Background While endometriosis is thought to be a heterogeneous disease, the pathophysiologic heterogeneity across the three visualized lesion macrophenotypes (i.e., superficial peritoneal endometriosis(SPE) lesions, endometriomas, and deep lesions) remains unclear. Objectives This study aimed to investigate associations between known and putative risk factors and co-existing comorbidities with odds of endometriosis lesion macrophenotypes. Study Design We conducted a pooled, cross-sectional analysis using data from 1,244 participants surgically diagnosed with endometriosis and 1,271 without endometriosis who participated in three World Endometriosis Research Foundation Endometriosis Phenome and Biobanking Harmonization Project compliant population-based studies from North America and Europe. Multivariable logistic regression models adjusting for age at questionnaire completion and studies were used to calculate odds ratios (OR) and 95% confidence intervals (CI) for the associations between participant characteristics of known and putative risk factors and co-existing comorbidities and surgically-confirmed endometriosis. Polytomous logistic regression was used to examine the associations among case groups defined by endometriosis macrophenotype, with likelihood ratio tests used to evaluate statistically significant differences between macrophenotypes presented as p-heterogeneity (p-het). Results Among endometriosis cases, 834(71%) had SPE only, 92(8%) had at least one endometrioma, 129(11%) had deep lesions, and 111(10%) had both endometrioma and deep lesions. Younger age at menarche was associated with significantly higher odds for having SPE only and deep+endometrioma macrophenotypes (≤11 vs. 12 years-old, OR=1.29, CI=1.01-1.65 and OR=2.07, CI=1.11-3.86, respectively), but not associated with endometrioma or deep lesions alone (p-heterogeneity=0.05). Presence of chronic overlapping pain conditions was associated with greater odds of endometriosis overall (OR=1.66, CI=1.49-1.85 per condition) and of SPE only(OR=1.80, CI=1.59-2.04 per condition) and deep lesions(OR=1.76, CI=1.44-2.15 per condition), but not with macrophenotypes including endometrioma(p-het=0.0001). Unsupervised clustering by risk factors and co-existing conditions showed distinct associative patterns by surgically-visualized lesion macrophenotypes. Conclusions These results showing heterogeneity of individual risk factors and co-existing conditions across endometriosis macrophenotypes support the concept that endometriosis macrophenotypes may have different etiologies and underscores the importance of evaluating risk factors and biomarkers by endometriosis lesion macrophenotypes.

BACKGROUND:Non-hormonal intrauterine devices (IUDs) create an inflammatory uterine environment while oral contraceptives (OC) suppress ovulation and have different associations with ovarian cancer risk. We have evaluated the associations of these two contraceptive exposures with ovarian tumor immune infiltration. METHODS:This study assessed associations of IUD and OC use with tumor immune features via multiplex immunofluorescence in 24 ovarian tumor tissue microarrays from four case-control and two cohort studies. Multivariable-adjusted beta-binomial models estimated the odds of tumor T cell positivity by contraceptive history. RESULTS:High-grade serous tumors had the highest percentage of tumor cells positive for total T cells (CD3+ mean=3.4%, SD = 6.1) and each T cell subtype. Ever (vs. never) IUD use was modestly associated with increased cytotoxic T cell infiltration (CD3+CD8+ OR:1.14, 95% CI:0.99-1.32), which was stronger among those with a history of endometriosis, postmenopausal women, and smokers. Conversely, OC use ≥1 year (vs. never) was associated with lower cytotoxic T cell odds (CD3+CD8+ OR:0.89, 95% CI:0.79-1.00; p-het=0.008). Increased odds of terminal T cell exhaustion were observed for IUD use only (CD3+PD1+TIM3+ OR:1.53, 95% CI:0.99-2.36), which was stronger among those who had ever used genital powder or BMI > 25 kg/m2. CONCLUSIONS:Pre-diagnostic contraception use may influence ovarian tumor immunity and may modulate cancer susceptibility.

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.

