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CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts

Authors
 Licai Huang  ;  Jing Wang  ;  Bingliang Fang  ;  Funda Meric-Bernstam  ;  Jack A Roth  ;  Min Jin Ha 
Citation
 SCIENTIFIC REPORTS, Vol.12(1) : 12984, 2022-07 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2022-07
MeSH
Animals ; Carcinoma, Non-Small-Cell Lung* / pathology ; Disease Models, Animal ; Drug Synergism ; Heterografts ; Humans ; Lung Neoplasms* / pathology ; Xenograft Model Antitumor Assays
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.
Files in This Item:
T202203248.pdf Download
DOI
10.1038/s41598-022-16933-6
Appears in Collections:
4. Graduate School of Public Health (보건대학원) > Graduate School of Public Health (보건대학원) > 1. Journal Papers
Yonsei Authors
Ha, Min Jin(하민진)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/191715
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