Survival analysis results using cellular abundance (1% threshold for low vs high) and Ripley's K degree of clustering (median as threshold for low vs high). Models were adjusted for age at diagnosis, high/low stage, and patient cohort and CD3 abundance for T-cell subsets. Absent = no immune marker present in tumor core; LL = low abundance / low spatial clustering, LH = low abundance / high spatial clustering; HL = high abundance / low spatial clustering; HH = high abundance / high spatial clustering. Bolded values indicate results that are statistically significant.

OBJECTIVE:To examine the association between post-diagnosis use of estrogen hormone therapy (EHT) and survival among patients diagnosed with invasive epithelial ovarian cancer by histotype before the age of 60. METHODS:In this retrospective, population-based, cohort study, we included all patients diagnosed with invasive epithelial ovarian cancer (EOC) between January 1, 1997, and December 31, 2020, who were under age 60 at diagnosis and survived at least one year after diagnosis. EHT use was restricted to systemic use, defined using provincial pharmacy dispensation records. We compared the survival of post-diagnosis EHT users to hormone therapy (HT) non-users using Cox proportional hazards regression with EHT use as a time-varying exposure and using a landmark analysis. RESULTS:Of the 2334 people included, 19.1% (n = 446) used EHT after their cancer diagnosis. EHT use was significantly associated with improved survival among people with serous ([adjusted hazard ratio [aHR], 0.71, 95% [CI] 0.58-0.87) and clear cell EOC (aHR, 0.52, 95% [CI] 0.28-0.97). As most of serous EOC is high-grade, this improved survival applies to high-grade serous cancer (HGSC). In contrast, EHT users showed worse survival among patients with the endometrioid histotype (aHR, 2.01, 95% [CI] 1.16-3.51). CONCLUSION:Post-diagnosis use of EHT is safe for patients diagnosed under age 60 with HGSC and likely for clear cell EOC. Thus, EHT can be used to manage menopausal sequelae in these patients. Our data suggest caution is warranted in patients with endometrioid EOC. More research is needed to understand the relationship between EHT and mucinous EOC survival.

Markers used for the detection of cell phenotypes with the Vectra3 Automated Quantitative Pathology Imaging System.

Survival analysis results from colocalization of CD3+CD8+ and CD3+CD8+CD69+ cells (“counted cell”) with CD3+CD4+Foxp3+, CD3+CD4+, and CD3+CD4+CD69+ using bivariate Ripley's K (median as threshold for low (L) vs high (H)) with cell abundance (present (P) vs absent (A)). Models were adjusted for age at diagnosis, high/low stage, and patient cohort and CD3 abundance for T-cell subsets. APN = absent of CD3+CD4+, CD3+CD4+Foxp3+, or CD3+CD4+CD69+ (A), present of other phenotype (CD3+CD8+ or CD3+CD8+CD69+) (P), and no spatial clustering computed (N); PAN = presence of CD3+CD4+, CD3+CD4+Foxp3+, or CD3+CD4+CD69+ (P), absence of other phenotype (CD3+CD8+ or CD3+CD8+CD69+) (A), and no spatial clustering computed (N); PPH = presence of both phenotypes and high level of colocalization; PPL = presence of both phenotypes and low level of colocalization; AAN = absence of both phenotypes and no level of colocalization computed. Bolded values indicate results that are statistically significant.

