Rosner, Bernard

Abstract Background: Clinical use of breast cancer risk prediction requires simplified models. We evaluate a simplified version of the validated Rosner–Colditz model and add percent mammographic density (MD) and polygenic risk score (PRS), to assess performance from ages 45–74. We validate using the Mayo Mammography Health Study (MMHS). Methods: We derived the model in the Nurses' Health Study (NHS) based on: MD, 77 SNP PRS and a questionnaire score (QS; lifestyle and reproductive factors). A total of 2,799 invasive breast cancer cases were diagnosed from 1990–2000. MD (using Cumulus software) and PRS were assessed in a nested case–control study. We assess model performance using this case–control dataset and evaluate 10-year absolute breast cancer risk. The prospective MMHS validation dataset includes 21.8% of women age <50, and 434 incident cases identified over 10 years of follow-up. Results: In the NHS, MD has the highest odds ratio (OR) for 10-year risk prediction: ORper SD = 1.48 [95% confidence interval (CI): 1.31–1.68], followed by PRS, ORper SD = 1.37 (95% CI: 1.21–1.55) and QS, ORper SD = 1.25 (95% CI: 1.11–1.41). In MMHS, the AUC adjusted for age + MD + QS 0.650; for age + MD + QS + PRS 0.687, and the NRI was 6% in cases and 16% in controls. Conclusion: A simplified assessment of QS, MD, and PRS performs consistently to discriminate those at high 10-year breast cancer risk. Impact: This simplified model provides accurate estimation of 10-year risk of invasive breast cancer that can be used in a clinical setting to identify women who may benefit from chemopreventive intervention. See related commentary by Tehranifar et al., p. 587

Abstract Background: This analysis aimed to determine if associations of prediagnostic mammographic percent breast density, absolute dense area, and absolute non-dense area and risk of breast cancer vary according to the subsequent breast tumors biomarker status. Methods: We included 1,154 postmenopausal women diagnosed with breast cancer between June 1, 1989, and June 30, 2004, and 2,078 matched controls from the Nurses' Health Study I and II. Percent breast density, absolute dense and non-dense areas were estimated from digitized film mammograms using computer assisted thresholding techniques. Density measures were modeled as quartiles. Information on estrogen receptor (ESR1 [ER]), progesterone receptor (PGR [PR]), and human epidermal growth factor receptor 2 (ERBB2 [HER2]) status was obtained from pathology reports and medical records, and if missing, from immunostains performed on paraffin sections of tumor tissue microarrays (TMAs). Other biomarkers assessed on TMAs were androgen receptor (AR), cytokines 5/6 (KRT5/KRT6 [CK5/6]), epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGFA [VEGF]), and cyclooxygenase 2 (PTGS2 [COX2]). Information on breast cancer risk factors was obtained prospectively from biennial questionnaires before the date of cancer diagnosis for cases and matched controls. Polychotomous logistic regression was used to assess associations of the three breast density measures with tumor subtypes based on the status of selected tissue markers. Results: Breast cancer risk was positively associated with percent density and absolute dense area and inversely associated with absolute non-dense area (p for trend<0.0001 for all). The associations of percent density with breast cancer risk did not statistically significantly differ by the status of any of the markers. However, we found stronger positive associations for absolute dense area and KRT5/KRT6- and EGFR- tumors as compared to respective marker-positive tumors (4th vs. 1st quartile: OR=2.99 for KRT5/KRT6- vs. OR=0.92 for KRT5/KRT6+ tumors, p-heterogeneity <0.001; OR=3.06 for EGFR- vs. OR=1.08 for EGFR+ tumors, p-heterogeneity=0.001). Stronger inverse associations of absolute non-dense area with breast cancer risk were found for ESR1- as compared to ESR1+ tumors, and for AR+, KRT5/KRT6+, and EGFR+ tumors as compared to respective marker-negative tumors (4th vs. 1st quartile: OR=0.21 for ESR1- vs. OR=0.59 for ESR1+ tumors, p-heterogeneity <0.0001; OR=0.22 for AR+ vs. OR=0.42 for AR- tumors, p-heterogeneity=0.04; OR=0.21 for KRT5/KRT6+ vs. OR=1.14 for KRT5/KRT6- tumors, p-heterogeneity<0.01; OR=0.37 for EGFR+ vs. OR=1.11 for EGFR- tumors, p-heterogeneity<0.01). There were no differences in associations of the density measures with breast cancer by status of other markers evaluated. Conclusions: The findings suggest that some mammographic density measures are differentially associated with the risk of subsequent breast tumor subtypes. Citation Format: Lusine Yaghjyan, Andreas Pettersson, Graham Colditz, Laura Collins, Stuart Schnitt, Andrew Beck, Bernard Rosner, Celine Vachon, Rulla Tamimi. Mammographic breast density and subsequent risk of breast cancer in postmenopausal women according to the status of selected tissue markers: A case-control study. [abstract]. In: Proceedings of the Twelfth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2013 Oct 27-30; National Harbor, MD. Philadelphia (PA): AACR; Can Prev Res 2013;6(11 Suppl): Abstract nr PR09.

Abstract Objective: Several attributes of female reproductive history, including age at natural menopause (ANM), have been related to primary open-angle glaucoma (POAG). We assembled 18 previously reported common genetic variants that predict ANM to determine their association with ANM or POAG. Methods: Using data from the Nurses’ Health Study (7,143 women), we validated the ANM weighted genetic risk score in relation to self-reported ANM. Subsequently, to assess the relation with POAG, we used data from 2,160 female POAG cases and 29,110 controls in the National Eye Institute Glaucoma Human Genetics Collaboration Heritable Overall Operational Database (NEIGHBORHOOD), which consists of 8 datasets with imputed genotypes to 5.6+ million markers. Associations with POAG were assessed in each dataset, and site-specific results were meta-analyzed using the inverse weighted variance method. Results: The genetic risk score was associated with self-reported ANM (P = 2.2 × 10–77) and predicted 4.8% of the variance in ANM. The ANM genetic risk score was not associated with POAG (Odds Ratio (OR) = 1.002; 95% Confidence Interval (CI): 0.998, 1.007; P = 0.28). No single genetic variant in the panel achieved nominal association with POAG (P ≥0.20). Compared to the middle 80 percent, there was also no association with the lowest 10th percentile or highest 90th percentile of genetic risk score with POAG (OR = 0.75; 95% CI: 0.47, 1.21; P = 0.23 and OR = 1.10; 95% CI: 0.72, 1.69; P = 0.65, respectively). Conclusions: A genetic risk score predicting 4.8% of ANM variation was not related to POAG; thus, genetic determinants of ANM are unlikely to explain the previously reported association between the two phenotypes.