Protein biomarkers of ovarian cancer: the forest and the trees

Circulating Proteins as Diagnostic Markers in Gastric Cancer

Gastric cancer (GC) is a highly malignant disease affecting humans worldwide and has a poor prognosis. Most GC cases are detected at advanced stages due to the cancer lacking early detectable symptoms. Therefore, there is great interest in improving early diagnosis by implementing targeted prevention strategies. Markers are necessary for early detection and to guide clinicians to the best personalized treatment. The current semi-invasive endoscopic methods to detect GC are invasive, costly, and time-consuming. Recent advances in proteomics technologies have enabled the screening of many samples and the detection of novel biomarkers and disease-related signature signaling networks. These biomarkers include circulating proteins from different fluids (e.g., plasma, serum, urine, and saliva) and extracellular vesicles. We review relevant published studies on circulating protein biomarkers in GC and detail their application as potential biomarkers for GC diagnosis. Identifying highly sensitive and highly specific diagnostic markers for GC may improve patient survival rates and contribute to advancing precision/personalized medicine.

Identification of serum miR-1246 and miR-150-5p as novel diagnostic biomarkers for high-grade serous ovarian cancer

Epithelial ovarian cancer (EOC) is one of the leading cancers in women, with high-grade serous ovarian cancer (HGSOC) being the most common and lethal subtype of this disease. A vast majority of HGSOC are diagnosed at the late stage of the disease when the treatment and total recovery chances are low. Thus, there is an urgent need for novel, more sensitive and specific methods for early and routine HGSOC clinical diagnosis. In this study, we performed miRNA expression profiling using the NanoString miRNA assay in 34 serum samples from patients with HGSOC and 36 healthy women. We identified 13 miRNAs that were differentially expressed (DE). For additional exploration of expression patterns correlated with HGSOC, we performed weighted gene co-expression network analysis (WGCNA). As a result, we showed that the module most correlated with tumour size, nodule and metastasis contained 8 DE miRNAs. The panel including miR-1246 and miR-150-5p was identified as a signature that could discriminate HGSOC patients with AUCs of 0.98 and 1 for the training and test sets, respectively. Furthermore, the above two-miRNA panel had an AUC = 0.946 in the verification cohorts of RT-qPCR data and an AUC = 0.895 using external data from the GEO public database. Thus, the model we developed has the potential to markedly improve the diagnosis of ovarian cancer.

A Novel Predictive Multi-Marker Test for the Pre-Surgical Identification of Ovarian Cancer

Ovarian cancer remains the most lethal of gynecological malignancies, with the 5-year survival below 50%. Currently there is no simple and effective pre-surgical diagnosis or triage for patients with malignancy, particularly those with early-stage or low-volume tumors. Recently we discovered that CXCL10 can be processed to an inactive form in ovarian cancers and that its measurement has diagnostic significance. In this study we evaluated the addition of processed CXCL10 to a biomarker panel for the discrimination of benign from malignant disease. Multiple biomarkers were measured in retrospectively collected plasma samples (n = 334) from patients diagnosed with benign or malignant disease, and a classifier model was developed using CA125, HE4, Il6 and CXCL10 (active and total). The model provided 95% sensitivity/95% specificity for discrimination of benign from malignant disease. Positive predictive performance exceeded that of "gold standard" scoring systems including CA125, RMI and ROMA% and was independent of menopausal status. In addition, 80% of stage I-II cancers in the cohort were correctly identified using the multi-marker scoring system. Our data suggest the multi-marker panel and associated scoring algorithm provides a useful measurement to assist in pre-surgical diagnosis and triage of patients with suspected ovarian cancer.

Practical delta check limits for tumour markers in different clinical settings

OBJECTIVES

Few studies have reported on delta checks for tumour markers, even though these markers are often evaluated serially. Therefore, this study aimed to establish a practical delta check limit in different clinical settings for five tumour markers: alpha-fetoprotein, cancer antigen 19-9, cancer antigen 125, carcinoembryonic antigen, and prostate-specific antigen.

METHODS

Pairs of patients' results (current and previous) for five tumour markers between 2020 and 2021 were retrospectively collected from three university hospitals. The data were classified into three subgroups, namely: health check-up recipient (subgroup H), outpatient (subgroup O), and inpatient (subgroup I) clinics. The check limits of delta percent change (DPC), absolute DPC (absDPC), and reference change value (RCV) for each test were determined using the development set (the first 18 months, n=179,929) and then validated and simulated by applying the validation set (the last 6 months, n=66,332).

RESULTS

The check limits of DPC and absDPC for most tests varied significantly among the subgroups. Likewise, the proportions of samples requiring further evaluation, calculated by excluding samples with both current and previous results within the reference intervals, were 0.2-2.9% (lower limit of DPC), 0.2-2.7% (upper limit of DPC), 0.3-5.6% (absDPC), and 0.8-35.3% (RCV99.9%). Furthermore, high negative predictive values >0.99 were observed in all subgroups in the in silico simulation.

CONCLUSIONS

Using real-world data, we found that DPC was the most appropriate delta-check method for tumour markers. Moreover, Delta-check limits for tumour markers should be applied based on clinical settings.

Comparison of Human Epididymis Protein 4, Cancer Antigen 125, and Ultrasound Prediction Model in Differentiating Benign from Malignant Adnexal Masses

Background

This study aimed to compare the diagnostic performance of carcinogenic antigen (CA) 125, (HE)-4 (Human epididymis protein 4), and ultrasound (International Ovarian Tumor Analysis [IOTA]) Simple Rules individually and to derive a composite score in the differentiating ovarian cancer from benign ovarian mass.

Subjects and Methods

Consecutive patients (n = 100) with pelvic mass admitted during February 2018-August 2019 were included prospectively. Patients with either known case of epithelial ovarian cancer (EOC) or metastatic EOC were excluded. The primary outcome was to assess the sensitivity and specificity of CA-125, HE-4, and IOTA Simple Rules in predicting benign from malignant mass independently, while secondary outcome was derivation of a new model incorporating these variables using multivariate logistic regression analysis to predict benign from malignant lesions. Receiver operator curve (ROC) was drawn to redefine the best-performing cutoff values and difference between area under the ROC (AUROC) were compared by DeLong's method.

Results

Out of 100 cases of adnexal mass selected, the sensitivity and specificity of CA-125 were 73.8% and 77.6%, HE-4 were 90.5% and 87.9%, and IOTA Simple Rules were 92.9% and 81.0%. CA-125, HE-4, and IOTA Simple Rules were independently associated with the likelihood of malignancy/borderline (P < 0.001). The area under the curve for the "composite score" (AUC = 0.93) was the highest and was significantly better than that of CA-125 (AUC = 0.786) (P = 0.004 using DeLong's test) and comparable with HE-4 (AUROC = 0.90; P = 0.128 using DeLong's Test).