Endometriosis is a chronic inflammatory condition that affects an estimated 1 in 10 women but is often mis- and underdiagnosed due to its non-specific symptoms and the lack of a non-invasive diagnostic test. This study aimed to identify and validate a potential non-invasive biomarker for endometriosis. This study applied quantitative proteomics discovery approaches to identify and validate non-invasive biomarkers of endometriosis with a long-term goal of leveraging them for purposes of diagnosis and therapeutic monitoring. Isobaric tags for relative and absolute quantification (iTRAQ) combined with mass spectrometry were used to identify and quantitate proteins and peptides in urine samples from participants with surgically-confirmed endometriosis (n = 73) and 1:1 age-matched participants never diagnosed with endometriosis (n = 73). Among those aged 25–47 at urine collection, Epidermal Growth Factor (EGF) (validated using monospecific enzyme-linked immunosorbent assays (ELISA)) was present at significantly lower levels in the urine of participants with surgically-confirmed endometriosis compared to controls (P = 0.02) and had an excellent negative predictive value (NPV) of 94.3

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.

Artificial intelligence-based histopathology image analysis using whole slide images (WSIs) has rapidly expanded in recent years with wide implications and opportunities for predicting clinical outcomes. Here we applied foundation model-based features to hematoxylin and eosin (H&E)-stained ovarian tumor WSIs and tumor microarrays (TMAs) and trained neural networks (NN) to predict survival and classify RNA-based tumor microenvironment (TME) subtype. We developed survival models for epithelial ovarian cancers using NNs enhanced with foundation model features from H&E-stained TMA and WSI across four cohorts (n=1,453). Tile-level features were extracted at 20x with foundation models: CHIEF, GIGAPATH, VIRCHOW, and input into a Weibull-based deep survival model (PyTorch). Models were trained separately on the TCGA (WSI) and the New England Case Control Study (NECC, TMA) using k-fold cross-validation with age-stratified sampling. Performance was assessed using held-out validation sets from TCGA and NECC, and externally on two independent datasets. TME subtypes were defined by bulk RNA-seq data based on cancer-associated fibroblasts (CAF), T cells, and endothelial cells. Unsupervised k-means clustering (k=4) with Euclidean distance over 1000 bootstrapped iterations was performed, and each cluster was annotated by the median expression scores of the selected marker sets and as one of the following subtypes:1) Immune-Enriched, 2) Immune-Enriched + Fibrotic, 3) Fibrotic, or 4) Depleted. We applied the classification NN for slide-level (multilayer perceptron) and core-level classification (Attention-Based Deep Multiple Instance Learning; ABMIL) to train models on TMA-derived H&E tiles and classify samples into the four transcriptomic-defined subtypes. Survival models trained on TMAs and WSIs achieved comparable performance across validation cohorts with strong prediction performance (c-index=0.76-0.96). In both NECC and TCGA, Immune-Enriched + Fibrotic and Depleted clusters were most distinct, marked by high and low expression of CAF, T cell, and endothelial scores, respectively. In contrast, Immune-Enriched and Fibrotic clusters were more difficult to differentiate, particularly based on CAF and endothelial scores, though T cell scores were higher in Immune-Enriched relative to Fibrotic clusters. The TMA-trained NN models were unable to reliably distinguish between the four RNA-defined TME subtypes. Despite TMA’s reduced spatial context, survival prediction was comparable to WSIs with consistent performance in validation datasets, suggesting that TMA-derived features paired with foundation models can capture biologically important signals in epithelial ovarian tumors. RNA-seq-defined TME subtype classification using TMA-derived H&E features was limited, likely due to sample size, spatial resolution, and the heterogeneity of the ovarian cancer TME. Future direction will leverage attention-based models to generate spatial maps highlighting tissue regions predictive of molecular features and clinical outcomes. Tina Yi Jin Hsieh, Shih-Yen Lin, Allison F. Vitonis, Kathryn L. Terry, Kun-Hsing Yu, Naoko Sasamoto. Applying histopathology foundation model-enhanced neural networks to ovarian tumor whole slide images and tissue microarray for survival prediction and tumor microenvironment subtyping [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr B040.

