scholarly journals A MYC-Driven Plasma Polyamine Signature for Early Detection of Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 913
Author(s):  
Johannes Fahrmann ◽  
Ehsan Irajizad ◽  
Makoto Kobayashi ◽  
Jody Vykoukal ◽  
Jennifer Dennison ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Stage ◽  
Area Under The Curve ◽  
Polyamine Metabolism ◽  
Healthy Controls ◽  
Test Set ◽  
Exact Test ◽  
Oncogenic Driver ◽  
Characteristic Area ◽  
Validation Set

MYC is an oncogenic driver in the pathogenesis of ovarian cancer. We previously demonstrated that MYC regulates polyamine metabolism in triple-negative breast cancer (TNBC) and that a plasma polyamine signature is associated with TNBC development and progression. We hypothesized that a similar plasma polyamine signature may associate with ovarian cancer (OvCa) development. Using mass spectrometry, four polyamines were quantified in plasma from 116 OvCa cases and 143 controls (71 healthy controls + 72 subjects with benign pelvic masses) (Test Set). Findings were validated in an independent plasma set from 61 early-stage OvCa cases and 71 healthy controls (Validation Set). Complementarity of polyamines with CA125 was also evaluated. Receiver operating characteristic area under the curve (AUC) of individual polyamines for distinguishing cases from healthy controls ranged from 0.74–0.88. A polyamine signature consisting of diacetylspermine + N-(3-acetamidopropyl)pyrrolidin-2-one in combination with CA125 developed in the Test Set yielded improvement in sensitivity at >99% specificity relative to CA125 alone (73.7% vs 62.2%; McNemar exact test 2-sided P: 0.019) in the validation set and captured 30.4% of cases that were missed with CA125 alone. Our findings reveal a MYC-driven plasma polyamine signature associated with OvCa that complemented CA125 in detecting early-stage ovarian cancer.

scholarly journals Serum Anti-PDLIM1 Autoantibody as Diagnostic Marker in Ovarian Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Cuipeng Qiu ◽  
Yaru Duan ◽  
Bofei Wang ◽  
Jianxiang Shi ◽  
Peng Wang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Detection ◽  
Early Stage ◽  
Diagnostic Value ◽  
Tissue Array ◽  
Enzyme Linked Immunosorbent Assay ◽  
Positive Staining ◽  
Healthy Controls ◽  
Autoantibody Response ◽  
Validation Set

BackgroundSerum autoantibodies (AAbs) against tumor-associated antigens (TAAs) could be useful biomarkers for cancer detection. This study aims to evaluate the diagnostic value of autoantibody against PDLIM1 for improving the detection of ovarian cancer (OC).MethodsImmunohistochemistry (IHC) test in tissue array containing 280 OC tissues, 20 adjacent tissues, and 8 normal ovarian tissues was performed to analyze the expression of PDLIM1 in tissues. Enzyme-linked immunosorbent assay (ELISA) was employed to measure the autoantibody to PDLIM1 in 545 sera samples from 182 patients with OC, 181 patients with ovarian benign diseases, and 182 healthy controls.ResultsThe results of IHC indicated that 84.3% (236/280) OC tissues were positively stained with PDLIM1, while no positive staining was found in adjacent or normal ovarian tissues. The frequency of anti-PDLIM1 autoantibody was significantly higher in OC patients than that in healthy and ovarian benign controls in both training (n=122) and validation (n=423) sets. The area under the curves (AUCs) of anti-PDLIM1 autoantibody for discriminating OC from healthy controls were 0.765 in training set and 0.740 in validation set, and the AUC of anti-PDLIM1 autoantibody for discriminating OC from ovarian benign controls was 0.757 in validation set. Overall, it was able to distinguish 35.7% of OC, 40.6% of patients with early-stage, and 39.5% of patients with late-stage. When combined with CA125, the AUC increased to 0.846, and 79.2% of OC were detected, which is statistically higher than CA125 (61.7%) or anti-PDLIM1(35.7%) alone (p<0.001). Also, anti-PDLIM1 autoantibody could identify 15% (18/120) of patients that were negative with CA125 (CA125 <35 U/ml).ConclusionsThe anti-PDLIM1 autoantibody response in OC patients was positively correlated with PDLIM1 high expression in OC tissues, suggesting that the autoantibody against PDLIM1 might have the potential to be a novel serological biomarker of OC, serving as a complementary measure of CA125, which could improve the power of OC detection.

