From a genomic risk model to clinical trial implementation in a learning health system: the ProGRESS Study
Authors: Vassy Jason L , Dornisch Anna M , Karunamuni Roshan , Gatzen Michael , Kachulis Christopher J , Lennon Niall J , Brunette Charles A , Danowski Morgan E , Hauger Richard L , Garraway Isla P , Kibel Adam S , Lee Kyung Min , Lynch Julie A , Maxwell Kara N , Rose Brent S , Teerlink Craig C , Xu George J , Hofherr Sean E , Lafferty Katherine A , Larkin Katie , Malolepsza Edyta , Patterson Candace J , Toledo Diana M , Donovan Jenny L , Hamdy Freddie , Martin Richard M , Neal David E , Turner Emma L , Andreassen Ole A , Dale Anders M , Mills Ian G , Batra Jyotsna , Clements Judith , Cussenot Olivier , Cybulski Cezary , Eeles Rosalind A , Fowke Jay H , Grindedal Eli Marie , Hamilton Robert J , Lim Jasmine , Lu Yong-Jie , MacInnis Robert J , Maier Christiane , Mucci Lorelei A , Multigner Luc , Neuhausen Susan L , Nielsen Sune F , Parent Marie-Élise , Park Jong Y , Petrovics Gyorgy , Plym Anna , Razack Azad , Rosenstein Barry S , Schleutker Johanna , Sørensen Karina Dalsgaard , Travis Ruth C , Vega Ana , West Catharine M L , Wiklund Fredrik , Zheng Wei ,   Profile Steering Committee,   IMPACT Study Steering Committee and Collaborators,   PRACTICAL Consortium,   Million Veteran Program, Seibert Tyler M 
Published: November 2024

Background

As 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.

Methods

We 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.

Results

In 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.govNCT05926102).

Conclusions

A 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.