Conclusion

The sensitivity and specificity of HE-4 and IOTA Simple Rules for predicting malignant ovarian tumor was better than those of CA-125. The diagnostic performance of "composite score" was comparable to those of either HE-4 or IOTA Simple Rules and significantly better than CA-125.

Tissue factor as a novel diagnostic target for early detection of ovarian cancer using ultrasound microbubbles

INTRODUCTION

Ovarian cancer (OC) is the deadliest gynecologic malignancy, with an overall 5-year survival rate of less than 30%. The existing paradigm for OC detection involves a serum marker, CA125, and ultrasound examination, neither of which is sufficiently specific for OC. This study addresses this deficiency through the use of a targeted ultrasound microbubble directed against tissue factor (TF).

METHODS

TF expression was examined in both OC cell lines and patient-derived tumor samples via western blotting and IHC. In vivo microbubble ultrasound imaging was analyzed using high grade serous ovarian carcinoma orthotopic mouse models.

RESULTS

While TF expression has previously been described on angiogenic, tumor-associated vascular endothelial cells (VECs) of several tumor types, this is first study to show TF expression on both murine and patient-derived ovarian tumor-associated VECs. Biotinylated anti-TF antibody was conjugated to streptavidin-coated microbubbles and in vitro binding assays were performed to assess the binding efficacy of these agents. TF-targeted microbubbles successfully bound to TF-expressing OC cells, as well as an in vitro model of angiogenic endothelium. In vivo, these microbubbles bound to the tumor-associated VECs of a clinically relevant orthotopic OC mouse model.

CONCLUSION

Development of a TF-targeted microbubble capable of successfully detecting ovarian tumor neovasculature could have significant implications towards increasing the number of early-stage OC diagnoses. This preclinical study shows potential for translation to clinical use, which could ultimately help increase the number of early OC detections and decrease the mortality associated with this disease.

Proteomic analysis identifies dysregulated proteins and associated molecular pathways in a cohort of gallbladder cancer patients of African ancestry

BACKGROUND

Gallbladder cancer (GBC) is a lethal cancer with a poor prognosis. The lack of specific and sensitive biomarkers results in delayed diagnosis with most patients presenting at late stages of the disease. Furthermore, there is little known about the molecular mechanisms associated with GBC, especially in patients of African ancestry. This study aimed to determine dysregulated proteins in South African GBC patients to identify potential mechanisms of the disease progression and plausible biomarkers.

METHODS

Tissues (27 GBC, 13 Gallstone disease, and 5 normal tissues) and blood plasma (54 GBC and 73 Benign biliary pathology) were obtained from consenting patients. Protein extraction was performed on all tissues and liquid chromatography-mass spectrometry was used for proteomic profiling. A project-specific spectral library was built using the Pulsar search algorithm. Principal component and Spearman's rank correlation analyses were performed using PAST (V4.07b). Pathway and Network analyses were conducted using REACTOME (v3.7) and stringAPP (v1.7.0), respectively.

RESULTS

In the tissue sample group, there were 62 and 194 dysregulated proteins in GBC compared to normal and gallstone groups, respectively. In the plasma group, there were 33 altered proteins in GBC compared to the benign biliary pathology group. We found 9 proteins (APOA1, APOA2, RET4, TTR, HEMO, HBB, HBA, PIGR, and APOE) to be commonly dysregulated in both tissue and plasma. Furthermore, a subset analysis demonstrated that 2 proteins, S100A8 and S100A9, were downregulated in GBC patients with GD history compared to those without. Pathway analysis showed that the dysregulated proteins in GBC patients were enriched in pathways involved in smooth muscle contraction, metabolism, ECM organization, and integrin cell surface interactions.

CONCLUSION

The identified dysregulated proteins help in understanding GBC molecular mechanisms in our patient group. Furthermore, the alteration of specific proteins in both tissue and plasma samples suggests their potential utility as biomarkers of GBC in this sample cohort.

Identification of two theranostic biomarker panels for epithelial ovarian cancer

BACKGROUND

Epithelial Ovarian cancer (EOC) is the leading cause of death associated with gynecologic tumors. Because the disease is asymptomatic in early-stage, the majority of patients are not diagnosed until late stages, highlighting the need for the development of novel diagnostic biomarkers. Mediators of tumoral microenvironment may affect EOC progression and resistance to treatment.

AIM OF THE STUDY

Analysis of serum proteins to identify a panel of theranostic biomarkers for EOC.

PATIENTS AND METHODS

Serum levels of 65 analytes were determined in EOC patients, and healthy controls with the ProcartaPlex Human Immune Monitoring 65-Plex Panel.

RESULTS

Twenty-one analytes: 7 cytokines (IFN-γ, IL-12p70, IL-13, IL-18 and TSLP), 7 chemokines (Eotaxin, eotaxin-2, IP-10, BLC, I-TAC, SDF-1α, and fractalkine), 2 growth factors (MMP-1, VEGF-α), and 5 soluble receptors (APRIL, CD40L, TWEAK, CD30 and TNFRII; were significantly differentially expressed between the two groups. ROC curves showed that only seven of them (IL-9, TNF-α, Eotaxin, IP-10, BLC, Fractalkine, and Tweak) had AUC values greater than 0.70 and thus had potential clinical utility. Moreover, five cytokines: IFN-γ, IL-1 β, IL-8, MIP-1β, and TNF-α are positively associated with patients who developed resistance to taxol-platinum-based chemotherapy (CT).

CONCLUSION

This study has revealed a first panel of 7 analytes (IL-9, TNF-α, Eotaxin, IP-10, BLC, Fractalkine and Tweak) that can be used for early detection of EOC and a second panel of five cytokines (IFN-γ, IL-1β, IL-8, MIP-1β, TNF-α) that can help clinicians to identify EOC patients who are at higher risk to develop resistance to CT of EOC.

Cancer-Specific miRNAs Extracted from Tissue-Exudative Extracellular Vesicles in Ovarian Clear Cell Carcinoma

Ovarian clear cell carcinomas (OCCs) arise from endometriotic cysts that many women develop. Biomarkers for early OCC detection need to be identified. Extracellular vesicles have attracted attention as biomarker carriers. This study aims to identify cancer-specific miRNAs as novel OCC biomarkers using tissue-exudative extracellular vesicles (Te-EVs). Te-EVs were collected from four patients with OCC on one side and a normal ovary on the other side. Microarray analysis was performed to identify cancer-specific miRNAs in Te-EVs. Serum samples obtained before and after surgery from patients with OCC and atypical endometrial hyperplasia (AEH) (controls) were compared using real-time PCR to examine changes in the detected EV miRNA levels. Thirty-seven miRNAs were >2-fold upregulated on the OCC side compared with the normal ovarian side. We selected 17 miRNAs and created specific primers for 12 of these miRNAs. The levels of six EV miRNAs were significantly decreased in postoperative OCC serum compared to those in preoperative OCC serum. In contrast, no significant change was observed between the pre and postoperative values in the control group. We identified OCC tissue-specific miRNAs in the EVs secreted by OCC tissues. These EV miRNAs have potential for use as biomarkers for the early diagnosis and detection of OCC.

