Temporal Integration of Serum Proteomics, Metabolomics and MRI Tumor Volumetrics via Deep Learning Identifies Systemic Mediators of Glioblastoma Response to Chemoradiotherapy.

Glioblastomas (GBM) are highly aggressive, treatment-resistant brain tumors lacking clinically actionable, noninvasive prognostic biomarkers. Tumor response after standard-of-care chemoradiation (CRT) is difficult to interpret on imaging, and post-CRT MRI changes have not been well linked to molecular features or potential biomarkers. We evaluated differential proteomic and metabolomic expression in patient serum in relation to AI-segmented MRI volume changes after CRT to integrate clinical, molecular, and imaging data with patient outcomes. Fifty- five clinically annotated GBM patients provided serum samples pre- and post-CRT, analyzed using the SomaScan® proteomic platform and SECIM metabolomic assay. Pathway signatures were derived from pre- vs. post-CRT differential expression. MRI scans underwent AI segmentation to quantify contrast-enhancing (CE), non-enhancing (NE), and edema volumes. We assessed correlations between early (immediately post-CRT) and late (six months post-CRT) imaging changes and molecular alterations. Integrated multiomic and imaging features were used for unsupervised clustering to identify survival-associated patient groups, followed by pathway re-identification. AI-derived CE volumes decreased significantly during the early period, while edema increased significantly during the late period. CE changes were associated with metabolic pathways relevant to GBM biology, including epithelial-mesenchymal transition, in ammatory response, coagulation, and interferon-γ signaling. Clustering revealed two groups with distinct survival outcomes; CE alterations were significantly greater in the low-survival cluster (p = 0.02). Multiomic analysis (MOGSA) showed downregulation of key metabolic pathways in the low-survival group, including the citric acid cycle, Warburg effect, amino acid metabolism, oncogenic 2-hydroxyglutarate activity, and purine metabolism. Contributing metabolites included fumarate, succinate, citrate, and 2-hydroxyglutarate, while major proteomic contributors included MPC1, PDHB, DLAT, DLST, IDH3, SDHB, and FH. AI-derived MRI tumor-volume changes after CRT correspond to specific serum proteomic and metabolomic alterations, highlighting metabolic pathways linked to contrast-enhancing tissue dynamics in GBM.