Aberrant glycosylation of α3 integrins as diagnostic markers in epithelial ovarian cancer

BACKGROUND

Glycans are strongly involved in stability and function of integrins (ITG) and tetraspanin protein CD63 and their respective interaction partners as they are dysregulated in the tumorigenic processes. Glycosylation changes is a universal phenomenon of cancer cells. In this study, glycosylation changes in epithelial ovarian cancer (EOC) are explored using tetraspanin and integrin molecules.

METHODS

ITG and CD63 were immobilized from 10 EOC and 5 benign ovarian cyst fluid on microtiter wells and traced with 3 glycan binding proteins (STn, WGA, UEA) conjugated on europium nanoparticles. Total protein measurements (ITG & CD63 immunoassays) were also performed. The most promising glycovariant candidates identified were then clinically evaluated on the whole cohort of 77 ovarian cyst fluids. Additional testing was performed in ascites fluid samples of liver cirrhosis (n=2) and EOC (n=4).

RESULTS

Sialylated Tn antibody based glycovariants of ITGα3 (ITGα3^STn) and CD63 (CD63^STn) performed better than corresponding protein epitope-based immunoassays, ITGα3^IA and CD63^IA respectively. Combined ITGα3 based assays (ITGα3^IA + ITGα3^STn) detected 49 out of 55 malignant & borderline cases without detecting any of the 22 benign and healthy cysts.

CONCLUSION

Our findings indicate the potential diagnostic application of ITGα3^STn along with total ITGα3^IA, which could help reduce the unnecessary surgeries. The results encourage studying further the potential use of these novel assays to detect EOC at earlier clinical stages.

OUP accepted manuscript

Currently, the only definitive method for diagnosing ovarian cancer involves histological examination of tissue obtained at time of surgery or by invasive biopsy. Blood has traditionally been the biofluid of choice in ovarian cancer biomarker discovery; however, there has been a growing interest in exploring urinary biomarkers, particularly as it is non-invasive. In this systematic review, we present the diagnostic accuracy of urinary biomarker candidates for the detection of ovarian cancer. A comprehensive literature search was performed using the MEDLINE/PubMed and EMBASE, up to 1 April 2021. All included studies reported the diagnostic accuracy using sensitivity and/or specificity and/or receiver operating characteristics (ROC) curve. Risk of bias and applicability of included studies were assessed using the QUADAS-2 tool. Twenty-seven studies were included in the narrative synthesis. Protein/peptide biomarkers were most commonly described (n = 18), with seven studies reporting composite scores of multiple protein-based targets. The most frequently described urinary protein biomarker was HE4 (n = 5), with three studies reporting a sensitivity and specificity > 80%. Epigenetic (n = 1) and metabolomic/organic compound biomarkers (n = 8) were less commonly described. Overall, six studies achieved a sensitivity and specificity of >90% and/or an AUC > 0.9. Evaluation of urinary biomarkers for the detection of ovarian cancer is a dynamic and growing field. Currently, the most promising biomarkers are those that interrogate metabolomic pathways and organic compounds, or quantify multiple proteins. Such biomarkers require external validation in large, prospective observational studies before they can be implemented into clinical practice.

Danish Gynecological Cancer Database

Aim of database

The Danish Gynecological Cancer Database (DGCD) is a nationwide clinical cancer database and its aim is to monitor the treatment quality of Danish gynecological cancer patients, and to generate data for scientific purposes. DGCD also records detailed data on the diagnostic measures for gynecological cancer.

Study population

DGCD was initiated January 1, 2005, and includes all patients treated at Danish hospitals for cancer of the ovaries, peritoneum, fallopian tubes, cervix, vulva, vagina, and uterus, including rare histological types.

Main variables

DGCD data are organized within separate data forms as follows: clinical data, surgery, pathology, pre- and postoperative care, complications, follow-up visits, and final quality check. DGCD is linked with additional data from the Danish “Pathology Registry”, the “National Patient Registry”, and the “Cause of Death Registry” using the unique Danish personal identification number (CPR number).

Descriptive data

Data from DGCD and registers are available online in the Statistical Analysis Software portal. The DGCD forms cover almost all possible clinical variables used to describe gynecological cancer courses. The only limitation is the registration of oncological treatment data, which is incomplete for a large number of patients.

