From inherited susceptibility to tumor state: toward a stage-aware architecture for genomic risk stratification in prostate cancer.

Biologically informed machine learning enables stage-aware genomic risk stratification in prostate cancer, integrating inherited susceptibility and tumor biology.

• Why it matters: Current risk assessment tools are limited by separating germline and tumor genomic data, which hampers accurate, transportable clinical decision-making across diverse populations and disease stages.
• What they did: The authors review approaches including single-cell, spatial atlases, and whole-omics models, emphasizing the need for stage-aware architectures that incorporate ancestry calibration, external validation, and longitudinal updates.
• The result: A unified, auditable AI framework linking inherited risk to tumor evolution may improve personalized screening and treatment, but progress depends on rigorous validation and calibration across populations.