Detection Of Early Stage Ovarian Cancer Research Articles

595P Multi-marker liquid biopsy for detection of early-stage ovarian cancer

Ovarian cancer is often not detected (∼70% of the cases) until it has spread to regional or distant sites, thus limiting curative options. In contrast, when detected in a localized state, the 5-year survival rate increases dramatically to ∼90% in comparison to the ∼30% survival rate for distant cases. Currently, only 15% of cases are detected at early stages highlighting the need for the development of non-invasive detection strategies such as liquid biopsies.

Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches.

One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient individuals to conduct predictive analytics for early diagnosis. In statistical analysis, Student’s t-test as well as log fold changes of two groups are used to find the significant blood biomarkers. Furthermore, a set of machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LGBM) are used to build classification models to stratify benign-vs.-malignant ovarian cancer patients. Both of the analysis techniques recognized that the serumsamples carbohydrate antigen 125, carbohydrate antigen 19-9, carcinoembryonic antigen and human epididymis protein 4 are the top-most significant biomarkers as well as neutrophil ratio, thrombocytocrit, hematocrit blood samples, alanine aminotransferase, calcium, indirect bilirubin, uric acid, natriumas as general chemistry tests. Moreover, the results from predictive analysis suggest that the machine learning models can classify malignant patients from benign patients with accuracy as good as 91%. Since generally, early-stage detection is not available, machine learning detection could play a significant role in cancer diagnosis.

Abstract 3390: Preliminary results for a novel single extracellular vesicle assay for early stage ovarian cancer: The power of co-localized detection of surface biomarkers

Abstract Introduction: Ovarian cancer (OC) is one of the deadliest cancers, with 314,000 new cases and 207,000 deaths globally in 2020. Serum CA125 has been explored as an OC biomarker for the past 40 years, but lacks sensitivity for early stage OC and is not recommended for screening average-risk, asymptomatic women. We hypothesize that co-localization of biomarkers on the surface of individual extracellular vesicles (EVs), which are shed into the circulation by cancer cells, may lead to development of a blood test for early stage OC. We evaluated the potential of our approach in detecting early stage OC in clinical samples. Methods: We isolated EVs using size-exclusion chromatography and immunoaffinity capture, and detected biomarkers co-localized on the surface of individual EVs with proximity ligation qPCR. Using this approach, we evaluated 49 antibody combinations recognizing 2 or more biomarkers. Each combination consisted of 1 capture antibody and 2 oligonucleotide-tagged detection antibodies. We tested plasma samples from women with early stage I/II high-grade serous ovarian carcinoma (HGSOC)(n=18; 48-80 yr, med 57) and late stage HGSOC (n=24; 37-80 yr, med 54). HGSOC samples were sourced from 2 commercial vendors. Controls comprised samples from women with benign ovarian masses (n=26; 23-76 yr, med 39.5) sourced from a single vendor, and samples prospectively collected by Mercy from healthy women with no cancer history (n=24; 22-72 yr, med 52.5). PCR cycle threshold (Ct) values were measured for each of 49 combinations and data was evaluated using univariate analysis. Performance was compared to plasma CA125 measured at Mercy by commercial ELISA. Results: 8 of 49 combinations distinguished all stages of HGSOC relative to benign and healthy controls with AUCs ranging from 0.86 (95% CI 0.78-0.94) to 0.95 (95% CI 0.90-1.00), comparable to CA125 with an AUC of 0.87 (95% CI 0.79-0.95). One of the most effective combinations (STn, BST2, MUC1) had a sensitivity of 0.78 (95% CI 0.52-0.94) at a specificity of 0.96 (95% CI 0.87-0.99) in detecting early stage HGSOC. This combination also detected HGSOC in 6 of 11 women (3 early stage, 3 late stage) with normal CA125 (< 25 U/mL) and correctly classified 7 of 8 women with benign masses and high CA125 (> 25 U/mL). Conclusions: These preliminary data suggest that co-localization of surface biomarkers in single EVs may provide an effective means to identify women with early stage HGSOC, including those with normal CA125, while avoiding false positives in women with benign masses and high CA125. Despite the inherent challenges associated with commercial samples, our finding that several combinations detected early stage HGSOC is promising. Statistically powered studies with curated repository specimens are underway to refine combinations and independently validate our assay for early stage OC detection. Citation Format: Laura T. Bortolin, Daniel P. Salem, Sanchari Banerjee, Kelly M. Biette, Delaney M. Byrne, Anthony D. Couvillon, Peter A. Duff, Jonian Grosha, MacKenzie Sadie King, Christopher R. Sedlak, Ibukunoluwapo O. Zabroski, Claire Alexander, Karen Copeland, Daniel Gusenleitner, Emily S. Winn-Deen, Eric K. Huang, Bo R. Rueda, Joseph Charles Sedlak. Preliminary results for a novel single extracellular vesicle assay for early stage ovarian cancer: The power of co-localized detection of surface biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3390.

