CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts
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 s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-16933-6 |
| _version_ | 1818492699234271232 |
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| author | Licai Huang Jing Wang Bingliang Fang Funda Meric-Bernstam Jack A. Roth Min Jin Ha |
| author_facet | Licai Huang Jing Wang Bingliang Fang Funda Meric-Bernstam Jack A. Roth Min Jin Ha |
| author_sort | Licai Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T17:46:29Z |
| format | Article |
| id | doaj.art-38683de5b53440cfb62abbde76cdcc3d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-10T17:46:29Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-38683de5b53440cfb62abbde76cdcc3d2022-12-22T01:39:13ZengNature PortfolioScientific Reports2045-23222022-07-0112111010.1038/s41598-022-16933-6CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenograftsLicai Huang0Jing Wang1Bingliang Fang2Funda Meric-Bernstam3Jack A. Roth4Min Jin Ha5Department of Biostatistics, The University of Texas MD Anderson Cancer CenterDepartments of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer CenterDepartment of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer CenterDepartment of Investigational Cancer Therapeutics, Division of Cancer Medicine, The University of Texas MD Anderson Cancer CenterDepartment of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer CenterDepartment of Biostatistics, Graduate School of Public Health, Yonsei UniversityAbstract 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.https://doi.org/10.1038/s41598-022-16933-6 |
| spellingShingle | Licai Huang Jing Wang Bingliang Fang Funda Meric-Bernstam Jack A. Roth Min Jin Ha CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts Scientific Reports |
| title | CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts |
| title_full | CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts |
| title_fullStr | CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts |
| title_full_unstemmed | CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts |
| title_short | CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts |
| title_sort | combpdx a unified statistical framework for evaluating drug synergism in patient derived xenografts |
| url | https://doi.org/10.1038/s41598-022-16933-6 |
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