Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement.
BACKGROUND
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.


METHODS
We investigate the performance of an up-to-date 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, empirically, in a well characterised Swedish case-control study of postmenopausal women. We examined the efficiency of various prediction models at a population level for individualised screening by extending a recently proposed analytical approach for estimating number of cases captured.


RESULTS
The performance of a risk prediction model based on an initial set of seven breast cancer risk SNPs is improved by additionally including eleven more recently established breast cancer risk SNPs (P = 4.69 × 10-4). Adding mammographic PD, BMI and all 18 SNPs to a Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk (P = 8.93 × 10-9). For scenarios we considered, we estimated that an individualised screening strategy based on risk models incorporating clinical risk factors, mammographic density and SNPs, captures 10% more cases than a screening strategy using the same resources, based on age alone. Estimates of numbers of cases captured by screening stratified by age provide insight into how individualised screening programs might appear in practice.


CONCLUSIONS
Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer.
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P,O, Box 281, Stockholm 177 71, Sweden. hatef.darabi@ki.se
Hatef
H
Darabi
Kamila
K
Czene
Wanting
W
Zhao
Jianjun
J
Liu
Per
P
Hall
Keith
K
Humphreys
D000328:Adult; D000368:Aged; D019540:Area Under Curve; D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000235:genetics*; D016022:Case-Control Studies; D055088:Early Detection of Cancer; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D042361:Mammary Glands, Human / Q000002:abnormalities; D008327:Mammography; D008875:Middle Aged; D008954:Models, Biological; D020641:Polymorphism, Single Nucleotide; D017698:Postmenopause; D057285:Precision Medicine; D012372:ROC Curve; D012307:Risk Factors