Detection of early-stage ovarian cancer using a liquid biopsy test based on extracellular vesicle proteins.

e17558 Background: We have recently conducted a study to evaluate the predictive value of proteins associated with extracellular vesicles (EVs) isolated using an alternating current electrokinetic (ACE) system, for the detection of early stage (I & II) ovarian cancer cases. Ovarian cancer patients, typically, have few symptoms in stages I & II; the disease is often undetected until it is advanced. Currently, only 15% of cases are detected at early stages when clinical treatment outcomes are improved. Methods: We tested the feasibility of detecting the presence of early stage (I-II) ovarian cancer using protein biomarkers carried by EVs in patient blood. Using an ACE microelectrode array, EVs were purified from ovarian cancer patient plasma (N = 70; Stage I = 51, Stage II = 19) and female non-cancer donors (N = 290) and tested for the presence of cancer biomarker proteins with multiple bead-based multiplex immunoassay kits. An adaptive boosted trees machine learning algorithm with 100x cross-validation was developed to identify the most important features for differentiation between cases and controls. Results: The machine learning algorithm identified relevant biomarkers for the detection of ovarian cancer with a receiver-operator characteristic area under the curve (AUC) of 0.952 (95% CI: 0.918 – 0.985). Overall sensitivity was 72.9% at 99% specificity, rising to 84.3% at 95% specificity. When further stratified, Stage I sensitivity was 70.6% and stage II sensitivity was 78.9% at the 99% specificity level, as expected. We also separated the cases into endometroid carcinoma (N = 24) and serous carcinoma (N = 36). At the 99% specificity level, the sensitivity was 87.5% and 63.9%, respectively. Conclusions: These results suggest that it is possible to detect early-stage ovarian cancer by analyzing the EV-bound proteins isolated with the ACE technology. EVs may carry the earliest signals of cancer development and a blood-based EV-protein biomarker test can yield high sensitivity and specificity. Stable biomarker detection with small input volumes may be possible. These results require further investigation in larger datasets to validate the current observations with the potential application for general, routine screening, as well as for populations at elevated disease risk.

Extracellular vesicle-based biomarker assay for the detection of early-stage ovarian cancer.

5542 Background: Detection of cancer with improved discrimination compared to current blood tests could be achieved using an approach that assesses extracellular vesicles (EVs). This approach should have high sensitivity (se) because of EVs abundance in blood and high specificity (sp) by assaying EVs with multiple cancer-related protein and glycosylation epitopes (PGEs) co-localized on their surfaces. We are developing a platform technology that detects multiple cancer-related PGEs co-localized on the same EV using immunoaffinity-capture and proximity-ligation qPCR. In this study, we compare the performance of this technology vs plasma CA125 for correctly categorizing early-stage high-grade serous ovarian cancer (HGSOC) vs healthy/benign ovarian tumors (OT). Methods: We evaluated our EV-based platform technology using 7 PGE combinations to discriminate HGSOC from benign adnexal masses. We first derived a prediction model on a retrospectively collected cohort of 42 HGSOC and 26 benign OT samples from 2 commercial vendors and 24 healthy controls (HC) using a machine-learning algorithm. We validated this model on an independent cohort [89 HGSOC: Stage I (17), II (35), III (37); 192 benign OT] from university-associated biobanks and 124 HC. We also assessed the assay’s performance in plasma from 87 women with off-target cancers and 42 women with inflammatory conditions from commercial vendors. For each sample, we also measured CA125 levels using a commercial ELISA. Results: The prediction model distinguishes HGSOC from benign and HC with an AUC of 0.965 (95% CI 0.93-0.99), with 89.9% (0.82-0.95) se at 98% sp. For stage I/II HGSOC, the model achieves an AUC of 0.942 (0.9-0.99), with 84.6% (0.72-0.93) se at 98% sp. By comparison, CA125 achieves an AUC of 0.875 (0.81-0.94) and 44.2% (0.3-0.59) se at 98% sp. Direct comparison of CA125 and our model shows a significant difference at 98% sp for both all and stage I/II HGSOC (McNemar p-val < 0.001). When comparing HGSOC to HC, there is no significant difference between our model and CA125 (p-val = 1.0). There is a significant difference when comparing patients with all stage and stage I/II HGSOC to patients with benign OT (p-val < 0.001). Our assay had 1 false positive and CA125 had 3 false positives out of 42 inflammatory cases. Conclusions: These preliminary data suggest our platform technology for detecting PGEs co-localized on individual EVs may detect all stages of HGSOC from plasma with high se at a very high sp. Our assay may improve on CA125 by distinguishing stage I/II HGSOC from benign OT and could have clinical utility for both early detection and surgical referral recommendation for benign and malignant OT. While the diverse cohorts in this study may present challenges in interpretation, the reproducibility in an independent cohort is encouraging and supports further investigation using cases and controls from well-defined cohort studies.