Gold Nanoparticles Based Optical Biosensors for Cancer Biomarker Proteins: A Review of the Current Practices

Cancer prognosis depends on the early detection of the disease. Gold nanoparticles (AuNPs) have attracted much importance in biomedical research due to their distinctive optical properties. The AuNPs are easy to fabricate, biocompatible, surface controlled, stable, and have surface plasmonic properties. The AuNPs based optical biosensors can intensely improve the sensitivity, specificity, resolution, penetration depth, contrast, and speed of these devices. The key optical features of the AuNPs based biosensors include localized surface plasmon resonance (LSPR), SERS, and luminescence. AuNPs based biomarkers have the potential to sense the protein biomarkers at a low detection level. In this review, the fabrication techniques of the AuNPs have been reviewed. The optical biosensors based on LSPR, SERS, and luminescence are also evaluated. The application of these biosensors for cancer protein detection is discussed. Distinct examples of cancer research that have a substantial impact on both scientific and clinical research are presented.

Biomarker-Based Models for Preoperative Assessment of Adnexal Mass: A Multicenter Validation Study

Ovarian cancer (OC) is the most lethal genital malignancy in women. We aimed to develop and validate new proteomic-based models for non-invasive diagnosis of OC. We also compared them to the modified Risk of Ovarian Malignancy Algorithm (ROMA-50), the Copenhagen Index (CPH-I) and our earlier Proteomic Model 2017. Biomarkers were assessed using bead-based multiplex technology (Luminex®) in 356 women (250 with malignant and 106 with benign ovarian tumors) from five European centers. The training cohort included 279 women from three centers, and the validation cohort 77 women from two other centers. Of six previously studied serum proteins (CA125, HE4, osteopontin [OPN], prolactin, leptin, and macrophage migration inhibitory factor [MIF]), four contributed significantly to the Proteomic Model 2021 (CA125, OPN, prolactin, MIF), while leptin and HE4 were omitted by the algorithm. The Proteomic Model 2021 revealed a c-index of 0.98 (95% CI 0.96, 0.99) in the training cohort; however, in the validation cohort it only achieved a c-index of 0.82 (95% CI 0.72, 0.91). Adding patient age to the Proteomic Model 2021 constituted the Combined Model 2021, with a c-index of 0.99 (95% CI 0.97, 1) in the training cohort and a c-index of 0.86 (95% CI 0.78, 0.95) in the validation cohort. The Full Combined Model 2021 (all six proteins with age) yielded a c-index of 0.98 (95% CI 0.97, 0.99) in the training cohort and a c-index of 0.89 (95% CI 0.81, 0.97) in the validation cohort. The validation of our previous Proteomic Model 2017, as well as the ROMA-50 and CPH-I revealed a c-index of 0.9 (95% CI 0.82, 0.97), 0.54 (95% CI 0.38, 0.69) and 0.92 (95% CI 0.85, 0.98), respectively. In postmenopausal women, the three newly developed models all achieved a specificity of 1.00, a positive predictive value (PPV) of 1.00, and a sensitivity of >0.9. Performance in women under 50 years of age (c-index below 0.6) or with normal CA125 (c-index close to 0.5) was poor. CA125 and OPN had the best discriminating power as single markers. In summary, the CPH-I, the two combined 2021 Models, and the Proteomic Model 2017 showed satisfactory diagnostic accuracies, with no clear superiority of either model. Notably, although combining values of only four proteins with age, the Combined Model 2021 performed comparably to the Full Combined Model 2021. The models confirmed their exceptional diagnostic performance in women aged ≥50. All models outperformed the ROMA-50.

Association Between Micronutrient Concentrations and Human Epididymis Protein 4

Background

Human epididymis protein 4 (HE4) has been frequently used to study in many malignant tumors, while serum nutritional markers are used to determine a person's health status. However, the link between serum micronutrient concentrations and HE4 has not yet been clarified.

Methods

A total of 2464 eligible female participants and serum concentrations of nutritional biomarkers were chosen from the National Health and Nutrition Examination Surveys (NHANES) 2001-2002. For statistical analysis, we used the χ ² test, multivariable linear regression, and analysis of variance. Adjusted models were used, and the concentrations of serum nutritional biomarkers were divided into quartiles.

Results

The mean age of the participants was 48.07 years. Among twelve micronutrients, five were negatively associated with HE4 in models 1, 2 and 3. Only α-carotene, trans-β-carotene, cis-β-carotene, trans-lycopene and retinol were associated with HE4, with beta coefficients of -0.102, -0.027, -0.506, -0.131 and -0.054, respectively. After performing quartile-based analysis, statistical significance was only found for serum α-carotene, trans-lycopene, and retinol in the three models. In model 3, the beta coefficients [95% confidence intervals (CIs)] of the fourth quartiles compared to the first quartiles for α-carotene, trans-lycopene, and retinol were -3.390 (-5.053, -1.727), -4.036 (-5.722, -2.351) and -4.146 (-5.899, -2.393), respectively. Serum concentrations of these three nutritional biomarkers were inversely related to serum HE4 concentration (p trend <0.001).

Conclusion

HE4 is a useful and novel biomarker that can be used with many diseases, especially ovarian cancer. Three of our selected micronutrients were inversely associated with HE4 concentration. Supplement of micronutrients may reduce the levels of HE4 and the subsequent of ovarian cancer's risk. Therefore, a formula that correlates HE4 with nutritional biomarkers needs to be established before use in clinical applications.