Conclusion

The very complete collection of available data from more registries form one of the unique strengths of DGCD compared to many other clinical databases, and provides unique possibilities for validation and completeness of data. The success of the DGCD is illustrated through annual reports, high coverage, and several peer-reviewed DGCD-based publications.

Semiparametric transformation models for multiple continuous biomarkers in ROC analysis

Recent technological advances continue to provide noninvasive and more accurate biomarkers for evaluating disease status. One standard tool for assessing the accuracy of diagnostic tests is the receiver operating characteristic (ROC) curve. Few statistical methods exist to accommodate multiple continuous-scale biomarkers in the framework of ROC analysis. In this paper, we propose a method to integrate continuous-scale biomarkers to optimize classification accuracy. Specifically, we develop semiparametric transformation models for multiple biomarkers. We assume that unknown and marker-specific transformations of biomarkers follow a multivariate normal distribution. Our models accommodate biomarkers subject to limits of detection and account for the dependence among biomarkers by including a subject-specific random effect. We also propose a diagnostic measure using an optimal linear combination of the transformed biomarkers. Our diagnostic rule does not depend on any monotone transformation of biomarkers and is not sensitive to extreme biomarker values. Nonparametric maximum likelihood estimation (NPMLE) is used for inference. We show that the parameter estimators are asymptotically normal and efficient. We illustrate our semiparametric approach using data from the Endometriosis, Natural History, Diagnosis, and Outcomes (ENDO) study.

Discovery of serum biomarkers of ovarian cancer using complementary proteomic profiling strategies

Purpose

Ovarian cancer is a devastating disease and biomarkers for its early diagnosis are urgently required. Serum may be a valuable source of biomarkers that may be revealed by proteomic profiling. Herein, complementary serum protein profiling strategies were employed for discovery of biomarkers that could discriminate cases of malignant and benign ovarian cancer.

Experimental design

Identically collected and processed serum samples from 22 cases of invasive epithelial ovarian cancer, 45 benign ovarian neoplasms, and 64 healthy volunteers were subjected to immunodepletion and protein equalization coupled to 2D‐DIGE/MS and multidimensional fractionation coupled to SELDI‐TOF profiling with MS/MS for protein identification. Selected candidates were verified by ELISA in samples from malignant (n = 70) and benign (n = 89) cases and combined marker panels tested against serum CA125.

Results

Both profiling platforms were complementary in identifying biomarker candidates, four of which (A1AT, SLPI, APOA4, VDBP) significantly discriminated malignant from benign cases. However, no combination of markers was as good as CA125 for diagnostic accuracy. SLPI was further tested as an early marker using prediagnosis serum samples. While it rose in cases toward diagnosis, it did not discriminate prediagnosis cases from controls.

Conclusions and clinical relevance

The candidate biomarkers warrant further validation in independent sample sets.

Tumor suppressive effects of bromodomain-containing protein 7 (BRD7) in epithelial ovarian carcinoma

PURPOSE

Bromodomain-containing protein 7 (BRD7), which is a subunit of SWI/SNF complex, has been recently suggested as a novel tumor suppressor in several cancers. In this study, we investigated the tumor suppressive effect of BRD7 in epithelial ovarian cancer.

EXPERIMENTAL DESIGN

We analyzed the expression of BRD7 in human ovarian tissues with real-time PCR. To investigate the functional role of BRD7, we transfected ovarian cancer cells (A2780 and SKOV3) with BRD7 plasmid and checked the cell viability, apoptosis, and invasion. The activities of BRD7 in the signaling pathways associated with carcinogenesis were also tested. In addition, we used the orthotopic mouse model for ovarian cancer to evaluate tumor growth-inhibiting effect by administration of BRD7 plasmid.

