BACKGROUND:Prior studies have examined survival in patients with epithelial ovarian cancer (EOC); however, few consider race and ethnicity, particularly disaggregating Asian and Native Hawaiian/Pacific Islander women. METHODS:We analysed data from 18 Ovarian Cancer Association Consortium studies, including women with EOC from Asian (n = 697), non-Hispanic Black (n = 267), Hispanic (n = 492), Native Hawaiian/Pacific Islander (n = 98) and non-Hispanic White (n = 12,998) racial and ethnic groups. We ran Cox proportional hazards models estimating overall survival by race and ethnicity, adjusting for age, stage, year of diagnosis, and histotype, with fully adjusted models accounting for body mass index, smoking, and postmenopausal hormone use. We also examined associations between hormone-related factors and family history and overall survival by race and ethnicity, testing for heterogeneity. RESULTS:Compared to non-Hispanic White women with EOC, Native Hawaiian/Pacific Islander and non-Hispanic Black women had poorer overall survival (Hazard Ratios, HR = 1.58, 95% CI = 1.16-2.16, and HR = 1.31, 95% CI = 1.12-1.54, respectively). The association was more pronounced for Native Hawaiian/Pacific Islander women with high-grade serous carcinoma (HR = 2.00, 95% CI = 1.37-2.92). There was no significant heterogeneity in the associations between epidemiological factors and survival by racial and ethnic groups (p ≥ 0.31). DISCUSSION:Native Hawaiian/Pacific Islander and non-Hispanic Black women with EOC had poorer survival, highlighting the need to address disparities in outcome.

Abstract Background: There is currently no ovarian cancer biomarker appropriate for screening which is partially due to using retrospective clinical samples obtained at the time of diagnosis for biomarker discovery. Thus, we sought to discover novel plasma proteomic biomarkers for ovarian cancer early detection using prospectively collected blood samples. Method: We evaluated 10,778 plasma proteins measured using the SomaScan v5.0 assay in blood drawn at least three years prior to ovarian cancer diagnosis and matched controls in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO; n=98, training dataset) and the Nurses’ Health Studies (NHS; n=99, replication dataset). We used a conditional logistic regression to identify individual proteins associated with ovarian cancer in the two datasets separately. We also compared plasma proteins for early-stage and late-stage ovarian cancer in blood collected at diagnosis in the PreOperative Pelvic Mass Study to age-matched population-based controls (PreOp; n=134). Then we used Elastic Net to develop a proteomic-based score to discriminate ovarian cancer cases from controls in PLCO, compared to a model with CA125 alone, and validated the proteomic-based score performance in NHS by calculating the area under the receiver operating characteristic curve (AUC) and 95% confidence interval (CI). Results: Plasma proteins associated with ovarian cancer in blood samples collected prospectively differed from those associated with blood samples collected at diagnosis of early-stage disease compared to controls. There were 99 proteins associated with ovarian cancer diagnosed at least 3 years from blood collection (p<0.05) in PLCO, where 2 proteins, RCN3 and OBP2B, replicated in NHS (p<0.05). Of these 99 proteins, majority were not associated with ovarian cancer in PreOp and only three proteins overlapped (i.e., SERPINF2, ASAH2, BAGE3). In PLCO, adding a proteomic-based score comprised of 56 proteins to a model with CA125 alone significantly (p=0.02) improved discriminating ovarian cancer cases from controls with an AUC (95%CI) from 0.65(0.50-0.80) to 0.86(0.67,1.00). In NHS, proteomic-based score resulted in an AUC of 0.60(0.49-0.72) with marginal significance. Conclusion: Our results revealed plasma proteomic profiles differ between prospectively collected blood samples at least 3 years prior to diagnosis and blood samples collected at time of diagnosis regardless of stage. We developed a proteomics-based score that improved upon CA-125 alone, although application to an independent cohort did not demonstrate a strong improvement. However, differences between studies (e.g., menopausal status and hormone therapy use) may explain this variation. Citation Format: Nan Lin, Ngo Long, Allison F. Vitonis, Tara Eicher, SHELLEY TWOROGER, Simon T. Dillon, Towia A. Libermann, Daniel W. Cramer, John Quackenbush, Kathryn L. Terry, Naoko Sasamoto. Development and validation of a plasma proteomics signature for earlier diagnosis of ovarian cancer using prospectively collected blood samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2310.