Multi-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection

Advanced ovarian cancer is one of the most lethal gynecological tumor, mainly due to late diagnoses and acquired drug resistance. MicroRNAs (miRNAs) are small-non coding RNA acting as tumor suppressor/oncogenes differentially expressed in normal and epithelial ovarian cancer and has been recognized as a new class of tumor early detection biomarkers as they are released in blood fluids since tumor initiation process. Here, we evaluated by droplet digital PCR (ddPCR) circulating miRNAs in serum samples from healthy (N = 105) and untreated ovarian cancer patients (stages I to IV) (N = 72), grouped into a discovery/training and clinical validation set with the goal to identify the best classifier allowing the discrimination between earlier ovarian tumors from health controls women. The selection of 45 candidate miRNAs to be evaluated in the discovery set was based on miRNAs represented in ovarian cancer explorative commercial panels. We found six miRNAs showing increased levels in the blood of early or late-stage ovarian cancer groups compared to healthy controls. The serum levels of miR-320b and miR-141-3p were considered independent markers of malignancy in a multivariate logistic regression analysis. These markers were used to train diagnostic classifiers comprising miRNAs (miR-320b and miR-141-3p) and miRNAs combined with well-established ovarian cancer protein markers (miR-320b, miR-141-3p, CA-125 and HE4). The miRNA-based classifier was able to accurately discriminate early-stage ovarian cancer patients from health-controls in an independent sample set (Sensitivity = 80.0%, Specificity = 70.3%, AUC = 0.789). In addition, the integration of the serum proteins in the model markedly improved the performance (Sensitivity = 88.9%, Specificity = 100%, AUC = 1.000). A cross-study validation was carried out using four data series obtained from Gene Expression Omnibus (GEO), corroborating the performance of the miRNA-based classifier (AUCs ranging from 0.637 to 0.979). The clinical utility of the miRNA model should be validated in a prospective cohort in order to investigate their feasibility as an ovarian cancer early detection tool.

The Molecular Landscape Influencing Prognoses of Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) is one of the major increasing lethal malignancies of the gynecological tract, mostly due to delayed diagnosis and chemoresistance, as well as its very heterogeneous genetic makeup. Application of high-throughput molecular technologies, gene expression microarrays, and powerful preclinical models has provided a deeper understanding of the molecular characteristics of EOC. Therefore, molecular markers have become a potent tool in EOC management, including prediction of aggressiveness, prognosis, and recurrence, and identification of novel therapeutic targets. In addition, biomarkers derived from genomic/epigenomic alterations (e.g., gene mutations, copy number aberrations, and DNA methylation) enable targeted treatment of affected signaling pathways in advanced EOC, thereby improving the effectiveness of traditional treatments. This review outlines the molecular landscape and discusses the impacts of biomarkers on the detection, diagnosis, surveillance, and therapeutic targets of EOC. These findings focus on the necessity to translate these potential biomarkers into clinical practice.

Within- and between-subject biological variation data for tumor markers based on the European Biological Variation Study

OBJECTIVES

Reliable biological variation (BV) data are required for the clinical use of tumor markers in the diagnosis and monitoring of treatment effects in cancer. The European Biological Variation Study (EuBIVAS) was established by the EFLM Biological Variation Working Group to deliver BV data for clinically important measurands. In this study, EuBIVAS-based BV estimates are provided for cancer antigen (CA) 125, CA 15-3, CA 19-9, carcinoembryonic antigen, cytokeratin-19 fragment, alpha-fetoprotein and human epididymis protein 4.

METHODS

Subjects from five European countries were enrolled in the study, and weekly samples were collected from 91 healthy individuals (53 females and 38 males; 21-69 years old) for 10 consecutive weeks. All samples were analyzed in duplicate within a single run. After excluding outliers and homogeneity analysis, the BVs of tumor markers were determined by CV-ANOVA on trend-corrected data, when relevant (Røraas method).

RESULTS

Marked individuality was found for all tumor markers. CYFRA 21-1 was the measurand with the highest index of individuality (II) at 0.67, whereas CA 19-9 had the lowest II at 0.07. The CV _I s of HE4, CYFRA 21-1, CA 19-9, CA 125 and CA 15-3 of pre- and postmenopausal females were significantly different from each other.

CONCLUSIONS

This study provides updated BV estimates for several tumor markers, and the findings indicate that marked individuality is characteristic. The use of reference change values should be considered when monitoring treatment of patients by means of tumor markers.

Clinical usefulness of high levels of C-reactive protein for diagnosing epithelial ovarian cancer

The purpose of the present study was to evaluate the diagnostic role of CRP in ovarian cancer and to assess whether CRP can be combined with tumor markers to enhance the diagnostic efficacy toward ovarian cancer. Area under the curve, sensitivity, and specificity were calculated to access the diagnostic ability of each singly and combined as markers for ovarian cancer. The CRP cut-off value was then calculated to evaluate the diagnostic efficacy of CRP for ovarian cancer. Our results showed that values for all markers were significantly higher in the cancer group than in the control group. Receiver operating characteristic curve results showed that CA125 had the highest diagnostic efficacy for ovarian cancer, while the sensitivity for CRP was higher than for CA125, and the specificity for CRP was equal to that of CA125. The combination of CRP, CA125, and HE4, however, provided the strongest diagnostic capability. Furthermore, the diagnostic cut-off value for CRP with regard to ovarian cancer was 9.8 mg/L, and high levels of CRP were correlated with stage and tumor size of ovarian cancer. Our study indicated that CRP is valuable in the diagnosis of ovarian cancer, and that combining CRP with CA125 and HE4 improved the diagnostic efficacy with respect to ovarian cancer.

NFAT Overexpression Correlates with CA72-4 and Poor Prognosis of Ovarian Clear-Cell Carcinoma Subtype

Current biomarkers did not overcome the limitations of clinical application due to the heterogeneity of ovarian tumors. The role of nuclear factor of activated T cells (NFAT) in the prognosis of different histological subtypes of ovarian cancer remains unclear. NFAT expression was analyzed in 302 ovarian tumors from The Cancer Genome Atlas (TCGA) dataset and was further confirmed by 88 ovarian tumor specimens, including 30 clear-cell carcinoma, 34 serous carcinoma, and 24 papillary serous cystadenocarcinoma. The correlations between NFAT expression, cancer biomarkers, and clinical characteristics in different subtypes of ovarian tumors were analyzed. ALGGEN PROMO, reporter assay, and NFAT overexpression and knockdown were used to identify chondroadherin (CHAD) as the downstream target of NFAT. NFAT was significantly upregulated only in late-stage clear-cell carcinoma, but not in other two subtypes. NFAT levels were correlated with CA72-4 levels and poor overall survival and disease-free survival (P < 0.05), suggesting that NFAT together with CA72-4 were specific prognostic markers for clear-cell carcinoma. Pathological stage and lymph node metastasis were the prognostic factors affecting serous carcinoma (P < 0.05), while CA-125 was the prognostic factor affecting papillary serous cystadenocarcinoma (P < 0.05). PROMO and reporter assay indicated that CHAD was the downstream target of NFAT. In addition, NFAT overexpression and silencing increased and reduced CHAD expression, respectively. NFAT together with CA72-4 were specific tumor markers for risk assessment of unique clear-cell subtype of ovarian tumors. CHAD was identified as the downstream target gene of NAFT and was associated with poor survival of ovarian cancer.