RESULTS

The BRD7 expression was downregulated in the ovarian cancer tissues compared with normal (P < 0.05), high-grade serous cancer exhibited significantly decreased expression of BRD7 compared with low-grade (P < 0.01) serous cancer. Transfection of BRD7 plasmid to A2780 (p53-wild) or SKOV3 (p53-null) ovarian cancer cells showed the tumor suppressive effects assessed by cell viability, apoptosis, and invasion assay and especially significantly decreased tumor weight in orthotopic mouse model (A2780). Moreover, we found that tumor suppressive effects of BRD7 are independent to the presence of p53 activity in ovarian cancer cells. BRD7 negatively regulated β-catenin pathway, resulting in decreased its accumulation in the nucleus.

CONCLUSIONS

These results suggested that BRD7 acts as a tumor suppressor in epithelial ovarian cancers independently of p53 activity, via negative regulation of β-catenin pathway.

Ovarian cyst fluid is a rich proteome resource for detection of new tumor biomarkers

Background

We aimed to investigate the use of ovarian cyst fluid as a source for biomarker discovery and to find novel biomarkers for use in the diagnosis of epithelial ovarian tumors.

Results

Ovarian cyst fluids from 218 women were collected and 192 (benign n = 129, malignant n = 63) were analyzed using mass spectrometry. 1180 peaks were detected, 221 of which were differently expressed between benign and malignant ovarian tumors. Seventeen peaks had receiver operating curve and area under the curve values >0.70; the majority of these represented peaks for apolipoproteins C-III and C-I (ApoC-I), transthyretin (TTR), serum amyloid A4 (SAA4), and protein C inhibitor (PCI). ApoC-III, PCI, and serum CA125, with an ROC AUC 0.94 was the best combination for diagnosing epithelial ovarian cancer. ApoC-III and PCI was analyzed with ELISA in the original cohort (n = 40) and in 40 new cyst fluid samples for confirmation with an independent method and validation. Results from MS and ELISA for ApoC-III correlated well (p = 0.04). In the validation set, ApoC-III was significantly (p = 0.001) increased in the malignant epithelial ovarian cancers.

Conclusions

Fluid from ovarian cysts connected directly to the primary tumor harbor many possible new tumor-specific biomarkers. Biomarkers found in ovarian cyst fluid may be used as molecular imaging targets for early diagnostics and prediction of therapy. Plasma abundant proteins are also influencing the cystic fluid proteome. Methods for isolating less frequent cyst fluid proteins are needed.

Reproducibility of mass spectrometry based protein profiles for diagnosis of ovarian cancer across clinical studies: A systematic review

The focus of this systematic review is to give an overview of the current status of clinical protein profiling studies using MALDI and SELDI MS platforms in the search for ovarian cancer biomarkers. A total of 34 profiling studies were qualified for inclusion in the review. Comparative analysis of published discriminatory peaks to peaks found in an original MALDI MS protein profiling study was made to address the key question of reproducibility across studies. An overlap was found despite substantial heterogeneity between studies relating to study design, biological material, pre-analytical treatment, and data analysis. About 47% of the peaks reported to be associated to ovarian cancer were also represented in our experimental study, and 34% of these redetected peaks also showed a significant difference between cases and controls in our study. Thus, despite known problems related to reproducibility an overlap in peaks between clinical studies was demonstrated, which indicate convergence toward a set of common discriminating, reproducible peaks for ovarian cancer. The potential of the discriminating protein peaks for clinical use as ovarian cancer biomarkers will be discussed and evaluated. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Multianalyte assay systems in the differential diagnosis of ovarian cancer

INTRODUCTION

The efficient triage of women diagnosed with a pelvic mass presents a current area of unmet need. Unnecessary surgical intervention performed on patients at a decreased risk of malignancy represents a significant source of preventable morbidity, anxiety and cost. Likewise, delayed or overlooked referral of patients harboring malignant tumors is strongly associated with diminished outcomes. Current tools including imaging modalities and the CA 125 blood test are of insufficient accuracy to overcome these challenges. The use of multianalyte assays systems which include additional biomarkers capable of complementing the performance of CA 125 may offer the best hope of improvement.

AREAS COVERED

Recent findings regarding the use of multianalyte biomarker panels for the differential diagnosis of a pelvic mass are reviewed and discussed. Particular attention is paid to to the FDA approved ROMA and OVA1 tests. The development, validation, recent evaluation and comparative performances of these two tests are reviewed in detail.

