Abstract Background: Chronic inflammation is implicated in ovarian carcinogenesis, but how different inflammation-related exposures individually or jointly affect histotype-specific associations remains unclear. Materials and Methods: We pooled data from 16 case-control studies in the Ovarian Cancer Association Consortium to evaluate associations of eight inflammation-related factors (anti-inflammatory: aspirin use, tubal ligation (TL); pro-inflammatory: endometriosis, obesity, lifetime ovulatory cycles (LOC), smoking, pelvic inflammatory disease (PID), polycystic ovary syndrome (PCOS)) with epithelial ovarian cancer (OvC) by histologic subtype. We examined individual associations and clustering of risk factors across histotypes and computed population attributable risk (PAR) for each factor. We assessed additive and multiplicative interactions for exposure combinations. Results: Associations with OvC risk differed by histotype (e.g., high-grade serous: aspirin: OR=0.90; 95%CI 0.82, 0.99; TL: OR=0.80; 95%CI 0.73, 0.88; overall serous: endometriosis: OR=1.17; 95%CI 1.03, 1.31; high LOC: OR=1.42; 95%CI 1.28, 1.58; obesity (low-grade serous): OR=1.50; 95%CI 1.14, 1.98). Clustering analyses showed highly correlated risk profiles in endometrioid and clear cell (r=0.91). High-grade serous and mucinous profiles were moderately correlated with endometrioid and clear cell (r=0.60) tumors. The profile for low-grade serous (r=0.36) tumors was distinct from other histotypes. PAR estimates suggested modifying aspirin use, TL, and LOCs could substantially reduce burdens of endometrioid, clear cell and mucinous tumors. Out of 28 exposure combinations tested in overall OvC and 189 by histotype, we observed 12 interactions. Not using aspirin regularly showed positive additive interactions with obesity and high LOCs, particularly in endometrioid tumors (obesity relative excess risk due to interaction (RERI)=0.74, 95%CI 0.31, 1.18; Pint=0.001 for; LOCs RERI=0.80, 95%CI 0.03, 1.56; Pint=0.04). Not using aspirin regularly also showed a positive additive interaction with endometriosis in clear cell tumors (RERI=1.77, 95%CI 0.03, 3.52; Pint=0.05). Lack of TL showed positive interactions with obesity in endometrioid (RERI=0.86, 95%CI 0.17, 1.53; Pint=0.01) and mucinous (RERI=1.10, 95%CI 0.23, 1.97; Pint=0.01) tumors, while negative additive interactions were observed for smoking and endometriosis in endometrioid tumors (RERI=-1.12, 95%CI -2.19, -0.05; Pint=0.04). A multiplicative interaction was observed between obesity and endometriosis in mucinous tumors (Pint=0.01). Conclusion: The findings suggest ovarian tumorigenesis is strongly shaped by pro- and anti-inflammatory pathways that act largely independently. Further examining these pathways may clarify the origins of histotype heterogeneity and guide prevention strategies. Citation Format: Maxwell Akonde, Britton Trabert, SHELLEY TWOROGER, Allan Jensen, Kathryn L. Terry, Joshua Sampson, Hoda Anton-Culver, David Bowtell, Elisa V. Bandera, Angela Brooks-Wilson, Andrew Berchuck, Daniel William Cramer, Linda S. Cook, Julie M. Cunningham, Jennifer A. Doherty, Ellen L. Goode, Marc T. Goodman, Holly Ruth Harris, Susanne K. Kjaer, Nhu Le, Alice Wen-Ron Lee, Francesmary Modugno, Kirsten B. Moysich, Celeste Pearce, Malcolm C. Pike, Harvey A. Risch, Mary A. Rossing, Joellen M. Schildkrau, Daniel O. Stram, Rebecca Sutphen, David Van Den Berg, Penelope M. Webb, Anna Wu, Argyrios Ziogas, Nicolas A. Wentzensen. Inflammation-related exposures and histotype- specific ovarian cancer risk in the Ovarian Cancer Association Consortium (OCAC) [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 6261.

Supplementary Figure 1: Methylation Signature Approximation

Supplementary Table 1: Characteristics of Study Participants with High Grade Serous Tubo-Ovarian Carcinoma by Contributing Study

Supplementary Figure 2: No Association between Methylation Signature and Baseline Clinical Factors

Supplementary Table 3: Differential Methylation Analysis for Mayo Training and Testing Sets for all QC-passed CpG loci from 450k

Supplementary Figure 3: Immune Analysis

Supplementary Table 6: Multivariate Modeling of Time to Recurrence/Death Among Studies with Gene Expression Data

Supplementary Table 5: Modeling of Time to Recurrence/Death Among Studies with Gene Expression Data, excluding data from Previously Published Participants

Supplementary Table 4: Multivariate Modeling of Methylation Signature Among Studies with Multiple Datatypes