Making new biomarkers a reality: the case of serum human epididymis protein 4

Background Measurement of human epididymis protein 4 (HE4) in serum has recently been proposed for clinical use in the framework of ovarian cancer (OvCa). We sought to retrace the translational phase and the clinical implementation steps boosting HE4's clinical value and discuss the effects of its introduction on the diagnostic and management pathways. Methods Meta-analyses of running evidence have preliminarily suggested that HE4 may overcome carbohydrate antigen 125 (CA125) in identifying OvCa, showing however several gaps that need to be considered, i.e. definition of biomarker diagnostic performance in the early detection of OvCa, added diagnostic value, biological and lifestyle factors of variation, and optimal interpretative criteria. Investigation of the influencing factors has shown that renal impairment represents a major limitation for HE4's diagnostic power. On the other hand, the demonstration of the substantial equivalence of results obtained by commercially available assays allows recommending harmonized thresholds for diagnostic purpose, even if the study of HE4's biological variation has clarified that the longitudinal interpretation of the biomarker changes according to the reference change value could be more appropriate. Summary We used HE4 as an example for describing the long and bumpy road for making a new biomarker a reality, and the issues that should be checked and the information that should be provided in moving a novel biomarker from its discovery to an effective clinical adoption.

Novel protein and immune response markers of human serous tubal intraepithelial carcinoma of the ovary

Ovarian cancer is the leading cause of death among gynecologic diseases in the USA and Europe. High-grade serous carcinoma (HGSC) of the ovary, the most aggressive type of ovarian cancer, is typically diagnosed at advanced stages when the 5-year survival is dismal. Since the cure rate for stage I HGSC is high, early detection of localized initial disease may improve patient outcomes. Serous tubal intraepithelial carcinoma (STIC) is considered to be a precursor lesion of HGSC. Discovery of biomarkers associated with STIC could aid in the development of an HGSC screening algorithm. Using immunohistochemical staining, we have demonstrated overexpression of UCHL1, ADAMTS13, and GAPDH in patients' STIC lesions, but not in cancer-free fallopian tubes. We additionally demonstrated a marked increase of T cells in perineoplastic stroma surrounding STIC lesions (largely CD4 + cells), but not in normal fallopian tubes and HGSC. FOXP3 + T regulatory cells are absent in STIC lesions but are present in HGSC. These observations indicate the microenvironment surrounding a STIC lesion may be immune promoting in contrast to the immune suppressive microenvironment of invasive carcinoma. In summary, we have identified UCHL1, ADAMTS13, and GAPDH as novel potentially useful markers associated with early stages of HGSC tumorigenesis and possibly contribute to STIC immunogenicity. The lack of immune suppression in the STIC microenvironment indicates that the immune system can still recognize and keep STIC controlled at this stage of the tumor development.

The diagnostic, prognostic and therapeutic potential of circulating microRNAs in ovarian cancer

Ovarian cancer (OC) is often diagnosed at an advanced stage because of the late onset of symptoms, and this together with the lack of effective treatments, has meant it is associated with a very high mortality. The aberrant expression of MicroRNA (miRNA) contributes to the initiation and development of human tumors including OC. Several miRNAs are secreted by tumor cells and can be identified in body fluids. Serum miRNAs levels are associated with several clinical conditions, and may be used to predict prognosis and response to treatments in some cancers including OC. This review summarizes the current progresses regarding the potential applications of circulating miRNA as innovative biomarkers in OC.

Proteomic Discovery of Biomarkers to Predict Prognosis of High-Grade Serous Ovarian Carcinoma

Initial identification of biomarkers predicting the exact prognosis of high-grade serous ovarian carcinoma (HGSOC) is important in precision cancer medicine. This study aimed to investigate prognostic biomarkers of HGSOC through proteomic analysis. We conducted label-free liquid chromatography-mass spectrometry using chemotherapy-naïve, fresh-frozen primary HGSOC specimens, and compared the results between a favorable prognosis group (progression-free survival (PFS) ≥ 18 months, n = 6) and a poor prognosis group (PFS < 18 months, n = 6). Among 658 differentially expressed proteins, 288 proteins were upregulated in the favorable prognosis group and 370 proteins were upregulated in the poor prognosis group. Using hierarchical clustering, we selected α1-antitrypsin (AAT), nuclear factor-κB (NFKB), phosphomevalonate kinase (PMVK), vascular adhesion protein 1 (VAP1), fatty acid-binding protein 4 (FABP4), platelet factor 4 (PF4), apolipoprotein A1 (APOA1), and α1-acid glycoprotein (AGP) for further validation via immunohistochemical (IHC) staining in an independent set of chemotherapy-naïve primary HGSOC samples (n = 107). Survival analyses revealed that high expression of AAT, NFKB, and PMVK were independent biomarkers for favorable PFS. Conversely, high expression of VAP1, FABP4, and PF4 were identified as independent biomarkers for poor PFS. Furthermore, we constructed models predicting the 18-month PFS by combining clinical variables and IHC results. Through leave-one-out cross-validation, the optimal model was based on initial serum CA-125, germline BRCA1/2 mutations, residual tumors after surgery, International Federation of Gynecology and Obstetrics (FIGO) stage, and expression levels of the six proteins. The present results elucidate the proteomic landscape of HGSOC and six protein biomarkers to predict the prognosis of HGSOC.

Improved early detection of ovarian cancer using longitudinal multimarker models

BACKGROUND

Ovarian cancer has a poor survival rate due to late diagnosis and improved methods are needed for its early detection. Our primary objective was to identify and incorporate additional biomarkers into longitudinal models to improve on the performance of CA125 as a first-line screening test for ovarian cancer.

METHODS

This case-control study nested within UKCTOCS used 490 serial serum samples from 49 women later diagnosed with ovarian cancer and 31 control women who were cancer-free. Proteomics-based biomarker discovery was carried out using pooled samples and selected candidates, including those from the literature, assayed in all serial samples. Multimarker longitudinal models were derived and tested against CA125 for early detection of ovarian cancer.

RESULTS

The best performing models, incorporating CA125, HE4, CHI3L1, PEBP4 and/or AGR2, provided 85.7% sensitivity at 95.4% specificity up to 1 year before diagnosis, significantly improving on CA125 alone. For Type II cases (mostly high-grade serous), models achieved 95.5% sensitivity at 95.4% specificity. Predictive values were elevated earlier than CA125, showing the potential of models to improve lead time.

CONCLUSIONS

We have identified candidate biomarkers and tested longitudinal multimarker models that significantly improve on CA125 for early detection of ovarian cancer. These models now warrant independent validation.