EXPERT OPINION

The performances achieved by the ROMA and OVA1 diagnostic tests represent significant milestones in the application of multianalyte assay systems into standard clinical practice. The overall impact and cost-effectiveness of widespread clinical use of these tools remains to be evaluated.

"The quest for biomarkers": are we on the right technical track?

The discovery phase of biomarkers of diagnostic or therapeutic interest started a decade ago with the very rapid development of proteomic investigations. In spite of the development of innovative technologies and multiple approaches, the "harvest" is still modest. Various reasons justified the encountered difficulties and most of them have been circumvented by specific sample treatments or dedicated analytical approaches. Nevertheless, the situation of very modest biomarker discovery level did not change much. This review intends to specifically analyze the main approaches used for biomarker discovery phase and evaluate related advantages and disadvantages. Thus, preliminary sample treatments such as fractionation, depletion and reduction of dynamic concentration range will critically be discussed and then the main differential expression investigation methods analyzed. Combinations of technologies are also discussed along with possible proposals to federate associations of complementary technologies for better chances of success.

-The advancement of biomarker-based diagnostic tools for ovarian, breast, and pancreatic cancer through the use of urine as an analytical biofluid

Despite considerable advancements, the development of effective cancer screening tools based on serum biomarker measurements has thus far failed to achieve a meaningful clinical impact. The incremental progress observed over the course of serum biomarker development suggests that further refinements based on novel approaches may yet result in a breakthrough. The use of urine as an analytical biofluid for biomarker development may represent such an approach. The unique characteristics of urine including a high level of stability, ease of sampling, and an inactive and low-complexity testing matrix offer several potential advantages over the use of serum. A number of recent reports have demonstrated the utility of urine in the identification of novel cancer biomarkers and also the improved performance of biomarkers previously evaluated in serum. In this review, advancements related to the use of urine biomarkers within the settings of ovarian, breast, and pancreatic cancer are presented and discussed. Findings regarding the identification of specific urine biomarkers for each disease are highlighted along with comparative analyses of urine and serum biomarkers as diagnostic tools.

Proteomic biomarkers for overall and progression-free survival in ovarian cancer patients

PURPOSE

To determine if the level of apolipoprotein A1, hepcidin, transferrin, inter-α trypsin IV internal fragment, transthyretin (TT), connective-tissue activating protein 3 (CTAP3), serum amyloid A1, β-2 microglobulin (B2M) might have impact on overall and progression-free survival for ovarian cancer (OC) patients.

EXPERIMENTAL DESIGN

Serum from 150 OC patients was tested using SELDI-TOF-MS.

RESULTS

A proteomic prognostic index (xb-pro) was constructed using the regression coefficients based on inter-α trypsin IV internal fragment, B2M and TT. A multivariable Cox survival analysis including the xb-pro index showed that xb-pro (p<0.0001, HR=2.50, 95% CI: 1.65-3.79), residual tumor after primary surgery (p=0.0005), age (p=0.01) and chemotherapy (p=0.0002) are of independent prognostic value for overall survival. International Federation of Gynecology and Obstetrics stage, performance status, histological type of tumor and serum CA125 were found of no independent value. A proteomic index (xb-pfs) based on B2M and CTAP3 was found to predict progression-free survival (xb-pfs: p=0.008, HR=1.77, 95% CI: 1.17-2.70 together with type of surgery, age and chemotherapy.

CONCLUSIONS AND CLINICAL RELEVANCE

We found an index with three proteomic biomarkers (xb-pro) to be of independent prognostic value for overall survival and an index with two proteomic biomarkers (xb-pfs) with evidence of independent prognostic value for progression-free survival.

Proteomics and biomarkers for ovarian cancer diagnosis

Ovarian cancer remains a leading cause of death from gynecological malignancy. Early diagnosis is the most important determinant of survival. Current diagnostic tools have had very limited success in early detection. In recent years, the advancing techniques for proteomics have accelerated the discovery of ovarian cancer biomarkers. Numerous proteomics-based molecular biomarkers/panels have been identified and hold great potential for diagnostic applications, but they need further development and validation. This article reviews recently published data on the diagnosis of ovarian cancer with proteomics, including the major proteomics technologies and promising strategies for biomarker discovery and development.