ADAM17-A Potential Blood-Based Biomarker for Detection of Early-Stage Ovarian Cancer.

Simple SummaryOvarian cancer has the highest lethality among gynecological tumors. Therefore, it is essential to find reliable biomarkers to improve early detection. This is the first report describing ADAM17 detection in serum and ascites fluid of ovarian cancer patients. A high ADAM17 concentration in serum at primary diagnosis is associated with early FIGO stages and predicts complete resection of the tumor mass. In addition, ADAM17 and CA-125 complement each other, especially in the diagnosis of early stages. In summary, ADAM17 appears to be a promising screening marker for detecting early-stage ovarian cancer.Ovarian cancer has the highest mortality rate among gynecological tumors. This is based on late diagnosis and the lack of early symptoms. To improve early detection, it is essential to find reliable biomarkers. The metalloprotease ADAM17 could be a potential marker, as it is highly expressed in many solid tumors, including ovarian and breast cancer. The aim of this work is to evaluate the relevance of ADAM17 as a potential diagnostic blood-based biomarker in ovarian cancer. Ovarian cancer cell lines IGROV-1 and A2780, as well as primary patient-derived tumor cells obtained from tumor tissue and ascitic fluid, were cultured to analyze ADAM17 abundance in the culture supernatant. In a translational approach, a cohort of 117 well-characterized ovarian cancer patients was assembled and ADAM17 levels in serum and corresponding ascitic fluid were determined at primary diagnosis. ADAM17 was quantified by enzyme-linked immunosorbent assay (ELISA). In the present study, ADAM17 was detected in the culture supernatant of ovarian cancer cell lines and primary cells. In addition, ADAM17 was found in serum and ascites of ovarian cancer patients. ADAM17 level was significantly increased in ovarian cancer patients compared to an age-matched control group (p < 0.0001). Importantly early FIGO I/II stages, which would not have been detected by CA-125, were associated with higher ADAM17 concentrations (p = 0.007). This is the first study proposing ADAM17 as a serum tumor marker in the setting of a gynecological tumor disease. Usage of ADAM17 in combination with CA-125 and other markers could help detect early stages of ovarian cancer.

Combining the biomarkers macrophage inhibitory factor, osteopontin and prolactin with CA-125 improves early detection of ovarian cancer

<h3>Objectives:</h3> To determine the combined efficacy of MIF, OPN, PROL and CA-125 in distinguishing ovarian cancer from healthy compared to CA-125 alone as a single biomarker. <h3>Methods:</h3> Serum samples from 153 ovarian cancer patients (37 Stage I/II and 116 Stage III/IV) and 279 healthy age-matched controls were utilized in this study. The ovarian cancer group (n=153, average age=57.1 yr) was composed of women with newly diagnosed ovarian cancer following discovery of pelvic mass. Mean concentration of MIF, OPN, PROL and CA-125 was measured using a novel, fully automated, multi-analyte immunoassay platform, Ella. The serum samples were randomly divided into two different cohorts (n=153 and n=279) to be interchangeably used as training and testing sets for statistical analysis. Four statistical classification models, k-nearest neighbors, logistic regression, random forest, and support vector machines were used to calculate accuracy, sensitivity, specificity, positive-predictive value (PPV), and negative-predictive value (NPV) of the four protein biomarker panel and CA-125 alone. <h3>Results:</h3> In distinguishing serum of ovarian cancer patients from healthy controls, the four-protein biomarker panel yielded an average accuracy of 91% compared to 85% when using CA-125 alone across the four classification models (p=3.224e-09). Further, the four-protein biomarker produced statistically significantly higher sensitivity (median of 76%), specificity (median of 98%), PPV (median of 91.5%), and NPV (median of 92%), compared to CA-125 alone in distinguishing ovarian cancer from healthy controls (Wilcoxon p-values: 2.22e-07, 0.02, 1.27e-2, and 3.629e-07, respectively; Figure 1). Importantly, the improved performance of the four-protein biomarker over CA-125 alone is maintained in the detection of early-stage ovarian cancer (Stage I/II) from health controls. <h3>Conclusions:</h3> Combining MIF, OPN, and PROL to CA-125 resulted in improved detection of ovarian cancer compared to CA-125 alone. This study is another step forward towards developing a useful serologic biomarker panel for early detection of ovarian cancer that is both highly accurate and specific. The multi-analyte platform, Ella, is fully automated, highly reproducible and a useful tool in future ovarian cancer biomarker discoveries.