Common and Unique microRNAs in Multiple Carcinomas Regulate Similar Network of Pathways to Mediate Cancer Progression

Cancer is a complex disease with a fatal outcome. Early detection of cancer, by monitoring appropriate molecular markers is very important for its therapeutic management. In this regard, the short non-coding RNA molecules, microRNAs (miRNAs) have shown great promise due to their availability in circulating fluids facilitating non-invasive detection of cancer. In this study, an in silico comparative analysis was performed to identify specific signature miRNAs dysregulated across multiple carcinomas and simultaneously identify unique miRNAs for each cancer type as well. The miRNA-seq data of cancer patient was obtained from GDC portal and their differential expressions along with the pathways regulated by both common and unique miRNAs were analyzed. Our studies show twelve miRNAs commonly dysregulated across seven different cancer types. Interestingly, four of those miRNAs (hsa-mir-210, hsa-mir-19a, hsa-mir-7 and hsa-mir-3662) are already reported as circulatory miRNAs (circRNAs); while, the miR-183 cluster along with hsa-mir-93 have been found to be incorporated in exosomes signifying the importance of the identified miRNAs for their use as prospective, non-invasive biomarkers. Further, the target mRNAs and pathways regulated by both common and unique miRNAs were analyzed, which interestingly had significant commonality. This suggests that miRNAs that are commonly de-regulated and specifically altered in multiple cancers might regulate similar pathways to promote cancer. Our data is of significance because we not only identify a set of common and unique miRNAs for multiple cancers but also highlight the pathways regulated by them, which might facilitate the development of future non-invasive biomarkers conducive for early detection of cancers.

Multi-platform Affinity Proteomics Identify Proteins Linked to Metastasis and Immune Suppression in Ovarian Cancer Plasma

A central reason behind the poor clinical outcome of patients with high-grade serous carcinoma (HGSC) of the ovary is the difficulty in reliably detecting early occurrence or recurrence of this malignancy. Biomarkers that provide reliable diagnosis of this disease are therefore urgently needed. Systematic proteomic methods that identify HGSC-associated molecules may provide such biomarkers. We applied the antibody-based proximity extension assay (PEA) platform (Olink) for the identification of proteins that are upregulated in the plasma of OC patients. Using binders targeting 368 different plasma proteins, we compared 20 plasma samples from HGSC patients (OC-plasma) with 20 plasma samples from individuals with non-malignant gynecologic disorders (N-plasma). We identified 176 proteins with significantly higher levels in OC-plasma compared to N-plasma by PEA (p < 0.05 by U-test; Benjamini-Hochberg corrected), which are mainly implicated in immune regulation and metastasis-associated processes, such as matrix remodeling, adhesion, migration and proliferation. A number of these proteins have not been reported in previous studies, such as BCAM, CDH6, DDR1, N2DL-2 (ULBP2), SPINT2, and WISP-1 (CCN4). Of these SPINT2, a protease inhibitor mainly derived from tumor cells within the HGSC microenvironment, showed the highest significance (p < 2 × 10⁻⁷) similar to the previously described IL-6 and PVRL4 (NECTIN4) proteins. Results were validated by means of the aptamer-based 1.3 k SOMAscan proteomic platform, which revealed a high inter-platform correlation with a median Spearman ρ of 0.62. Likewise, ELISA confirmed the PEA data for 10 out of 12 proteins analyzed, including SPINT2. These findings suggest that in contrast to other entities SPINT2 does not act as a tumor suppressor in HGSC. This is supported by data from the PRECOG and KM-Plotter meta-analysis databases, which point to a tumor-type-specific inverse association of SPINT2 gene expression with survival. Our data also demonstrate that both the PEA and SOMAscan affinity proteomics platforms bear considerable potential for the unbiased discovery of novel disease-associated biomarkers.

Diagnostic significance assessment of the circulating cell-free DNA in ovarian cancer: An updated meta-analysis

BACKGROUND

Recently, disagreements remain in increasing evidence about the potential value of circulating cell-free DNA (cfDNA) as a noninvasive diagnostic biomarker for ovarian cancer (OC). Here, this update meta-analysis was performed to further assess the diagnostic performance of circulating cfDNA in discriminating OC from non-cancerous individuals.

METHODS

We performed a systemic literature search of PubMed, Embase, Web of Science, Cochrane Library, OVID, Chinese National Knowledge Infrastructure (CNKI) and Wanfang databases to obtain 22 eligible articles including a total of 1125 patients and 1244 controls. The pooled sensitivity, specificity, diagnostic odds ratio (DOR) and area under receiver operating characteristics curves (AUROC) of the included studies for cfDNA in diagnosing OC patients were used to estimate the diagnostic value. The clinical utility of cfDNA was evaluated by Fagan nomogram. Heterogeneity was explored utilizing subgroup analysis and meta-regression.

RESULTS

The pooled sensitivity and specificity were 73% and 90%, the DOR and AUROC were 25.29 and 0.90, respectively. Subgroup analyses and meta-regression, according to patients' region, study design, clinical stage, specimen types, detection indicators, simple size, publication year revealed there were no significant sources of heterogeneity. Additionally, subgroup analyses showed qualitative detection (methylation detection); TNM stage I-IV, publication year 2011-2018, serum-based cfDNA assays exhibited better diagnostic performance as compared to quantitative detection, TNM stage III-IV, publication year 2002-2010; plasma-based cfDNA assays, and more participants and prospective studies manifested superior diagnostic accuracy. The result of sensitivity analysis indicated no study exclusively contributed to the heterogeneity and Deeks' funnel plot suggested no evidence of significant publication bias.

CONCLUSIONS

Our meta-analysis found the qualitative detection (methylation); TNM stage I-IV, publication year 2011-2018 were related to more effective diagnostic accuracy for OC. However, serum-based cell-free DNA detection should be cautiously interpreted due to unclear factors. Hence, further large-scale longitudinal studies are required to validate the diagnostic potential of cell-free DNA. The present study provides to accrue knowledge of cell-free DNA levels for future researches.

Potential of mid-infrared spectroscopy as a non-invasive diagnostic test in urine for endometrial or ovarian cancer

The current lack of an accurate, cost-effective and non-invasive test that would allow for screening and diagnosis of gynaecological carcinomas, such as endometrial and ovarian cancer, signals the necessity for alternative approaches. The potential of spectroscopic techniques in disease investigation and diagnosis has been previously demonstrated. Here, we used attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy to analyse urine samples from women with endometrial (n = 10) and ovarian cancer (n = 10), as well as from healthy individuals (n = 10). After applying multivariate analysis and classification algorithms, biomarkers of disease were pointed out and high levels of accuracy were achieved for both endometrial (95% sensitivity, 100% specificity; accuracy: 95%) and ovarian cancer (100% sensitivity, 96.3% specificity; accuracy 100%). The efficacy of this approach, in combination with the non-invasive method for urine collection, suggest a potential diagnostic tool for endometrial and ovarian cancers.

A two-step strategy for identification of plasma protein biomarkers for endometrial and ovarian cancer

Background

Over 500,000 women worldwide are diagnosed with ovarian or endometrial cancer each year. We have used a two-step strategy to identify plasma proteins that could be used to improve the diagnosis of women with an indication of gynecologic tumor and in population screening.

