The utility of proteomics in gynecologic cancers

Identification of Potentially Novel Molecular Targets of Endometrial Cancer Using a Non-Biased Proteomic Approach

The present study was aimed at identifying novel proteins in endometrial cancer (EC), employing proteomic analysis of tissues obtained after surgery. A differential MS-based proteomic analysis was conducted from whole tissues dissected from biopsies from post-menopausal women, histologically confirmed as endometrial cancer (two endometrioid and two serous; n = 4) or normal atrophic endometrium (n = 4), providing 888 differentially expressed proteins with 246 of these previously documented elsewhere as expressed in EC and 372 proteins not previously demonstrated to be expressed in EC but associated with other types of cancer. Additionally, 33 proteins not recorded previously in PubMed as being expressed in any forms of cancer were also identified, with only 26 of these proteins having a publication associated with their expression patterns or putative functions. The putative functions of the 26 proteins (GRN, APP, HEXA, CST3, CAD, QARS, SIAE, WARS, MYH8, CLTB, GOLIM4, SCARB2, BOD1L1, C14orf142, C9orf142, CCDC13, CNPY4, FAM169A, HN1L, PIGT, PLCL1, PMFBP1, SARS2, SCPEP1, SLC25A24 and ZC3H4) in other tissues point towards and provide a basis for further investigation of these previously unrecognised novel EC proteins. The developmental biology, disease, extracellular matrix, homeostatic, immune, metabolic (both RNA and protein), programmed cell death, signal transduction, molecular transport, transcriptional networks and as yet uncharacterised pathways indicate that these proteins are potentially involved in endometrial carcinogenesis and thus may be important in EC diagnosis, prognostication and treatment and thus are worthy of further investigation.

Proteomic Screening for Serum Biomarkers for Cervical Cancer and Their Clinical Significance

Background

The present study aimed to determine serum markers for cervical cancer (CC) and to provide valuable references for clinical diagnosis and treatment.

Material/Methods

Serum samples were collected from age-matched healthy control women, and from female CC patients before and after surgery. Serum biomarkers were selected by comparing serum peptides profiles among the 3 groups by magnetic bead-based weak cation – exchange chromatography fractionation combined with matrix-assisted laser desorption/ionization-time of flight mass spectrometry. Probable serum biomarkers for cervical cancer were then further identified by liquid chromatography-electrospray ionization-tandem mass spectrometry system and the identified proteins were verified by enzyme-linked immunosorbent assay (ELISA).

Results

Three peptide biomarkers were identified for distinguishing CC patients from normal individuals, and distinguishing preoperative CC patients from postoperative CC patients. Of these 3 identified protein peptide regions, 2 peptide regions – TKT (Peak 2, 2435.63 m/z, 499–524) and FGA (Peak 4, 2761.79 m/z, 603–629) – were identified as upregulated markers, and peptide region of APOA1 (Peak 9, 2575.3 m/z, 245–260) was identified as a downregulated biomarker in preoperative CC patients compared with healthy women.

Conclusions

The present study provides a new method for identifying potential serum biomarkers for CC patients.

HE4 can Help Discriminate Women with Malignant Ovarian Tumors only if CA125 Levels are Elevated

Objective

To evaluate the performance of mesothelin, CA125, HE4 and ROMA index in the detection of malignant ovarian tumors.

Study Design

This is a cross-sectional study including 199 women with adnexal masses (67 with malignant tumors and 132 with benign tumors) and 150 healthy women (controls). We used a recursive multivariate partitioning algorithm to assess the contribution of each of the serum markers and the ROMA index to the discrimination of women with malignant tumors. Also, the areas under the ROC curves (AUC) for each of the serum markers and for the ROMA index were compared using the Venkatraman's projection-permutation test.

Results

In the recursive model, CA125 had the best performance in discriminating women with malignant tumors. In women with normal-range CA125 levels, none of the other markers contributed to the discrimination of women with malignant tumors. In women with elevated CA125 levels, elevated HE4 levels were associated with an increased risk of harboring a malignant tumor. The AUC for mesothelin was smaller than that for all the other curves, and both the ROMA index and CA125 had better AUC than HE4.

Conclusion

In women with normal CA125 levels, neither mesothelin nor HE4 contributed to discriminate women with malignant ovarian tumors; however, for women with elevated CA125 levels HE4 may help discriminating those who have a malignant ovarian tumor.

Proteomics approaches in cervical cancer: focus on the discovery of biomarkers for diagnosis and drug treatment monitoring

INTRODUCTION

The HPV virus accounts for the majority of cervical cancer cases. Although a diagnostic tool (Pap Test) is widely available, cervical cancer incidence still remains high worldwide, and especially in developing countries, attributed to a large extent to suboptimal sensitivities of the Pap test and unavailability of the test in developing countries.

