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Previous studies have shown that a greater number of ovulatory cycles, cumulatively summed as lifetime number of ovulatory cycles (LOC), increases ovarian cancer risk, but there is no uniform algorithm with which to compute LOC. The association between LOC and endometrial cancer is less certain. Accordingly, we identified 14 different LOC algorithms in a literature review and calculated LOCs in the Polish Cancer Study (2001-2003). We evaluated the associations of LOC with ovarian and endometrial cancer risks using unconditional logistic regression, with and without adjustment for individual risk factors used in the LOC computations. Our analysis included 302 ovarian cancer cases with 1,356 controls and 532 endometrial cancer cases with 1,286 controls. We found a high correlation between LOC values among the combined controls (r ≥ 0.88) and identified 5 groups of similar LOC algorithms. A LOC value in the highest quartile was associated with ovarian cancer risk as computed by 2 algorithms (odds ratio (OR) = 2.22 (95% confidence interval (CI): 1.07, 4.62) and OR = 2.44 (95% CI: 1.22,4.87)) and with endometrial cancer risk as computed by 1 algorithm (OR = 1.95,95% CI: 1.11, 3.44). LOC algorithms using a core set of variables widely available in epidemiologic studies may be independently associated with risk of gynecological cancers beyond the contribution of the individual risk factors, such as ages at menopause and menarche. [ABSTRACT FROM AUTHOR]

Introduction: Studies suggest that high circulating levels of prolactin increase breast cancer risk. It is unclear if genetic variations in prolactin (PRL) or prolactin receptor (PRLR) genes also play a role. Thus, we examined the relationship between single nucleotide polymorphisms (SNPs) in PRL and PRLR, serum prolactin levels and breast cancer risk in a population-based case-control study. Methods: We genotyped 8 PRL and 20 PRLR tag SNPs in 1965 breast cancer cases and 2229 matched controls, aged 20-74, and living in Warsaw or Łódź, Poland. Serum prolactin levels were measured by immunoassay in a subset of 773 controls. Odds ratios (ORs) and 95% confidence intervals (CIs) for genotype associations with breast cancer risk were estimated using unconditional logistic regression, adjusted for age and study site. Geometric mean prolactin levels were estimated using linear regression models adjusted for age, study site, blood collection time, and menstrual cycle day (premenopausal women). Results: Three SNPs were associated with breast cancer risk: in premenopausal women, PRLR rs249537 (T vs. C perallele OR 1.39, 95% CI 1.07 - 1.80, P = 0.01); and in postmenopausal women, PRLR rs7718468 (C vs. T per-allele OR 1.16, 95% CI 1.03 - 1.30, P = 0.01) and PRLR rs13436213 (A vs. G per-allele OR 1.13 95% CI 1.01 - 1.26, P = 0.04). However, mean serum prolactin levels for these SNPs did not vary by genotype (P-trend > 0.05). Other SNPs were associated with serum prolactin levels: PRLR rs62355518 (P-trend = 0.01), PRLR rs10941235 (P-trend = 0.01), PRLR rs1610218 (P-trend = 0.01), PRLR rs34024951 (P-trend = 0.02), and PRLR rs9292575 (P-trend = 0.03) in premenopausal controls and PRL rs849872 (P-trend = 0.01) in postmenopausal controls. Conclusions: Our data provide limited support for an association between common variations in PRLR and breast cancer risk. Altered serum prolactin levels were not associated with breast cancer risk-associated variants, suggesting that common genetic variation is not a strong predictor of prolactin-associated breast cancer risk in this population. [ABSTRACT FROM AUTHOR]

PURPOSE: Physical activity may reduce endogenous estrogens, but few studies have assessed effects on estrogen metabolism and none have evaluated sedentary behavior in relation to estrogen metabolism. We assessed relationships between accelerometer-measured physical activity and sedentary behavior and 15 urinary estrogens and estrogen metabolites (EM) among postmenopausal controls from a population-based breast cancer case-control study conducted in Poland (2000-2003). METHODS: Postmenopausal women (N = 542) were ages 40 to 72 yr and not currently using hormone therapy. Accelerometers, worn for 7 d, were used to derive measures of average activity (counts per day) and sedentary behavior (<100 counts per minute per day). Estrogen metabolites were measured in 12-h urine samples using liquid chromatography-tandem mass spectrometry. Estrogen metabolites were analyzed individually, in metabolic pathways (C-2, -4, or -16), and as ratios relative to parent estrogens. Geometric means of estrogen metabolites by tertiles of accelerometer-measures, adjusted for age and body mass, were computed using linear models. RESULTS: High activity was associated with lower levels of estrone and estradiol (P trend = 0.01), whereas increased sedentary time was positively associated with these parent estrogens (P trend = 0.04). Inverse associations were observed between high activity and 2-methoxyestradiol, 4-methoxyestradiol, 17-epiestriol, and 16-epiestriol (P trend = 0.03). Sedentary time was positively associated with methylated catechols in the 2- and 4-hydroxylation pathways (P trend ≤ 0.04). Women in the highest tertile of activity had increased hydroxylation at the C-2, -4, and -16 sites relative to parent estrogens (P trend ≤ 0.02), whereas increased sedentary time was associated with a lower 16-pathway/parent estrogen ratio (P trend = 0.01). CONCLUSIONS: Higher activity was associated with lower urinary estrogens, possibly through increased estrogen hydroxylation and subsequent metabolism, whereas sedentary behavior may reduce metabolism. [ABSTRACT FROM AUTHOR]