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 Phosphatase Regenerating Liver3 and ECadherin Expression in Epithelial Ovarian Cancer

2017 ◽  
pp. 170-175
Author(s):  
Risma Maharani ◽  
Syahrul Rauf ◽  
Rina Masadah
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Early Stage ◽  
Cross Sectional Study ◽  
Teaching Hospitals ◽  
Advanced Stage ◽  
Cross Sectional ◽  
Exact Test ◽  
Significant Difference ◽  
E Cadherin

Objective: To determine the expression of Phosphatase Regenerating Liver-3 (PRL-3) and E-Cadherin in the epithelial ovarian cancer on various stages and differentiation grades. Method: This was a cross-sectional study design conducted at Obstetrics and Gynecology Department of several teaching hospitals, Faculty of Medicine Universitas Hasanuddin from January to June 2015. The expression of PRL-3 and E-cadherin was assessed immunohistochemically in 40 patients with epithelial ovarian cancer including 15 patients in early stage and 25 patients in advanced stage. We used the Fisher’s exact test with the significance of p0.05). The significant difference was found in the expression of E-cadherin whereas the high expression was shown at early stage than advanced stage (p0.05). This study also pointed out no correlation between the expression of PRL-3 and E-cadherin in epithelial ovarian cancer (p>0.05). Conclusion: PRL-3 overexpression does not decrease E-cadherin expression in epithelial ovarian cancer. Keywords: E-cadherin, epithelial ovarian cancer, PRL-3

scholarly journals Multi-Marker Longitudinal Algorithms Incorporating HE4 and CA125 in Ovarian Cancer Screening of Postmenopausal Women

Cancers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1931
Author(s):  
Aleksandra Gentry-Maharaj ◽  
Oleg Blyuss ◽  
Andy Ryan ◽  
Matthew Burnell ◽  
Chloe Karpinskyj ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Screening ◽  
Postmenopausal Women ◽  
Area Under The Curve ◽  
Serum Samples ◽  
Ovarian Cancer Screening ◽  
Serum Ca125 ◽  
Line Test ◽  
Validation Set ◽  
Serial Samples

Longitudinal CA125 algorithms are the current basis of ovarian cancer screening. We report on longitudinal algorithms incorporating multiple markers. In the multimodal arm of United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), 50,640 postmenopausal women underwent annual screening using a serum CA125 longitudinal algorithm. Women (cases) with invasive tubo-ovarian cancer (WHO 2014) following outcome review with stored annual serum samples donated in the 5 years preceding diagnosis were matched 1:1 to controls (no invasive tubo-ovarian cancer) in terms of the number of annual samples and age at randomisation. Blinded samples were assayed for serum human epididymis protein 4 (HE4), CA72-4 and anti-TP53 autoantibodies. Multimarker method of mean trends (MMT) longitudinal algorithms were developed using the assay results and trial CA125 values on the training set and evaluated in the blinded validation set. The study set comprised of 1363 (2–5 per woman) serial samples from 179 cases and 181 controls. In the validation set, area under the curve (AUC) and sensitivity of longitudinal CA125-MMT algorithm were 0.911 (0.871–0.952) and 90.5% (82.5–98.6%). None of the longitudinal multi-marker algorithms (CA125-HE4, CA125-HE4-CA72-4, CA125-HE4-CA72-4-anti-TP53) performed better or improved on lead-time. Our population study suggests that longitudinal HE4, CA72-4, anti-TP53 autoantibodies adds little value to longitudinal serum CA125 as a first-line test in ovarian cancer screening of postmenopausal women.

