CombPDX: a Unified Statistical Framework for Evaluating Drug Synergism in Patient-derived Xenografts

Overview

Journal Sci Rep

Specialty Science

Date 2022 Jul 29

PMID 35906256

Authors

Licai Huang

Jing Wang

Bingliang Fang

Funda Meric-Bernstam

Jack A Roth

Min Jin Ha

Affiliations

Soon will be listed here.

Abstract

Anticancer combination therapy has been developed to increase efficacy by enhancing synergy. Patient-derived xenografts (PDXs) have emerged as reliable preclinical models to develop effective treatments in translational cancer research. However, most PDX combination study designs focus on single dose levels, and dose-response surface models are not appropriate for testing synergism. We propose a comprehensive statistical framework to assess joint action of drug combinations from PDX tumor growth curve data. We provide various metrics and robust statistical inference procedures that locally (at a fixed time) and globally (across time) access combination effects under classical drug interaction models. Integrating genomic and pharmacological profiles in non-small-cell lung cancer (NSCLC), we have shown the utilities of combPDX in discovering effective therapeutic combinations and relevant biological mechanisms. We provide an interactive web server, combPDX ( https://licaih.shinyapps.io/CombPDX/ ), to analyze PDX tumor growth curve data and perform power analyses.

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