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 (Rx-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.
翻译:准确确定协同治疗组合及其基本生物机制在许多疾病领域,特别是癌症领域至关重要。在翻译肿瘤研究中,临床前系统,如病人衍生的基因移植(PDX),已经成为一种独特的研究设计,评价对植入基因相同的小鼠的同一种人类肿瘤样本的多种治疗方法。在本文中,我们提议为PDX数据建立一个新的巴伊西亚概率树基框架,以通过推断现有治疗树群树(称为治疗树(Rx树))来调查治疗之间的等级关系。这个框架激励了两种或多种治疗方法之间一种新的机械相似性指标,其中考虑到树的内在不确定性;高估计相似性的治疗方法可能具有很高的机械性协同作用。在Drichlet Difult 树的基础上,我们得出了一种封闭式的边际可能性,通过一个新的两阶段的调控算法来计算出高效的事后推断。模拟研究表明,拟议方法在恢复树结构及治疗相似性方面表现优异性。我们最近对PX/多层次的直观性治疗方法进行的一项分析,我们所了解的精确性研究,在生物分析后期生物分析中,我们所了解的精准的精准的精准的精准性研究结果显示了我们所了解的精准的精准的精准的精准的精准性研究。