Distinct spatial immune architectures in tumor and tumor-adjacent tissues of early-stage non-small cell lung cancer
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background Lung cancer remains the leading cause of cancer-related deaths in the United States with over 124,000 estimated deaths for 2026. Previous studies have found that tumor-adjacent lung tissues may provide additional insight into the immune microenvironment of early-stage NSCLC. Methods Multiplex immunofluorescence (mIF) imaging was performed on 192 tissues from 101 early-stage non-small cell lung cancer patients including 91 matched tumor-adjacent pairs, using three mIF panels. Spatial analyses were performed to identify distinctions between tissues and identify associations with clinical and genomic features. Results Tumor tissue showed significantly higher densities of T cells, macrophages, B cells, and memory/regulatory populations than adjacent tissue (p<0.001). Tumors exhibited greater spatial heterogeneity, with higher prevalence of spatial patterning (47.7% vs. 31.2%) and more consistently localized organization, whereas adjacent tissue showed stronger individual-cell clustering. Pairwise colocalization (Ripley’s L-cross) revealed selective spatial segregation in tumors (including B-cells from memory/regulatory cells and among cytotoxic T-cell subsets) and reduced immune proximity to malignant cells relative to the strong immune-epithelial association in adjacent tissue. Spatial features were linked to genomic features or patient outcomes: tumor neoantigen burden exhibited a positive association with CD3+ T cells in the tumor, while colocalization between CD45RO + CD57 + GZMB + cells and CD57 + GZMB + cells was negatively associated with overall survival outside the tumor. Conclusion Tumor and adjacent tissues harbor distinct spatial immune architectures, with spatial features displaying associations with genomic features or patient outcomes. These findings highlight immune microenvironment reorganization and the importance of incorporating spatial context from both compartments into risk assessment in NSCLC.
Source: Frontiers in Immunology
Authors: Isabella Polic, Claudio Arrechedera, Jared Slone, Amanda Montoya, Emily Bontekoe, Peixin Jiang, Anika Patel, Tatiana Karpinets, Xiaogang Wu, Xingzhi Song, Qianyun Luo, Heladio Ibarguen, Auriole Tamegnon, Mei Jiang, Cara Haymaker, Annikka Weissferdt, Ara A. Vaporciyan, Lydia Kavraki, Tina Cascone, Ignacio Wistuba, ICON Study Team, Luisa M. Solis Soto, John V. Heymach, Jianjun Zhang, Don L. Gibbons, Alexandre Reuben
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
Funders: U.S. Department of Defense, LUNGevity Foundation, Cancer Prevention and Research Institute of Texas, Small Business Technology Transfer, Cardinal Health, National Institutes of Health, University of Texas MD Anderson Cancer Center, National Cancer Institute, U.S. National Library of Medicine
Awards: G2417015594, G4000061730, Developing knowledge guided deep learning models to predict the binding between T cell receptors and neoantigens for facilitating TCR-T therapies, Targeting Lung Cancer Vulnerabilities, Development of TCR-based immunotherapies targeting hotspot EGFR mutations for the treatment of non-small cell lung cancer, Unbiased electrothermal flow-enhanced identification of antigen-specific T cells in lung cancer, Leveraging the ATTACH platform to optimize T cell therapies in lung cancer, Cancer Therapeutics Training Program (CTTP)., G75567730, G8156940795, G8505174276, Cancer Center Support Grant, NLM Research Training Program in Biomedical Informatics and Data Science for Predoctoral and Postdoctoral Fellows, MD Anderson Cancer Immune Monitoring and Analysis Center MDA-CIMAC, PASSCODE (Pancreatic Adenocarcinoma Stromal Reprograming ConSortium COordination, Data Management and Education)
OpenAlex ID: W7221533981