Sampson, Joshua N.

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.

Abstract Black women in the United States are disproportionately affected by early-onset, triple-negative breast cancer. DNA methylation has shown differences by race in healthy and tumor breast tissues. We examined associations between genome-wide DNA methylation levels in breast milk and breast cancer risk factors, including race, to explain how this reproductive stage influences a woman's risk for, and potentially contributes to racial disparities in, breast cancer. Breast milk samples and demographic, behavioral, and reproductive data, were obtained from cancer-free, uniparous, and lactating U.S. black (n = 57) and white (n = 82) women, ages 19–44. Genome-wide DNA methylation analysis was performed on extracted breast milk DNA using the Infinium HumanMethylation450 BeadChip. Statistically significant associations between breast cancer risk factors and DNA methylation beta values, adjusting for potential confounders, were determined using linear regression followed by Bonferroni Correction (P < 1.63 × 10−7). Epigenetic analysis in breast milk revealed statistically significant associations with race and lactation duration. Of the 284 CpG sites associated with race, 242 were hypermethylated in black women. All 227 CpG sites associated with lactation duration were hypomethylated in women who lactated longer. Ingenuity Pathway Analysis of differentially methylated promoter region CpGs by race and lactation duration revealed enrichment for networks implicated in carcinogenesis. Associations between DNA methylation and lactation duration may offer insight on its role in lowering breast cancer risk. Epigenetic associations with race may mediate social, behavioral, or other factors related to breast cancer and may provide insight into potential mechanisms underlying racial disparities in breast cancer incidence.

Abstract Purpose: Ovarian cancer is a heterogeneous disease that can be divided into multiple subtypes with variable etiology, pathogenesis, and prognosis. We analyzed DNA methylation profiling data to identify biologic subgroups of ovarian cancer and study their relationship with histologic subtypes, copy number variation, RNA expression data, and outcomes. Experimental Design: A total of 162 paraffin-embedded ovarian epithelial tumor tissues, including the five major epithelial ovarian tumor subtypes (high- and low-grade serous, endometrioid, mucinous, and clear cell) and tumors of low malignant potential were selected from two different sources: The Polish Ovarian Cancer study, and the Surveillance, Epidemiology, and End Results Residual Tissue Repository (SEER RTR). Analyses were restricted to Caucasian women. Methylation profiling was conducted using the Illumina 450K methylation array. For 45 tumors array copy number data were available. NanoString gene expression data for 39 genes were available for 61 high-grade serous carcinomas (HGSC). Results: Consensus nonnegative matrix factorization clustering of the 1,000 most variable CpG sites showed four major clusters among all epithelial ovarian cancers. We observed statistically significant differences in survival (log-rank test, P = 9.1 × 10−7) and genomic instability across these clusters. Within HGSC, clustering showed three subgroups with survival differences (log-rank test, P = 0.002). Comparing models with and without methylation subgroups in addition to previously identified gene expression subtypes suggested that the methylation subgroups added significant survival information (P = 0.007). Conclusions: DNA methylation profiling of ovarian cancer identified novel molecular subgroups that had significant survival difference and provided insights into the molecular underpinnings of ovarian cancer. See related commentary by Ishak et al., p. 5729

Abstract Background: The gut metabolome may be associated with the incidence and progression of numerous diseases. The composition of the gut metabolome can be captured by measuring metabolite levels in the feces. However, there are little data describing the effect of fecal sample collection methods on metabolomic measures. Methods: We collected fecal samples from 18 volunteers using four methods: no solution, 95% ethanol, fecal occult blood test (FOBT) cards, and fecal immunochemical test (FIT). One set of samples was frozen after collection (day 0), and for 95% ethanol, FOBT, and FIT, a second set was frozen after 96 hours at room temperature. We evaluated (i) technical reproducibility within sample replicates, (ii) stability after 96 hours at room temperature for 95% ethanol, FOBT, and FIT, and (iii) concordance of metabolite measures with the putative “gold standard,” day 0 samples without solution. Results: Intraclass correlation coefficients (ICC) estimating technical reproducibility were high for replicate samples for each collection method. ICCs estimating stability at room temperature were high for 95% ethanol and FOBT (median ICC > 0.87) but not FIT (median ICC = 0.52). Similarly, Spearman correlation coefficients (rs) estimating metabolite concordance with the “gold standard” were higher for 95% ethanol (median rs = 0.82) and FOBT (median rs = 0.70) than for FIT (median rs = 0.40). Conclusions: Metabolomic measurements appear reproducible and stable in fecal samples collected with 95% ethanol or FOBT. Concordance with the “gold standard” is highest with 95% ethanol and acceptable with FOBT. Impact: Future epidemiologic studies should collect feces using 95% ethanol or FOBT if interested in studying fecal metabolomics. Cancer Epidemiol Biomarkers Prev; 25(11); 1483–90. ©2016 AACR.

