SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data - Osher, Nathaniel; Kang, Jian; Krishnan, Santhoshi; Rao, Arvind; Baladandayuthapani, Veerabhadran (Frontiers in Genetics, 2023)
The acquisition of high-resolution digital pathology imaging data has sparked the development of methods to extract context-specific features from such complex data. In the context of cancer, this has led to increased exploration of the tumor microenvironment with respect to the presence and spatial composition of immune cells. Spatial statistical modeling of the immune microenvironment may yield insights into the role played by the immune system in the natural development of cancer as well as downstream therapeutic interventions. In this paper, we present SPatial Analysis of paRtitioned Tumor-Immune imagiNg (SPARTIN) method, a Bayesian method for the spatial quantification of immune cell infiltration from pathology images. SPARTIN uses Bayesian point processes to characterize a novel measure of local tumor-immune cell interaction, Cell Type Interaction Probability (CTIP). CTIP allows rigorous incorporation of uncertainty and is highly interpretable, both within and across biopsies, and can be used to assess associations with genomic and clinical features. Through simulations, we show SPARTIN can accurately distinguish various patterns of cellular interactions as compared to existing methods. Using SPARTIN, we characterized the local spatial immune cell infiltration within and across 335 melanoma biopsies and evaluate their association with genomic, phenotypic, and clinical outcomes. The R-package for implementing SPARTIN is available at https://github.com/nateosher/SPARTIN along with a visualization tool for the images and results at: https://nateosher.github.io/SPARTIN.