Abstract B28: Optimizing DNA processing and ovarian cancer methylation-specific PCR assays for the detection of early-stage ovarian cancer

Abstract Background: Ovarian cancer screening has the potential to greatly reduce mortality by shifting the time of diagnosis to an early stage when the tumor is confined to a single site and can be completely cured by surgical removal. Ovarian cancer DNA is present in blood and has a distinct methylation profile that distinguishes it from healthy blood cells, thus making it a target for ovarian cancer diagnosis via a blood test. However, while the presence of this DNA in the blood of cancer patients is well established, low abundance and DNA fragmentation present a barrier to developing clinical applications that target it. Methods: We are systematically evaluating and optimizing each step of DNA extraction, processing, and analysis to increase the sensitivity of cancer DNA detection by PCR. At the same time, we are developing PCR assays to detect ovarian cancer specific methylation based on a previous tumor and blood screen that identified 73 methylated candidate biomarker regions. Results: We have found that inhibitors of the PCR reaction introduced into the sample during DNA extraction and bisulphite treatment limit the total amount of DNA that can be analyzed, and thus the sensitivity of the test. Precipitation of the DNA samples prior to PCR analysis removes the inhibitors and increases useable sample volume and thus sensitivity. We have also developed methylation-specific PCR assays for DNA regions that are methylated in ovarian tumor tissue, and unmethylated in white blood cells. Conclusions: We are progressing our candidate methylated DNA ovarian cancer biomarkers closer to testing in clinical samples. Our results on sample processing and DNA analysis methods to enable ovarian cancer DNA detection are relevant to all cancer types for which cell-free tumor DNA has clinical applications. Citation Format: Kristina Warton, Nicole Yuwono, Claire Henry, Neville Hacker, Caroline Ford. Optimizing DNA processing and ovarian cancer methylation-specific PCR assays for the detection of early-stage ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B28.

The generally low sensitivity of CA 125 for FIGO stage I ovarian cancer diagnosis increases for endometrioid histotype.

The serum marker CA 125 is still the most widely used biomarker for ovarian cancer (OC) diagnosis in gynecological and oncological setting, but its predictive role in early-stage OC is still debated. The aim of this study was to explore the value of CA 125 in distinguishing between early-stage OC and borderline ovarian tumor (BOT) and to evaluate the accuracy of CA 125 in the detection of early stage OC. A retrospective cohort study was performed at the University Hospital of Bologna (Italy) on 1296 consecutive women suffering from OC or BOT (diagnosed at histology) between 1988-2017. Patients for whom CA 125 level was determined preoperatively were included. The positive cut-off level used was >35 U/mL. Of 910 patients, 192 (21.1%) were diagnosed with BOT and 718 (78.9%) with OC. The sensitivity of CA 125 for stage I OC was 54.4 (95% CI: 45.3-63.3) (51.5 for IA, 54.6 for IB, 58.3 for IC), but it increased to 78.0 (95% CI: 63.7-88.0) for stage II. Interestingly, in stage I OC, CA 125 presented a significantly higher sensitivity for the endometrioid histotype [72.4 (95% CI: 52.5-86.5) vs. 49.0 (95% CI: 38.6-59.4), P=0.026]. The positive likelihood ratio of CA 125 for early-stage OC compared to BOT was 1.29 (95% CI: 1.06-1.58). Despite its limited sensitivity for early-stage OCs, CA 125 still represents a useful serum marker to early differentiate between OCs and BOTs. Its sensitivity for stage I OC increases in endometrioid histotype.