Methods

In the discovery step we screened 441 proteins in plasma using the proximity extension assay (PEA) and five Olink Multiplex assays (CVD II, CVD III, INF I, ONC II, NEU I) in women with ovarian cancer (n = 106), endometrial cancer (n = 74), benign ovarian tumors (n = 150) and healthy population controls (n = 399). Based on the discovery analyses a set of 27 proteins were selected and two focused multiplex PEA assays were developed. In a replication step the focused assays were used to study an independent set of cases with ovarian cancer (n = 280), endometrial cancer (n = 228), women with benign ovarian tumors (n = 76) and healthy controls (n = 57).

Results

In the discovery step, 27 proteins that showed an association to cancer status were identified. In the replication analyses, the focused assays distinguished benign tumors from ovarian cancer stage III-IV with a sensitivity of 0.88 and specificity of 0.92 (AUC = 0.92). The assays had a significantly higher AUC for distinguishing benign tumors from late stage ovarian cancer than using CA125 and HE4 (p = 9.56e-22). Also, population controls could be distinguished from ovarian cancer stage III-IV with a sensitivity of 0.85 and a specificity of 0.92 (AUC = 0.89).

Conclusion

The PEA assays represent useful tools for identification of new biomarkers for gynecologic cancers. The selected protein assays could be used to distinguish benign tumors from ovarian and endometrial cancer in women diagnosed with an unknown suspicious pelvic mass. The panels could also be used in population screening, for identification of women in need of specialized gynecologic transvaginal ultrasound examination.

Funding

The Swedish Cancer Foundation, Vinnova (SWELIFE), The Foundation for Strategic Research (SSF), Assar Gabrielsson Foundation.

SELEX methods on the road to protein targeting with nucleic acid aptamers

Systematic evolution of ligand by exponential enrichment (SELEX) is an efficient method used to isolate high-affinity single stranded oligonucleotides from a large random sequence pool. These SELEX-derived oligonucleotides named aptamer, can be selected against a broad spectrum of target molecules including proteins, cells, microorganisms and chemical compounds. Like antibodies, aptamers have a great potential in interacting with and binding to their targets through structural recognition and are therefore called "chemical antibodies". However, aptamers offer advantages over antibodies including smaller size, better tissue penetration, higher thermal stability, lower immunogenicity, easier production, lower cost of synthesis and facilitated conjugation or modification with different functional moieties. Thus, aptamers represent an attractive substitution for protein antibodies in the fields of biomarker discovery, diagnosis, imaging and targeted therapy. Enormous interest in aptamer technology triggered the development of SELEX that has underwent numerous modifications since its introduction in 1990. This review will discuss the recent advances in SELEX methods and their advantages and limitations. Aptamer applications are also briefly outlined in this review.

CD146 mediates an E-cadherin-to-N-cadherin switch during TGF-β signaling-induced epithelial-mesenchymal transition

Cadherin switch is an initiating factor of epithelial-mesenchymal transition (EMT) and is intimately correlated with cancer metastatic potential; however, its underlying mechanisms remain unclear. Here, using a transforming growth factor-β (TGF-β)-induced EMT model, we provide explicit evidence that CD146, with elevated expression and activity in a variety of cancers, is a key factor involved in the cadherin switch. We show that CD146 can be induced by TGF-β signaling. Moreover, CD146 expression is positively correlated with the activation levels of STAT3/Twist and ERK pathways. Transcriptional response of the CD146/STAT3/Twist cascade inhibits E-cadherin expression, whereas the CD146/ERK cascade enhances N-cadherin expression. CD146 overexpression also significantly promotes EMT in both mouse embryonic fibroblasts (MEFs) and ovarian cancer cells. Clinically, ovarian cancer patients with detectable CD146 expression had a significantly lower survival rate than that of patients without CD146 expression. Furthermore, CD146-deficient MEFs exhibited decreased motility as a result of reversion in this cadherin switch, strongly suggesting that targeting CD146 is a potential strategy for cancer treatment. Therefore, CD146-mediated regulation of the E-cadherin-to-N-cadherin switch provides an insight into the general mechanisms of EMT as well as cancer metastasis.

Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach

Background

Early cancer diagnosis is one of the most important challenges of cancer research, since in many cancers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers.

Methods

Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection.

Results

We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solution diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolution of cancer biomarkers, thus allowing us to make predictions on cancer detection times.

Conclusions

Combining cancer and immune biomarkers could improve cancer detection times, and any predictions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific.

A Combinatorial Proteomic Biomarker Assay to Detect Ovarian Cancer in Women

Ovarian cancer is often fatal and incidence in the general population is low, underscoring the necessity (and the challenges) for advancements in screening and early detection. The goal of this study was to design a serum-based biomarker panel and corresponding multivariate algorithm that can be used to accurately detect ovarian cancer. A combinatorial protein biomarker assay (CPBA) that uses CA125, HE4, and 3 tumor-associated autoantibodies resulted in an area under the curve of 0.98. The CPBA Ov algorithm was trained using subjects who were suspected to have gynecological cancer and were scheduled for surgery. As a surgical rule-out test, the clinical performance achieves 100% sensitivity and 83.7% specificity. Although sample size (n = 60) is a limiting factor, the CPBA Ov algorithm performed better than either CA-125 alone or the Risk of Ovarian Malignancy Algorithm.

A combination of circulating miRNAs for the early detection of ovarian cancer

Ovarian cancer is the leading cause of gynecologic cancer mortality, due to the difficulty of early detection. Current screening methods lack sufficient accuracy, and it is still challenging to propose a new early detection method that improves patient outcomes with less-invasiveness. Although many studies have suggested the utility of circulating microRNAs in cancer detection, their potential for early detection remains elusive. Here, we develop novel predictive models using a combination of 8 circulating serum miRNAs. This method was able to successfully distinguish ovarian cancer patients from healthy controls (area under the curve, 0.97; sensitivity, 0.92; and specificity, 0.91) and early-stage ovarian cancer from patients with benign tumors (0.91, 0.86 and 0.83, respectively). This method also enables subtype classification in 4 types of epithelial ovarian cancer. Furthermore, it is found that most of the 8 miRNAs were packaged in extracellular vesicles, including exosomes, derived from ovarian cancer cells, and they were circulating in murine blood stream. The circulating miRNAs described in this study may serve as biomarkers for ovarian cancer patients. Early detection and subtype determination prior to surgery are crucial for clinicians to design an effective treatment strategy for each patient, as is the goal of precision medicine.

