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.

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Emerging Role of Inhibin as a Biomarker for Ovarian Cancer

Women s Health ◽

10.1517/17455057.1.1.051 ◽

2005 ◽

Vol 1 (1) ◽

pp. 51-57 ◽

Cited By ~ 1

Author(s):

David M Robertson ◽

Martin K Oehler

Keyword(s):

Ovarian Cancer ◽

Granulosa Cell ◽

Early Stage ◽

Clinical Stage ◽

Survival Rates ◽

Ovarian Carcinomas ◽

Gynecological Malignancy ◽

Granulosa Cell Tumors ◽

Ovarian Cancers ◽

Specificity And Sensitivity

Ovarian cancer is the most lethal gynecological malignancy as it is diagnosed at a late clinical stage in more than 80% of patients. The development of diagnostic tests that can detect all types of ovarian cancers with high specificity and sensitivity, and at an early stage would improve survival rates. Serum inhibin is an ovarian hormone involved in the regulation of fertility, decreasing to undetectable levels after menopause. Certain ovarian malignancies, such as mucinous carcinomas and granulosa cell tumors, continue to produce inhibin, which is detectable in serum. A test for serum inhibin has been developed which is able to diagnose granulosa cell tumors and mucinous carcinomas with high accuracy. When the inhibin assay is used in conjunction with the CA125 test, which detects epithelial ovarian carcinomas, the two tests detect the majority of ovarian cancers with high sensitivity (95%) and specificity (95%). This article discusses the application of the inhibin test in ovarian cancer.

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Biological Aspects and Clinical Applications of Serum Biomarkers in Ovarian Cancer

10.1093/med/9780190248208.003.0002 ◽

2018 ◽

Author(s):

Rouba Ali-Fehmi ◽

Eman Abdulfatah

Keyword(s):

Ovarian Cancer ◽

Early Detection ◽

Early Stage ◽

Disease Diagnosis ◽

Ca 125 ◽

Diagnostic Tools ◽

Gynecological Malignancy ◽

Advanced Stages ◽

Novel Biomarkers ◽

Brca1 Brca2

Ovarian cancer, the most aggressive gynecological malignancy, presents at advanced stages with metastatic disease. Diagnosis at an early stage is the most important determinant of survival; however, the majority of patients are asymptomatic at early stages and the current diagnostic tools used in clinics show limited success in early detection and hence the need for new diagnostic biomarkers. With the advance of techniques in genomic and proteomics, numerous biomarkers are emerging which may serve as a platform for early detection of ovarian cancer. These include gene-, protein-, miRNAs, and metabolite- based biomarkers. Examples of gene-based biomarkers include HE4, FLOR1, p16INK4a, BRCA1, BRCA2, MLH1, and MSH2. Protein- based biomarkers include leptin, prolactin, osteopontin, IGF-II, and MIF. This chapter discusses the serum tumor markers (CA-125) in current use for screening, diagnosis and monitoring of ovarian cancer as well as the novel biomarkers that are under investigation and validation.

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Development of a gene signature for earlier detection and diagnosis of ovarian cancer

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.5573 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 5573-5573

Author(s):

G. Vansant ◽

P. Pezzoli ◽

W. Goldstein ◽

J. Monforte ◽

M. Birrer

Keyword(s):

Ovarian Cancer ◽

Early Detection ◽

Survival Rates ◽

High Specificity ◽

Gene Signature ◽

Stage I ◽

Patient Treatment ◽

Normal Surface ◽

Detection And Diagnosis ◽

Significant Expression

5573 Background: Ovarian cancer is the 5th leading cause of death from cancer among women and is the most fatal cancer among the gynecological malignancies. One of the major reasons for poor prognosis among ovarian cancer patients is the disease is often detected at stages III and IV, where survival rates are very low. For patients at these stages, less than 25% will survive 5 years and ultimately 75% of women diagnosed at these late stages will die. The current biomarker predominantly used for detection of ovarian cancer and monitoring of patients for relapse during therapy is the mucin 16 gene product CA125. The utility of this biomarker is limited because its expression is not always isolated to tumor tissue and in fact its expression is not associated with 40% of stage I tumors. Methods: Genes were selected that have been shown to have significant expression differences in primary ovarian tumors versus normal surface ovarian epithelium and were used to develop a multiplex RT-PCR assay for monitoring the expression of these genes. We extracted RNA from samples representative of patients with cancers of various stages (1A through 4B) and diagnoses as well as disease free subjects and monitored the expression of the potential genetic biomarkers in these samples with the developed multiplex assay. Results: Cluster analysis of the gene expression data successfully grouped the different stages together with very high specificity including the samples from the stage I patients. One stage I sample clustered with the stage III and IVs. This was from a patient with a poorly differentiated serous adenocarcinoma, a characteristically aggressive tumor which may be physiologically more similar to the later stage tumors. Conclusions: In these experiments we sought to identify and evaluate potential biomarkers that could be used in parallel for early detection and for monitoring patients during treatment. Initial results from this study revealed genes that have the potential to be part of a gene signature assay panel for early detection and patient treatment monitoring for ovarian cancer. No significant financial relationships to disclose.