A novel biosensor for the monitoring of ovarian cancer tumor protein CA 125 in untreated human plasma samples using a novel nano-ink: a new platform for efficient diagnosis of cancer using paper based microfluidic technology

Carcinoma antigen 125 (CA 125) has been recognized as a clinical glycoprotein/biomarker for the early-stage detection of ovarian cancer.

Pre-operative Circulating Plasma Gelsolin Predicts Residual Disease and Detects Early Stage Ovarian Cancer

Ovarian cancer (OVCA) patients with suboptimal residual disease (RD) and advanced stages have poor survival. pGSN is an actin binding protein which protects OVCA cells from cisplatin-induced death. There is an urgent need to discover reliable biomarkers to optimize individualized treatment recommendations. 99 plasma samples with pre-determined CA125 were collected from OVCA patients and pGSN assayed using sandwich-based ELISA. Associations between CA125, pGSN and clinicopathological parameters were examined using Fisher’s exact test, T test and Kruskal Wallis Test. Univariate and multivariate Cox proportional hazard models were used to statistically analyze clinical outcomes. At 64 µg/ml, pGSN had sensitivity and specificity of 60% and 60% respectively, for the prediction of RD where as that of CA125 at 576.5 U/mL was 43.5% and 56.5% respectively. Patients with stage 1 tumor had increased levels of pre-operative pGSN compared to those with tumor stage >1 and healthy subjects (P = 0.005). At the value of 81 µg/mL, pGSN had a sensitivity and specificity of 75% and 78.4%, respectively for the detection of early stage OVCA. At the value of 0.133, the Indicator of Stage 1 OVCA (ISO1) provided a sensitivity of 100% at a specificity of 67% (AUC, 0.89; P < 0.001). In the multivariate Cox regression analysis, pGSN (HR, 2.00; CI, 0.99–4.05; P = 0.05) was an independent significant predictor of progression free survival (PFS) but not CA125 (HR, 0.68; CI, 0.41–1.13; P = 0.13). Pre-operative circulating pGSN is a favorable and independent biomarker for early disease detection, RD prediction and patients’ prognosis.

Abstract 912: Macrophage inhibitory factor (MIF), osteopontin (OPN) and anti-interleukin-8 (IL-8) autoantibodies (AAb) complement CA125 for detection of early-stage ovarian cancer

Abstract Background: Early detection of ovarian cancer promises to reduce mortality. At present, only 20 percent of ovarian cancer is found in early stage (I-II). CA125 is found in sera from approximately 60% of patients with early stage disease. Greater sensitivity could be observed with a panel of blood tests. Methods: A panel of 10 antigen biomarkers (MIF, Leptin, IL-6, IL-8, TNFa, OPN, FGF2, HE4, TGFa and a panel of autoantibodies (AAbs) against 12 antigens (IL-8, MDM2, c-myc, PLAT, EpCAM, HOXA7, ENO1, PDI, HSPA5, HSPA8, ANXA2, Cathepsin D) were analyzed in sera of 77 ovarian cancer patients with early stage (I-II), 50 patients with late stage (III-IV), and 216 healthy women from the Normal Risk Ovarian Cancer Screening Study (NROSS) using Luminex-based immunoassays. Serum levels of serum tumor marker CA-125 were routinely measured in all patients using a Roche assay. The most promising markers (CA125, MIF, OPN, and anti-IL-8 autoantibodies were validated using an independent panel with sera from 69 early stage and 50 late ovarian cancer patients and 200 healthy controls. Result: Serum level of CA125, MIF, OPN and IL-8 AAb were elevated in early stage (I-II) and late stage (III-IV) ovarian cancer patients compared with healthy controls. Analyzing the positive biomarkers in combination with CA125 increased detection of early stage disease from 62% with CA125 alone to 92% a panel of CA125, MIF, OPN and IL-8 AAb at 98% specificity. For patients with late stage disease sensitivity increased from 90% to 98% with the panel. Complementarity of CA125 with MIF, OPN and IL-8 AAb was confirmed with an independent validation set, where sensitivity for early stage disease was improved from 84% to 91% at 98% specificity. Conclusion: MIF, OPN and IL8 AAb could be used in combination with CA125 to distinguish ovarian cancer patients from health controls with high sensitivity. Citation Format: Jing Guo, Wei-lei Yang, Joseph Celestino, Karen Lu, Anna E. Lokshin, Zhen Lu, Robert C. Bast. Macrophage inhibitory factor (MIF), osteopontin (OPN) and anti-interleukin-8 (IL-8) autoantibodies (AAb) complement CA125 for detection of early-stage ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 912.