Recently, intrauterine device (IUD) use has increased and oral contraceptive (OC) use has decreased. While OC use is known to reduce epithelial ovarian cancer (EOC) risk, previous studies of IUD use and EOC risk have shown inconsistent associations. Here, we examined the association between primarily non-hormonal IUD use and EOC risk by leveraging harmonized individual-level data from 11 case-control studies, 10 in the Ovarian Cancer Association Consortium and the African American Cancer Epidemiology Study, and 7 prospective cohort studies from the Ovarian Cancer Cohort Consortium. Contraception use was self-reported through questionnaires. Study-specific odds ratios (OR) and 95% confidence intervals (CI) were estimated using unconditional logistic regression (case-control studies) or time varying polytomous logistic regression models (cohort studies), adjusted for age, duration of OC use, parity, and family history of breast or ovarian cancer. Heterogeneity between studies was assessed for case-control and cohort studies separately using Cochran Q. There was no evidence of heterogeneity between studies, thus, data were pooled within each study design. Case-control and cohort pooled results were combined by random effects meta-analysis. Associations were estimated for overall EOC risk and by histotype (high-grade serous, low-grade serous, endometrioid, clear cell, mucinous). Heterogeneity across histotypes was evaluated using a Wald test for case-control and cohort models separately. Among case-control studies, data were pooled for 11, 033 cases and 13, 358 controls. A total of 1, 933 (17.5%) cases reported ever IUD use compared to 2, 656 (19.9%) controls. Among cohort studies, data were pooled for 629, 008 participants with 3, 549 incident cases. A total of 72, 546 (11.5%) participants reported ever IUD use at enrollment. The associations of ever vs. never IUD use and EOC risk were similar in case-control (pooled OR: 0.92; 95% CI: 0.86-0.98) and cohort studies (pooled OR: 1.00; 95% CI: 0.88-1.13). When meta-analyzed, there was a suggestive 5% lower odds of overall EOC among IUD users (OR: 0.94; 95% CI: 0.88-1.01; p-het = 0.26). We observed heterogeneity by histotype in case-control studies (p=0.0001) but not in cohort studies (p=0.77). After meta-analysis, results suggested that IUD use was associated with a lower risk of mucinous (OR: 0.85; 95% CI: 0.73-0.98), clear cell (OR: 0.69; 95% CI: 0.64-0.86), and low grade serous EOC (OR: 0.75; 95% CI: 0.55-1.01). IUD use was not associated with an increased risk of EOC in this analysis of over 12, 000 ovarian cancer cases. Our findings suggest there may be an inverse association between IUD use and risk of mucinous, clear cell, and low grade serous EOC. Analyses are ongoing and will include assessment of effect modification by parity and birth cohort, as well as consideration of timing of IUD use. Jennifer M. Mongiovi, Ana Babic, Allison F. Vitonis, Naoko Sasamoto, Mark K. Townsend, Brett M. Reid, Mollie E. Barnard, Holly R. Harris, Lauren Peres, Britton Trabet, Brooke L. Fridley, Katie M. O'Brien, Anita Koushik, Renee Turzanski Fortner, Jennifer A. Doherty, Joelle M. Schildkraut, Shelley S. Tworoger, Kathryn L. Terry, Ovarian Cancer Association Consortium, Ovarian Cancer Cohort Consortium. Intrauterine device use and ovarian cancer risk among 11 case-control studies and 7 prospective cohort studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7377.

Inflammatory biomarker associations stratified by BMI at blood draw

Inflammatory biomarker associations stratified analysis by menopausal status

Metabolic biomarker associations stratified by BMI at blood draw

BACKGROUND:Parity and breastfeeding are associated with systemic changes in maternal inflammation and reduced risk of ovarian cancer, but little is known about their impact on the ovarian tumor immune microenvironment. METHODS:We evaluated the associations of self-reported parity and history of breastfeeding with tumor-infiltrating T cells among 1,706 ovarian carcinoma cases with tumor tissue collected across four studies. The abundance of tumor-infiltrating T cells was measured by multiplex immunofluorescence in tumor tissue microarrays. ORs and 95% confidence intervals (CI) for the positivity of tumor immune cells were calculated using beta-binomial models and stratified by histotype. RESULTS:Compared with ovarian tumors in nulliparous women, there was no association between parity and ovarian tumor T-cell abundance among all histotypes combined but suggestion of increased cytotoxic T cells and T-cell exhaustion among parous women with clear-cell tumors. When restricted to parous women, history of breastfeeding was associated with increased odds for all T-cell types [i.e., total T, cytotoxic T, helper T (Th), regulatory T, and exhausted T cells], with ORs ranging from 1.11 to 1.42. For every 6 months of breastfeeding, we observed increased odds of activated Th-cell infiltration (CD3+CD4+CD69+; OR, 1.13, 95% CI, 0.99-1.29), with a similar association for high-grade serous tumors, but lower odds in clear-cell tumors (OR, 0.43, 95% CI, 0.21-0.87). CONCLUSIONS:History of breastfeeding may alter the ovarian tumor immune microenvironment by modulating the abundance of tumor-infiltrating T cells. IMPACT:Although replication is required, history of breastfeeding may play a role in the activation of the ovarian tumor immune response.

