Contribution of Risk Factors, Including Polygenic Score, to the Multifactorial Risk Assessment for the Implementation...

Risk-based breast cancer (BC) screening can provide tailored recommendations based on individual risk. We aimed to identify key predictors for BC risk stratification to inform implementation in screening programs.

We estimated 10-year BC risks using BOADICEA v.6 (CanRisk) in 3753 women aged 40-70 with no cancer history from the PERSPECTIVE I&I cohort. The primary endpoint was risk reclassification, assessed as the proportion of women whose assigned 10-year risk category changed when using different risk factor combinations against a full multifactorial model including questionnaire-based risk factors (QRFs), polygenic score (PGS), mammographic density (MD), and pedigree-structured first- and second-degree family history (FH) of breast, ovarian, pancreatic and prostate cancer, including both affected and unaffected relatives. Relative risk thresholds were set as <1.5 (average), 1.5-2.7 (higher-than-average), and ≥2.7 (high), equivalent to the remaining lifetime risk categories of <15%, 15-25% and ≥25% for women aged 30 (the anchor) to age 80. We quantified individual-level reclassification flows by direction and magnitude.

Excluding PGS from risk calculations led to the highest overall reclassification. Using only the BC status in first- and second-degree relatives produced comparable risk classification to that of the full FH data that included breast, ovarian, prostate and pancreatic cancer (reclassification = 0.5%). However, collecting only affected relatives led to overestimation of risk. Excluding either PGS, MD or FH resulted in a greater proportion of reclassification among younger women. Adding the PGS to risk factors already collected in provincial screening programs reduced reclassification from 23% to ~13%.

PGS, MD, QRFs and FH of BC in affected and unaffected first- and second-degree relatives are key for refining risk stratification. These findings provide real-world evidence on how incorporating different sets of risk factors, both those routinely collected in screening programs and those requiring additional data collection, affect individual-level risk classification amongst a population-based cohort, and how the impacts differ across age groups. While risk classification reflects model-based changes in estimated risk categories rather than direct evidence of mis-screening or clinical outcomes, comparison with the current eligibility criteria used to identify women at higher-than-average risk highlights the potential clinical value of a multifactorial risk assessment approach in ensuring more appropriate screening strategies.