OBJECTIVE:To evaluate the association between per- and polyfluoroalkyl substances (PFAS) in follicular fluid (FF) and live birth after in vitro fertilization (IVF) and characterize the FF metabolome in relation to both. DESIGN:Retrospective cohort. SUBJECTS:Thirty-six women who underwent IVF treatment at 1 of the 3 centers in Eastern Massachusetts between 1999 and 2003. EXPOSURE:Twenty-four PFAS were measured in FF retrieved from the first follicle aspirated during the first treatment cycle. The 8 PFAS detected in >90% of samples were evaluated. MAIN OUTCOME MEASURES:We analyzed the FF metabolome using untargeted liquid chromatography high-resolution mass spectrometry. We used linear regression to estimate associations between FF PFAS and metabolic feature intensities, and logistic regression to estimate associations of FF PFAS and metabolites with live birth, adjusting for age and infertility type. We subsequently conducted pathway enrichment analyses and used a meet-in-the-middle approach to screen for overlapping metabolic pathways associated with FF PFAS and live birth. RESULTS:Higher FF perfluoroheptanesulfonic acid (PFHpS) was associated with lower odds of live birth (odds ratio = 0.42 [95% confidence interval: 0.15-0.97]). All other FF PFAS, except perfluorobutanesulfonic acid (PFBS), were also inversely associated with live birth, although confidence intervals included the null value. We evaluated 27,903 features detected in >25% of participant samples. For PFAS-feature associations, the top 5% of features for each PFAS were enriched for bile acid biosynthesis. Thirty-eight FF metabolic pathways enriched for features associated with live birth (yes/no) overlapped with pathways enriched for features associated with perfluorooctanoic acid (PFOA), PFHpS, or perfluorohexanesulfonic acid (PFHxS). These included pathways related to lipid (n = 11), carbohydrate (n = 9), and vitamin and cofactor metabolism (n = 6), among others. CONCLUSION:Higher FF PFAS concentrations were associated with lower odds of achieving live birth, although most 95% confidence intervals included the null. Metabolic pathways enriched for features associated with live birth overlapping with those associated with FF PFAS were related to lipid, carbohydrate, vitamin, and cofactor metabolism. Although our sample size was small, our findings align with other studies characterizing environmental exposures in reproductive organs and using the metabolome to assess their impact on fertility.

BACKGROUND:Prognosis after a diagnosis of invasive epithelial ovarian cancer is poor. Some studies have suggested modifiable behaviors, like diet, are associated with survival but the evidence is inconsistent. OBJECTIVES:This study aims to pool data from studies conducted around the world to evaluate the relationships among dietary indices, foods, and nutrients from food sources and survival after a diagnosis of ovarian cancer. METHODS:This analysis from the Multidisciplinary Ovarian Cancer Outcomes Group within the Ovarian Cancer Association Consortium included 13 studies with 7700 individuals with ovarian cancer, who completed food-frequency questionnaires regarding their prediagnosis diet. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CI) for associations with overall survival were estimated using Cox proportional hazards models. RESULTS:Overall, there was no association between any of the 7 dietary indices (representing prediagnosis diet) evaluated and survival; however, associations differed by tumor stage. Although there were no consistent associations among those with advanced disease, among those with earlier stage (local/regional) disease, higher scores on the alternate Healthy Eating Index (aHR quartile 4 compared with 1 = 0.66, 95% CI: 0.50, 0.87), Healthy Eating Index-2015 (aHR: 0.75; 95% CI: 0.59, 0.97), and alternate Mediterranean diet (aHR: 0.76; 95% CI: 0.60, 0.98) were associated with better survival. Better survival was also observed for individuals with early-stage disease who reported higher intakes of dietary components that contribute to the healthy diet indices (aHR for Q4 compared with Q1: vegetables 0.71; 95% CI: 0.56, 0.91), tomatoes (aHR: 0.72; 95% CI: 0.57, 0.91) and nuts and seeds (aHR 0.71; 95% CI: 0.55, 0.92). In contrast, there were suggestions of worse survival with higher scores on 2 of the 3 inflammatory indices and higher intake of trans-fatty acids. CONCLUSIONS:Adherence to a more healthy, less-inflammatory diet may confer a survival benefit for individuals with early-stage ovarian cancer.

