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A0989
Title: Probabilistic learning of treatment trees in cancer Authors:  Tsung-Hung Yao - University of Michigan at Ann Arbor (United States) [presenting]
Zhenke Wu - University of Michigan at Ann Arbor (United States)
Karthik Bharath - University of Nottingham (United Kingdom)
Jinju Li - University of Michigan at Ann Arbor (United States)
Veera Baladandayuthapani - University of Michigan (United States)
Abstract: Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many domains. In oncology, 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 mice. We propose a novel Bayesian probabilistic tree-based framework for PDX data to investigate the hierarchy 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 the inherent uncertainty in tree estimation. 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 the 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.