Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Quantitative Sciences Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
Sci Rep. 2022 Jul 29;12(1):12984. doi: 10.1038/s41598-022-16933-6.
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.
抗癌联合治疗的发展旨在通过增强协同作用来提高疗效。患者来源的异种移植物（PDX）已成为转化癌症研究中开发有效治疗方法的可靠临床前模型。然而，大多数 PDX 联合研究设计侧重于单一剂量水平，而剂量反应曲面模型不适合测试协同作用。我们提出了一个全面的统计框架，用于从 PDX 肿瘤生长曲线数据评估药物组合的联合作用。我们提供了各种指标和稳健的统计推断程序，可在经典药物相互作用模型下对局部（在固定时间）和全局（跨时间）的组合效果进行评估。在非小细胞肺癌（NSCLC）中整合基因组和药理学特征，我们已经展示了 combPDX 在发现有效治疗组合和相关生物学机制方面的效用。我们提供了一个交互式网络服务器 combPDX（https://licaih.shinyapps.io/CombPDX/），用于分析 PDX 肿瘤生长曲线数据并进行功效分析。
