Abstract
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.
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Acknowledgements
Research reported in this publication was supported by the National Cancer Institute under award numbers R01CA268287A1, U01 CA269181, R01 CA26820701A1, R01CA249992- 01A1, R01CA202752- 01A1, R01CA208236- 01A1, R01CA216579- 01A1, R01CA220581-01A1, R01CA257612- 01A1, 1U01CA239055- 01, 1U01CA248226- 01, 1U54CA254566- 01, National Heart, Lung and Blood Institute 1R01HL15127701A1, R01HL15807101A1, National Institute of Biomedical Imaging and Bioengineering 1R43EB028736- 01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service the Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program (W81XWH- 19- 1-0668), the Prostate Cancer Research Program (W81XWH- 20-1- 0851), the Lung Cancer Research Program (W81XWH-18-1-0440, W81XWH-20-1-0595), the Peer Reviewed Cancer Research Program (W81XWH- 18-1-0404, W81XWH- 21-1-0345, W81XWH- 21-1-0160), the Kidney Precision Medicine Project (KPMP) Glue Grant and sponsored research agreements from Bristol Myers-Squibb, Boehringer-Ingelheim, Eli-Lilly and Astrazeneca. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the U.S. Department of Veterans Affairs, the Department of Defense, or the United States Government.
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Arabyarmohammadi, S., Corredor, G., Zhou, Y., López de Rodas, M., Schalper, K., Madabhushi, A. (2023). Triangular Analysis of Geographical Interplay of Lymphocytes (TriAnGIL): Predicting Immunotherapy Response in Lung Cancer. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_77
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