Supplemental Table 1: Adjusted geometric mean hormone levels by lifetime ovulatory years quartile among postmenopausal women not using hormone therapy at the time of blood draw in the Nurses' Health Study; excluding hysterectomized women whose age at menopause was n=1683

BACKGROUND:Ovarian high-grade serous carcinomas (HGSC) comprise four distinct molecular subtypes based on mRNA expression patterns, with differential survival. Understanding risk factor associations is important to elucidate the etiology of HGSC. We investigated associations between different epidemiologic risk factors and HGSC molecular subtypes. METHODS:We pooled data from 11 case-control studies with epidemiologic and tumor gene expression data from custom NanoString CodeSets developed through a collaboration within the Ovarian Tumor Tissue Analysis consortium. The PrOTYPE-validated NanoString-based 55-gene classifier was used to assign HGSC gene expression subtypes. We examined associations between epidemiologic factors and HGSC subtypes in 2,070 cases and 16,633 controls using multivariable-adjusted polytomous regression models. RESULTS:Among the 2,070 HGSC cases, 556 (27%) were classified as C1.MES, 340 (16%) as C5.PRO, 538 (26%) as C2.IMM, and 636 (31%) as C4.DIF. The key factors, including oral contraceptive use, parity, breastfeeding, and family history of ovarian cancer, were similarly associated with all subtypes. Heterogeneity was observed for several factors. Former smoking [OR = 1.25; 95% confidence interval (CI) = 1.03, 1.51] and genital powder use (OR = 1.42; 95% CI = 1.08, 1.86) were uniquely associated with C2.IMM. History of endometriosis was associated with C5.PRO (OR = 1.46; 95% CI = 0.98, 2.16) and C4.DIF (OR = 1.27; 95% CI = 0.94, 1.71) only. Family history of breast cancer (OR = 1.44; 95% CI = 1.16, 1.78) and current smoking (OR = 1.40; 95% CI = 1.11, 1.76) were associated with C4.DIF only. CONCLUSIONS:This study observed heterogeneous associations of epidemiologic and modifiable factors with HGSC molecular subtypes. IMPACT:The different patterns of associations may provide key information about the etiology of the four subtypes.

This table shows the associations between epidemiologic factors and HGSC subtypes restricted to HGSC cases with probability of subtype assignment >80%.

BACKGROUND:Cancers of ductal origin often express glycoprotein mucin 1 (MUC1), also known as CA15.3, with higher levels leading to poor prognosis. Conversely, anti-MUC1 antibodies develop in some patients, leading to better prognosis. We sought to identify epidemiologic factors associated with CA15.3 antigen or antibody levels. METHODS:Levels of CA15.3 antigen and anti-CA15.3 IgG antibodies were measured in archived sera from 2,302 mostly healthy women from the National Health and Nutritional Survey; and epidemiologic predictors of their levels were examined using multivariate and correlational analyses. RESULTS:Among racial groups, Black women had the highest levels of CA15.3 antigen and lowest levels of antibodies. Increasing body mass index and current smoking were associated with low anti-CA15.3 antibody levels. Low CA15.3 antigen levels were seen in oral contraceptive users and high levels in women who were pregnant or lactating at the time of blood collection, with the latter group also having high antibody levels. Past reproductive events associated with high antigen levels included the following: later age at menarche, having given birth, and history of endometriosis. Lower antigen levels were seen with increasing duration of OC use. Anti-CA15.3 antibody levels decreased with an increasing estimated number of ovulatory years. CONCLUSIONS:Key determinants of CA.15.3 antigen or antibody levels include the following: race, body mass index, smoking, later menarche, childbirth, number of ovulatory cycles, and endometriosis. IMPACT:This study supports the premise that known epidemiologic factors affecting risk for or survival after MUC1-expressing cancers may, at least partially, operate through their association with CA15.3 antigen or antibody levels.