AREAS COVERED

Proteomics approaches have been used in order to understand the HPV virus correlation to cervical cancer pathology, as well as to discover putative biomarkers for early cervical cancer diagnosis and drug mode of action. Expert commentary: The present review summarizes the latest in vitro and in vivo proteomic studies for the discovery of putative cervical cancer biomarkers and the evaluation of available drugs and treatments.

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.

Annexin-A2 as predictor biomarker of recurrent disease in endometrial cancer

Endometrial carcinomas, the most common malignant tumour of the female genital tract, are usually diagnosed at an early stage with uterine-confined disease and an overall favourable prognosis. However, up to 20% of endometrial carcinomas will end up in recurrent disease, associated with a drop in survival and representing the major clinical challenge. Management of this group of risk patients relies on robust biomarkers that may predict which endometrial carcinomas will relapse. For this, we performed a proteomic analysis comparing primary lesions with recurrences and identified ANXA2 as a potential biomarker associated with recurrent disease that we further validated in an independent series of samples by immunohistochemistry. We demonstrated in vitro a role for ANXA2 in the promotion of metastasis rather than interfering with sensitivity to radio/chemotherapy. In addition, ANXA2 silencing resulted in a reduced metastatic pattern in a mice model of endometrial cancer dissemination, with a limited presence of circulating tumor cells. Finally, a retrospective study in a cohort of 93 patients showed that ANXA2 effectively predicted those endometrioid endometrial carcinomas that finally recurred. Importantly, ANXA2 demonstrated a predictive value also among low risk Stage I endometrioid endometrial carcinomas, highlighting the clinical utility of ANXA2 biomarker as predictor of recurrent disease in endometrial cancer. Retrospective and prospective studies are ongoing to validate ANXA2 as a potential tool for optimal stratification of patients susceptible to receive radical surgery and radio/chemotherapy.

Unilocular adnexal cysts with papillary projections but no other solid components: is there a diagnostic method that can classify them reliably as benign or malignant before surgery?

OBJECTIVES

To develop a logistic regression model for discrimination between benign and malignant unilocular solid cysts with papillary projections but no other solid components, and to compare its diagnostic performance with that of subjective evaluation of ultrasound findings (subjective assessment), CA  125 and the risk of malignancy index (RMI).

METHODS

Among the 3511 adnexal masses in the International Ovarian Tumor Analysis (IOTA) database there were 252 (7%) unilocular solid cysts with papillary projections but no other solid components ('unilocular cysts with papillations'). All had been examined with transvaginal ultrasound using the IOTA standardized research protocol. The ultrasound examiner had also classified each mass as certainly or probably benign, unclassifiable, or certainly or probably malignant. A logistic regression model to discriminate between benignity and malignancy was developed for all unilocular cysts with papillations (175 tumors in the training set and 77 in the test set) and for unilocular cysts with papillations for which the ultrasound examiner was not certain about benignity/malignancy (113 tumors in the training set and 53 in the test set). The gold standard was the histological diagnosis of the surgically removed adnexal mass.

RESULTS

A model containing six variables was developed for all unilocular cysts with papillations. The model had an area under the receiver-operating characteristics curve (AUC) on the test set of 0.83 (95% CI, 0.74-0.93). The optimal risk cut-off, as defined on the training set (0.35), resulted in sensitivity 69% (20/29), specificity 79% (38/48), positive likelihood ratio (LR +) 3.31 and negative likelihood ratio (LR-) 0.39 on the test set. The corresponding values for subjective assessment when using the ultrasound examiner's dichotomous classification of the mass as benign or malignant were 97% (28/29), 79% (38/48), 4.63 and 0.04. A model containing four variables was developed for unilocular cysts with papillations for which the ultrasound examiner was not certain about benignity/malignancy. The model had an AUC of 0.74 (95% CI, 0.60-0.88) on the test set. The optimal risk cut-off of the model, as defined on the training set (0.30), resulted in sensitivity 57% (12/21), specificity 78% (25/32), LR + 2.61 and LR- 0.55 on the test set. The corresponding values for subjective assessment were 95% (20/21), 78% (25/32), 4.35 and 0.06. CA  125 and RMI had virtually no diagnostic ability.

CONCLUSIONS

Even though logistic regression models to predict malignancy in unilocular cysts with papillations can be developed, they have at most moderate performance and are not superior to subjective assessment for discrimination between benignity and malignancy.