We evaluated inflammatory markers among 389 surgically confirmed endometriosis cases and 505 controls from the Women’s Health Study: From Adolescence to Adulthood (A2A) cohort. Participants reported dysmenorrhea, acyclic pelvic pain, dyspareunia, and pain with bowel movements. Using multiplex assays, we measured their levels of plasma interleukin (IL)-1β, -6, -8, -10, and -16, tumor necrosis factor (TNF)-α, monocyte chemotactic protein (MCP)-1 and -4, thymus and activation-regulated chemokine (TARC), and interferon gamma-induced protein (IP)-10. For each symptom, we computed biomarker-level geometric means (GMs) with 95% confidence intervals (95% CI) using multivariate linear regression among the endometriosis cases and controls, with interactions with case/control status tested using Wald statistics. Among the controls, those with dyspareunia had lower levels of IL-8 (GMpresent = 4.64 [95% CI = 4.41–4.89] pg/mL vs. GMabsent = 4.99 [95% CI = 4.82–5.17] pg/mL; p = 0.02), and the IL-8 levels were lower for controls reporting pain with bowel movements (GMpresent = 4.66 [95% CI = 4.43–4.89] vs. GMabsent = 4.96 [95% CI = 4.82–5.11] pg/mL, p = 0.03). No significant associations between pelvic pain symptoms and inflammatory markers were observed among the endometriosis cases; however, the relationship between inflammatory marker levels and pain experience varied by analgesic use at blood draw. Dyspareunia and pain with bowel movements were associated with inflammatory markers among the controls, while the associations between pelvic pain symptoms and inflammatory markers among the endometriosis cases differed by analgesic use.

BACKGROUND:Endometriosis is a chronic inflammatory condition characterised by pain and infertility. We conducted a prospective study to elucidate the pathophysiological mechanisms underlying endometriosis development. METHODS:We examined the association between 1305 proteins measured by SomaScan proteomics and risk of endometriosis diagnosis in prospectively collected plasma from 200 laparoscopically-confirmed endometriosis cases and 200 risk-set sampling matched controls within the Nurses' Health Study II (NHSII) cohort. Using conditional logistic regression, we calculated odds ratios (OR) and 95% confidence intervals (CI) per one standard deviation increase in protein levels and area under the curve (AUC) to assess the multi-protein model in discriminating cases from controls. Analytical validation for three proteins was performed using immunoassays. Ingenuity Pathway Analysis and STRING analyses identified biological pathways and protein interactions. FINDINGS:Blood samples from cases were collected up to 9 years before diagnosis (median = 4 years). Among 61 individual proteins nominally significantly associated with risk of endometriosis diagnosis compared to controls, endometriosis cases had higher plasma levels of S100A9 (OR = 1.52, 95%CI = 1.19-1.94), ICAM2 (OR = 1.47, 95%CI = 1.17-1.85), HIST1H3A (OR = 1.42, 95%CI = 1.31-1.78), TOP1 (OR = 1.95, 95%CI = 1.24-3.06), CD5L (OR = 1.23, 95%CI = 1.00-1.51) and lower levels of IGFBP1 (OR = 0.70, 95%CI = 0.52-0.94). We further evaluated three of the proteins in an independent set of 103 matched case-control pairs within the NHSII cohort. Pathway analyses revealed upregulation of multiple immune-related pathways in blood samples collected years before endometriosis diagnosis. INTERPRETATION:In this prospective analysis using aptamer-based proteomics, we identified multiple proteins and biological pathways related to innate immune response upregulated years before endometriosis surgical diagnosis, suggesting the role of immune dysregulation in endometriosis development. FUNDING:This study was supported by the Department of Defence, the 2017 Boston Center for Endometriosis Trainee Award. Investigators were supported by Aspira Women's Health and NIH which were not directly related to this project.

