Polygenic Risk Score (PRS) Combined with NGS Panel Testing Increases Accuracy in Hereditary Breast Cancer Risk Estimation

Abstract

Breast cancer (BC) is the most prominent tumor type among women, accounting for 32% of newly diagnosed cancer cases. BC risk factors include inherited germline pathogenic gene variants and family history of disease. However, the etiology of the disease remains occult in most cases. Therefore, in the absence of high-risk factors, a polygenic basis has been suggested to contribute to susceptibility. This information is utilized to calculate the Polygenic Risk Score (PRS) which is indicative of BC risk. This study aimed to evaluate retrospectively the clinical usefulness of PRS integration in BC risk calculation, utilizing a group of patients who have already been diagnosed with BC. The study comprised 105 breast cancer patients with hereditary genetic analysis results obtained by NGS. The selection included all testing results: high-risk gene-positive, intermediate/low-risk gene-positive, and negative. PRS results were obtained from an external laboratory (Allelica). PRS-based BC risk was computed both with and without considering additional risk factors, including gene status and family history. A significantly different PRS percentile distribution consistent with higher BC risk was observed in our cohort compared to the general population. Higher PRS-based BC risks were detected in younger patients and in those with FH of cancers. Among patients with a pathogenic germline variant detected, reduced PRS values were observed, while the BC risk was mainly determined by a monogenic etiology. Upon comprehensive analysis encompassing FH, gene status, and PRS, it was determined that 41.90% (44/105) of the patients demonstrated an elevated susceptibility for BC. Moreover, 63.63% of the patients with FH of BC and without an inherited pathogenic genetic variant detected showed increased BC risk by incorporating the PRS result. Our results indicate a major utility of PRS calculation in women with FH in the absence of a monogenic etiology detected by NGS. By combining high-risk strategies, such as inherited disease analysis, with low-risk screening strategies, such as FH and PRS, breast cancer risk stratification can be improved. This would facilitate the development of more effective preventive measures and optimize the allocation of healthcare resources.

Keywords: breast cancer; next-generation sequencing (NGS); polygenic risk score (PRS).

Figure 1

PRS percentile distribution in 105 patients with breast cancer. The dotted line represents the average PRS expected in the general population.

Figure 2

PRS percentile distribution in patients with and without family history (FH) of breast cancer. Blue: PRS-based risk estimation in patients with FH of BC. Green: PRS-based risk estimation in patients without FH of BC.

Figure 3

(A) PRS percentile distribution in patients without pathogenic gene alterations and with alterations in high-, moderate-, and low/unspecified-risk genes. (B) PRS percentile distribution in patients <45 years without pathogenic gene alterations and with alterations in high-, moderate-, and low/unspecified-risk genes. (C) PRS percentile distribution in patients >45 years without pathogenic gene alterations and with alterations in high-, moderate-, and low/unspecified-risk genes. Blue: PRS-based risk estimation in patients without a cancer gene alteration detected by NGS. Green: PRS-based risk estimation in patients with a variant in a low-risk gene. Orange: PRS-based risk estimation in patients with a variant in a moderate-risk gene. Red: PRS-based risk estimation in patients with a variant in a high-risk gene.

Figure 4

Density curves of BC risk distribution in the cohort of BC patients. Red density curve: BC risk estimation based on PRS only (Risk PRS), Blue: BC risk estimation based on FH and gene status without PRS (risk_noPRS). Grey: BC risk estimation including all factors in addition to PRS (Risk total).

Figure 5

Density curves of PRS-based risk estimation in patients with and without FH of BC. Blue: PRS-based risk estimation in patients with FH of BC. Green: PRS-based risk estimation in patients without FH of BC.

Figure 6

Risk distribution with and without the integration of PRS results in patients categorized according to their FH/gene status.