Plasma concentrations of the vitamin E-binding protein afamin are associated with overall and progression-free survival and platinum sensitivity in serous ovarian cancer--a study by the OVCAD consortium

OBJECTIVE

Comparative proteomics identified the plasma protein afamin as potential biomarker for ovarian cancer (OC). Significantly decreased afamin plasma concentrations in pre-therapeutic OC patients reconstituted to control values after successful tumor surgery. This study evaluates the association of afamin with survival and response to therapy in serous OC patients within the OVCAD consortium project.

METHODS

We measured afamin in 215 pre-therapeutic plasma samples, 246 tumor lysates and 109 plasma samples taken 6months after finishing platinum-based chemotherapy. Differences in afamin plasma concentrations among FIGO stages were tested by Kruskal-Wallis test; association of afamin concentrations with overall and progression-free survival was evaluated using Kaplan-Meier survival plots and multivariate adjusted COX regression analysis.

RESULTS

Pre-therapeutic afamin correlated significantly with FIGO stages (p=0.012) and was lower in the presence of metastases (p=0.013) and poorly differentiated OC in patients responding to therapy (p=0.016). Afamin ≥48.0mg/L was also associated with a lower hazard ratio for recurrent disease as compared to afamin <48.0mg/L (p=0.007). Post-therapeutic afamin ≥48mg/L was positively correlated with overall (p<0.001) and progression-free (p=0.012) survival and was lower in non-responders than in responders (p=0.048). Thus, afamin returned post-therapeutically to values of healthy controls in responders (p<0.001) but not in non-responders (p=0.114). Afamin in tumor lysates was lower in poorly differentiated OC than in G 1+2 tumors (p=0.041). Higher afamin concentrations in tumor lysates were associated with increased overall survival (p=0.003).

CONCLUSION

These data indicate that afamin is associated with therapy response and survival rate in advanced OC patients.

The utility of human epididymal protein 4, cancer antigen 125, and risk for malignancy algorithm in ovarian cancer and endometriosis

BACKGROUND

In women with pelvic mass, cancer antigen 125 (CA125) had not achieved satisfactory sensitivity and specificity in the detection of ovarian cancer, particularly in patients with underlying endometriosis. The aim of this study was to determine the diagnostic potential of human epididymal protein 4 (HE4), the combination of HE4+CA125, and the Risk of Ovarian Malignancy Algorithm (ROMA) for patients with pelvic mass, particularly in differentiating endometriosis from carcinoma.

METHODS

A prospective cross-sectional study was conducted at the Clinic for Gynecology and Obstetrics, Clinical Center of Serbia. Serum samples were obtained preoperatively from 108 women undergoing surgery for pelvic mass; 29 of them had ovarian carcinoma, and 79 had a nonmalignant ovarian disease (39 with benign tumor, 20 with endometriosis, 20 healthy controls). Sera were analyzed for the levels of HE4 and CA125 and were then compared with the final pathologic results. The diagnostic performance of HE4 and CA125 was estimated using receiver operating characteristic curve and area under the receiver operating characteristic curve.

RESULTS

The level of HE4 and CA125 was significantly higher among the patients with malignant tumors, compared with patients with nonmalignant disease. At the predefined specificity of 95%, HE4 and CA125 showed sensitivity of 65.5% and 58.6%, respectively, whereas the combination of HE4+CA125 reached 68.9% at the same specificity. Importantly, the level of HE4 did not differ significantly between the patients with endometriosis and with other nonmalignant diseases (which was not the case with CA125). Risk of Ovarian Malignancy Algorithm classified 96% of benign premenopausal cases as at low risk for ovarian cancer.

CONCLUSIONS

HE4 showed satisfactory capability of distinguishing endometriosis from ovarian cancer, which CA125 lacked. The Risk of Ovarian Malignancy Algorithm score proved to be useful in excluding malignant diagnosis in premenopausal women.

Serum peptidome patterns for early screening of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers in the world. Early diagnosis is critical for guiding the therapeutic management of ESCC. The present study aims to determine serum peptidome patterns for diagnosing ESCC. To identify novel peptidome patterns for diagnosing ESCC, sera from 31 healthy volunteers and 32 ESCC patients were subjected to a comparative proteomic analysis using a ClinProt™ Kit combined with mass spectrometry (MS). This approach enables the determination of peptidome patterns that can differentiate between ESCC sera and sera from healthy volunteers. For further validation, the diagnostic and differential diagnostic capabilities of the peptidome patterns were verified blindly by using an independent group of sera, consisting of sera from 31 ESCC patients, 33 healthy volunteers, 38 colorectal patients, and 36 gastric cancer patients. A Quick Classifier Algorithm was used to construct the peptidome patterns for the identification of ESCC from the control samples. Five of the identified peaks at mass to charge ratios 759, 786, 1,866, 3,316, and 6,634 were used to construct the peptidome patterns with almost 100% accuracy. Furthermore, the peptidome patterns could also differentiate the validation group with high accuracy. These results suggest that the ClinProt™ Kit combined with MS achieves significantly high accuracy for ESCC diagnosis and differential diagnosis.