Abstract 5404: RNA sequencing-based discovery and validation of a noninvasive microRNA signature for the detection of early-stage ovarian cancer

Abstract Purpose Ovarian cancer (OC) is one of the most lethal gynecological malignancies with a 5-year survival rate of less than 50%. While only 15% of OCs are diagnosed at an early stage (stage 1), more than 75% of these neoplasms at first presentation are already advanced (stage 3 or 4), where a cure is very unlikely and recurrent disease is quite common. Previous attempts at discovering early detection biomarkers for OC have largely failed because of reliance on sub-optimal discovery approaches and the lack of comprehensive patient cohorts. MicroRNAs (miRNAs), which belong to the family of small non-coding RNAs, are frequently dysregulated in cancers including OC. Although several miRNA biomarkers have previously been reported for OC patients, lack of systematic biomarker discovery and validation approach has hampered their clinical translation. To address some of these limitations, we performed a comprehensive and systematic biomarker discovery and validation to identify miRNAs dysregulated in early stage OCs, with the ultimate aim of developing a blood-based early diagnostic assay. Experimental design We performed miRNA expression profiling using a high-throughput small RNA-sequencing in stage 1 ovarian cancer (N=23) and matched normal (N=10) tissues. Differentially expressed miRNAs between cancer and normal tissues were identified using the limma package, which were subsequently validated in a cohort of plasma samples (N=32, healthy individuals and N=32 stage 1 OC patients) using Taqman-based RT-PCR assays. Results Our genome-wide systematic discovery approach led to the identification of eight miRNAs which were significantly upregulated in OC vs. adjacent normal tissues. Of note, even individual miRNAs achieved AUROC values ranging from 0.80 to 0.89; however, the combined panel of these 8 miRNAs achieved an even greater AUROC of 0.90 in detecting stage 1 OC patients (p&amp;lt;0.001). Subsequently, we analyzed this 8-miRNA panel in a set of 64 plasma (N=32, Healthy subjects and N=32, stage 1 OC patients) using RT-PCR assays. We performed binary logistic regression and ROC analysis to evaluate the diagnostic accuracy of these biomarkers. Interestingly, in accordance with the tissue findings, we achieved AUROC values ranging from 0.70 to 0.90 for individual biomarkers, while the combination panel achieved an impressive AUROC of 0.99 (95% CI: 0.98-1.00, p&amp;lt;0.0001) for the identification of early stage ovarian cancer patients. Conclusions In conclusion, our RNA sequencing based comprehensive biomarker discovery identified an 8-miRNA panel that demonstrated a robust diagnostic accuracy in not only tissue specimens, but also in the plasma specimens from early stage OC patients. Further evaluation of these miRNAs in multicenter retrospective and prospective cohorts might lead to the development of a non-invasive diagnostic, as well as a population-screening assay for OC patients. Citation Format: Raju Kandimalla, Wei Wang, Michael Hsieh, Gerald C. Gooden, Monique Spillman, Xin Wang, Bodour Salhia, Ajay Goel. RNA sequencing-based discovery and validation of a noninvasive microRNA signature for the detection of early-stage ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5404.

Abstract 4122: Acidic tumor microenvironment targeted wormhole-shaped mesoporous silica nanoparticles to detect ovarian cancer by multispectral optoacoustic tomography

