CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts
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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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In CombPDX, TV data at a single day are used to calculate a local CI, and the average of all local CI values is the global CI.
“TV data at a single day were used to calculate a local CI, the average of all local CI values is the global CI (27).”
From Statistical Assessment of Drug Synergy from <i>In Vivo</i> Combination Studies Using Mouse Tumor Models (2023) · cites this paper as [27]
Huang and colleagues established CombPDX as a statistical framework for Bliss independence, HSA, and response additive models, with Bliss CI equivalent to Wu and colleagues and HSA and RA newly introduced.
“More recently, Huang and colleagues established CombPDX as a statistical framework for three synergy models: Bliss independence, HSA, and response additive (RA; ref. 27), their definition of Bliss CI is equivalent to that in Wu and colleagues (24), while HSA and RA were newly introduced.”
From Statistical Assessment of Drug Synergy from <i>In Vivo</i> Combination Studies Using Mouse Tumor Models (2023) · cites this paper as [27]
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- Authors
- Huang, L., Wang, J., Fang, B., Meric-Bernstam, F., Roth, J. A., & Ha, M. J.
- Affiliations
- The University of Texas MD Anderson Cancer Center (US)
- Yonsei University (KR)
- Corresponding author
- Ha, Min Jin
- Venue
- Scientific Reports
- Published
- 2022-07-29
- Volume
- 12
- Issue
- 1
- Pages
- 12984-12984
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC9338066
- OpenAlex
- W4288694721
- Cited by
- 21
- References
- 55
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 5 · 2023: 4
- FWCI
- 2.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
- Funders
- National Institutes of Health (5U54CA224065-04)
- National Institutes of Health,United States (2P50CA070907-21A1)
- Yonsei University College of Medicine (2022-32-0029)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Keywords
- drug synergy, patient-derived xenografts, combination therapy, tumor growth curves, non-small cell lung cancer, statistical inference, power analysis, anticancer drug combination
- MeSH terms
- Animals, Carcinoma, Non-Small-Cell Lung, Disease Models, Animal, Drug Synergism, Humans, Lung Neoplasms, Xenograft Model Antitumor Assays, Heterografts
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