Proteomic biomarkers predicting lymph node involvement in serum of cervical cancer patients. Limitations of SELDI-TOF MS

BACKGROUND

Lymph node status is not part of the staging system for cervical cancer, but provides important information for prognosis and treatment. We investigated whether lymph node status can be predicted with proteomic profiling.

MATERIAL & METHODS

Serum samples of 60 cervical cancer patients (FIGO I/II) were obtained before primary treatment. Samples were run through a HPLC depletion column, eliminating the 14 most abundant proteins ubiquitously present in serum. Unbound fractions were concentrated with spin filters. Fractions were spotted onto CM10 and IMAC30 surfaces and analyzed with surface-enhanced laser desorption time of flight (SELDI-TOF) mass spectrometry (MS). Unsupervised peak detection and peak clustering was performed using MASDA software. Leave-one-out (LOO) validation for weighted Least Squares Support Vector Machines (LSSVM) was used for prediction of lymph node involvement. Other outcomes were histological type, lymphvascular space involvement (LVSI) and recurrent disease.

RESULTS

LSSVM models were able to determine LN status with a LOO area under the receiver operating characteristics curve (AUC) of 0.95, based on peaks with m/z values 2,698.9, 3,953.2, and 15,254.8. Furthermore, we were able to predict LVSI (AUC 0.81), to predict recurrence (AUC 0.92), and to differentiate between squamous carcinomas and adenocarcinomas (AUC 0.88), between squamous and adenosquamous carcinomas (AUC 0.85), and between adenocarcinomas and adenosquamous carcinomas (AUC 0.94).

CONCLUSIONS

Potential markers related with lymph node involvement were detected, and protein/peptide profiling support differentiation between various subtypes of cervical cancer. However, identification of the potential biomarkers was hampered by the technical limitations of SELDI-TOF MS.

Organ Specific Tumor Markers: What's New?

Tumor markers are molecules produced in the body in response to cancer. An ideal tumor marker should have high sensitivity and specificity, should be cheap, and should be easily detected in body fluids. Identification of novel markers is important and it is expected that with the advent of newer technologies, more reliable markers will be discovered. This review discusses the currently available tumor markers for different malignancies.

Serum amyloid a protein in clinical cancer diagnosis

The serum amyloid A (SAA) protein is an acute phase protein that is synthesized under the regulation of inflammatory cytokines during both acute and chronic inflammation. It is suggested that the SAA increases correlate with many types of carcinogenesis and neoplastic diseases. Th changes in SAA in serum could therefore indicate the progress and malignancy of the disease, as well as the host responses. The present paper reviewed the rationale of using SAA as potential cancer biomarker in clinical diagnosis, including the contribution and involvement of SAA in cancer growth and development. Then we discussed the current applications of SAA in diagnosis and tracing of different types of cancers. Finally the proteomics techniques, especially the SELDI-TOF MS to identify SAA in serum from patients were appreciated as an important manner in clinical diagnosis.

Potential markers for detection and monitoring of ovarian cancer

This paper reviews current screening techniques as well as novel biomarkers and their potential role in early detection of ovarian cancer. Ovarian cancer is one of the most common reproductive cancers and has the highest mortality rate amongst gynecologic cancers. Because most ovarian cancer diagnoses occur in the late stages of the disease, five-year survival rates fall below 20%. To improve survival rates and to lower mortality rates for ovarian cancer, improved detection at early stages of the disease is needed. Current screening approaches include tumor markers, ultrasound, or a combination. Efforts are underway to discover new biomarkers of ovarian cancer in order to surmount the obstacles in early-stage diagnosis. Among serum protein markers, HE4 and mesothelin can augment CA125 detection providing higher sensitivity and specificity due to the presence of these proteins in early-stage ovarian cancer. Detection testing that includes methylation of the MCJ gene and increased expression of vascular endothelial growth factor is correlated to poor prognosis and may predict patient survival outcome. Detection testing of biomarkers with long-term stability and combination panels of markers, will likely lead to effective screening strategies with high specificity and sensitivity for early detection of ovarian cancer.

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.