Abstract Background: Understanding the tumor immune microenvironment (TIME) is essential for advancing cancer research and improving treatment strategies. Multiplex immunofluorescence (mIF) is a spatial proteomics imaging technique enabling simultaneous analysis of multiple markers in preserved tissues. However, mIF-derived cell abundance data pose statistical challenges, such as zero-inflation, over-dispersion, hierarchical cell relationships, and repeated measures, that must be addressed to extract meaningful insights and enhance translational impact. Methods: We developed a novel Bayesian multi-cell type analysis model that simultaneously models the relationship of immune cell abundances with clinical and epidemiological factors, while incorporating the biological relationships between immune cell populations. We applied this model to three large studies assessing the TIME of high-grade serous ovarian cancer: Nurses’ Health Study I/II (NHSI/II) (N=321), African American Cancer Epidemiology Study (AACES) (N=92), and University of Colorado Ovarian Cancer Study (UCOCS) (N=103). The mIF staining for these studies was performed using the AKOYA Biosciences OPALTM 7-Color Automation IHC Kit with the Vectra®3 Automated Quantitative Pathology Imaging System (0.499µm/pixel) utilized for image collection. InForm and HALO were utilized for spectral unmixing and cell phenotyping, respectively. Our analysis examined associations between immune cell infiltration (T-cells, B-cells, macrophages) and clinical variables (cancer stage, age at diagnosis, debulking status) with comparisons to the single-cell type model. Results: In the NHSI/II analysis, our multi-cell type model detected a positive association between age at diagnosis and abundance levels of 6 of the 7 cell types in the analysis while the single-cell type model only detected 2 of the 7. This indicates improved association detection, with our multi-cell type model. We also observed that our multi-cell type model had narrower credible intervals (CIs) for all 7 cell types demonstrating higher accuracy in the association estimation. With cancer stage as the predictor in the NHSI/II analysis, neither model detected an association although our multi-cell type model had narrower CIs for 3 of the 7 cell types compared to the single-cell type model. Despite not capturing any associations between the predictors (age, stage, debulking status) and immune cell populations in the AACES or UCOCS, our Bayesian multi-cell type model had narrower CIs for every cell type in both studies for each predictor. Discussion: Our Bayesian multi-cell type model offers a flexible framework for incorporating immune cell relationships and is well-suited for cancer studies of the TIME utilizing TMAs, regions of interest, or whole-slide imaging data. Citation Format: Chase Sakitis, Jose Laborde, Julia Wrobel, Alex C. Soupir, Christelle M. Colin-Leitzinger, Benjamin G. Bitler, Mary K. Townsend, Andrew B. Lawson, Joellen M. Schildkraut, Shelley S. Tworoger, Kathryn L. Terry, Lauren C. Peres, Brooke L. Fridley. Multi-cell type model for analyzing spatial single-cell protein imaging data with application to ovarian cancer [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 6851.

Abstract Background: Researchers can study both the abundance and spatial architecture of cell types within the tumor microenvironment (TME) using spatial technologies. Often, Ripley’s K or nearest-neighbor G are used to measure spatial clustering of cells. These measures can be computed at various radii to assess clustering at different spatial ranges. We propose the use of functional principal component analysis (FPCAs) to model the association of the spatial clustering of T cell populations in the TME with survival from high grade serous ovarian cancer (HGSOC). Methods: We applied FPCA to study the clustering of CD3+ and CD3+CD8+ cells in the ovarian TME with survival. Five ovarian cancer studies were included in the analysis: Nurses’ Health Study (N=239), Nurses’ Health Study II (N=68), New England Case Control Study of Ovarian Cancer (N=175), African American Cancer Epidemiology Study (N=155), and the North Carolina Ovarian Cancer Study (N=136). Protein imaging data was collected using AKOYA Biosciences OPALTM IHC Kit with image analysis completed using Vectra®3 Automated Quantitative Pathology Imaging System. Spatial trajectories using G statistic were computed for samples with at least 8 positive cells for a cell type. FPCA was applied to the spatial curves with the top two components (FPC1, FPC2) associated with survival, adjusting for stage, age of diagnosis, and abundance of the cell population (high vs low using 1% threshold). A second model was fit to assess interaction between the abundance and spatial clustering. Analyses were completed for each study with results combined using a random-effect meta-analysis. Results: From the model without spatial information, we observed that high abundance of CD3+ (hazard ratio (HR): 0.81, 95% confidence interval (0.66, 0.98)) and CD3+CD8+ cells (HR: 0.64 (0.52, 0.79)) were associated with improved survival. The model with both abundance and spatial clustering detected a significant effect for CD3+CD8+ clustering (FPC1 HR: 1.17 (1.04, 1.33)) and a borderline association for CD3+ cells (FPC1 HR: 1.06 (0.99, 1.14)). When fitting a model with interactions for abundance and spatial clustering, a significant interaction for CD3+ cells (HR: 1.23 (1.07, 1.42)) and a borderline interaction for CD3+CD8+ cells (HR: 1.19 (0.98, 1.43)) was observed. Hence, we estimated the HRs for 4 tumor types (high/low abundance and high/low spatial clustering). We observed that patients with high abundance but low spatial clustering of CD3+ and CD3+CD8+ cells had the improved survival, with HRs for the high abundance / low spatial clustering group being 0.74 and 0.41, respectively. Discussion: In studying the HGSOC TME using spatial proteomics and FPCA, we found that not only is the abundance of T cell populations related to survival, but also the spatial clustering of these cell populations, with improved survival for women with tumors with diffuse T cell infiltration. Citation Format: Brooke L. Fridley, Alex C. Soupir, Daisy Liao, Chase Sakitis, Joellen Schildkraut, Andrew B. Lawson, Mary K. Townsend, Shelley Tworoger, Kathryn L. Terry, Julia Wrobel, Lauren Cole Peres. Functional data analysis of spatial protein imaging data using spatial trajectories with application to ovarian cancer [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 6844.