Diagnostic Performance of Risk of Ovarian Malignancy Algorithm Against CA125 and HE4 in Connection With Ovarian Cancer: A Meta-analysis

2016 ◽  
Vol 26 (9) ◽  
pp. 1586-1593 ◽  
Author(s):  
Farshid Dayyani ◽  
Steffen Uhlig ◽  
Bertrand Colson ◽  
Kirsten Simon ◽  
Vinzent Rolny ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Confidence Interval ◽  
Late Stage ◽  
Diagnostic Performance ◽  
Clinical Decision Making ◽  
Early Stage ◽  
Area Under The Curve ◽  
Ovarian Malignancy ◽  
Eligibility Criteria ◽  
Sensitivity Specificity

ObjectivesThe aim of this study was to determine whether the Risk of Ovarian Malignancy Algorithm (ROMA) is more accurate than the human epididymis 4 (HE4) or carbohydrate antigen 125 (CA125) biomarkers with respect to the differential diagnosis of women with a pelvic mass. The secondary objective is to assess the performance of ROMA in early-stage ovarian cancer (OC) and late-stage OC, as well as premenopausal and postmenopausal patient populations.Methods/MaterialsThe PubMed and Google Scholar databases were searched for relevant clinical studies. Eligibility criteria included comparison of ROMA with both HE4 and CA125 levels in OC (unspecified, epithelial, and borderline ovarian tumors), use of only validated ROMA assays, presentation of area under the curve and sensitivity/specificity data, and results from early-stage OC, late-stage OC and premenopausal and postmenopausal women. Area under the curve (AUC), sensitivity/specificity, and the diagnostic odds ratio (DOR) results were summarized.ResultsFive studies were selected comprising 1975 patients (premenopausal, n = 1033; postmenopausal, n = 925; benign, n = 1387; early stage, n = 192; and late stage, n = 313). On the basis of the AUC (95% confidence interval) data for all patients, ROMA (0.921 [0.855–0.960]) had a numerically greater diagnostic performance than CA125 (0.883 [0.771–0.950]) and HE4 (0.899 [0.835–0.943]). This was also observed in each of the subgroup populations, in particular, the postmenopausal patients and patients with early OC. The sensitivity and specificity (95% confidence interval) results showed ROMA (sensitivity, 0.873 [0.752–0.940]; specificity, 0.855 [0.719–0.932]) to be numerically superior to CA125 (sensitivity, 0.796 [0.663–0.885]; specificity, 0.825 [0.662–0.919]) and HE4 (sensitivity, 0.817 [0.683–0.902]; specificity, 0.851 [0.716–0.928]) in all patients and for the early- and late-stage OC subgroups. Finally, the ROMA log DOR results were better than HE4 and CA125 log DOR results especially for the early-stage patient group.ConclusionsThe results presented support the use of ROMA to improve clinical decision making, most notably in patients with early OC.

scholarly journals Diagnostic Value of Plasma Annexin A2 in Early-Stage High-Grade Serous Ovarian Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 69
Author(s):  
Noor A. Lokman ◽  
Carmela Ricciardelli ◽  
Andrew N. Stephens ◽  
Thomas W. Jobling ◽  
Peter Hoffmann ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Detection ◽  
Early Stage ◽  
Diagnostic Value ◽  
Annexin A2 ◽  
High Grade ◽  
Healthy Controls ◽  
Operating Characteristics ◽  
Logistic Regression Models ◽  
Stage Ia

Ovarian cancer (OC) is commonly diagnosed at advanced stage when prognosis is poor. Consequently, there is an urgent clinical need to identify novel biomarkers for early detection to improve survival. We examined the diagnostic value of the calcium phospholipid binding protein annexin A2 (ANXA2), which plays an important role in OC metastasis. Annexin A2 plasma levels in patients with high grade serous OC (n = 105), benign ovarian lesions (n = 55) and healthy controls (n = 143) were measured by ELISA. Annexin A2 levels were found to be significantly increased in patients with stage I (p < 0.0001) and stage IA (p = 0.0027) OC when compared to healthy controls. In the logistic regression models followed by receiver operating characteristics (ROC) curve analyses, plasma annexin A2 showed 46.7% sensitivity at 99.6% specificity in distinguishing stage IA OC patients from healthy controls and 75% sensitivity at 65.5% specificity in the diagnosis of stage IA versus benign ovarian tumors. In the diagnosis of stage IA OC versus normal controls, the combination of plasma annexin A2 and CA125 showed 80% sensitivity at 99.6% specificity (AUC = 0.970) which was significantly higher than for CA125 (53.3% sensitivity at 99.6% specificity; AUC = 0.891) alone. The diagnostic accuracy in distinguishing stage IA OC from benign ovarian disease when combining annexin A2 and CA125 (71.4% accuracy at 100% sensitivity) was almost twice as high compared to CA125 (37.1% accuracy at 100% sensitivity) alone. In conclusion, annexin A2 in combination with CA125 has potential as a biomarker for the early detection of OC and to predict malignancy in patients with ovarian lesions, warranting further investigations.