While inflammation and gonadotropin levels are hypothesized to contribute to ovarian cancer initiation, specific proteomic pathways and other potential mechanisms are unclear. Thus, we aimed to identify novel blood-based non-invasive proteomic risk biomarkers for invasive epithelial ovarian cancer. We measured 7, 310 pre-diagnostic plasma proteins using SomaScan v4.1 in 169 incident ovarian cancer cases [140 high-grade serous (HGS) cases] and 169 matched controls nested within the Nurses’ Health Studies. We excluded proteins with coefficient of variation ≥ 25% and influenced by delayed processing, resulting in 3, 131 proteins included in this analysis. We transformed protein levels to probit scores to achieve normality and used conditional logistic regression modeling to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) per one standard deviation increase in protein levels. We used Gene Set Enrichment Analysis to identify biological pathways and STRING to conduct protein-protein interaction network and functional enrichment analysis. We also conducted analyses restricted to HGS histotype. All ovarian cancer cases included in the current analysis had blood samples collected years prior to diagnosis (median=14 years). Average age at blood collection was 56.3 years in both ovarian cancer cases and controls. When examining individual proteins associated with ovarian cancer risk, 94 proteins were significantly associated with ovarian cancer risk with nominal p-value<0.05. Biological pathway analysis revealed complement cascade (NES=2.0; FDR p=0.049) and innate immune system (NES=1.5; FDR p=0.049) pathways being upregulated in ovarian cancer cases compared to controls. STRING analysis showed protein-protein interactions were enriched in pathways such as tissue remodeling (e.g., COL6A1, COL18A1, IGFBP2), lipid metabolism (e.g., NCOA2, ME1), and B cell function (e.g., IGLL5, MZB1) in ovarian cancer cases compared to controls. Similar results were observed for HGS ovarian cancer. We conducted a comprehensive examination of the plasma proteins associated with ovarian cancer risk in a prospective cohort study and observed upregulation of pathways related to innate immune system and complement cascade being associated with ovarian cancer risk. Nan Lin, Allison F. Vitonis, Simon T. Dillion, Ngo Long, Shelley S. Tworoger, Towia A. Libermann, Kathryn L. Terry, Naoko Sasamoto. Plasma proteomics associated with ovarian cancer risk in the Nurses' Health Studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2338.

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

Oral contraceptives (OC) strongly protect against ovarian cancer, however, OC use has been declining while intrauterine device (IUD) use is increasing and the impact of this shift on ovarian cancer risk is largely unknown. IUD use may increase local inflammation, leading to alteration of immune microenvironment profiles whereas OC use causing ovulation cessation may be associated with reduced local inflammation. As chronic inflammation may contribute to immunosuppressive tumor microenvironment, we hypothesized that IUD use may be associated with a more immunosuppressive tumor microenvironment compared to OC use, which may be more evident in the stromal components of the tumor. Therefore, we assessed the associations of tumor-associated macrophages (TAMs) and tumor-infiltrating lymphocytes (TILs) in the stromal cells of the tumor with contraception use to better understand their influence on ovarian cancer carcinogenesis. We used ovarian tumor samples embedded into tissue microarrays (TMAs) and questionnaire data from multiple studies (African American Cancer Epidemiology Study, Diseases of the Ovary and their Evaluation, Hormone and Ovarian Cancer Prediction, New England Case Control Study, and Nurses’ Health Studies I and II). TAMs (CD68+, M1+, M2+, M0+) and TILs (CD3+, CD3+CD8+, CD3+CD4+FOXP3+) were assessed using multiplex immunofluorescence (mIF). Contraception use was self-reported as ever used an IUD (predominant non-hormonal) and/or OC. We used beta binomial models to calculate odds ratios (OR) and 95% confidence intervals (CI) for stromal cell positivity for TAM and TIL markers in association with IUD and OC use, adjusting for age at cancer diagnosis, parity, family history of breast or ovarian cancer, histotype, study site, and mutually adjusting for contraception method. Of the 1,801 cases included in the study, 314 had ever used IUD (17.4%) and 924 had ever used OC for at least one year (51.3%). Overall, there were no statistically significant associations between abundance of different immune markers in the stroma with ever use of IUD or OC. However, ever use of IUD was associated with a higher odds of CD3+CD8+ positivity (OR: 1.08, CI: 0.94-1.24), while ever use of OC was associated with lower odds of CD3+CD8+ (OR: 0.92, CI: 0.83-1.03; p-het=0.07). We observed no statistically significant associations of contraception use with TAMs and TILs in the stromal component of the tumor microenvironment. Ongoing analyses include examination of modifiers contributing to inflammation, including body mass index and endometriosis. Additional analyses will assess duration and timing of OC and IUD use, potential modification by parity, as well as differences in the tumor microenvironment by histotype. Jennifer M. Mongiovi, Roshni Babel, Ellie Lee, Ana Babic, Naoko Sasamoto, Mary K. Townsend, Allison F. Vitonis, Mollie Barnard, Jonathan Hecht, T. Rinda Soong, Lauren C. Peres, Joellen M. Schildkraut, Holly R. Harris, Jennifer A. Doherty, Francesmary Modugno, Brooke L. Fridley, Shelley S. Tworoger, Kathryn L. Terry. Ovarian cancer tumor associated macrophages and tumor infiltrating lymphocytes in the stromal compartment associated with intrauterine device and oral contraception use [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr B023.