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.

Survival analysis results using cellular abundance (1% threshold for low vs high) and Ripley's K degree of clustering (median as threshold for low vs high). Models were adjusted for age at diagnosis, high/low stage, and patient cohort and CD3 abundance for T-cell subsets. Absent = no immune marker present in tumor core; LL = low abundance / low spatial clustering, LH = low abundance / high spatial clustering; HL = high abundance / low spatial clustering; HH = high abundance / high spatial clustering. Bolded values indicate results that are statistically significant.

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.

Many epithelial cancers orchestrate prognostically significant intratumoral tertiary lymphoid structures (TLSs), yet the mechanisms behind their antitumor activity remain unclear. Here we show that intratumoral TLS in ∼14% of human high-grade serous ovarian cancer (HGSOC) harbor highly oligoclonal B cells expressing IgA or IgG, which are not found in distant tumor regions. By engineering a recombinant antibody with the dominant B cell receptor (BCR) sequence from a mature ovarian cancer TLS, we inhibited the growth of the corresponding autologous tumor in vivo by targeting the extracellular domain of tumor-promoting GPR85. Additionally, tumor regions surrounding TLS exhibited an inflammatory environment that restricts tumor growth. TLS therefore exert anti-tumor immune pressure through the local production of isotype-switched anti-tumor antibodies and by promoting antitumor inflammation. Our results provide mechanistic insight into the elusive nature of antitumor immunity associated with TLS and underscore the importance of coordinated cellular and humoral responses in human cancer.

