Brinton, Louise A.

Abstract Background: Nulliparity is an established endometrial cancer risk factor, but whether this association is related to infertility is unclear. Although there are many underlying causes of infertility, few studies have assessed risk relationships according to specific causes. Despite concerns that most treatments induce ovulation and that the endometrium is highly hormonally-responsive, previous studies have provided conflicting results on treatment associations. Methods: To address these issues, we conducted a pooled analysis of 8,151 cases and12,471 controls from 2 cohort and 12 case-control studies conducted in 1982 to 2009 (N. America, Europe, Australia, Asia). All studies provided self-reported infertility, causes and treatments, except for 1 study that relied on data from national registries. Study-specific exposures and covariate data were harmonized across studies. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI), adjusted for study, age, interview year, race, parity, oral contraceptive and menopausal hormone use, and body mass index. If data were not reported for a particular variable, that study was excluded from the variable-specific analysis. Results: Nulliparous women had an elevated endometrial cancer risk compared with parous women (adjusted OR=1.73; 95% CI: 1.58-1.90).Women with self-reported infertility also had an increased risk (adjusted OR=1.22; 95% CI: 1.13-1.32) compared with those without infertility issues, even after adjusting for parity; the strongest associations was seen among studies that defined infertility as unsuccessfully trying to conceive for 2+ years (adjusted OR=1.31; 95% CI: 1.07-1.60). Infertility was associated with similar risks among nulliparous (1.22; 1.10-1.48) and parous (1.22; 1.12-1.34) women, although there was no increased risk associated with infertility among those who had given birth to 3+ children. Two infertility causes associated with risk elevation were endometriosis (adjusted OR=2.08; 95% CI: 1.46-2.95 compared with women no infertility problems) and anovulation (adjusted OR=1.40; 95% CI: 1.16-1.70). Based on relatively small numbers, none of the individual treatments (in vitro fertilization, selective estrogen response modulators, gonatropins, progestins, estrogens) were substantially related to risk. Conclusion: Based on mainly self-reported infertility data, we found that parity and infertility independently contribute to endometrial cancer risk, with parity seemingly being the predominant risk predictor. Understanding residual endometrial cancer relationships related to causes of infertility and infertility treatment may benefit from documented medical information on causes of infertility and treatment parameters. Citation Format: Hannah P. Yang, Linda S. Cook, Elisabete Weiderpass, Hans-Olov Adami, Kristin E. Anderson, Hui Cai, James R. Cerhan, Tess Clendenen, Ashley S. Felix, Christine Friedenreich, Montserrat Garcia-Closas, Marc T. Goodman, Xiaolin Liang, Jolanta Lissowska, Lingeng Lu, Anthony M. Magliocco, Susan E. McCann, Kristen B. Moysich, Sara H. Olson, Malcolm C. Pike, Silvia Polidoro, Fulvio Ricceri, Harvey Risch, Carlotta Sacerdote, V. Wendy Setiawan, Xiao Ou Shu, Amanda B. Spurdle, Britton Trabert, Penelope M. Webb, Nicolas Wentzensen, Yong-Bing Xiang, Youming Xu, Herbert Yu, Anne Zeleniuch-Jacquotte, Louise A. Brinton. Infertility and risk of incident endometrial carcinoma: a pooled analysis from the Epidemiology of Endometrial Cancer Consortium. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2167. doi:10.1158/1538-7445.AM2014-2167

AbstractBreast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large‐scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65–70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa = 0.76), followed by human epidermal growth factor receptor 2 (Kappa = 0.69) and progesterone receptor (Kappa = 0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose‐response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96–98%), but yielded many false positives (positive predictive value = 30–32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large‐scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker‐specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results.

