Abstract 1293: A multimodal AI framework integrating spatial omics and radiomics for recurrence prediction in glioblastoma.

Abstract Introduction/Rationale: Glioblastoma (GBM) has a dismal prognosis, yet times to recurrence vary substantially. We integrated multimodal data to identify tumor- and microenvironment-level features associated with timing of recurrence. Methods: Primary GBMs tumors with documented time to recurrence were analyzed by Xenium spatial transcriptomics (ST). ST data were processed with Seurat, manually annotated, and organized into five reproducible spatial niches. Differential expression was performed within tumor-dense niches comparing that shortest recurrence (SR) and those with the longer time to recurrence (LR). ST embeddings were extracted using the graph attention network based Novae foundation model (FM), while embeddings from the pathology whole-slide Hematoxylin and Eosin (H H 54 in LR). SR tumors overexpressed CD38, HLA-DQA1, CX3CR1, TMEM119, and P2RY12, indicating microglial activation and inflammatory signaling. LR tumors upregulated POSTN, LOX, ACKR1, VEGFA, and CAV1, consistent with extracellular matrix organization, angiogenesis, and vascular remodeling. SR GBM exhibits widespread tumor infiltration and immune activation, whereas LR tumors preserve structured vascular-immune niches and more diverse malignant-cell states. Conclusion: ST, SP, pathology slide H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1293.