Abstract Background: The need to develop valid methods for sampling and analyzing fecal specimens for microbiome studies is increasingly important, especially for large population studies. Methods: Some of the most important attributes of any sampling method are reproducibility, stability, and accuracy. We compared seven fecal sampling methods [no additive, RNAlater, 70% ethanol, EDTA, dry swab, and pre/post development fecal occult blood test (FOBT)] using 16S rRNA microbiome profiling in two laboratories. We evaluated nine commonly used microbiome metrics: abundance of three phyla, two alpha-diversities, and four beta-diversities. We determined the technical reproducibility, stability at ambient temperature, and accuracy. Results: Although microbiome profiles showed systematic biases according to sample method and time at ambient temperature, the highest source of variation was between individuals. All collection methods showed high reproducibility. FOBT and RNAlater resulted in the highest stability without freezing for 4 days. In comparison with no-additive samples, swab, FOBT, and 70% ethanol exhibited the greatest accuracy when immediately frozen. Conclusions: Overall, optimal stability and reproducibility were achieved using FOBT, making this a reasonable sample collection method for 16S analysis. Impact: Having standardized method of collecting and storing stable fecal samples will allow future investigations into the role of gut microbiota in chronic disease etiology in large population studies. Cancer Epidemiol Biomarkers Prev; 25(2); 407–16. ©2015 AACR.

Abstract Background: Endometrial cancers account for over 46,000 cases and 8,100 deaths annually in the U.S. Endometrial cancers that are confined to the uterus portend an excellent prognosis, suggesting that development of early detection methods for these tumors could reduce mortality. To identify and validate markers with potential utility in early detection of endometrial cancers, we compared DNA methylation profiling data for endometrial cancer tissues collected in a population-based epidemiological study and a comparison set of benign endometrial tissues, and replicated promising candidates in a separate clinical study. Methods: We used the Illumina Golden Gate platform to profile DNA methylation of CpGs at 1536 sites (807 genes) using fixed tissues from 148 endometrial cancers included in the population-based Polish Endometrial Cancer Study (cases) and 23 benign endometrial tissues collected at Magee Women's Hospital (controls). Tissue cores (1.0-mm) removed from target-rich areas of blocks were used for DNA extraction, bisulfite treatment and profiling. We replicated top candidate markers by applying pyrosequencing assays to endometrial brushings performed prior to hysterectomy among 38 women with endometrial cancer and 37 with benign gynecological diseases treated at the Mayo Clinic and calculated odds ratios comparing the highest tertile of methylation values in controls for each marker with middle and low tertiles. In addition, we assessed discrimination across the whole range of cutoffs using Receiver Operator Characteristics (ROC) curves. Results: Methylation profiling identified 114 CpG sites with differential methylation levels between cases and controls (P≤10-7). Eight genes (ADCYAP1, ASCL2, HS3ST2, HTR1B, MME, NPY, and SOX1) which showed large case-control differences in methylation levels (≥ 0.4), and low values in controls (<0.2) were chosen for replication by pyrosequencing in pre-hysterectomy patients. In the replication study, higher methylation levels were found for 7 of 8 genes in endometrial brush samples from women with cancer as compared to women without cancer. In the replication study, odds ratios for association of methylation markers with endometrial cancer ranged from 3.6 (95%-CI: 1.35-9.47) for HTR1B to 10.9 (95%-CI: 3.23-36.91) for ASCL2. Discrimination of cases and controls was best for ADCYAP1 (area under ROC curve=0.89; 95% CI: 0.80-0.97), CDH13 (0.87; 95% CI: 0.80-0.97), and MME (0.87; 95% CI: 0.78-0.97). Conclusions: Analysis of DNA methylation markers in endometrial brushings is strongly associated with prevalent endometrial cancer at hysterectomy. Development and validation of a robust early detection marker panel for endometrial cancers, including non-endometrioid cancers and precursor lesions is ongoing. Citation Format: Nicolas A. Wentzensen, Jamie Bakkum, Keith Killian, Viji Shridhar, Lisa Adams, Richard Guido, Lori D'Ambrosio, Patricia Luhn, Joshua Sampson, Hannah Yang, Louise Brinton, Jolanta Lissowska, Karl Podratz, Mark Sherman. Discovery and validation of methylation markers for early detection of endometrial cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3477. doi:10.1158/1538-7445.AM2013-3477

Abstract Background: With over 13 million cancer survivors in the United States today, second cancers are of rapidly growing importance. However, data on nontreatment risk factors for second cancers are sparse. We explored the feasibility of pooling data from cohort studies of cancer incidence to investigate second cancer etiology. Methods: We combined data from five prospective studies including more than 800,000 individuals. We compared study designs and populations; evaluated availability of and ability to harmonize risk factor data; compared incidence and survival for common first primary malignancies and incidence of second primary malignancies; and estimated sample size requirements. Results: Overall, 96,513 incident, first primary malignancies were diagnosed during 1985 to 2009. Incidence rates and survival following the first primary varied among the cohorts, but most of the heterogeneity could be explained by characteristics of the study populations (age, sex, smoking, and screening rates). A total of 7,890 second primary cancers (excluding original primary site) were identified, yielding sufficient statistical power (≥80%) for detecting modest associations with risk of all second cancers among survivors of common first primary malignancies (e.g., colorectal cancer); however, there were insufficient events for studying survivors of rarer cancers or identifying risk factors for specific second cancers. Conclusions: Pooling data from cohort studies to investigate nontreatment risk factors for second primary cancers seems feasible but there are important methodologic issues—some of which are barriers to specific research questions—that require special attention. Impact: Increased understanding of nontreatment risk factors for second cancers will provide valuable prevention and surveillance information. Cancer Epidemiol Biomarkers Prev; 23(8); 1598–608. ©2014 AACR.