Prostate cancer screening is entering a new phase, one defined not just by earlier detection but by smarter detection.
A recent study, the ProGRESS trial, evaluated a genomic risk model designed to personalise prostate cancer screening. By combining polygenic risk scores, family history, and genetic ancestry across more than 585,000 men, researchers developed a framework to better predict who is most likely to develop prostate cancer, including aggressive and metastatic disease.
This signals a meaningful shift in how screening is approached, moving from age-based population testing toward structured, risk-stratified prevention.
Why Traditional Screening Has Limits
Prostate-specific antigen (PSA) testing has long been the foundation of prostate cancer screening. While PSA testing has saved lives, it also presents challenges:
- It does not account for inherited genetic susceptibility
- It can lead to overdiagnosis of indolent disease
- It may miss men at elevated genetic risk who would benefit from earlier or more intensive monitoring
In short, traditional screening applies similar thresholds to biologically diverse individuals.
Genomic risk modelling introduces a more nuanced approach, identifying individuals whose inherited risk places them at substantially higher lifetime or early-onset risk while distinguishing those whose genetic profile suggests lower probability of aggressive disease.
What the Genomic Risk Model Changes
The ProGRESS model integrates three core elements:
- Polygenic risk scores (PRS) capturing the combined impact of many common genetic variants.
- Family history incorporating inherited clustering of disease.
- Genetic ancestry improving predictive accuracy across diverse populations.
Rather than replacing PSA testing, this approach refines who should be screened, when screening should begin, and potentially how frequently it should occur.
This reflects an emerging shift toward biologically informed screening strategies rather than demographic averages.
PRS performance varies across ancestry groups, and ongoing work to improve genomic diversity in predictive models remains essential to ensure equitable clinical application.
From Reactive Diagnosis to Proactive Risk Identification
Genomic risk modelling reflects a wider transformation occurring across healthcare:
- Moving upstream by identifying risk before symptoms develop
- Identifying high-risk individuals outside traditional guideline triggers
- Reducing unnecessary intervention in lower-risk populations
- Integrating structured genetic interpretation into routine care pathways
The goal is not simply more testing; it is better-targeted testing.
Risk-stratification has the potential to reduce both under-detection of aggressive disease and overdiagnosis of clinically insignificant cancers, two longstanding challenges in prostate screening.
Implementation Is Where Value Is Realised
While the science is promising, the true impact depends on clinical integration.
Risk models are only useful when:
- Results are carefully interpreted
- Patients receive clear, contextualised explanations
- Screening decisions align with evidence and patient values
- Governance frameworks ensure equity across ancestry groups
- Counselling pathways support informed decision-making
Genomic information without structured delivery risks confusion. Genomic information within a defined clinical workflow enables confident prevention.
Ethical and Equity Considerations
Genomic prediction models must perform well across diverse populations. Historically, polygenic risk scores have been more accurate in individuals of European ancestry, raising concerns about exacerbating disparities if applied without validation in diverse cohorts.
The ProGRESS study’s scale and its inclusion of ancestry as a model component reflect increasing recognition of this challenge. Equitable genomic prevention requires inclusive datasets, transparent reporting boundaries, and ongoing evaluation in real-world clinical settings.
Clinical Relevance and Next Steps
Genomic risk modelling for prostate cancer highlights how prevention strategies can be better aligned with individual risk profiles. Rather than relying solely on age-based screening, incorporating genetic risk enables more targeted identification of patients who may benefit from earlier or more structured surveillance.
The key consideration is not increased testing but appropriate integration. Genomic risk assessment requires clear thresholds, careful interpretation, and structured pathways to support decision-making. Genetic counselling and contextualised result reporting remain essential to ensure findings are understood and used appropriately.
As these models are evaluated in real-world care, their value will depend on how effectively genomic risk information is embedded into existing screening workflows and communicated to patients in a clear, evidence-based manner.
Reference:
Vassy, J.L., Dornisch, A.M., Karunamuni, R. et al. Genomic risk model to implement precision prostate cancer screening in clinical care: the ProGRESS study. Nat Cancer (2026). https://doi.org/10.1038/s43018-025-01103-0