Abstract Background: Ovarian cancer has a poor prognosis with a 5-year survival less than 50%, resulting > 14,000 deaths in the US annually and > 150,000 globally. Identifying prognostic biomarkers at time of diagnosis and elucidating the biological dysregulation among those who are more likely to progress after first-line treatment could inform personalized treatment strategies. Thus, we evaluated metabolites and metabolomic profiles in pretreatment blood associated with survival in women with high-grade serous ovarian cancer, the most common and most deadly histologic subtype. Method: We examined plasma metabolites in blood samples collected prior to ovarian cancer treatment in 80 high-grade serous ovarian cancer patients who participated in the PreOperative Pelvic Mass Study, a clinic-based study enrolling women undergoing surgery for a pelvic mass. Liquid chromatography tandem mass spectrometry was used to measure 524 known metabolites. We excluded metabolites with coefficient of variation ≥ 25% (n = 104), then removed metabolites with missing in > 20% of the samples (n = 3). Metabolites missing in < 20% of the samples were imputed to be half the minimum value for that metabolite, resulting in 417 unduplicated metabolites. All metabolite values were transformed to probit scores to achieve normality. Cox proportional hazard models were used to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) adjusting for age at diagnosis, body mass index (BMI), and stage. False discovery rate (FDR) was used for multiple testing correction. Metabolite set enrichment analysis (MSEA) was used to identify metabolite classes associated with prognosis. Results: Overall, none of the individual metabolites were significantly associated with survival after multiple testing correction (FDR > 0.05). However, 32 metabolites were associated with survival with p< 0.05, with 12 metabolites being associated with worse survival (HR range: 1.38 - 1.98) and 20 metabolites being associated with better survival (HR range: 0.54 - 0.73). MSEA revealed triglycerides (normalized enrichment score (NES) = 3.14), cholesteryl esters (NES = 2.43), and phosphatidylcholines (NES = 2.30) were positively associated with risk of death (FDR < 0.001). Steroids and steroid derivatives (NES = -1.93), sphingomyelins (NES = -2.45), and carnitines (NES = -2.27) were negatively associated with risk of death (FDR < 0.001). Diglycerides (NES = 1.96), lysophosphatidylethanolamines (NES = 1.72), and phosphatidylcholine plasmalogens (NES = 1.58) were positively associated and phosphatidylethanolamines (NES = -1.93) was negatively associated with risk of death with FDR < 0.05. Discussion: In this study, we explored plasma metabolites associated with mortality in high-grade serous ovarian cancer patients and observed multiple classes of lipid-related metabolites being associated with worse prognosis. Citation Format: Nan Lin, Oana A. Zeleznik, Julian Avila-Pacheco, Clary B. Clish, Allison F. Vitonis, Daniel W. Cramer, Kathryn L. Terry, Naoko Sasamoto. Pre-surgical blood metabolites associated with ovarian cancer prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3682.

Supplementary Figure 1. Distributions of CA15.3 antigens and antibodies.

