Abstract 7437: Decoding tumor microenvironment heterogeneity through spatial microdomains and network biology to predict immunotherapy outcomes

Abstract Background: Spatial niches, often quantified through the co-occurrence or colocalization of various cell types, are a common measure of tissue organization in health and disease. However, a clear link is missing between how these spatial measures quantify tissue heterogeneity, how that heterogeneity relates to functional organization within the tissue, and how the functional organization impacts patient outcomes. Methods: The SpaceIQ™ multi-omics analysis platform addresses tissue heterogeneity through a formal analysis of spatial cell-cell communication from a mutual information perspective. This methodology allows for the virtual dissection of tissue into distinct tumor microenvironment (TME) programs. Inter-patient heterogeneity is then revealed by clustering these spatial TME programs based on their compositional fractions, a process independent of patient outcomes. Crucially, these spatial TME programs serve as highly effective "microdomains" that provide critical local context for cell-to-cell interactions and spatially modulated "network biology," demonstrating strong predictive power for patient outcomes. Results: We analyzed a publicly available 51-plex immunofluorescence based spatial proteomics data (CODEX platform) from checkpoint-treated cutaneous T-cell lymphoma patients, using spatial analysis based on microdomains and network biology. Our findings demonstrate that: (i) checkpoint expressions alone are poor predictors of patient response; (ii) spatial interactions between different cell types moderately improve prediction accuracy compared to multi-marker phenotypes; and (iii) microdomains significantly outperform non-spatial methods and checkpoint expression-based approaches in predicting response. Conclusions: Spatial analysis leveraging microdomains and network biology significantly enhance prediction accuracy compared to non-spatial or checkpoint expression-based methods. This advancement enables improved biomarker-driven patient selection and targeted therapy optimization. Citation Format: A. Burak Tosun, Raymond Yan, Brian Falkenstein, Filippo Pullara, S. Chakra Chennubhotla. Decoding tumor microenvironment heterogeneity through spatial microdomains and network biology to predict immunotherapy outcomes abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7437.