From a genomic risk model to clinical trial implementation in a learning health system: the ProGRESS Study
Vassy, J. L.; Dornisch, A. M.; Karunamuni, R.; Gatzen, M.; Kachulis, C. J.; Lennon, N. J.; Brunette, C. A.; Danowski, M. E.; Hauger, R. L.; Garraway, I. P.; Kibel, A. S.; Lee, K. M.; Lynch, J. A.; Maxwell, K. N.; Rose, B. S.; Teerlink, C. C.; Xu, G. J.; Hofherr, S. E.; Lafferty, K. A.; Larkin, K.; Malolepsza, E.; Patterson, C. J.; Toledo, D. M.; Donovan, J. L.; Hamdy, F.; Martin, R. M.; Neal, D. E.; Turner, E. L.; Andreassen, O. A.; Dale, A. M.; Mills, I. G.; Batra, J.; Clements, J.; Cussenot, O.; Cybulski, C.; Eeles, R. A.; Fowke, J. H.; Grindedal, E. M.; Hamilton, R. J.; Lim, J.; Lu, Y.-J.;
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BackgroundAs healthcare moves from a one-size-fits-all approach towards precision care, individual risk prediction is an important step in disease prevention and early detection. Biobank-linked healthcare systems can generate knowledge about genomic risk and test the impact of implementing that knowledge in care. Risk-stratified prostate cancer screening is one clinical application that might benefit from such an approach. MethodsWe developed a clinical translation pipeline for genomics-informed prostate cancer screening in a national healthcare system. We used data from 585,418 male participants of the Veterans Affairs (VA) Million Veteran Program (MVP), among whom 101,920 self-identify as Black/African-American, to develop and validate the Prostate CAncer integrated Risk Evaluation (P-CARE) model, a prostate cancer risk prediction model based on a polygenic score, family history, and genetic principal components. The model was externally validated in data from 18,457 PRACTICAL Consortium participants. A novel blended genome-exome (BGE) platform was used to develop a clinical laboratory assay for both the P-CARE model and rare variants in prostate cancer-associated genes, including additional validation in 74,331 samples from the All of Us Research Program. ResultsIn overall and ancestry-stratified analyses, the polygenic score of 601 variants was associated with any, metastatic, and fatal prostate cancer in MVP and PRACTICAL. Values of the P-CARE model at [≥]80th percentile in the multiancestry cohort overall were associated with hazard ratios (HR) of 2.75 (95% CI 2.66-2.84), 2.78 (95% CI 2.54-2.99), and 2.59 (95% CI 2.22-2.97) for any, metastatic, and fatal prostate cancer in MVP, respectively, compared to the median. When high- and low-risk groups were defined as P-CARE HR>1.5 and HR<0.75 for metastatic prostate cancer, the 220,062 (37.6%) high-risk vs.146,826 (25.1%) low-risk participants in MVP had a 47.9% vs. 14.1%, 9.3% vs. 2.0%, and 3.6% vs. 0.8% cumulative cause-specific incidence of any, metastatic, and fatal prostate cancer by age 90, respectively. The clinical assay and reports are now being implemented in a clinical trial of precision prostate cancer screening in the VA healthcare system (Clinicaltrials.gov NCT05926102). ConclusionsA model consisting of a polygenic score, family history, and genetic principal components describes a clinically important gradient of prostate cancer risk in a diverse patient population and demonstrates the potential of learning health systems to implement and evaluate precision health care approaches.
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