PROBABILISTIC LEARNING OF TREATMENT TREES IN CANCER.

ABSTRACT: 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_x-tree). The framework motivates a new metric of mechanistic similarity between two or more treatments accounting for inherent uncertainty

SUBMITTER: Yao TH 

PROVIDER: S-EPMC10501503 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature