Olson, Janet E.

Abstract Background: Early adolescent weight may affect the risk of postmenopausal breast cancer, and this association may be modified by a family history of breast or ovarian cancer in a first-degree relative, and/or estrogen (ER) and progesterone (PR) receptor status of the disease. Methods: Relative weight at age 12 years (above, below, or average weight compared with peers) and family history were ascertained using a mailed questionnaire in 1986, in the Iowa Women's Health Study, a prospective cohort study of postmenopausal women. Incident breast cancer cases (including ER and PR status) were identified using the Iowa Surveillance, Epidemiology, and End Results Cancer Registry. Relative risks (RR) and 95% confidence intervals (95% CI) were estimated using Cox proportional hazards regression, and were adjusted for breast cancer risk factors, including body mass index at age 18 years and body mass index at study baseline. Results: Through 2003, 2,503 cases of postmenopausal breast cancer were identified among 35,941 women in the analytic cohort. Compared with women with average weight at age 12 years, there was no association of below average weight with risk of breast cancer (RR, 1.02; 95% CI, 0.92-1.13), whereas women with above average weight had a lower risk (RR, 0.85; 95% CI, 0.74-0.98). There was no evidence of an interaction between weight at age 12 years and family history (P = 0.44). The inverse association of above average weight with risk of breast cancer was strongest for PR− tumors (RR, 0.62; 95% CI, 0.43-0.89), intermediate for ER+ (RR, 0.80; 95% CI, 0.67-0.96) and ER− (RR, 0.77; 95% CI, 0.50-1.19) tumors, and weakest for PR+ tumors (RR, 0.90; 95% CI, 0.74-1.09). These associations were not modified by a family history (all P > 0.18). In a joint ER/PR analyses, the strongest inverse association with above average weight at age 12 years was seen for ER+/PR− (RR, 0.49; 95% CI, 0.29-0.85). Conclusion: Above average weight at age 12 years was inversely associated with risk of postmenopausal breast cancer, and was not modified by a family history of the disease. The inverse association was strongest for ER+/PR− tumors. (Cancer Epidemiol Biomarkers Prev 2008;17(2):374–8)

AbstractThe AURKA oncogene is associated with abnormal chromosome segregation and aneuploidy and predisposition to cancer. Amplification of AURKA has been detected at higher frequency in tumors from BRCA1 and BRCA2 mutation carriers than in sporadic breast tumors, suggesting that overexpression of AURKA and inactivation of BRCA1 and BRCA2 cooperate during tumor development and progression. The F31I polymorphism in AURKA has been associated with breast cancer risk in the homozygous state in prior studies. We evaluated whether the AURKA F31I polymorphism modifies breast cancer risk in BRCA1 and BRCA2 mutation carriers from the Consortium of Investigators of Modifiers of BRCA1/2. Consortium of Investigators of Modifiers of BRCA1/2 was established to provide sufficient statistical power through increased numbers of mutation carriers to identify polymorphisms that act as modifiers of cancer risk and can refine breast cancer risk estimates in BRCA1 and BRCA2 mutation carriers. A total of 4,935 BRCA1 and 2,241 BRCA2 mutation carriers and 11 individuals carrying both BRCA1 and BRCA2 mutations was genotyped for F31I. Overall, homozygosity for the 31I allele was not significantly associated with breast cancer risk in BRCA1 and BRCA2 carriers combined [hazard ratio (HR), 0.91; 95% confidence interval (95% CI), 0.77-1.06]. Similarly, no significant association was seen in BRCA1 (HR, 0.90; 95% CI, 0.75-1.08) or BRCA2 carriers (HR, 0.93; 95% CI, 0.67-1.29) or when assessing the modifying effects of either bilateral prophylactic oophorectomy or menopausal status of BRCA1 and BRCA2 carriers. In summary, the F31I polymorphism in AURKA is not associated with a modified risk of breast cancer in BRCA1 and BRCA2 carriers. (Cancer Epidemiol Biomarkers Prev 2007;16(7):1416–21)

Abstract Motivation: Using stable isotopes in global proteome scans, labeled molecules from one sample are pooled with unlabeled molecules from another sample and subsequently subjected to mass-spectral analysis. Stable-isotope methodologies make use of the fact that identical molecules of different stable-isotope compositions are differentiated in a mass spectrometer and are represented in a mass spectrum as distinct isotopic clusters with a known mass shift. We describe two multivariable linear regression models for 16O/18O stable-isotope labeled data that jointly model pairs of resolved isotopic clusters from the same peptide and quantify the abundance present in each of the two biological samples while concurrently accounting for peptide-specific incorporation rates of the heavy isotope. The abundance measure for each peptide from the two biological samples is then used in down-stream statistical analyses, e.g. differential expression analysis. Because the multivariable regression models are able to correct for the abundance of the labeled peptide that appear as an unlabeled peptide due to the inability to exchange the natural C-terminal oxygen for the heavy isotope, they are particularly advantageous for a two-step digestion/labeling procedure. We discuss how estimates from the regression model are used to quantify the variability of the estimated abundance measures for the paired samples. Although discussed in the context of 16O/18O stable-isotope labeled data, the multivariable regression models are generalizable to other stable-isotope labeled technologies. Contact: eckel@mayo.edu

Abstract Recent experimental evidence has shown that catechol estrogens can be activated through metabolism to form depurinating DNA adducts and thereby initiate cancer. Limited data are available regarding this pathway in epithelial ovarian cancer. We conducted a case-control study of 503 incident epithelial ovarian cancer cases at the Mayo Clinic in Rochester, MN, and Jacksonville, FL, and a 48-county region in North Carolina. Six hundred nine cancer-free controls were frequency matched to the cases on age, race, and residence. After an interview to obtain data on risk factors, a sample of blood was collected for DNA isolation. Subjects were genotyped for seven common single nucleotide polymorphisms in four genes involved in catechol estrogen formation (CYP1A1 and CYP1B1) or conjugation (COMT and SULT1A1). Data were analyzed using logistic regression, stratified by race, and with adjustment for design factors and potential confounders. None of the individual genotypes were significantly associated with ovarian cancer risk. However, an oligogenic model that considered the joint effects of the four candidate genes provided evidence for an association between combinations of these genes and ovarian cancer status (P = 0.015). Although preliminary, this study provides some support for the hypothesis that low-penetrance susceptibility alleles may influence risk of epithelial ovarian cancer.