Abstract BACKGROUND: While over 80% of sexually active premenopausal women use oral contraception (OC), the biological mechanisms linking OC to hormone-sensitive cancers are not well understood. OC use lowers ovarian cancer risk while current use modestly increases breast cancer risk, suggesting complex underlying pathways. We sought to identify plasma metabolites associated with OC use in premenopausal women and relationships with ovarian and breast cancer. METHODS: Analysis included 2,072 premenopausal women in Nurses’ Health Study II with self-reported OC history. OC use was defined as ≥1 years of previous use, excluding current users. All metabolite values were transformed to probit scores to achieve normality. Associations of prior use ≥1 years versus never and per 5 years use with individual metabolites were evaluated through multivariable linear regression, accounting for the number of effective tests (NEF) based on the number of principal components explaining 90% of the variance. Metabolite groups were identified through metabolite set enrichment analysis (MSEA) using false discovery rate (FDR) for multiple testing. OC use models were adjusted for blood draw variables (age, time, season, fasting status), BMI smoking, diet (Alternative Healthy Eating Index score), alcohol consumption, and physical activity at the time of blood draw. Associations of metabolite groups (FDR<0.20) and metabolites (NEF<0.20) were considered nominally associated with OC use. Associations with incident breast (n=663) and ovarian cancers (n=34) diagnosed at least ≥3 years after blood draw were assessed using conditional logistic regression, using controls matched 1:1 on blood draw variables, adjusting for age, OC duration, parity, and family history of breast or ovarian cancer and using MSEA. RESULTS: Of 2,703 women, 1,681 (81.1%) reported having previously used OC for ≥1 year, with a mean duration of 4.0±3.9 years. Each additional 5 years of OC use was associated with higher C40:6 phosphatidylethanolamines (PE) levels (β=0.09; 95% CI: 0.04, 0.14), and both phosphatidylcholines and PEs were negatively associated with OC use and duration but positively for breast and ovarian cancer risk. Increasing OC duration was associated with lower tryptophan (β=-0.11; 95% CI: -0.16, -0.05) and higher 1,7-methyluric acid (β=-0.11; 95% CI: -0.16, -0.05), which were both lower among breast cancer cases and higher among ovarian cancer cases, but the overall organoheterocyclic compound group was not significantly associated with OC use. CONCLUSION: Our results suggest long-lasting alterations of systemic metabolism may in part explain associations of OC use with breast and ovarian cancers, particularly among metabolites that contribute to lipid metabolism. Analyses are ongoing and will include a deeper investigation into timing and formulation of OC use as well as cancer subtypes. Citation Format: Jennifer M. Mongiovi, Nan Lin, Oana Alina Zeleznik, Naoko Sasamoto, Britton Trabert, Julian Avila-Pacheco, Clary B. Clish, A. Heather Eliassen, Shelley TWOROGER, Kathryn L. Terry. Plasma metabolomic profiles of oral contraception use associated with breast and ovarian cancers among premenopausal women in Nurse’s Health Study II [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 2317.

Spatial proteomic imaging technologies enable the simultaneous assessment of immune cell abundance and spatial organization within the tumor microenvironment. Spatial clustering is commonly summarized using measures such as Ripley's K or nearest neighbor G-functions at a fixed radius. However, these approaches depend on scale selection and may obscure biologically relevant patterns occurring across spatial ranges. We propose a functional data analysis (FDA) framework to model spatial clustering trajectories derived across a continuum of radii. Functional principal component analysis (FPCA) was used to summarize dominant modes of spatial variation, and resulting scores were incorporated into Cox proportional hazards models as both main effects and interaction with immune cell abundance. The approach was applied to multiplex immunofluorescence data from five ovarian cancer studies, comprising 773 high grade ovarian serous tumors. Analyses focused on CD3+ and CD8+ T cell populations within the tumor compartment of the tissue, adjusting for age at diagnosis and cancer stage, with study-specific estimates combined using random-effects meta-analysis. Higher abundance of both T cells and CD8+ T cells was consistently associated with improved overall survival. Beyond abundance, spatial features captured by the leading functional principal component were independently associated with survival, particularly for CD8+ T cells. Interaction models further showed that the prognostic effect of immune infiltration depended on spatial clustering, with tumors characterized by high abundance and low spatial clustering exhibiting the most favorable outcomes. These findings indicate that spatial organization provides complementary prognostic information beyond abundance alone and suggests that more diffuse immune infiltration may reflect more effective anti-tumor activity in ovarian cancer. Overall, FDA offers a flexible and interpretable framework for modeling spatial clustering across scales and identifying prognostic spatial features not captured by fixed-radius or distance analyses.

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.

