An Ensemble Transfer Learning Framework for Robust Polygenic Prediction of Drug Response

Thomas M. Menino Convention & Exhibition Center 

Accurate prediction of drug response in pharmacogenomics (PGx) is hindered by traditional methods, such as disease-specific polygenic risk scores (PRS-Dis), which often fail to capture the genetic basis of drug efficacy. Direct PGx PRS approaches could improve predictions, but their application is limited by the lack of large, relevant PGx datasets. To address these challenges, we present PRS-PGx-ETL, a novel Ensemble Transfer Learning (ETL) framework that combines transfer learning (TL) and ensemble learning (EL) to improve drug response prediction. TL leverages genetic data from large disease or PGx-T (treatment-only) base cohorts and transfers knowledge to a target PGx cohort. EL integrates multiple PRSs, generated from different base cohorts and various PRS methods, into a single, optimally weighted score. This integrative approach expands the genetic information used and automates parameter tuning. In simulations and application to the IMPROVE-IT PGx GWAS dataset, PRS-PGx-ETL significantly improves drug response prediction accuracy and patient stratification over existing methods. Our framework offers a robust PRS tool for advancing precision medicine.

GWAS

Pharmacogenomics

Polygenic risk score

Transfer learning

Ensemble learning 

Biopharmaceutical Section