Tumor-immune partitioning and clustering algorithm for identifying tumor-immune cell spatial interaction signatures within the tumor microenvironment
Fig 4
Comparison of TIPC performance with existing analysis methods, using CD3+ T-cell data.
Tumor subtypes were identified using (a-c) TIPC, (d-f) Morisita-Horn (M-H) tumor cell:CD3+, (g-i) G-cross tumor cell:CD3+, and (j-l) L-cross tumor cell:CD3+. Box plots show that (a) TIPC subtypes were less confounded by the overall CD3+ T-cell density as compared to (d,g,j) other methods. Kaplan-Meier and log-rank test show (b,c,e,f,h,i) subtyped derived by TIPC, M-H, and G-cross harbored significant associations with colorectal cancer-specific survival, but otherwise for (k,l) L-cross method. G-cross and L-cross AUC quartiles were measured using radius of 20 µm based on stromal CD3+ cells (S7 and S8 Figs); M-H index was calculated using a 5-by-5 µm rectangular grid and 80th percentile dichotomization cut-off (S5 Fig); TIPC subtypes were obtained at the optimal subregion size of 35 µm and input number of clusters of 9 (S2 Fig). Abbreviations: CSR = Cold, stroma-rich; CTR = Cold, tumor-rich; HTCC = Hot, tumor-centric clustering; HD = Host and disperse; HSCC = Hot, stroma-centric clustering; HC = Hot and clustered.