Abstract Purpose: The implementation of the innovative technologies remains the top priority for the development of potential modalities for the diagnosis and treatment of various cancers. Despite all the recent advances, ovarian cancer is considered as a lethal gynecologic malignancy in which vast majority of the cases are diagnosed at the late metastatic stage at which the prognosis is poor. Because of the few apparent early symptoms, significant effort was made for developing efficient methods to identify early progression of the disease. We demonstrated that the bioconjugation of the V7 peptide on 27nm wormhole mesoporous silica nanoparticles (V7-CWMSN) particles specifically release the IR780 imaging probe at the orthotopically implanted early-stage ovarian tumor to detect by multispectral optoacoustic imaging technology (MSOT). Methods: Wormhole mesoporous silica nanoparticles (WMSN) were synthesized by sol-gel chemistry. The WMSN were characterized by dynamic light scattering (DLS) and transmission electron microscopy (TEM). The surface of the WMSN particle was functionalized with pH sensitizer chitosan to obtain CWMSN. The CWMSN particle was loaded with propidium Iodide (PI) or IR780 infrared imaging dye. Further, the conjugation of the CWMSN with V7 pHLIP peptide (V7-CWMSN) developed pH sensitive cargo release from the nanoparticles. Female athymic mice were orthotopically implanted with ES-2 ovarian cancer cells. Following 10 days of orthotopic implantation of the tumor cells, mice were intravenously injected with V7-CWMSN particles containing IR780 dye and were imaged with MSOT. Results: In the current study, we have synthesized the wormhole-shaped mesoporous silica particles (WMSN) with the 27 nm diameter carrying IR780 imaging probes for the detection of early-stage ovarian cancer. The V7 peptide undergoes a conformational change upon contact with the acidic tumor microenvironment. Also, the chitosan crosslinking on the surface of the particles (V7-CWMSN) acts as a gatekeeper that is degradable upon contact with acidic pH tumor to prevent off-target release. As a model for ovarian cancer, we have used athymic mice orthotopically implanted with ovarian cancer cells. This model closely resembles the human ovarian cancer pathophysiology. Our results have demonstrated that the intravenous delivery of V7-CWMSN particles could detect the orthotopically implanted early-stage ovarian tumors (p&amp;lt;0.0001,n=5). Conclusion: The development of the distinct nanoformulations with potent imaging technology enables the visualization of early-stage ovarian tumors. Translating these modalities may allow clinicians to identify early-stage malignancies that are currently undetectable through conventional imaging techniques. Citation Format: Abhilash Samykutty, Molly W. McNally, William E. Grizzle, Akiko Chiba, Alexandra Thomas, Lacey R. McNally. Acidic tumor microenvironment targeted wormhole-shaped mesoporous silica nanoparticles to detect ovarian cancer by multispectral optoacoustic tomography [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4122.

Folic Acid Targeting for Efficient Isolation and Detection of Ovarian Cancer CTCs from Human Whole Blood Based on Two-Step Binding Strategy.

Studies regarding circulating tumor cells (CTCs) have great significance for cancer prognosis, treatment monitoring, and metastasis diagnosis. However, due to their extremely low concentration in peripheral blood, isolation and enrichment of CTCs are the key steps for early detection. To this end, targeting the folic acid receptors (FRs)on the CTC surface for capture with folic acid (FA) using bovine serum albumin (BSA)-tether for multibiotin enhancement in combination with streptavidin-coated magnetic nanoparticles (MNPs-SA) was developed for ovarian cancer CTC isolation. The streptavidin-biotin-system-mediated two-step binding strategy was shown to capture CTCs from whole blood efficiently without the need for a pretreatment process. The optimized parameters for this system exhibited an average capture efficiency of 80%, which was 25% higher than that of FA-decorated magnetic nanoparticles based on the one-step CTC separation method. Moreover, the isolated cells remained highly viable and were cultured directly without detachment from the MNPs-SA-biotin-CTC complex. Furthermore, when the system was applied for the isolation and detection of CTCs in ovarian cancer patients' peripheral blood samples, it exhibited an 80% correlation with clinical diagnostic criteria. The results indicated that FA targeting, in combination with BSA-based multibiotin enhancement magnetic nanoparticle separation, is a promising tool for CTC enrichment and detection of early-stage ovarian cancer.

Early screening of ovarian cancer

The mortality of ovarian cancer is higher than any other female genital malignant tumors, while there exists a strong correlation between early-stage detection and cure for it. CA125 and HE4 are two most common and effective serum markers in recent screening research of ovarian cancer. This paper derives a sequential screening strategy for ovarian cancer by jointly modeling the longitudinal profiles of CA125 and HE4. We construct a Bayesian hierarchical mixture model with changepoint, and propose two approaches for diagnosis: the risk of cancer index and the hypothesis test on the true incidence time. We simulated a 7-year sequential screening research and compared with the standard approach based on a fixed cutoff level. Our approach achieves a 15% higher sensitivity for a fixed specificity, indicating that the sequential strategy combining multiple markers is more effective in the early-stage detection of ovarian cancer.

