Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification

Abstract

Background Published genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aimed to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies. Methods Four different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) were compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors were studied in both cohorts. Results The metaGRS that combines two genetic risk scores (metaGRS 2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) had the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS 2 corresponded to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10 − 135 ) in the UK Biobank and accounting for family history marginally attenuated the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 7.8*10 − 129 ). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS 2 compared to women in the lowest 50% was 4.2 (95% CI 2.8 to 6.2, p = 8.1*10 − 13 ). The different GRSs were only moderately correlated with each other and were associated with different known predictors of BC. The classification of genetic risk for the same individual varied considerably depending on the chosen GRS. Conclusions We have shown that metaGRS 2, that combined on the effects of more than 900 SNPs, provided best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS 2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women. Electronic supplementary material The online version of this article (10.1186/s12885-019-5783-1) contains supplementary material, which is available to authorized users.

Polygenic risk scores and breast and epithelial ovarian cancer risks for carriers of BRCA1 and BRCA2 pathogenic variants

“… 12 This difference is unlikely to be an overestimation of the ORs in the general population (“winner’s curse” 26 ), because the effect sizes were estimated in prospective studies that were independent of the data used in their development. 12 , 27 Adjustment for family history, a potential confounder in this study, did not influence the associations. Therefore, these most likely represent real differences, in which PRS modify breast cancer risk for BRCA1 / 2 carriers to a smaller relative extent than the general population.…”

Uptake of polygenic risk information among women at increased risk of breast cancer

“…42 Finally, it should be noted that research is still ongoing to identify SNPs associated with breast cancer risk 43 and to determine the best performing PRS. 44,45 Hence, it is possible the present PRS will need to be updated in the future. Despite these limitations, our findings point to potential areas for intervention to facilitate genetic testing decisions that include improving knowledge of familial breast cancer and PRS and exploring perceived benefits and barriers to accessing polygenic information.…”

Personalized early detection and prevention of breast cancer: ENVISION consensus statement

“…Some empirical models, which are commercially available, have been modified to incorporate breast cancer PRSs, but without accounting for the fact that PRSs explain a large fraction of the familial relative risk of breast cancer. The failure to adjust these models to account for family history of breast cancer results in substantial levels of miscalibration in different risk categories and subsequently compromises the clinical validity of the model 46 .…”