Polygenic risk and rare variant gene clustering enhance cancer risk stratification for breast and prostate cancers

Abstract

Polygenic risk score (PRS) and rare monogenic variant screening are valuable tools for predicting cancer risk and identifying individuals at high risk. Integrating both common and rare genetic variants is crucial for accurate risk assessment. However, estimating the impacts of rare variants on cancer and combining them with PRS remains challenging. Here, we analyze 454,711 exome sequencing and 487,409 array UK Biobank samples, focusing on breast and prostate cancers. We introduce an expanded PRS (EPRS) approach, yielding a systematic model for more effective risk stratification. By prioritizing and clustering genes with cancer-specific rare variants based on odds ratios and population-attributable fraction, we refine risk stratification by combining both monogenic and polygenic effects. Individuals in high-PRS groups with rare high-impact gene variants show up to 15- and 22-fold higher risk for breast and prostate cancers, respectively, compared to those in the intermediate-PRS groups without rare variants. Combined risk profiles vary across distinct rare variant clusters within the same PRS group for both cancers. Our EPRS approach enhances risk stratification for breast and prostate cancers, offering important insights for future research and potential applications to other cancer types.

Fig. 1. Cancer odds ratio among individuals categorized according to the presence of monogenic variants and polygenic risk scores (PRS).

Participants with or without monogenic variants were categorized into three strata based on their PRS (low, intermediate, or high). The odds ratio and 95% confidence intervals were calculated from a logistic regression model for (a) breast and (b) prostate cancer.

Fig. 2. Cancer odds ratio among individuals categorized according to monogenic effect clusters and polygenic risk score (PRS).

Participants were categorized into three strata based on their PRS (low, intermediate, or high) and monogenic effect cluster. The odds ratios and 95% confidence intervals (CIs) were calculated from a logistic regression model for (a) breast and (b) prostate cancer.

Fig. 3. Prevalence of risk groups.

The prevalence of stratified risk groups according to their monogenic effect cluster and polygenic risk score (PRS) risk group were calculated for UK Biobank samples for (a) breast and (b) prostate cancer.