A Tailored Polygenic Risk Score for Predicting Progression in Prostate Cancer Under Active Surveillance

Prostate cancer (PCa) is the most frequently diagnosed malignancy among men in many countries. For many patients with low-risk disease-defined by favorable clinical and pathological features-active surveillance (AS) represents the preferred initial management strategy, aiming to avoid overtreatment and related morbidity by delaying radical therapy until progression is detected. Nonetheless, AS has limitations: long-term data indicate that up to 50% of men initially classified as low-risk experience histological upgrading or clinical progression within five years, ultimately requiring curative treatment. Accurate baseline risk stratification is therefore essential to improve patient selection and tailor follow-up intensity. A positive family history is a well-established risk factor for PCa, underscoring the contribution of inherited genetic susceptibility. Beyond rare high-penetrance mutations, genome-wide association studies have identified numerous common variants associated with PCa risk, enabling the development of polygenic risk scores (PRS) that aggregate the effects of multiple low-penetrance loci weighted by their effect sizes. While PRS have traditionally been used to estimate lifetime PCa risk, emerging evidence suggests that they may also correlate with tumor aggressiveness and progression, including in men undergoing AS. Progression-specific or aggressiveness-tailored PRS could therefore enhance the prognostic accuracy of existing clinical models and support more precise decision-making. Based on currently available biobank samples, 185 patients are included and distributed into four groups: (1) familial history with progression during AS; (2) familial history without progression; (3) no familial history with progression; and (4) no familial history without progression. Germline DNA is extracted from already biobanked peripheral blood using the Maxwell® RSC Whole Blood DNA Kit from patients enrolled at the institution who have already provided informed consent for research use (ClinicalTrials.gov ID: NCT06187870). SNP analysis is performed, after pseudonymization, using the Applied Biosystems™ Axiom™ Precision Medicine Diversity Research Array (PMDA). PRS is calculated as the weighted sum of risk allele dosages. One or more PRS models are expected to demonstrate significant predictive utility for AS progression, particularly among men with a family history of PCa. Participants with high PRS may benefit from intensified monitoring or earlier intervention, whereas those with low PRS may safely undergo less intensive surveillance, reducing the burden of repeat biopsies and associated complications. Integrating PRS with established clinical parameters may strengthen risk discrimination and support a precision oncology framework that merges genomic and clinical data to optimize outcomes for men with early-stage prostate cancer.