scholarly journals Validation of Candidate Serum Ovarian Cancer Biomarkers for Early Detection

2007 ◽  
Vol 2 ◽  
pp. 117727190700200 ◽  
Author(s):  
Feng Su ◽  
Jennifer Lang ◽  
Ashutosh Kumar ◽  
Carey Ng ◽  
Brian Hsieh ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Detection ◽  
Regression Models ◽  
Serum Proteins ◽  
Early Stage ◽  
Ovarian Tumors ◽  
Ca 125 ◽  
Endometrioid Adenocarcinoma ◽  
Serum Samples ◽  
Early Stage Ovarian Cancer

Objective We have previously analyzed protein profiles using Surface Enhanced Laser Desorption and Ionization Time-Of-Flight Mass Spectroscopy (SELDI-TOF-MS) [Kozak et al. 2003, Proc. Natl. Acad. Sci. U.S.A. 100:12343–8] and identified 3 differentially expressed serum proteins for the diagnosis of ovarian cancer (OC) [Kozak et al. 2005, Proteomics, 5:4589–96], namely, apolipoprotein A-I (apoA-I), transthyretin (TTR) and transferin (TF). The objective of the present study is to determine the efficacy of the three OC biomarkers for the detection of early stage (ES) OC, in direct comparison to CA125. Methods The levels of CA125, apoA-I, TTR and TF were measured in 392 serum samples [82 women with normal ovaries (N), 24 women with benign ovarian tumors (B), 85 women with ovarian tumors of low malignant potential (LMP), 126 women with early stage ovarian cancer (ESOC), and 75 women with late stage ovarian cancer (LSOC)], obtained through the GOG and Cooperative Human Tissue Network. Following statistical analysis, multivariate regression models were built to evaluate the utility of the three OC markers in early detection. Results Multiple logistic regression models (MLRM) utilizing all biomarker values (CA125, TTR, TF and apoA-I) from all histological subtypes (serous, mucinous, and endometrioid adenocarcinoma) distinguished normal samples from LMP with 91% sensitivity (specificity 92%), and normal samples from ESOC with a sensitivity of 89% (specificity 92%). MLRM, utilizing values of all four markers from only the mucinous histological subtype showed that collectively, CA125, TTR, TF and apoA-I, were able to distinguish normal samples from mucinous LMP with 90% sensitivity, and further distinguished normal samples from early stage mucinous ovarian cancer with a sensitivity of 95%. In contrast, in serum samples from patients with mucinous tumors, CA125 alone was able to distinguish normal samples from LMP and early stage ovarian cancer with a sensitivity of only 46% and 47%, respectively. Furthermore, collectively, apoA-I, TTR and TF (excluding CA-125) distinguished i) normal samples from samples representing all histopathologic subtypes of LMP, with a sensitivity of 73%, ii) normal samples from ESOC with a sensitivity of 84% and iii) normal samples from LSOC with a sensitivity of 97%. More strikingly, the sensitivity in distinguishing normal versus mucinous ESOC, utilizing apoA-I, TF and TTR (CA-125 excluded), was 95% (specificity 86%; AUC 95%). Conclusions These results suggest that the biomarker panel consisting of apoA-I, TTR and TF may significantly improve early detection of OC.

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

Cancers ◽  
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.

scholarly journals Development of a Multimarker Assay for Early Detection of Ovarian Cancer

2010 ◽  
Vol 28 (13) ◽  
pp. 2159-2166 ◽  
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.

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255804
Author(s):  
Priscila D. R. Cirillo ◽  
Katia Margiotti ◽  
Marco Fabiani ◽  
Mateus C. Barros-Filho ◽  
David Sparacino ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Detection ◽  
Cancer Patients ◽  
Serum Proteins ◽  
Early Stage ◽  
Multivariate Logistic Regression Analysis ◽  
Advanced Ovarian Cancer ◽  
Ca 125 ◽  
Data Series ◽  
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.

