RESUMEN
Several different approaches are available to clinicians for determining prostate cancer (PCa) risk. The clinical validity of various PCa risk assessment methods utilizing single nucleotide polymorphisms (SNPs) has been established; however, these SNP-based methods have not been compared. The objective of this study was to compare the three most commonly used SNP-based methods for PCa risk assessment. Participants were men (n = 1654) enrolled in a prospective study of PCa development. Genotypes of 59 PCa risk-associated SNPs were available in this cohort. Three methods of calculating SNP-based genetic risk scores (GRSs) were used for the evaluation of individual disease risk such as risk allele count (GRS-RAC), weighted risk allele count (GRS-wRAC), and population-standardized genetic risk score (GRS-PS). Mean GRSs were calculated, and performances were compared using area under the receiver operating characteristic curve (AUC) and positive predictive value (PPV). All SNP-based methods were found to be independently associated with PCa (all P 0.05 for comparisons between the three methods), and all three SNP-based methods had a significantly higher AUC than family history (all P < 0.05). Results from this study suggest that while the three most commonly used SNP-based methods performed similarly in discriminating PCa from non-PCa at the population level, GRS-PS is the method of choice for risk assessment at the individual level because its value (where 1.0 represents average population risk) can be easily interpreted regardless of the number of risk-associated SNPs used in the calculation.
RESUMEN
Unprecedented progress has been made in genomic personalized medicine in the last several years, allowing for more individualized healthcare assessments and recommendations than ever before. However, most of this progress in prostate cancer (PCa) care has focused on developing and selecting therapies for late-stage disease. To address this issue of limited focus, we propose a model for incorporating genomic-based personalized medicine into all levels of PCa care, from prevention and screening to diagnosis, and ultimately to the treatment of both early-stage and late-stage cancers. We have termed this strategy the "Pyramid Model" of personalized cancer care. In this perspective paper, our objective is to demonstrate the potential application of the Pyramid Model to PCa care. This proactive and comprehensive personalized cancer care approach has the potential to achieve three important medical goals: reducing mortality, improving quality of life and decreasing both individual and societal healthcare costs.