomarkers that are suitable for early diagnosis, monitoring, and prediction of therapeutic response. We performed an in-depth proteomics analysis of ovarian cancer ascites fluid. Size exclusion chromatography and ultrafiltration were used to remove high abundance proteins with molecular mass >/=30 kDa. After trypsin digestion, the subproteome (</=30 kDa) of ascites fluid was determined by two-dimensional liquid chromatography-tandem mass spectrometry. Filtering criteria were used to select potential ovarian cancer biomarker candidates. By combining data from different size exclusion and ultrafiltration fractionation protocols, we identified 445 proteins from the soluble ascites fraction using a two-dimensional linear ion trap mass spectrometer. Among these were 25 proteins previously identified as ovarian cancer biomarkers. After applying a set of filtering criteria to reduce the number of potential biomarker candidates, we identified 52 proteins for which further clinical validation is warranted. Our proteomics approach for discovering novel ovarian cancer biomarkers appears to be highly efficient because it was able to identify 25 known biomarkers and 52 new candidate biomarkers that warrant further validation.

Gene-expression profiling in epithelial ovarian cancer

DNA-microarray technology has made it possible to simultaneously analyze the expression of thousands of genes in a small sample of tumor tissue. In epithelial ovarian cancer, gene-expression profiling has been used to provide prognostic information, to predict response to first-line platinum-based chemotherapy, and to discriminate between different histologic subtypes. Furthermore, DNA-microarray technology might permit identification of novel markers for early detection of disease and provide insights into the mechanisms of cancer growth and chemotherapy resistance. In this Review, we summarize the contributions of gene-expression profiling to the diagnosis and management of epithelial ovarian cancer and discuss ways in which this technique could become a useful tool in clinical management.

Evaluation of biomarker panels for early stage ovarian cancer detection and monitoring for disease recurrence

OBJECTIVE

To determine the utility of novel combinations of biomarkers, using both a one-step and two-step assay format, to distinguish serum of early ovarian cancer patients from that of healthy controls and to discern the utility of these biomarkers in a monitoring capacity.

METHODS

For ovarian cancer detection, HE4, Glycodelin, MMP7, SLPI, Plau-R, MUC1, Inhibin A, PAI-1, and CA125 were evaluated in a cohort of 200 women with ovarian cancer and 396 healthy age-matched controls. Each biomarker was assessed by serum-based immunoassays utilizing novel monoclonal antibody pairs or commercial kits. For detection of disease recurrence, HE4, Glycodelin, MMP7 and CA125 were evaluated in 260 samples from 30 patients with OC monitored longitudinally after diagnosis.

RESULTS

Based upon ROC curve analysis, the sensitivity/specificity of specific biomarker combination algorithms ranged from 59.0%/99.7% to 80.5%/96.5% for detection of early stage ovarian cancer and 76.9%/99.7% to 89.2%/97.2% for detection of late stage cancer. In monitoring evaluation of 27 patients who experienced recurrence of OC, sensitivity for predicting recurrence was 100% for the biomarker panel and 96% for CA125. At least one of the panel biomarkers was elevated earlier (range 6-69 weeks) than CA125 and prior to clinical evidence of recurrence in 14/27 (52%) patients.

CONCLUSIONS

We have developed and demonstrated the utility of several one- and two-step multi-marker combinations with acceptable test characteristics for possible use in an ovarian cancer screening population. A subset of this panel may also provide adjunctive information to rising CA125 levels in disease monitoring.