scholarly journals Role of PET/CT in Assessment of Recurrent Ovarian Tumors

QJM ◽  
2020 ◽  
Vol 113 (Supplement_1) ◽  
Author(s):  
S Kadry ◽  
A Haggag ◽  
A Ekbal
Keyword(s):  
Ovarian Cancer ◽  
Tumor Marker ◽  
Early Stage ◽  
Ovarian Tumors ◽  
University Hospital ◽  
Ca 125 ◽  
Early Stage Disease ◽  
Major Health Problem ◽  
Pet Ct ◽  
Suspected Recurrence

Abstract Background Ovarian cancer remains a major health problem worldwide, with over 225,000 new cases and 140,000 deaths reported annually. Despite high response after initial treatment, 20-30% of patients with early-stage disease and up to 75% of patients with advanced disease present with recurrence within two years. Early diagnosis of recurrence is crucial for determination of the best treatment. Aim of the Work is to detect the significance of PET/CT in the early detection of recurrent ovarian tumors. Patients and Method The study included 25 patients who have been diagnosed with ovarian cancer, received treatment and achieved complete response. All of the 25 patients had suspected recurrence either due to elevated tumor markers or suspicious clinical findings. The 25 patients have been referred for PET/CT scan at ElDemerdash university hospital from July 2017 to August 2018. Results Total of 25 patients were included in the study. 18 of 25 patients had high tumor marker (CA 125) level. The remaining 7 patients had suspected recurrence with normal tumor marker levels. Recurrence was confirmed by histopathology or clinical and imaging follow up in 19 patients of the 25 patients. Recurrent disease was not shown in 5 of 19 patients on CECT imaging and 1 of 19 patients on PET/CT imaging. PET/CT had a sensitivity of (94.74%), specificity of (100%) and accuracy of (96%). CECT has been reported with sensitivity of (73.68), specificity of (83.33%) and accuracy of (76%). Conclusion PET/CT is a useful tool and has a higher sensitivity, specificity and accuracy than CECT in detection of recurrent ovarian cancer.

Changes of serum glycome in patients suffering from ovarian cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 5047-5047
Author(s):  
Veronique Blanchard ◽  
Karina Biskup ◽  
Elena Ioana Braicu ◽  
Jalid Sehouli ◽  
Rudolf Tauber ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Serum Proteins ◽  
Early Stage ◽  
Research Work ◽  
Protein Glycosylation ◽  
Ca 125 ◽  
Pngase F ◽  
Cancer Tumor ◽  
Spss Software ◽  
And Control

5047 Background: Protein glycosylation plays an important role in many biological processes. Most human serum proteins, with the exception of albumin, are glycosylated. Glycosylation is known to be altered with development of diseases such as cancer. In the case of ovarian cancer, tumor markers among them CA-125 that are clinically used are known to have poor specificity. In addition, they fail to detect the disease at an early stage. Therefore, better biomarkers are needed. The aim of the present research work is to identify new potential glycan biomarkers by analyzing the serum N-glycome of patients suffering from ovarian cancer Methods: Serum was collected from 67 patients as well as from 33 healthy age-matching women. N-glycans were released from 10 ul serum by PNGase F digestion, permethylated and subsequently analyzed by means of MALDI-TOF mass spectrometry. The SPSS software was used for the statistical analysis. Results: The N-glycome of patients was found to have more fucosylated structures, especially in tri- and tetraantennary sialylated glycans. The PCA analysis indicates that there are significant differences between the glycome of ovarian cancer patients in all stages of the disease and the glycome of healthy controls. We identified 14 potential structures that were divided in two categories, one of monofucosylated structures with high antennarity (sensitivity 94%, specificity 97%) and one containing high-mannose structures and an asialylated structures (sensitivity 97%, specificity 97%). Conclusions: Our study is the first trial to identify major differences between ovarian cancer sera and control sera, which could potentially be used in the future as biomarkers.