Significance of adding progesterone to the Risk of Ovarian Malignancy Algorithm for early stage ovarian cancer detection in patients with a pelvic mass: A single-center case–control study

ObjectiveTo evaluate the clinical significance of the combination of cancer antigen-125 (CA-125), human epididymis protein 4 (HE4), and progesterone for the identification of ovarian masses in patients with suspected early stage ovarian cancer (OC). Materials and methodsThis was a case–control, single-center study of 225 women with a pelvic mass of suspected ovarian origin, including 75 patients with Stage I/II OC and 150 controls. Diagnostic procedures included pelvic and rectal examinations, transvaginal ultrasound, evaluation of CA-125 and HE4 levels alone and in the Risk of Ovarian Malignancy Algorithm (ROMA), and a new algorithm combining ROMA and progesterone. ResultsMedian CA-125 and HE4 levels were significantly higher in patients with OC compared with women with benign ovarian tumors, irrespective of menopausal status. The highest median progesterone levels occurred in premenopausal women with benign ovarian tumors, compared with premenopausal women with OC with or without benign ovarian disease. The combination of ROMA and progesterone was significantly more accurate at detecting OC compared with ROMA or CA-125 or HE4 alone, but only in premenopausal patients. ConclusionDifferent algorithms should be used for diagnosing OC, and the addition of progesterone might improve the performance of ROMA for the diagnosis of pelvic masses in premenopausal women.

Highly-accurate metabolomic detection of early-stage ovarian cancer.

High performance mass spectrometry was employed to interrogate the serum metabolome of early-stage ovarian cancer (OC) patients and age-matched control women. The resulting spectral features were used to establish a linear support vector machine (SVM) model of sixteen diagnostic metabolites that are able to identify early-stage OC with 100% accuracy in our patient cohort. The results provide evidence for the importance of lipid and fatty acid metabolism in OC and serve as the foundation of a clinically significant diagnostic test.

A multiplex methylation-specific PCR assay for detection of early-stage ovarian cancer using cell-free serum DNA.

5535 Background: Epithelial ovarian cancer (EOC) remains the most lethal disease among gynecological malignancies. Prompt diagnosis is challenging because of the non-specific symptoms exhibited during the early stage of the disease. So there is an urgent need for better detection methods. Here we performed this work to build up a platform of multiplex methylation-specific PCR (MSP) assay to improve the early detection of ovarian cancer, via identifying the methylation status of cell-free serum DNA. Methods: After screening, we chose seven genes (APC, RASSF1A, CDH1, RUNX3, TFPI2, SFRP5 and OPCML) with a high frequency of methylation as candidate genes to construct the multiplex-MSP assay. When methylation of at least one of the seven genes was observed, the multiplex-MSP assay was considered positive. We performed the retrospective and screening study to verify its specificity and sensitivity in the detection of EOC. Results: The methylation status of cell-free serum DNA was examined in the preoperative serum of 202 patients, including 87 EOC cases (stage I, n=41, stage II-IV, n=46), 53 benign ovarian tumors and 62 healthy controls. As expected, multiplex MSP assay achieved a sensitivity of 85.3% and a specificity of 90.5% in stage I EOC, strikingly higher than that of single CA125, producing a sensitivity of 56.1% at 64.15% specificity [p=0.0036](Table). Conclusions: Multiplex MSP assay analyzing the methylation status of cell-free serum DNA is a suitable and reliable approach to improve the early detection of ovarian cancer, potentially benefiting a broad range of applications in clinical oncology. [Table: see text]

A multiplex methylation-specific PCR assay for the detection of early-stage ovarian cancer using cell-free serum DNA

ObjectiveEpithelial ovarian cancer (EOC) remains the most lethal disease among gynecological malignancies. Prompt diagnosis is challenging because of the non-specific symptoms exhibited during the early stage of the disease. As a result, there is an urgent need for improved detection methods. In this study, we established a multiplex methylation-specific PCR (MSP) assay to improve the early detection of ovarian cancer, via identification of the methylation status of cell-free serum DNA. MethodsAfter screening, we chose seven candidate genes (APC, RASSF1A, CDH1, RUNX3, TFPI2, SFRP5 and OPCML) with a high frequency of methylation to construct the multiplex-MSP assay. When methylation of at least one of the seven genes was observed, the multiplex-MSP assay was considered positive. We performed retrospective and screening studies to verify the specificity and sensitivity of the assay in the detection of EOC. ResultsThe methylation status of cell-free serum DNA was examined in the preoperative serum of 202 patients, including 87 EOC patients (stage I, n=41; stage II–IV, n=46), 53 patients with benign ovarian tumors and 62 healthy controls. As expected, the multiplex MSP assay achieved a sensitivity of 85.3% and a specificity of 90.5% in stageI EOC, strikingly higher rates compared with a single CA125, which produced a sensitivity of 56.1% at 64.15% specificity [P=0.0036]. ConclusionA multiplex MSP assay that analyzes the methylation status of cell-free serum DNA is a suitable and reliable approach to improve the early detection of ovarian cancer, potentially benefiting a broad range of applications in clinical oncology.