Abstract Background: Serum miRNAs have been proposed as potential biomarkers for the early detection of ovarian cancer (OC). The heterogenous histological subtypes of ovarian cancer show different morphologic and genetic alterations influencing outcome and survival. We aimed to evaluate if serum miRNA levels differ with histological OC subtype and FIGO stage. Methods: We included women with histological confirmed OC from three study sets: 36 specimens from the Mass General Brigham Biobank collected between 2012 and 2019, 51 specimens from women in the pelvic mass study treated at the Brigham and Women’s Hospital (BWH) or Massachusetts General Hospital (MGH) between 1998 and 2009 and 93 specimens from Aspira Women’s Heath collected between 2007 and 2012. Data from 1790 women without OC from the Mass General Brigham Biobank collected between 2012 and 2019 were used as controls. Serum levels of a focused panel of 179 circulating miRNAs were measured by flow cytometry using the Abcam Fireplex® assay. Differences in miRNA serum profiles were analyzed according to histological subtypes and early (FIGO stage I/II) vs. late stages (FIGO stage III/IV) by univariate analysis, adjusting for multiple comparisons using Bonferroni correction. Results: The study population of OC patients showed a median age range of 50-59 years and 70.2% were postmenopausal, while women in the control group were younger (median age range of 40-49 years) and 48.3% were postmenopausal. Among the study subjects, most OC were of epithelial origin (174 OC (96.1%)) and consisted of the following histological subtypes: 87 (48.1%) serous, 29 (16.0%) endometrioid, 15 (8.3%) clear cell, 18 (9.9%) mucinous, 1 (0.6%) transitional cell, 24 (13.3%) other epithelial OC, and 7 (3.9%) non-epithelial OC. Most patients were diagnosed with advanced stage disease (59.1%) and showed a high-grade histology (59.7%). Univariate analysis showed significant changes in circulating miRNA profiles for epithelial OC compared to the control group with significant changes in 133/179 miRNAs (corrected p<0.05). No significant changes in miRNA expressions were detected comparing serous vs. non-serous epithelial OC and comparing any histological epithelial subtype (serous, endometrioid, clear cell or mucinous OC) to all other OCs in the study population. Further sub-group analyses comparing early vs. late-stage OCs among serous epithelial and non-serous epithelial OC did not reveal any significant differences in miRNA profiles, either. Conclusion: Ovarian cancers show a distinct miRNA profile, but different histological epithelial OC subtypes and tumor stages cannot be distinguished by miRNA expression levels. Detection of OC by miRNAs as potential biomarkers can be performed independent of histological subtype and tumor stage. Citation Format: Laura Wollborn, James W. Webber, Sudhanshu Mishra, Chad B. Sussman, Cameron E. Comrie, Daniel G. Packard, Allison Vitonis, Stephanie Alimena, Ryan Phan, Todd Pappas, Daniel W. Cramer, Dipanjan Chowdhury, Kevin M. Elias. Serum miRNA expression in ovarian cancer patients is independent of histological subtype and FIGO stage [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B044.

Survival from ovarian cancer depends on the resection status after primary surgery. We performed genome-wide association analyses for resection status of 7705 ovarian cancer patients, including 4954 with high-grade serous carcinoma (HGSOC), to identify variants associated with residual disease. The most significant association with resection status was observed for rs72845444, upstream of MGMT , in HGSOC ( p = 3.9 × 10 −8 ). In gene-based analyses, PPP2R5C was the most strongly associated gene in HGSOC after stage adjustment. In an independent set of 378 ovarian tumours from the AGO-OVAR 11 study, variants near MGMT and PPP2R5C correlated with methylation and transcript levels, and PPP2R5C mRNA levels predicted progression-free survival in patients with residual disease. MGMT encodes a DNA repair enzyme, and PPP2R5C encodes the B56γ subunit of the PP2A tumour suppressor. Our results link heritable variation at these two loci with resection status in HGSOC.