A combined biomarker panel shows improved sensitivity for the early detection of ovarian cancer allowing the identification of the most aggressive type II tumours

Background:

There is an urgent need for biomarkers for the early detection of ovarian cancer (OC). The purpose of this study was to assess whether changes in serum levels of lecithin-cholesterol acyltransferase (LCAT), sex hormone-binding globulin (SHBG), glucose-regulated protein, 78 kDa (GRP78), calprotectin and insulin-like growth factor-binding protein 2 (IGFBP2) are observed before clinical presentation and to assess the performance of these markers alone and in combination with CA125 for early detection.

Methods:

This nested case–control study used samples from the United Kingdom Collaborative Trial of Ovarian Cancer Screening trial. The sample set consisted of 482 serum samples from 49 OC subjects and 31 controls, with serial samples spanning up to 7 years pre-diagnosis. The set was divided into the following: (I) a discovery set, which included all women with only two samples from each woman, the first at<14 months and the second at >32 months to diagnosis; and (ii) a corroboration set, which included all the serial samples from the same women spanning the 7-year period. Lecithin-cholesterol acyltransferase, SHBG, GRP78, calprotectin and IGFBP2 were measured using ELISA. The performance of the markers to detect cancers pre-diagnosis was assessed.

Results:

A combined threshold model IGFBP2 >78.5 ng ml⁻¹ : LCAT <8.831 μg ml⁻¹ : CA125 >35 U ml⁻¹ outperformed CA125 alone for the earlier detection of OC. The threshold model was able to identify the most aggressive Type II cancers. In addition, it increased the lead time by 5–6 months and identified 26% of Type I subjects and 13% of Type II subjects that were not identified by CA125 alone.

Conclusions:

Combined biomarker panels (IGFBP2, LCAT and CA125) outperformed CA125 up to 3 years pre-diagnosis, identifying cancers missed by CA125, providing increased diagnostic lead times for Type I and Type II OC. The model identified more aggressive Type II cancers, with women crossing the threshold dying earlier, indicating that these markers can improve on the sensitivity of CA125 alone for the early detection of OC.

Novel risk models for early detection and screening of ovarian cancer

PURPOSE

Ovarian cancer (OC) is the most lethal gynaecological cancer. Early detection is required to improve patient survival. Risk estimation models were constructed for Type I (Model I) and Type II (Model II) OC from analysis of Protein Z, Fibronectin, C-reactive protein and CA125 levels in prospectively collected samples from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).

RESULTS

Model I identifies cancers earlier than CA125 alone, with a potential lead time of 3-4 years. Model II detects a number of high grade serous cancers at an earlier stage (Stage I/II) than CA125 alone, with a potential lead time of 2-3 years and assigns high risk to patients that the ROCA Algorithm classified as normal.

MATERIALS AND METHODS

This nested case control study included 418 individual serum samples serially collected from 49 OC cases and 31 controls up to six years pre-diagnosis. Discriminatory logit models were built combining the ELISA results for candidate proteins with CA125 levels.

CONCLUSIONS

These models have encouraging sensitivities for detecting pre-clinical ovarian cancer, demonstrating improved sensitivity compared to CA125 alone. In addition we demonstrate how the models improve on ROCA for some cases and outline their potential future use as clinical tools.

Protein Z: A putative novel biomarker for early detection of ovarian cancer

Ovarian cancer (OC) has the highest mortality of all gynaecological cancers. Early diagnosis offers an approach to achieving better outcomes. We conducted a blinded-evaluation of prospectively collected preclinical serum from participants in the multimodal group of the United Kingdom Collaborative Trial of Ovarian Cancer Screening. Using isobaric tags (iTRAQ) we identified 90 proteins differentially expressed between OC cases and controls. A second targeted mass spectrometry analysis of twenty of these candidates identified Protein Z as a potential early detection biomarker for OC. This was further validated by ELISA analysis in 482 serial serum samples, from 80 individuals, 49 OC cases and 31 controls, spanning up to 7 years prior to diagnosis. Protein Z was significantly down-regulated up to 2 years pre-diagnosis (p = 0.000000411) in 8 of 19 Type I patients whilst in 5 Type II individuals, it was significantly up-regulated up to 4 years before diagnosis (p = 0.01). ROC curve analysis for CA-125 and CA-125 combined with Protein Z showed a statistically significant (p = 0.00033) increase in the AUC from 77 to 81% for Type I and a statistically significant (p= 0.00003) increase in the AUC from 76 to 82% for Type II. Protein Z is a novel independent early detection biomarker for Type I and Type II ovarian cancer; which can discriminate between both types. Protein Z also adds to CA-125 and potentially the Risk of Ovarian Cancer algorithm in the detection of both subtypes.

Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis

PURPOSE

To discover novel prognostic biomarkers in ovarian serous carcinomas.

METHODS

A meta-analysis of all single genes probes in the TCGA and HAS ovarian cohorts was performed to identify possible biomarkers using Cox regression as a continuous variable for overall survival. Genes were ranked by p-value using Stouffer's method and selected for statistical significance with a false discovery rate (FDR) <.05 using the Benjamini-Hochberg method.

RESULTS

Twelve genes with high mRNA expression were prognostic of poor outcome with an FDR <.05 (AXL, APC, RAB11FIP5, C19orf2, CYBRD1, PINK1, LRRN3, AQP1, DES, XRCC4, BCHE, and ASAP3). Twenty genes with low mRNA expression were prognostic of poor outcome with an FDR <.05 (LRIG1, SLC33A1, NUCB2, POLD3, ESR2, GOLPH3, XBP1, PAXIP1, CYB561, POLA2, CDH1, GMNN, SLC37A4, FAM174B, AGR2, SDR39U1, MAGT1, GJB1, SDF2L1, and C9orf82).

CONCLUSION

A meta-analysis of all single genes identified thirty-two candidate biomarkers for their possible role in ovarian serous carcinoma. These genes can provide insight into the drivers or regulators of ovarian cancer and should be evaluated in future studies. Genes with high expression indicating poor outcome are possible therapeutic targets with known antagonists or inhibitors. Additionally, the genes could be combined into a prognostic multi-gene signature and tested in future ovarian cohorts.

Proteomics in cancer biomarkers discovery: challenges and applications

With the introduction of recent high-throughput technologies to various fields of science and medicine, it is becoming clear that obtaining large amounts of data is no longer a problem in modern research laboratories. However, coherent study designs, optimal conditions for obtaining high-quality data, and compelling interpretation, in accordance with the evidence-based systems biology, are critical factors in ensuring the emergence of good science out of these recent technologies. This review focuses on the proteomics field and its new perspectives on cancer research. Cornerstone publications that have tremendously helped scientists and clinicians to better understand cancer pathogenesis; to discover novel diagnostic and/or prognostic biomarkers; and to suggest novel therapeutic targets will be presented. The author of this review aims at presenting some of the relevant literature data that helped as a step forward in bridging the gap between bench work results and bedside potentials. Undeniably, this review cannot include all the work that is being produced by expert research groups all over the world.