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Automated Assay of a Four-Protein Biomarker Panel for Improved Detection of Ovarian Cancer

Cancers ◽

10.3390/cancers13020325 ◽

2021 ◽

Vol 13 (2) ◽

pp. 325

Author(s):

Christopher Walker ◽

Tuan-Minh Nguyen ◽

Shlomit Jessel ◽

Ayesha B. Alvero ◽

Dan-Arin Silasi ◽

...

Keyword(s):

Ovarian Cancer ◽

Early Detection ◽

Predictive Value ◽

Early Stage ◽

Ca 125 ◽

Healthy Controls ◽

Classification Models ◽

Serum Samples ◽

Protein Biomarker ◽

Biomarker Panel

Background: Mortality from ovarian cancer remains high due to the lack of methods for early detection. The difficulty lies in the low prevalence of the disease necessitating a significantly high specificity and positive-predictive value (PPV) to avoid unneeded and invasive intervention. Currently, cancer antigen- 125 (CA-125) is the most commonly used biomarker for the early detection of ovarian cancer. In this study we determine the value of combining macrophage migration inhibitory factor (MIF), osteopontin (OPN), and prolactin (PROL) with CA-125 in the detection of ovarian cancer serum samples from healthy controls. Materials and Methods: A total of 432 serum samples were included in this study. 153 samples were from ovarian cancer patients and 279 samples were from age-matched healthy controls. The four proteins were quantified using a fully automated, multi-analyte immunoassay. The serum samples were divided into training and testing datasets and analyzed using four classification models to calculate accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: The four-protein biomarker panel yielded an average accuracy of 91% compared to 85% using CA-125 alone across four classification models (p = 3.224 × 10−9). Further, in our cohort, the four-protein biomarker panel demonstrated a higher sensitivity (median of 76%), specificity (median of 98%), PPV (median of 91.5%), and NPV (median of 92%), compared to CA-125 alone. The performance of the four-protein biomarker remained better than CA-125 alone even in experiments comparing early stage (Stage I and Stage II) ovarian cancer to healthy controls. Conclusions: Combining MIF, OPN, PROL, and CA-125 can better differentiate ovarian cancer from healthy controls compared to CA-125 alone.

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Glycomic-Based Biomarkers for Ovarian Cancer: Advances and Challenges

Diagnostics ◽

10.3390/diagnostics11040643 ◽

2021 ◽

Vol 11 (4) ◽

pp. 643

Author(s):

Francis Mugeni Wanyama ◽

Véronique Blanchard

Keyword(s):

Ovarian Cancer ◽

Survival Rates ◽

Post Translational Modification ◽

Diagnostic Biomarkers ◽

Diagnostic Strategies ◽

Delay In Diagnosis ◽

Common Causes ◽

New Biomarkers ◽

Cure Rates ◽

Developed World

Ovarian cancer remains one of the most common causes of death among gynecological malignancies afflicting women worldwide. Among the gynecological cancers, cervical and endometrial cancers confer the greatest burden to the developing and the developed world, respectively; however, the overall survival rates for patients with ovarian cancer are worse than the two aforementioned. The majority of patients with ovarian cancer are diagnosed at an advanced stage when cancer has metastasized to different body sites and the cure rates, including the five-year survival, are significantly diminished. The delay in diagnosis is due to the absence of or unspecific symptoms at the initial stages of cancer as well as a lack of effective screening and diagnostic biomarkers that can detect cancer at the early stages. This, therefore, provides an imperative to prospect for new biomarkers that will provide early diagnostic strategies allowing timely mitigative interventions. Glycosylation is a protein post-translational modification that is modified in cancer patients. In the current review, we document the state-of-the-art of blood-based glycomic biomarkers for early diagnosis of ovarian cancer and the technologies currently used in this endeavor.

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Preoperative Lymphocyte-to-monocyte Ratio and CA125 Level as Risk Factors for Advanced Ovarian Cancer

10.21203/rs.3.rs-77009/v1 ◽

2020 ◽

Author(s):

Ying Tang ◽

Huiquan Hu ◽

Yalan Tang ◽

Fangxiang Tang ◽

Dan Lin ◽

...

