Tumor-immune partitioning and clustering algorithm for identifying tumor-immune cell spatial interaction signatures within the tumor microenvironment
Fig 2
Implementation of TIPC, a computational method utilizing hexagonal tessellation and a classifier that evaluates multiple spatial parameters against a tumor region-specific null model represented by two global ratios based on the total number of immune (user-selected cell type of interest), tumor (global I:T) and stromal cells (global I:S).
Using the Cartesian coordinates of these cells, TIPC divides the space into a hexagonal grid of subregions of specified subregion size and calculates two local ratios namely I:T and I:S for each subregion. The subregions are then classified into six different categories based on comparing the local to the global I:T and I:S ratios. In this mIF-stained image example, there were 19 “Tumor only” subregions containing only tumor cells; 25 “I:T low” subregions with a local I:T ratio less than the global I:T ratio; and 31 “I:T high” subregions with a local I:T ratio greater than the global I:T ratio. The three stromal categories were counted in a similar way. The number of subregions in each category are then normalized using the total number of subregions containing cells of any type. The resultant six-element numerical vector encodes the tumor-immune spatial organization of the TME for an ROI. Abbreviations: I:T, immune-to-tumor, I:S, immune-to-stroma.