Abstract Background: The recent literature proposes several diagnostic Machine Learning (ML) models for ovarian cancer based on miRNA expression profiling. These ML models are trained and validated on subjects whose miRNA sample was drawn proximate to cancer diagnosis (e.g., within one month). Whether these models remain useful remote from the time of diagnosis, when early detection or prevention would be most relevant, is unclear. Therefore, we examine the effect of time between blood draw and cancer diagnosis on miRNA-based ML model accuracy and the relevance of a cancer probability score, Pc, for estimating long-term cancer risk. Methods: The study is based on the miRNA expression profiles of 2983 total subjects, which is comprised of 1829 subjects collected as part of the Biobank at Mass General Brigham (MGB), 110 samples from the Pelvic Mass Protocol at Brigham and Women's Hospital (Cramer), and 1044 samples which were obtained from the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial. miRNA expression was measured using a pre-specified panel of 179 miRNAs optimized for serum detection using the Fireplex® circulating miRNA assay. We trained ML models using 1865 controls and 74 ovarian cancer subjects (1829 Biobank + 55 Cramer + 55 PLCO). The training cancer samples were drawn between 1 day and 14 years from diagnosis, most within 30 days. The models were then validated on 769 control and 275 ovarian cancer subjects (989 PLCO + 55 Cramer) whose time to cancer diagnosis ranges between 1 and 1814 days (up to 5 years) after blood draw. Performance was reported as Area Under the receiver operator characteristic Curve (AUC). Results: Among the validation set cases, we observe a decreasing trend in predicted cancer probability (Pc) with increasing log time (R = -0.34, p < 0.0001), and we see a similar trend in AUC score with time. On samples drawn within 21 days of cancer diagnosis, the ML model offers an AUC = 0.88, which decreases to AUC = 0.72 on samples drawn between 21 days and one year from diagnosis, and later plateaus at AUC ~ 0.72 up to five years from diagnosis. Out of 2928 total subjects considered in the study, 286 had multiple blood draws taken over a 5-year time period. Using this data, we analyze the change in Pc over time per subject. The results show that Pc, for the average case subject, increased by 7% per year (p = 0.02). In contrast, when we applied the same analysis to control subjects, Pc increased by only 1% per year on average (p = 0.62). Thus, monitoring changes in Pc at regular intervals could be an informative cancer diagnostic. In terms of relative risk, subjects with Pc < 0.5 had a relative 5-year cancer risk of 0.74, whereas subjects with Pc > 0.5 had relative risk 7.4 (i.e., an order of magnitude higher). Conclusion: The results indicate that miRNA-based ML models can be used to identify individuals at increased long-term risk of ovarian cancer and provide a tool for profiling at regular intervals to allow earlier diagnosis of disease. Citation Format: James Webber, Laura Wollborn, Sudhanshu Mishra, Stephanie Alimena, Bryanna Testino, Allison Vitonis, Daniel Cramer, Dipanjan Chowdhury, Kevin Elias. Time to diagnosis analysis using miRNA-based ovarian cancer prediction models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3896.

BACKGROUND:Ovarian cancer remains the deadliest of the gynecologic cancers in the United States. There have been limited advances in treatment strategies that have seen marked increases in overall survival. Thus, it is essential to continue developing and validating new treatment strategies and markers to identify patients who would benefit from the new strategy. In this report, we sought to further validate applications for a novel humanized anti-Sialyl Tn antibody-drug conjugate (anti-STn-ADC) in ovarian cancer.METHODS:We aimed to further test a humanized anti-STn-ADC in sialyl-Tn (STn) positive and negative ovarian cancer cell line, patient-derived organoid (PDO), and patient-derived xenograft (PDX) models. Furthermore, we sought to determine whether serum STn levels would reflect STn positivity in the tumor samples enabling us to identify patients that an anti-STn-ADC strategy would best serve. We developed a custom ELISA with high specificity and sensitivity, that was used to assess whether circulating STn levels would correlate with stage, progression-free survival, overall survival, and its value in augmenting CA-125 as a diagnostic. Lastly, we assessed whether the serum levels reflected what was observed via immunohistochemical analysis in a subset of tumor samples.RESULTS:Our in vitro experiments further define the specificity of the anti-STn-ADC. The ovarian cancer PDO, and PDX models provide additional support for an anti-STn-ADC-based strategy for targeting ovarian cancer. The custom serum ELISA was informative in potential triaging of patients with elevated levels of STn. However, it was not sensitive enough to add value to existing CA-125 levels for a diagnostic. While the ELISA identified non-serous ovarian tumors with low CA-125 levels, the sample numbers were too small to provide any confidence the STn ELISA would meaningfully add to CA-125 for diagnosis.CONCLUSIONS:Our preclinical data support the concept that an anti-STn-ADC may be a viable option for treating patients with elevated STn levels. Moreover, our STn-based ELISA could complement IHC in identifying patients with whom an anti-STn-based strategy might be more effective.

