Abstract To understand how the tumor immune microenvironment (TIME) impacts clinical outcomes and treatment response, researchers have been leveraging single-cell protein multiplex imaging techniques. These technologies measure multiple protein markers simultaneously within a tissue sample, providing a more complete assessment of the TIME. However, statistical challenges arise from the over-dispersed and zero-inflated nature of the data and from relationships among different immune cell populations. To address these challenges, we developed a Bayesian hierarchical method using a beta-binomial (BB) distribution to model the abundance of multiple immune cell types simultaneously while incorporating relationships and immune cell differentiation paths. We applied the model to data from three large studies of high-grade serous ovarian tumors (Nurses’ Health Study I/II: N = 321, African American Cancer Epidemiology Study: N = 92, University of Colorado Ovarian Cancer Study: N = 103). We examined associations between cancer stage, age at diagnosis, and debulking status and the abundance of immune cell populations. We compared the multi-cell type model to individual cell type analyses using a Bayesian BB model. The multi-cell type model detected more associations, when present, with narrower credible intervals. To support broader application, we developed an R package, BTIME, with a detailed tutorial. In conclusion, the Bayesian multi-cell type model is flexible in how relationships between cell types are incorporated and can be used for cancer studies that interrogate the TIME.
Sakitis et al. (2026) studied this question.