Keyword(s):

Risk Factors ◽

Ovarian Cancer ◽

Logistic Regression ◽

Early Stage ◽

Advanced Ovarian Cancer ◽

High Specificity ◽

Advanced Stage ◽

Binary Logistic Regression Model ◽

Lymphocyte To Monocyte Ratio ◽

The Relationship

Abstract Background: Detailed descriptions of the relationship between lymphocyte-to-monocyte ratio alone and combined with CA125 (COLC) and advanced stage of ovarian cancer (OC) have been lacking to date. This study is to analyze the relationship between LMR, CA125 and COLC and advanced stage of OC.Methods: A retrospective clinicopathologic review was performed. The receiver-operating characteristic (ROC) curves of LMR, CA125, and COLC staging OC were constructed. Furthermore, a binary logistic regression model was used to assay the independent risk factors.Results: A total of 225 patients with OC were identified in this cohort. Eighty-five patients with OC were diagnosed at an early stage, and 140 OC patients were diagnosed at an advanced stage. The median of LMR at the early stage was higher than the advanced stage (4.39 vs. 2.78), and the median of CA125 was lower than the advanced stage (80 U/mL vs. 251.25 U/mL). Multivariate logistic regression indicated that LMR (OR=0.314, 95% confidence interval [CI]: 0.143–0.687, P=0.004) and CA125 (OR=4.045, 95%CI: 1.883–8.692, P<0.001) were associated with OC staging. Furthermore, the area under the curve of COLC was higher than that of LMR (0.779 vs. 0.732) or CA125 (0.779 vs. 0.708) in staging OC. The specificity of COLC was higher than that of LMR (87.11% vs. 70.61%) or CA125 (87.11% vs. 61.21%) in staging OC.Conclusions: LMR alone or in combination with CA125 might be associated with OC staging. Besides, as a predictive factor, COLC may have high specificity in staging OC.

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Development of a Multimarker Assay for Early Detection of Ovarian Cancer

Journal of Clinical Oncology ◽

10.1200/jco.2008.19.2484 ◽

2010 ◽

Vol 28 (13) ◽

pp. 2159-2166 ◽

Cited By ~ 178

Author(s):

Zoya Yurkovetsky ◽

Steven Skates ◽

Aleksey Lomakin ◽

Brian Nolen ◽

Trenton Pulsipher ◽

...

Keyword(s):

Ovarian Cancer ◽

Early Detection ◽

Late Stage ◽

Early Stage ◽

Screening Strategy ◽

Ca 125 ◽

Great Promise ◽

Biomarker Panel ◽

Healthy Women ◽

Early Stage Ovarian Cancer

PurposeEarly detection of ovarian cancer has great promise to improve clinical outcome.Patients and MethodsNinety-six serum biomarkers were analyzed in sera from healthy women and from patients with ovarian cancer, benign pelvic tumors, and breast, colorectal, and lung cancers, using multiplex xMAP bead-based immunoassays. A Metropolis algorithm with Monte Carlo simulation (MMC) was used for analysis of the data.ResultsA training set, including sera from 139 patients with early-stage ovarian cancer, 149 patients with late-stage ovarian cancer, and 1,102 healthy women, was analyzed with MMC algorithm and cross validation to identify an optimal biomarker panel discriminating early-stage cancer from healthy controls. The four-biomarker panel providing the highest diagnostic power of 86% sensitivity (SN) for early-stage and 93% SN for late-stage ovarian cancer at 98% specificity (SP) was comprised of CA-125, HE4, CEA, and VCAM-1. This model was applied to an independent blinded validation set consisting of sera from 44 patients with early-stage ovarian cancer, 124 patients with late-stage ovarian cancer, and 929 healthy women, providing unbiased estimates of 86% SN for stage I and II and 95% SN for stage III and IV disease at 98% SP. This panel was selective for ovarian cancer showing SN of 33% for benign pelvic disease, SN of 6% for breast cancer, SN of 0% for colorectal cancer, and SN of 36% for lung cancer.ConclusionA panel of CA-125, HE4, CEA, and VCAM-1, after additional validation, could serve as an initial stage in a screening strategy for epithelial ovarian cancer.

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The Use of Biomarkers in Early Diagnostics of Pancreatic Cancer

Canadian Journal of Gastroenterology and Hepatology ◽

10.1155/2018/5389820 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 10

Author(s):

Lumir Kunovsky ◽

Pavla Tesarikova ◽

Zdenek Kala ◽

Radek Kroupa ◽

Petr Kysela ◽

...

Keyword(s):

Radical Surgery ◽

Early Stage ◽

Locally Advanced ◽

High Specificity ◽

Cost Effective ◽

Ductal Adenocarcinoma ◽

Specificity And Sensitivity ◽

Solid Malignancies ◽

Late Detection ◽

Resistance To Chemotherapy

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal solid malignancies with increasing incidence. The poor prognosis is due to the aggressive nature of the tumor, late detection, and the resistance to chemotherapy and radiotherapy. A radical surgery procedure is the only treatment that has been shown to improve the 5-year survival rate to 20-25%. However, the majority of patients (80-85%) are diagnosed with locally advanced or metastatic disease and just 15-20% patients are diagnosed in an early stage allowing them to undergo the potentially curative surgical resection. The early detection of PDAC without the use of invasive methods is challenging and discovery of a cost-effective biomarker with high specificity and sensitivity could significantly improve the treatment and survival in these patients. In this review, we summarize current and newly examined biomarkers in early PDAC detection.

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