# Integrating Polygenic Scores into Multifactorial Breast Cancer Risk Assessment: Insights from the First Year of Clinical Implementation in Western Austria

## TL;DR

This study shows that adding polygenic scores to breast cancer risk assessments can significantly change risk predictions and influence treatment decisions for women with moderate-risk genetic variants.

## Contribution

The study demonstrates the clinical impact of integrating polygenic scores into multifactorial breast cancer risk assessments for carriers of moderate-penetrance variants.

## Key findings

- PGS integration changed risk estimates by more than 10% in 5 of 13 cases.
- MFRA with PGS influenced surgical decisions in 38% of cases.
- PGS z-scores were significantly higher in breast cancer patients compared to controls.

## Abstract

Genetic cancer risk assessment in clinical practice often focuses on single cancer risk genes without considering the level of variation in the genetic background. In this study, we employed polygenic score (PGS) analysis in the setting of multifactorial risk assessment (MFRA), combining a person’s genetic information, family history, personal health data and lifestyle factors to estimate their risk of getting breast cancer, in a cohort of 13 women with monogenic moderate-risk variants in Western Austria. The personal risk was either estimated for contralateral breast cancer in women diagnosed with breast cancer (n = 8) or for primary breast cancer in women without a cancer diagnosis (n = 5). Integrating PGS information sometimes led to substantial changes (more than 10% change in 5 of 13 cases) in the predicted inherited breast cancer risks and influenced surgical management decisions in 5 cases (38%). Our findings support the use of PGS in future breast cancer genetic assessments with MFRA to improve patient diagnosis and care.

Background/Objectives: The implementation of polygenic scores (PGSs) and multifactorial risk assessments (MFRAs) has the potential to enhance breast cancer risk stratification, particularly in carriers of moderate-penetrance pathogenic variants (PVs), whose risk profiles often remain unclear if testing is limited to monogenic risk factors. Methods: To enhance breast cancer risk stratification, we included the BCAC313 polygenic score, together with MFRA, for carriers of moderate-penetrance pathogenic variants (PVs) during routine diagnostics and assessed its effect on the classification of patients’ risk categories in a real-world cohort at our center in its first year of implementation. Seventeen carriers with PVs in moderate-risk breast cancer genes were included in this study. Thirteen of them qualified for analysis for a full MFRA, including PGS, according to ancestry estimation and clinical criteria. The MFRA was performed using the CanRisk tool, which incorporates clinical, lifestyle, familial, and genetic data, including the BCAC313 score. Results: PGS z-scores were significantly higher in breast cancer patients compared to the unaffected control cohort (p = 0.016). The MFRA, including PGS, increased risk estimates for contralateral breast cancer in seven of eight patients with breast cancer and for primary breast cancer in three of five healthy carriers, compared to the risk conferred by the MFRA and moderate-penetrance pathogenic variant alone. Risk estimates varied widely, demonstrating the value of MFRA in personalized care. In five cases, one with a CHEK2-PV and four with an ATM-PV, the modified risk assessment contributed to the surgical decision for a prophylactic mastectomy. Conclusions: The MFRA, including PGS, provides the clinically meaningful refinement of breast cancer risk estimates in individuals with moderate-risk PVs. Personalized risk predictions can inform clinical management and support decision-making, which highlights the utility of this approach in clinical practice.