Abstract Background Evidence suggests chronic distress influences ovarian cancer (OC) etiology and metabolomic profiles. Here, we evaluated the association of a metabolite-based distress score (MDS) and OC risk. Methods We included two matched case-control studies nested within the Nurses’ Health Studies (N=584) and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (N=348). Metabolites were measured 3-27 years before diagnosis using liquid-chromatography tandem mass spectrometry. We examined the association of quintiles of MDS and 19 constituent metabolites with OC risk using unconditional logistic regression and stratified by tumor histotype, menopausal status, and age at diagnosis. Results We observed women in the highest versus lowest quintile of MDS had an increased OC risk (OR=1.62,95%CI=1.03-2.54,p trend =0.07), and type 2 tumors (OR=1.71,95%CI=1.03-2.83,p trend =0.11). Associations were suggestively stronger for premenopausal and <69-year-old women, and driven by pseudouridine, and N2,N2- dimethylguanosine. Conclusion Our findings suggest chronic distress-associated metabolic dysregulation may represent a novel OC risk factor, especially among younger women.

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.

BACKGROUND:A previous study found that adding the spatial context of tumor-infiltrating lymphocytes (TIL) and cytotoxic T lymphocytes (CTL) to abundance improved associations with overall survival (OS) of non-Hispanic Black women with high-grade serous ovarian cancer (HGSOC). This study set out to replicate previous findings in predominantly non-Hispanic White women HGSOC cohorts. METHODS:Multiplex immunofluorescence was used to characterize TILs in HGSOC from women enrolled in three epidemiologic studies (N = 433). Spatial clustering of TILs was derived using a permutation approach for Ripley K. Cox proportional hazards models were used for associations of spatial clustering and abundance with OS. RESULTS:In models assessing differences in spatial clustering within tumors with a high abundance of TILs (>1%), we found that spatial information significantly improved the model fit for the recently activated CTLs (likelihood ratio test P = 0.008), in which low spatial clustering in high abundance was associated with a decreased risk of mortality (hazard ratio = 0.31; 95% confidence interval, 0.14-0.70; P = 0.004) compared with when the recently activated CTLs were highly clustered. CONCLUSIONS:In this study, we replicated the significantly improved association with OS in mostly White women with HGSOC by including spatial information for the recently activated CTLs. Further research is needed to understand the mechanisms by which recently activated spatial architecture affects survival from HGSOC. IMPACT:This study validates our previous findings that adding spatial context to abundance, especially CTLs or the recently activated CTLs, when performing survival analyses improves the fit of the models.

BACKGROUND:Given that primary prevention strategies for ovarian cancer, such as surgery and medications, have inherent risks, identifying those at high risk of lethal ovarian cancer is critical. We examined prediagnosis factors and risk of developing and dying from ovarian cancer among cancer-free women. METHODS:Analyses were conducted in three 12-year periods from 1980 to 2017 in the Nurses' Health Study (NHS) and NHSII cohorts. Potential risk factors were reproductive and hormonal variables, endometriosis history, smoking, low-dose aspirin, self-identified race, family history, depression, and adiposity over the life course. We used a multistate survival model to estimate relative risks and 95% lower and upper confidence limits for lethal ovarian cancer among 211,420 cancer-free women, among whom 1,730 developed ovarian cancer and 660 died because of ovarian cancer in the same risk period as diagnosis. RESULTS:Of the 22 exposures evaluated, 10 were associated with lethal ovarian cancer. For example, nulliparity had an amplified association with lethal ovarian cancer (1.62, 1.23-2.13) due to associations with both incidence and mortality in the same direction. Oral contraceptive use ≥10 versus 0 years was associated with lethal ovarian cancer (0.65, 0.43-0.97) primarily due to association with incidence, whereas ≥20 versus 0 pack-years of smoking was associated with lethal ovarian cancer (1.25, 1.02-1.53) primarily due to the mortality relationship. CONCLUSIONS:Several reproductive factors, depression, and self-identified race were associated with risk of lethal ovarian cancer. IMPACT:Evaluations of lethal ovarian cancer risk must consider differential associations of exposures with incidence and mortality.

Marker combinations used for both univariate clustering and colocalization, along with the percent of samples of 1244 total that had at least 1 cell positive for that marker combination and the percent of samples of 1244 that had at least 2 cells positive for the marker combination allowing degree of spatial clustering to be computed.