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. 2023 May 28;15(11):2957.
doi: 10.3390/cancers15112957.

Polygenic Risk Score Predicts Modified Risk in BRCA1 Pathogenic Variant c.4035del and c.5266dup Carriers in Breast Cancer Patients

Egija Berga-Švītiņa  1   2 , Jeļena Maksimenko  2   3 , Edvīns Miklaševičs  2   4 , Krista Fischer  5   6 , Baiba Vilne  1 , Reedik Mägi  5
Affiliations

Polygenic Risk Score Predicts Modified Risk in BRCA1 Pathogenic Variant c.4035del and c.5266dup Carriers in Breast Cancer Patients

Egija Berga-Švītiņa et al. Cancers (Basel). .

Abstract

The aim of this study was to assess the power of the polygenic risk score (PRS) in estimating the overall genetic risk of women carrying germline BRCA1 pathogenic variants (PVs) c.4035del or c.5266dup to develop breast (BC) or ovarian cancer (OC) due to additional genetic variations. In this study, PRSs previously developed from two joint models using summary statistics of age-at-onset (BayesW model) and case-control data (BayesRR-RC model) from a genome-wide association analysis (GWAS) were applied to 406 germline BRCA1 PV (c.4035del or c.5266dup) carriers affected by BC or OC, compared with unaffected individuals. A binomial logistic regression model was used to assess the association of PRS with BC or OC development risk. We observed that the best-fitting BayesW PRS model effectively predicted the individual's BC risk (OR = 1.37; 95% CI = 1.03-1.81, p = 0.02905 with AUC = 0.759). However, none of the applied PRS models was a good predictor of OC risk. The best-fitted PRS model (BayesW) contributed to assessing the risk of developing BC for germline BRCA1 PV (c.4035del or c.5266dup) carriers and may facilitate more precise and timely patient stratification and decision-making to improve the current BC treatment or even prevention strategies.

Keywords: BRCA1 pathogenic variant carriers; breast cancer; ovarian cancer; polygenic risk score (PRS).

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Boxplots and binomial logistic regression analysis p values of polygenic risk scores in 406 BRCA1 PV carriers. Controls, no cancer; BC, breast cancer; OC, ovarian cancer. * p value below 0.05.
Figure 2
Figure 2
A comparison of the AUC (area under the receiver operating characteristic curve) to select the most optimal binomial logistic regression analysis model. In black—the model with only age and age squared as covariates; in red—the model with the BRCA1 PV added; in blue—the model with the BRCA1 PV and the best performing PRS added (i.e., score1).
See this image and copyright information in PMC

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