Abstract Background: Terminal duct lobular units (TDLU) are the predominant source of breast cancers. Lesser degrees of age-related TDLU involution have been associated with increased breast cancer risk, but factors that influence involution are largely unknown. We assessed whether circulating hormones, implicated in breast cancer risk, are associated with levels of TDLU involution using data from the Susan G. Komen Tissue Bank (KTB) at the Indiana University Simon Cancer Center (2009–2011). Methods: We evaluated three highly reproducible measures of TDLU involution, using normal breast tissue samples from the KTB (n = 390): TDLU counts, median TDLU span, and median acini counts per TDLU. RRs (for continuous measures), ORs (for categorical measures), 95% confidence intervals (95% CI), and Ptrends were calculated to assess the association between tertiles of estradiol, testosterone, sex hormone–binding globulin (SHBG), progesterone, and prolactin with TDLU measures. All models were stratified by menopausal status and adjusted for confounders. Results: Among premenopausal women, higher prolactin levels were associated with higher TDLU counts (RRT3vsT1:1.18; 95% CI: 1.07–1.31; Ptrend = 0.0005), but higher progesterone was associated with lower TDLU counts (RRT3vsT1: 0.80; 95% CI: 0.72–0.89; Ptrend < 0.0001). Among postmenopausal women, higher levels of estradiol (RRT3vsT1:1.61; 95% CI: 1.32–1.97; Ptrend < 0.0001) and testosterone (RRT3vsT1: 1.32; 95% CI: 1.09–1.59; Ptrend = 0.0043) were associated with higher TDLU counts. Conclusions: These data suggest that select hormones may influence breast cancer risk potentially through delaying TDLU involution. Impact: Increased understanding of the relationship between circulating markers and TDLU involution may offer new insights into breast carcinogenesis. Cancer Epidemiol Biomarkers Prev; 23(12); 2765–73. ©2014 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

ScopeWe reevaluated previously reported associations between variants in pathways of one‐carbon (1‐C) (folate) transfer genes and ovarian carcinoma (OC) risk, and in related pathways of purine and pyrimidine metabolism, and assessed interactions with folate intake.Methods and resultsOdds ratios (OR) for 446 genetic variants were estimated among 13 410 OC cases and 22 635 controls, and among 2281 cases and 3444 controls with folate information. Following multiple testing correction, the most significant main effect associations were for dihydropyrimidine dehydrogenase (DPYD) variants rs11587873 (OR = 0.92; p = 6 × 10−5) and rs828054 (OR = 1.06; p = 1 × 10−4). Thirteen variants in the pyrimidine metabolism genes, DPYD, DPYS, PPAT, and TYMS, also interacted significantly with folate in a multivariant analysis (corrected p = 9.9 × 10−6) but collectively explained only 0.2% of OC risk. Although no other associations were significant after multiple testing correction, variants in SHMT1 in 1‐C transfer, previously reported with OC, suggested lower risk at higher folate (pinteraction = 0.03–0.006).ConclusionVariation in pyrimidine metabolism genes, particularly DPYD, which was previously reported to be associated with OC, may influence risk; however, stratification by folate intake is unlikely to modify disease risk appreciably in these women. SHMT1 SNP‐by‐folate interactions are plausible but require further validation. Polymorphisms in selected genes in purine metabolism were not associated with OC.

Abstract Survival in epithelial ovarian cancer (EOC) is influenced by the host immune response, yet the key genetic determinants of inflammation and immunity that affect prognosis are not known. The nuclear factor-κB (NF-κB) transcription factor family plays an important role in many immune and inflammatory responses, including the response to cancer. We studied common inherited variation in 210 genes in the NF-κB family in 10,084 patients with invasive EOC (5,248 high-grade serous, 1,452 endometrioid, 795 clear cell, and 661 mucinous) from the Ovarian Cancer Association Consortium. Associations between genotype and overall survival were assessed using Cox regression for all patients and by major histology, adjusting for known prognostic factors and correcting for multiple testing (threshold for statistical significance, P < 2.5 × 10−5). Results were statistically significant when assessed for patients of a single histology. Key associations were with caspase recruitment domain family, member 11 (CARD11) rs41324349 in patients with mucinous EOC [HR, 1.82; 95% confidence interval (CI), 1.41–2.35; P = 4.13 × 10−6] and tumor necrosis factor receptor superfamily, member 13B (TNFRSF13B) rs7501462 in patients with endometrioid EOC (HR, 0.68; 95% CI, 0.56–0.82; P = 2.33 × 10−5). Other associations of note included TNF receptor–associated factor 2 (TRAF2) rs17250239 in patients with high-grade serous EOC (HR, 0.84; 95% CI, 0.77–0.92; P = 6.49 × 10−5) and phospholipase C, gamma 1 (PLCG1) rs11696662 in patients with clear cell EOC (HR, 0.43; 95% CI, 0.26–0.73; P = 4.56 × 10−4). These associations highlight the potential importance of genes associated with host inflammation and immunity in modulating clinical outcomes in distinct EOC histologies. Cancer Epidemiol Biomarkers Prev; 23(7); 1421–7. ©2014 AACR.