Abstract 6666: Deriving high-fidelity, low-plex clinical signatures from ultra-high-plex spatial data for immunotherapy response prediction

Abstract Background: Ultra-high-plex multiplex immunofluorescence (mIF) imaging enables detailed characterization of the tumor microenvironment (TME), yet translating these rich datasets into clinically deployable, low-plex biomarkers remains a major barrier for precision immuno-oncology. Existing predictive models rely heavily on high-dimensional features or broad phenotypic panels, limiting scalability, interpretability, and routine pathology integration. A systematic framework is needed to compress spatially resolved molecular information into minimal, clinical-plex, yet highly informative signatures capable of predicting immunotherapy response. Methods: Our SpaceIQ™ platform is a multi-omic analysis tool that integrates spatial proteomics data with optimized feature selection algorithms to distill molecular signatures. We employ unbiased cell typing and microdomain discovery to identify a differentially expressed network of spatial interactions between these unbiased cell types, utilizing pointwise mutual information (PMI) analysis. Each interaction (either pairwise or higher-order cliques) within this network represents a potential spatial prognostic model capable of predicting patient response. A key component of our approach is the identification of a subset threshold cell population that is enriched for a given unbiased cell type using a low-plex panel. The final prognostic model for a differential clique combines a spatial proximity score with these low-plex marker intensities. Results: Analysis of ultra-high-plex (=51 markers) spatial data from trial specimens of checkpoint-treated cutaneous T-cell lymphoma patients demonstrated that tumor-immune and immune-immune interactions emerge as microdomains with minimal signatures of 6-8 markers and high prediction accuracies (AUC = 0.87, 95% CI (0.865-0.881)). Conclusions: The SpaceIQ platform enables the extraction of compact, clinically practical biomarker panels from ultra-high-plex mIF datasets without sacrificing predictive power. By linking spatial microdomain biology to sparse signature derivation, this framework supports scalable deployment of precision immunotherapy biomarkers and enhances patient-selection strategies in clinical practice. Citation Format: Raymond Yan, Brian Falkenstein, A. Burak Tosun, Filippo Pullara, S. Chakra Chennubhotla. Deriving high-fidelity, low-plex clinical signatures from ultra-high-plex spatial data for immunotherapy response prediction [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 6666.