Triangular Analysis of Geographical Interplay of Lymphocytes (TriAnGIL): Predicting Immunotherapy Response in Lung Cancer.

Quantitative immunofluorescence (QIF) enables identifying immune cell subtypes across histopathology images. There is substantial evidence to show that spatial architecture of immune cell populations (e.g. CD4+, CD8+, CD20+) is associated with therapy response in cancers, yet there is a paucity of approaches to quantify spatial statistics of interplay across immune subtypes. Previously, analyzing spatial cell interplay have been limited to either building subgraphs on individual cell types before feature extraction or capturing the interaction between two cell types. However, looking at the spatial interplay between more than two cell types reveals complex interactions and co-dependencies that might have implications in predicting response to therapies like immunotherapy. In this work we present, Triangular Analysis of Geographical Interplay of Lymphocytes (TriAnGIL), a novel approach involving building of heterogeneous subgraphs to precisely capture the spatial interplay between multiple cell families. Primarily, TriAnGIL focuses on triadic closures, and uses metrics to quantify triads instead of two-by-two relations and therefore considers both inter- and intra-family relationships between cells. The TriaAnGIL’s efficacy for microenvironment characterization from QIF images is demonstrated in problems of predicting (1) response to immunotherapy (N = 122) and (2) overall survival (N = 135) in patients with lung cancer in comparison with four hand-crafted approaches namely DenTIL, GG, CCG, SpaTIL, and deep learning with GNN. For both tasks, TriaAnGIL outperformed hand-crafted approaches, and GNN with AUC = .70, C-index = .64. In terms of interpretability, TriAnGIL easily beats GNN, by pulling biological insights from immune cells interplay and shedding light on the triadic interaction of CD4+-Tumor-stromal cells.