From a genomic risk model to clinical trial implementation in a learning health system: the ProGRESS Study DOI Creative Commons
Jason L. Vassy,

Anna Dornisch,

Roshan Karunamuni

и другие.

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 4, 2024

ABSTRACT 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 systems can generate knowledge about genomic test the impact of implementing that care. Risk-stratified prostate cancer screening one clinical application might benefit such approach. Methods We developed translation pipeline for genomics-informed national system. used data 585,418 male participants Veterans Affairs (VA) Million Veteran Program (MVP), among whom 101,920 self-identify as Black/African-American, to develop validate Prostate CAncer integrated Risk Evaluation (P-CARE) model, model based on polygenic score, family history, genetic principal components. The was externally validated 18,457 PRACTICAL Consortium participants. A novel blended genome-exome (BGE) platform laboratory assay both P-CARE rare variants cancer-associated genes, including additional validation 74,331 samples All Us Research Program. Results In overall ancestry-stratified analyses, score 601 associated with any, metastatic, fatal MVP PRACTICAL. Values at ≥80th percentile multiancestry cohort were hazard ratios (HR) 2.75 (95% CI 2.66-2.84), 2.78 2.54-2.99), 2.59 2.22-2.97) MVP, respectively, compared median. When high– low-risk groups defined HR>1.5 HR<0.75 metastatic cancer, 220,062 (37.6%) high-risk vs.146,826 (25.1%) had 47.9% vs. 14.1%, 9.3% 2.0%, 3.6% 0.8% cumulative cause-specific incidence by age 90, respectively. reports are now being implemented trial VA system (Clinicaltrials.gov NCT05926102 ). Conclusions consisting components describes clinically gradient diverse patient population demonstrates potential learning health implement evaluate care approaches.

Язык: Английский

From a genomic risk model to clinical trial implementation in a learning health system: the ProGRESS Study DOI Creative Commons
Jason L. Vassy,

Anna Dornisch,

Roshan Karunamuni

и другие.

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 4, 2024

ABSTRACT 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 systems can generate knowledge about genomic test the impact of implementing that care. Risk-stratified prostate cancer screening one clinical application might benefit such approach. Methods We developed translation pipeline for genomics-informed national system. used data 585,418 male participants Veterans Affairs (VA) Million Veteran Program (MVP), among whom 101,920 self-identify as Black/African-American, to develop validate Prostate CAncer integrated Risk Evaluation (P-CARE) model, model based on polygenic score, family history, genetic principal components. The was externally validated 18,457 PRACTICAL Consortium participants. A novel blended genome-exome (BGE) platform laboratory assay both P-CARE rare variants cancer-associated genes, including additional validation 74,331 samples All Us Research Program. Results In overall ancestry-stratified analyses, score 601 associated with any, metastatic, fatal MVP PRACTICAL. Values at ≥80th percentile multiancestry cohort were hazard ratios (HR) 2.75 (95% CI 2.66-2.84), 2.78 2.54-2.99), 2.59 2.22-2.97) MVP, respectively, compared median. When high– low-risk groups defined HR>1.5 HR<0.75 metastatic cancer, 220,062 (37.6%) high-risk vs.146,826 (25.1%) had 47.9% vs. 14.1%, 9.3% 2.0%, 3.6% 0.8% cumulative cause-specific incidence by age 90, respectively. reports are now being implemented trial VA system (Clinicaltrials.gov NCT05926102 ). Conclusions consisting components describes clinically gradient diverse patient population demonstrates potential learning health implement evaluate care approaches.

Язык: Английский

Процитировано

0