scholarly journals Discrimination Between Invasive and In Situ Melanomas Using Clinical Close-Up Images and a De Novo Convolutional Neural Network

2021 ◽  
Vol 8 ◽  
Author(s):  
Sam Polesie ◽  
Martin Gillstedt ◽  
Gustav Ahlgren ◽  
Hannah Ceder ◽  
Johan Dahlén Gyllencreutz ◽  
...  
Keyword(s):  
De Novo ◽  
Area Under The Curve ◽  
Classification Problem ◽  
Dense Layer ◽  
Training Set ◽  
Clinical Images ◽  
Test Set ◽  
Time Period ◽  
Validation Set

Background: Melanomas are often easy to recognize clinically but determining whether a melanoma is in situ (MIS) or invasive is often more challenging even with the aid of dermoscopy. Recently, convolutional neural networks (CNNs) have made significant and rapid advances within dermatology image analysis. The aims of this investigation were to create a de novo CNN for differentiating between MIS and invasive melanomas based on clinical close-up images and to compare its performance on a test set to seven dermatologists.Methods: A retrospective study including clinical images of MIS and invasive melanomas obtained from our department during a five-year time period (2016–2020) was conducted. Overall, 1,551 images [819 MIS (52.8%) and 732 invasive melanomas (47.2%)] were available. The images were randomized into three groups: training set (n = 1,051), validation set (n = 200), and test set (n = 300). A de novo CNN model with seven convolutional layers and a single dense layer was developed.Results: The area under the curve was 0.72 for the CNN (95% CI 0.66–0.78) and 0.81 for dermatologists (95% CI 0.76–0.86) (P &lt; 0.001). The CNN correctly classified 208 out of 300 lesions (69.3%) whereas the corresponding number for dermatologists was 216 (72.0%). When comparing the CNN performance to each individual reader, three dermatologists significantly outperformed the CNN.Conclusions: For this classification problem, the CNN was outperformed by the dermatologist. However, since the algorithm was only trained and validated on 1,251 images, future refinement and development could make it useful for dermatologists in a real-world setting.

scholarly journals Elevated Interleukin-6 Levels in the Circulation and Peritoneal Fluid of Patients with Ovarian Cancer as a Potential Diagnostic Biomarker: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 11 (12) ◽  
pp. 1335
Author(s):  
Hina Amer ◽  
Apriliana E. R. Kartikasari ◽  
Magdalena Plebanski
Keyword(s):  
Ovarian Cancer ◽  
Late Stage ◽  
Diagnostic Performance ◽  
Diagnostic Marker ◽  
Early Stage ◽  
Meta Analysis ◽  
Healthy Controls ◽  
Diagnostic Biomarker ◽  
The Mean ◽  
Higher Sensitivity

