RESUMEN
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.
RESUMEN
BACKGROUND: An estimated 0.6% of the U.S. population identifies as transgender and an increasing number of patients are presenting for gender-related medical and surgical services. Utilization of health care services, especially surgical services, by transgender patients is poorly understood beyond survey-based studies. In this article, our aim is 2-fold; first, we intend to demonstrate the utilization of datasets generated by insurance claims data as a means of analyzing gender-related health services, and second, we use this modality to provide basic demographic, utilization, and outcomes data about the insured transgender population. METHODS: The Truven MarketScan Database, containing data from 2009 to 2015, was utilized, and a sample set was created using the Gender Identity Disorder diagnosis code. Basic demographic information and utilization of gender-affirming procedures was tabulated. RESULTS: We identified 7,905 transgender patients, 1,047 of which underwent surgical procedures from 2009 to 2015. Our demographic results were consistent with previous survey-based studies, suggesting transgender patients are on average young adults (average age = 29.8), and geographically diverse. The most common procedure from 2009 to 2015 was mastectomy. Complications of all gender-affirming procedures was 5.8%, with the highest rate of complications occurring with phalloplasty. There was a marked year-by-year increase in utilization of surgical services. CONCLUSION: Transgender care and gender confirming surgery are an increasing component of health care in the United States. The data contained in existing databases can provide demographic, utilization, and outcomes data relevant to providers caring for the transgender patient population.
