6th International Workshop on Breast Densitometry and Breast ...

More documents

Recommendations

Info

6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry and Breast Cancer Risk Assessment THE RELATIVE CONTRIBUTIONS OF BI-RADS DENSITY AND GENETICS TO BREAST CANCER RISK Celine M. Vachon 1 , Peter Fasching 2 , Fergus Couch 1,3 , Christopher Scott 1 , Matthew Jensen 1 , Dana Whaley 4 , Kathleen R. Brandt 4 , Elad Ziv 5 , Karla Kerlikowske 5 , V. Shane Pankratz 1 ABSTRACTS FOR SPEAKERS 1 Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic College of Medicine, Rochester, MN; 2 Department of Gynecology and Obstetrics, University Hospital Erlangen Friedrich- Alexander-University Erlangen-Nuremberg Erlangen, Germany; 3 Division of Experimental Pathology, Department of Laboratory Medicine and Pathology , Mayo Clinic College of Medicine, Rochester, MN; 4 Divison of Breast Imaging, Department of Radiology, Mayo Clinic College of Medicine, Rochester, MN; 5 Departments of Epidemiology and Biostatistics and General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA Background: Breast density is one of the strongest risk factors for breast cancer (BC), with a high population attributable risk. Recent genetic studies have identified 77 common susceptibility loci for BC which together explain 14% of familial risk. Only one study to date has examined the contribution of a subset of these variants (18 SNPs) to risk models that include breast density. We examine whether the 77 genetic variants provide additional discrimination of BC risk relative to age- and BMI- adjusted BI- RADS density. Further, we extend the BCSC risk model that incorporates Breast Imaging Reporting and Data System (BI-RADS) density and clinical factors to examine the additional contribution of the 77 SNPs to BC risk. Methods: The sample consisted of 1643 cases and 2497 controls from three clinic-based studies with clinical risk factors, including BI-RADS density, and genotype information on the 77 loci. We formulated a polygenic SNP score (77-SNP) from the reported per-SNP odds ratios, and used quartiles of this score in analyses. We evaluated whether BI-RADS density and the 77-SNP score were independent risk factors for BC, adjusted for age, BMI and study, and assessed whether the BIRADS-BC association differed by SNP quartiles. AUC values were compared between models with and without the 77-SNP score, using DeLong’s method, to evaluate whether inclusion of the 77-SNP score significantly improved the discriminatory ability of the model. We extended the BCSC risk model by incorporating into it the odds ratios (OR) for the 77-SNP groups, and estimated 5-year risks for the subjects in one of the three studies with 456 cases, 1166 controls. Five-year risks from the BCSC risk model were compared to those from the BCSC+77-SNP model by computing net reclassification improvement (NRI) for cases and controls using published 5-year risk groupings (4.0). Results: BI-RADS density, adjusted for age, BMI and study, was a significant risk factor for BC (OR (95% CI): 0.6 (0.4-0.7), 1.3 (1.1-1.5) and 1.8 (1.4-2.3) for BI-RADS 1, 3 and 4 vs. 2; AUC=0.66). The 77-SNP score (adjusted for age, BMI and study) was similarly associated with BC (OR (95% CI): 0.6 (0.5-0.7); 1.5 (1.3-1.8); 1.7 (1.4-2.0) for Quartiles 1, 3 and 4 vs. 2; AUC=0.675). BI-RADS density and 77-SNP score were independent risk factors for BC, and together showed greater discrimination of risk compared to the model with BI-RADS alone (adjusted for age and BMI) (AUC=0.69; UC=0.028, p
6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry and Breast Cancer Risk Assessment BREAST CANCER RISK PREDICTION AND INDIVIDUALIZED SCREENING BASED ON COMMON GENETIC VARIATIONS AND BREAST DENSITY MEASUREMENTS Hatef Darabi Karolinska Institutet, Stockholm, Sweden Over the last decade several breast cancer risk alleles have been identified which has led to an increased interest in individualised risk prediction for clinical purposes. In the present work we examine several models for predicting absolute risk, in particular we examine the performance of 18 breast cancer risk single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer. Adding mammographic PD, BMI and all 18 SNPs to a baseline Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement (NRI) was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk, where significant positive reclassification was observed (NRI= 0.170). Researchers participating in the Collaborative Oncological Gene-environment Study (COGS) have now identified more than 40 new SNPs associated with breast cancer. In addition to the discovery of new susceptibility loci, the findings reported can be applied to derive estimates for disease risk based on an individual’s genetic profile, and it is expected to improve the ability to predict which women are at greater risk of developing breast cancer. ABSTRACTS FOR SPEAKERS 19
Page 1: 6 th International
Page 4 and 5: 6 th International
Page 6 and 7: PROGRAM 6 th International<
Page 8 and 9: BREAST DENSITY - WHAT IS A WOMAN TO
Page 10 and 11: ABSTRACTS FOR SPEAKERS 6 th <strong
Page 32 and 33: P1 6 th International</stro
Page 44 and 45: P10 6 th International</str
Page 70 and 71:
6 th International
Page 72 and 73:
P35 6 th International</str
Page 74 and 75:
Page 76 and 77:
6 th International
Page 78 and 79:
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
Page 86 and 87:
6 th International
Page 88 and 89:
6 th International
Page 90 and 91:
88 6 th International</stro
Page 92 and 93:
6 th International
Page 94 and 95:
6 th International
Page 96 and 97:
6 th International
Page 98:
6 th International
show all

6th International Workshop on Breast Densitometry and Breast ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?