GENOMIC AND TRANSCRIPTOME PROFILING OF EPITHELIAL OVARIAN CANCER

2008 ◽  
Vol 31 (4) ◽  
pp. 17
Author(s):  
Rebecca JZ Menzies ◽  
Yury V Bukhman ◽  
Nancy F Ng ◽  
Patricia A Shaw ◽  
Tak W Mak
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Early Stage ◽  
Snp Array ◽  
Ovarian Tumors ◽  
Ca 125 ◽  
Molecular Heterogeneity ◽  
Presenting Symptoms

Background: Epithelial ovarian cancer is the leading cause of death by gynecological malignancy. Due to inadequate screening modalities, a lack of characteristic presenting symptoms, limited treatments and a poor understanding of the molecular underpinnings of the disease, only 25% of ovarian cancers are diagnosed at an early stage. Current 5-year survival rates range from 80%, for disease diagnosed in Stage I to as low as 13% for Stage IV. Current screening for ovarian cancer involves measuring CA-125 levels. However, CA-125 testing has low sensitivity since it can be elevated in a variety of other gynecological diseases. Numerous studies have found molecular heterogeneity between the four histological subtypes of ovarian cancer (serous, endometrioid, clear cell and mucinous). However, treatments remain the same for all subtypes regardless of molecular heterogeneity. Thus, better treatment targets and biomarkers must be found for this disease. Methods:In our study 300 ovarian tumors will be genomically profiled using the Affymetrix Genome-Wide SNP Array 6.0 to identify loci and genes implicated in ovarian cancer. To date, 51 ovarian tumors have been analyzed using the SNP array. Results:Preliminary analysis of copy number variation in these tumors using Partek software has revealed a total of 978 loci. Known amplifications derived from the literature were seen at CCNE1 and ERBB2. Similarly, well known deletions ofp53 and RB1 in ovarian cancer were detected. Novel amplified loci at 18q11.2 and 4q33 were also detected. Novel deletions were detected at 7p13 and 8q22.2. Conclusion: Future work will include running the remaining 249 tumor samples on the SNP array and analyzing the complete dataset using Partek software. Future validation of identified genes in vitro and in vivo may provide insight and possible biomarkers that may be used clinically to benefit the ovarian cancer patient.

Biological Aspects and Clinical Applications of Serum Biomarkers in Ovarian Cancer

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.

scholarly journals Early-Stage Ovarian Malignancy Score versus Risk of Malignancy Indices: Accuracy and Clinical Utility for Preoperative Diagnosis of Women with Adnexal Masses

Medicina ◽  
2020 ◽  
Vol 56 (12) ◽  
pp. 702
Author(s):  
Phichayut Phinyo ◽  
Jayanton Patumanond ◽  
Panprapha Saenrungmuaeng ◽  
Watcharin Chirdchim ◽  
Tanyong Pipanmekaporn ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Clinical Utility ◽  
Early Stage ◽  
Ca 125 ◽  
Lower Sensitivity ◽  
Ovarian Malignancy ◽  
Threshold Probability ◽  
Adnexal Masses ◽  
Risk Of Malignancy ◽  
Early Stage Ovarian Cancer

Background and objectives: To compare the diagnostic accuracy and clinical utility of the Early-stage Ovarian Malignancy (EOM) score with the Risk of Malignancy Index (RMI) in the presurgical assessment of women presenting with adnexal masses. Materials and Methods: A secondary analysis was carried out in a retrospective cohort of women who presented with an adnexal mass and were scheduled for surgery at Phrapokklao Hospital between September 2013 and December 2017. The clinical characteristics, ultrasonographic features of the masses, and preoperative CA-125 levels were recorded. The EOM and the RMI score were calculated and compared in terms of accuracy and clinical utility. Decision curve analysis (DCA), which examined the net benefit (NB) of applying the EOM and the RMI in practice at a range of threshold probabilities, was presented. Results: In this study, data from 270 patients were analyzed. Fifty-four (20.0%) women in the sample had early-stage ovarian cancer. All four RMI versions demonstrated a lower sensitivity for the detection of patients with early-stage ovarian cancer compared to an EOM score ≥ 15. An EOM ≥ 15 resulted in a higher proportion of net true positive or NB than all versions of the RMIs from a threshold probability of 5% to 30%. Conclusions: It also showed a higher capability to reduce the number of inappropriate referrals than the RMIs at a threshold probability between 5% and 30%. The EOM score showed higher diagnostic sensitivity and has the potential to be clinically more useful than the RMIs to triage women who present with adnexal masses for referral to oncologic gynecologists. Further external validation is required to support our findings.

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