Ovarian cancer (OC) is one of the most lethal cancers, largely due to a late diagnosis. This study aimed to provide a comprehensive meta-analysis on the diagnostic performance of IL6 in the blood and ascites separately for advanced and early-stage OC. We included 37 studies with 6948 participants detecting serum or plasma IL6. The plasma/serum IL6 mean level in the late-stage OC was 23.88 pg/mL (95% CI: 13.84–41.23), and the early-stage OC was 16.67 pg/mL (95% CI: 510.06–27.61), significantly higher than the healthy controls at 3.96 pg/mL (95% CI: 2.02–7.73), but not significantly higher than those found in the controls with benign growths in the ovary, which was 9.63 pg/mL (95% CI: 4.16–22.26). To evaluate IL6 in ascites as a diagnostic marker, we included 26 studies with 1590 participants. The mean level of ascitic IL6 in the late-stage OC was 3676.93 pg/mL (95% CI: 1891.7–7146.7), and the early-stage OC was 1519.21 pg/mL (95% CI: 604.6–3817.7), significantly higher than the benign controls at 247.33 pg/mL (95% CI: 96.2–636.0). There was no significant correlation between the levels of circulating and ascitic IL6. When pooling all OC stages for analysis, we found that serum/plasma IL6 provided 76.7% sensitivity (95% CI: 0.71–0.92) and 72% specificity (95% CI: 0.64–0.79). Ascitic IL6 provided higher sensitivity at 84% (95% CI: 0.710–0.919) and specificity at 74% (95% CI: 0.646–0.826). This study highlights the utility of ascitic IL6 for early detection of OC.

scholarly journals Untargeted Metabolomic Characterization of Ovarian Tumors

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3642
Author(s):  
Xiaona Liu ◽  
Gang Liu ◽  
Lihua Chen ◽  
Fei Liu ◽  
Xiaozhe Zhang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Diagnosis ◽  
High Performance ◽  
Clinical Symptoms ◽  
Early Stage ◽  
Ovarian Tumors ◽  
Diagnostic Model ◽  
Plasma Samples ◽  
Flight Mass Spectrometry ◽  
Validation Set

Diagnosis of ovarian cancer is difficult due to the lack of clinical symptoms and effective screening algorithms. In this study, we aim to develop models for ovarian cancer diagnosis by detecting metabolites in urine and plasma samples. Ultra-high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) in positive ion mode was used for metabolome quantification in 235 urine samples and 331 plasma samples. Then, Urine and plasma metabolomic profiles were analyzed by univariate and multivariate statistics. Four groups of samples: normal control, benign, borderline and malignant ovarian tumors were enrolled in this study. A total of 1330 features and 1302 features were detected from urine and plasma samples respectively. Based on two urine putative metabolites, five plasma putative metabolites and five urine putative metabolites, three models for distinguishing normal-ovarian tumors, benign-malignant (borderline + malignant) and borderline-malignant ovarian tumors were developed respectively. The AUC (Area Under Curve) values were 0.987, 0876 and 0.943 in discovery set and 0.984, 0.896 and 0.836 in validation set for three models. Specially, the diagnostic model based on 5 plasma putative metabolites had better early-stage diagnosis performance than CA125 alone. The AUC values of the model were 0.847 and 0.988 in discovery and validation set respectively. Our results showed that normal and ovarian tumors have unique metabolic signature in urine and plasma samples, which shed light on the ovarian cancer diagnosis and classification.

scholarly journals Validation of a Blood-Based Laboratory Test to Aid in the Confirmation of a Diagnosis of Schizophrenia

2010 ◽  
Vol 5 ◽  
pp. BMI.S4877 ◽  
Author(s):  
Emanuel Schwarz ◽  
Rauf Izmailov ◽  
Michael Spain ◽  
Anthony Barnes ◽  
James P. Mapes ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Area Under The Curve ◽  
Healthy Controls ◽  
Receiver Operating Characteristic Area ◽  
Specific Prediction ◽  
Characteristic Area ◽  
Disease Signature ◽  
Based Medicine ◽  
And Control ◽  
Independent Cohort

We describe the validation of a serum-based test developed by Rules-Based Medicine which can be used to help confirm the diagnosis of schizophrenia. In preliminary studies using multiplex immunoassay profiling technology, we identified a disease signature comprised of 51 analytes which could distinguish schizophrenia (n = 250) from control (n = 230) subjects. In the next stage, these analytes were developed as a refined 51-plex immunoassay panel for validation using a large independent cohort of schizophrenia (n = 577) and control (n = 229) subjects. The resulting test yielded an overall sensitivity of 83% and specificity of 83% with a receiver operating characteristic area under the curve (ROC-AUC) of 89%. These 51 immunoassays and the associated decision rule delivered a sensitive and specific prediction for the presence of schizophrenia in patients compared to matched healthy controls.

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