A Bayesian precision medicine framework for calibrating individualized therapeutic indices in cancer

Abstract

The development and clinical implementation of evidence-based precision medicine strategies has become a realistic possibility, primarily due to the rapid accumulation of large-scale genomics and pharmacological data from diverse model systems: patients, cell lines and drug perturbation studies. We introduce a novel Bayesian modeling framework called the individualized theRapeutic index (iR${}_{\mathit{x}}$) model to integrate high-throughput pharmacogenomic data across model systems. Our iR${}_{\mathit{x}}$ model achieves three main goals: first, it exploits the conserved biology between patients and cell lines to calibrate therapeutic response of drugs in patients; second, it finds optimal cell line avatars as proxies for patient(s); and finally, it identifies key genomic drivers explaining cell line-patient similarities. This is achieved through a semi-supervised learning approach that conflates (unsupervised) sparse latent factor models with (supervised) penalized regression techniques. We propose a unified and tractable Bayesian model for estimation, and inference is conducted via efficient posterior sampling schemes. We illustrate and validate our approach using two existing clinical trial data sets in multiple myeloma and breast cancer studies. We show that our iR${}_{\mathit{x}}$ model improves prediction accuracy compared to naive alternative approaches, and it consistently outperforms existing methods in literature in both multiple simulation scenarios as well as real clinical examples.