Context: Endometrial cancer is associated with metabolic disturbances related to its underlying risk factors, including obesity and diabetes. Identifying metabolite biomarkers associated with endometrial cancer may have value for early detection, risk assessment, and understanding etiology., Objective: The objective of the study was to evaluate the reliable measurement of metabolites in epidemiological studies with nonstandardized blood collection; confirm previously reported correlations of metabolites with body size; and assess differences in metabolite levels between cases and controls., Design: This was the Polish Endometrial Cancer Study (2001-2003)., Setting: This study was a population-based case-control study., Patients: Patients included 250 cases and 250 controls., Intervention: The intervention included the measurement of serum metabolite levels of 15 amino acids, 45 acylcarnitines, and nine fatty acids., Main Outcome Measure: The main outcome measure was endometrial cancer., Results: Body mass index was correlated with levels of valine (r = 0.26, P = 3.4 × 10(-5)), octenoylcarnitine (r = 0.24, P = 1.5 × 10(-4)), palmitic acid (r = 0.26, P = 4.4 × 10(-5)), oleic acid (r = 0.28, P = 9.9 × 10(-6)), and stearic acid (r = 0.26, P = 2.9 × 10(-5)) among controls. Only stearic acid was inversely associated with endometrial cancer case status (quartile 4 vs. quartile 1: odds ratio 0.37, 95% confidence interval 0.20-0.69, P for trend = 1.2 × 10(-4)). Levels of the C5-acylcarnitines, octenoylcarnitine, decatrienoylcarnitine, and linoleic acid were significantly lower in cases than controls (odds ratios ranged from 0.21 to 0.38)., Conclusions: These data demonstrate that previously reported variations in metabolomic profiles with body mass index can be replicated in population-based studies with nonfasting blood collection protocols. We also provide preliminary evidence that large differences in metabolite levels exist between cases and controls, independent of body habitus. Our findings warrant assessment of metabolic profiles, including the candidate markers identified herein, in prospectively collected blood samples to define biomarkers and etiological factors related to endometrial cancer.

The transforming growth factor beta (TGF-beta) pathway can play either a tumor-suppressing or a tumor-promoting role in human breast carcinogenesis. In order to determine whether expression of TGF-beta signaling factors varies by age at onset and breast tumor characteristics that have prognostic significance, we undertook a study of 623 women with invasive breast carcinoma enrolled in a population-based case-control study conducted in Poland from 2000 to 2003. TGF-beta signaling factors were analyzed by immunohistochemistry in tumor tissue microarrays. We found that most tumors expressed extracellular-TGF-beta1 (78%), TGF-beta2 (91%), TGF-beta3 (93%), TGF-betaR2 (72%), and phospho-SMAD2 (61%), whereas intracellular-TGF-beta1 was expressed in 32% of tumors. Expression of TGF-beta ligands (beta1, beta2, and beta3) was associated with prognostically favorable pathological features including small size, and low grade, and these associations were similar for ER-positive and negative tumors. On the contrary, expression of the receptor TGF-betaR2 was primarily associated with small tumor size among ER-negative tumors, while expression of the transcription factor phospho-SMAD2 was associated with positive nodal status among ER-negative tumors. The greater frequency of expression of phospho-SMAD2 in cancers associated with lymph node metastases is consistent with a pro-progression role for TGF-beta. In addition, expression of extracellular-TGF-beta1 (P = 0.005), TGF-betaR2 (P = 8.2E-11), and phospho-SMAD2 (P = 1.3E-8) was strongly associated with earlier age at onset, independent of ER status. Our data provide evidence that TGF-beta signaling patterns vary by age and pathologic features of prognostic significance including ER expression. These results warrant analysis in studies of clinical outcomes accounting for age, ER status and treatment.