List of nested case-control studies included in this study

BACKGROUND:Breastfeeding history has been associated with reduced risk of chronic diseases, although the underlying biological link is unclear. METHODS:The study included 16,165 parous women in the Nurses' Health Studies who reported lactation history and biomarkers measured using plasma samples collected at midlife. We calculated multivariable-adjusted geometric means of 10 inflammatory biomarkers [high-sensitivity C-reactive protein, IL6, IL8, IL10, insulin-like growth factor 1 (IGF1), soluble TNFα receptor 2, B cell-activating factor, C-X-C motif chemokine ligand 13, soluble IL2 receptor α, and soluble IL6 receptor-α] and eight metabolic biomarkers (triglyceride, total cholesterol, high- and low-density lipoprotein, leptin, soluble leptin receptor, adiponectin, and retinol-binding protein 4) by the self-reported history of breastfeeding prior to blood collection. The FDR was used for multiple testing corrections. RESULTS:The average age at blood collection was 52.6 years. Ever breastfeeding was associated with higher IGF1 (149.22 vs. 143.76 ng/mL; P value = 0.0002/FDR = 0.004) compared with never breastfeeding. Longer breastfeeding duration was associated with lower IL10 (P-trend = 0.001/FDR = 0.01) and higher IGF1 (P-trend = 0.0005/FDR = 0.01). No significant associations were observed for other biomarkers. Longer breastfeeding duration was associated with higher IGF1 among premenopausal women but not among postmenopausal women (P-interaction = 0.02). Longer breastfeeding duration was associated with lower soluble leptin receptor levels among those with body mass index ≥25 kg/m2 (P-trend = 0.01/FDR = 0.09) but not among those with body mass index <25 kg/m2 (P-interaction = 0.0002). CONCLUSIONS:Ever breastfeeding and longer breastfeeding duration were associated with higher IGF1 levels measured in midlife. IMPACT:Our results support the potential long-term systemic impact of breastfeeding on circulating IGF1 levels, which may influence future chronic disease risk.

Abstract Chronic pelvic pain (CPP) is a common and burdensome symptom in women yet current clinical management frequently leaves many with persistent pain. The Translational Research in Pelvic Pain (TRiPP) project adopts a pain-focused strategy, aiming to better phenotype CPP through multimodal assessment. Here, we integrated questionnaire, physiological, and biological data to (1) determine whether perturbations in the function of pain-relevant systems in women with CPP can be demonstrated and (2) explore whether these data can stratify women with CPP into meaningful subgroups, independent of diagnostic group. Participants included 108 women, aged 18 to 50 years, with CPP including endometriosis-associated pain (EAP), bladder pain syndrome (BPS), comorbid EAP and BPS, and pelvic pain with no underlying pathology alongside 50 pain-free controls. Analyses were conducted in 3 stages: (1) group comparisons, (2) latent profile analysis to identify CPP subgroups, and (3) clinical characterization of resulting clusters. Compared with controls, CPP participants reported significantly greater fatigue, poorer sleep, higher anxiety, depression, pain catastrophising, and more childhood trauma ( P < 0.01). However, no significant differences were observed in physiological measures. Latent profile analysis revealed 3 distinct CPP subgroups, differentiated by questionnaires rather than physiological measures. Cluster 1 represents a group with predominantly higher-impact pain compared with others, that may be associated with nociplastic mechanisms. These findings support alternative approaches to stratification of CPP separate from standard diagnostic groupings. The role of questionnaire measures in this stratification facilitates translation to clinical settings; however, further work is required to determine whether differing therapeutic approaches are appropriate for each cluster.