Limited estimates exist on risk factors for epithelial ovarian cancer (EOC) in Asian, Hispanic, and Native Hawaiian/Pacific Islander women. Participants in this study included 1734 Asian (n = 785 case and 949 control participants), 266 Native Hawaiian/Pacific Islander (n = 99 case and 167 control participants), 1149 Hispanic (n = 505 case and 644 control participants), and 24 189 White (n = 9981 case and 14 208 control participants) from 11 studies in the Ovarian Cancer Association Consortium. Logistic regression models estimated odds ratios (ORs) and 95% CIs for risk associations by race and ethnicity. Heterogeneity in EOC risk associations by race and ethnicity (P ≤ .02) was observed for oral contraceptive (OC) use, parity, tubal ligation, and smoking. We observed inverse associations with EOC risk for OC use and parity across all groups; associations were strongest in Native Hawaiian/Pacific Islander and Asian women. The inverse association for tubal ligation with risk was most pronounced for Native Hawaiian/Pacific Islander participants (odds ratio (OR) = 0.25; 95% CI, 0.13-0.48) compared with Asian and White participants (OR = 0.68 [95% CI, 0.51-0.90] and OR = 0.78 [95% CI, 0.73-0.85], respectively). Differences in EOC risk factor associations were observed across racial and ethnic groups, which could be due, in part, to varying prevalence of EOC histotypes. Inclusion of greater diversity in future studies is essential to inform prevention strategies. This article is part of a Special Collection on Gynecological Cancers.

BACKGROUND:Tea and coffee are the most frequently consumed beverages in the world. Green tea in particular contains compounds with potential anti-cancer effects, but its association with survival after ovarian cancer is uncertain. METHODS:We investigated the associations between tea and coffee consumption before diagnosis and survival using data from 10 studies in the Ovarian Cancer Association Consortium. Data on tea (green, black, herbal), coffee and caffeine intake were available for up to 5724 women. We used Cox proportional hazards regression to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CI). RESULTS:Compared with women who did not drink any green tea, consumption of one or more cups/day was associated with better overall survival (aHR = 0.84, 95% CI 0.71-1.00, p-trend = 0.04). A similar association was seen for ovarian cancer-specific survival in five studies with this information (aHR = 0.81, 0.66-0.99, p-trend = 0.045). There was no consistent variation between subgroups defined by clinical or lifestyle characteristics and adjustment for other aspects of lifestyle did not appreciably alter the estimates. We found no evidence of an association between coffee, black or herbal tea, or caffeine intake and survival. CONCLUSION:The observed association with green tea consumption before diagnosis raises the possibility that consumption after diagnosis might improve patient outcomes.

OBJECTIVE:To determine whether a multimodal assay combining serum microRNA with protein biomarkers and metadata improves triage assessment of an adnexal mass. METHODS:Serum samples from 468 training subjects (191 cancer cases and 277 benign adnexal mass controls or healthy controls) were analyzed for seven protein biomarkers and 180 miRNA. Circulating analyte data were combined with age and menopausal status (metadata) into a neural network model to classify samples as cases or controls. Forward regression with ten-fold cross-validation minimized the dimensionality of the model while maximizing linear separation between cases and controls. Model validation proceeded using both internal (44 cases and 56 controls) and external validation sets (51 cases and 59 controls). RESULTS:The total study population comprised 678 subjects, including 286 cases and 392 controls. Overall, 290 (43%) of the subjects were premenopausal. A panel of 10 miRNA delivered optimal performance when combined with protein and metadata features. The combined model improved the Receiver Operator Characteristic Area Under the Curve (ROC AUC) on the internal (AUC = 0.9; 95% CI 0.81-0.95) and external validation sets (AUC = 0.95; 95% CI 0.90-0.98) compared to miRNA alone or proteins plus metadata (without miRNA). On external validation, the combined model offered 92% sensitivity at 80% specificity overall, with 80% and 100% sensitivity for early and late-stage cancers, respectively, including 78% sensitivity for early-stage, serous ovarian cancers and 82% sensitivity for early-stage, non-serous cancers. CONCLUSIONS:A multimodal assay combining miRNA with protein biomarkers, age, and menopausal status improves surgical triage of an adnexal mass.