Yao Tsung-Hung, Wu Zhenke, Bharath Karthik, Li Jinju, Baladandayuthapani Veerabhadran
Department of Biostatistics, University of Michigan at Ann Arbor.
School of Mathematical Sciences, University of Nottingham.
Ann Appl Stat. 2023 Sep;17(3):1884-1908. doi: 10.1214/22-aoas1696. Epub 2023 Sep 7.
Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems such as patient-derived xenografts (PDX) have emerged as a unique study design evaluating multiple treatments administered to samples from the same human tumor implanted into genetically identical mice. In this paper, we propose a novel Bayesian probabilistic tree-based framework for PDX data to investigate the hierarchical relationships between treatments by inferring treatment cluster trees, referred to as treatment trees (R-tree). The framework motivates a new metric of mechanistic similarity between two or more treatments accounting for inherent uncertainty in tree estimation; treatments with a high estimated similarity have potentially high mechanistic synergy. Building upon Dirichlet Diffusion Trees, we derive a closed-form marginal likelihood encoding the tree structure, which facilitates computationally efficient posterior inference via a new two-stage algorithm. Simulation studies demonstrate superior performance of the proposed method in recovering the tree structure and treatment similarities. Our analyses of a recently collated PDX dataset produce treatment similarity estimates that show a high degree of concordance with known biological mechanisms across treatments in five different cancers. More importantly, we uncover new and potentially effective combination therapies that confer synergistic regulation of specific downstream biological pathways for future clinical investigations. Our accompanying code, data, and shiny application for visualization of results are available at: https://github.com/bayesrx/RxTree.
准确识别协同治疗组合及其潜在的生物学机制在许多疾病领域都至关重要，尤其是在癌症领域。在转化肿瘤学研究中，诸如患者来源的异种移植瘤（PDX）等临床前系统已成为一种独特的研究设计，用于评估对植入基因相同小鼠体内的来自同一人类肿瘤的样本进行的多种治疗。在本文中，我们为PDX数据提出了一种基于贝叶斯概率树的新颖框架，通过推断治疗聚类树（称为治疗树，R树）来研究治疗之间的层次关系。该框架激发了一种新的度量标准，用于衡量两种或更多种治疗之间的机制相似性，同时考虑到树估计中固有的不确定性；估计相似性高的治疗可能具有较高的机制协同作用。基于狄利克雷扩散树，我们推导了一种编码树结构的闭式边缘似然，这通过一种新的两阶段算法促进了计算效率高的后验推断。模拟研究表明，所提出的方法在恢复树结构和治疗相似性方面具有卓越的性能。我们对最近整理的PDX数据集的分析得出的治疗相似性估计显示，在五种不同癌症的各种治疗中，与已知生物学机制高度一致。更重要的是，我们发现了新的且可能有效的联合疗法，这些疗法对特定下游生物学途径具有协同调节作用，以供未来的临床研究使用。我们随附的代码、数据以及用于结果可视化的闪亮应用程序可在以下网址获取：https://github.com/bayesrx/RxTree 。