Supplementary Table 2: CpGs That Defined Previously Reported Methylation Subtypes Using a Semi-Supervised Clustering Approach

Supplementary Figure 4: Correlations between Methylation at CpG Sites and TAP1 mRNA Expression

Supplementary Methods 1: The original 60 CpG loci defining methylation-based subtypes, and the approximation method for methylation signature

AbstractBackground: Women with an inherited pathogenic variant in BRCA1 or BRCA2 have a greatly increased risk of developing ovarian cancer, but the importance of behavioral factors is less clear. We used a case-only design to compare the magnitude of associations with established reproductive, hormonal, and lifestyle risk factors between BRCA mutation carriers and noncarriers. Methods: We pooled data from five studies from the Ovarian Cancer Association Consortium including 637 BRCA carriers and 4,289 noncarriers. Covariate-adjusted generalized linear mixed models were used to estimate interaction risk ratios (IRR) and 95% confidence intervals (CI), with BRCA (carrier vs. noncarrier) as the response variable. Results: IRRs were above 1.0 for known protective factors including ever being pregnant (IRR = 1.29, 95% CI; 1.00–1.67) and ever using the oral contraceptive pill (1.30, 95% CI; 1.07–1.60), suggesting the protective effects of these factors may be reduced in carriers compared with noncarriers. Conversely, the IRRs for risk factors including endometriosis and menopausal hormone therapy were below 1.0, suggesting weaker positive associations among BRCA carriers. In contrast, associations with lifestyle factors including smoking, physical inactivity, body mass index, and aspirin use did not appear to differ by BRCA status. Conclusions: Our results suggest that associations with hormonal and reproductive factors are generally weaker for those with a pathogenic BRCA variant than those without, while associations with modifiable lifestyle factors are similar for carriers and noncarriers. Impact: Advice to maintain a healthy weight, be physically active, and refrain from smoking will therefore benefit BRCA carriers as well as noncarriers.

Supplementary Methods for multiple imputation and sensitivity analysis

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.

Supplementary Table 3: Case-only comparisons between BRCA variant carriers and non-carriers, high grade serous cancer and endometrioid/clear cell carcinomas

Supplementary Table 1: Characteristics of the contributing studies

Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks is a standard method in biomedical research. However, when used on tissues with diverse cell type compositions, it yields averaged gene expression profiles, complicating biomarker identification due to variations in cell proportions. To address the need for optimized strategies for defining individual cell type compositions from bulk FFPE samples, we constructed single-cell RNA-seq reference data for breast tissue and tested cell type deconvolution methods. Initial simulation experiments showed similar performances across multiple commonly used deconvolution methods. However, the introduction of FFPE artifacts significantly impacted their performances, with a root mean squared error (RMSE) ranging between 0.04 and 0.17. Scaden, a deep learning-based method, consistently outperformed the others, demonstrating robustness against FFPE artifacts. Testing these methods on our 62-sample RNA-seq benign breast disease cohort in which cell type composition was estimated using digital pathology approaches, we found that pre-filtering of the reference data enhanced the accuracy of most methods, realizing up to a 32% reduction in RMSE. To support further research efforts in this domain, we introduce SCdeconR, an R package designed for streamlined cell type deconvolution assessments and downstream analyses.

Background Despite the introduction of the Centers for Disease Control and Prevention's opioid prescribing guidelines, studies indicate that a significant proportion of opioids prescribed at hospital discharge remains unused. Little is known if improved provider awareness of guidelines and metrics would facilitate rightsizing opioid prescriptions at hospital discharge. Our institution created opioid prescribing guidelines and a key metrics dashboard and subsequently disseminated these tools to our institution's leadership and prescribers. We aim to evaluate the effectiveness of these efforts in reducing hospital discharge opioid prescriptions, especially those for acute pain exceeding 100 morphine milligram equivalents (MME). Methods Following the development of practice-specific opioid prescribing guidelines in 2017, a key metrics dashboard was created in 2021 to display the percentage of hospital discharges with opioids prescribed and the percentage of discharges with opioids prescribed for acute pain exceeding 100 MME. These metrics were broken down into calendar years between 2018 and 2022, and by the 7 major practice regions across our institution spanning 5 U.S. States. Results From 2018 to 2022, all regions showed a decline in the percentage of hospital discharges with opioids prescribed (range 2.7%-9.4%). In the same period, 5 of 7 regions showed a decline in discharge opioid prescriptions exceeding 100 MME for acute pain (range 8.8%-23.2%). Two sites showed an increase of 2.4% and 2.7%. Conclusion Downward trends in hospital discharge opioid prescriptions were observed for most practice regions following the introduction of our institution's opioid prescribing guidelines and key metrics dashboard.

Supplementary Table 2: Case-only comparisons between BRCA variant carriers and non-carriers, all invasive cancers