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Background MET alterations are oncogenic drivers in non-small cell lung cancer (NSCLC), but their impact on the tumor microenvironment (TME) remains unclear. Spatial analysis of whole slide images (WSIs) enables high-resolution TME characterization, overcoming limitations of bulk sequencing. This study used AI-powered spatial analysis to profile immune phenotypes and cellular composition across MET mutations in NSCLC.Methods We retrospectively analyzed 371 H&E-stained WSIs from NSCLC biopsies collected during routine clinical care using the AI-based SCOPE IO algorithm (Lunit) to quantify tumor cellular densities and immune phenotypes (inflamed, immune-excluded, immune-desert). Cases were stratified by MET status: exon 14 skipping (METex14, N=241), amplification (METamp, N=31), and wildtype (METwt, N=99). SCOPE IO metrics and targeted sequencing-based immune gene expression (iGEX) were compared across groups. METex14 tumors were further grouped into inflamed (N=63) and non-inflamed (N=158) subtypes. A machine learning (ML) model trained on iGEX features associated with METex14 subtypes was used to impute subtypes in a second NSCLC cohort with immunotherapy outcomes (N=205).Results METex14 tumors had more inflamed phenotypes than METamp and METwt (29% vs 10% and 15%, P=2E-3), with higher densities of endothelial cells, fibroblasts, and lymphocytes in cancer areas (P≤2E-3), elevated inflamed scores (P=0.01), and lower immune-desert scores (P=0.02). METamp tumors showed more immune-desert phenotypes (79% vs 52% and 63%, P=0.01), increased presence of mitotic cells (P=4E-8), fewer non-tumor cells (P<0.05), and higher immune-desert scores (P=0.047). iGEX confirmed these findings: METex14 tumors had higher overall iGEX (192 genes), while METamp tumors showed elevated proliferation-associated genes (18 genes) (P<0.05). Inflamed METex14 tumors had more lymphocytes, macrophages, and other non-tumor cells in cancer and stromal areas (P≤2E-3), with increased iGEX in immune activation pathways (166 genes, P<0.05). ML-based feature selection identified 46 differentially expressed genes distinguishing METex14 subtypes with high accuracy (ROC-AUC=0.94). In a second cohort, tumors classified as inflamed were associated with improved survival under immunotherapy (HR=0.5, P=0.004) ( figure 1).Conclusions AI-powered spatial analysis and iGEX profiling revealed distinct TME profiles across MET mutations in NSCLC. METex14 tumors exhibited immune-active TMEs, while METamp tumors were immune-deficient and proliferative. A subset of METex14 tumors showed high immune activity, suggesting potential responsiveness to immunotherapy ( figure 2). These findings highlight MET-driven NSCLC heterogeneity and the utility of spatial AI tools for immunotherapy stratification and biomarker development.Ethics Approval Ethics approval for this study, including waiver of informed consent, was obtained from WCG IRB (Study #1340120), an independent institutional review board.Abstract 1347 Figure 1(A) Distribution of survival time (months) between machine learning imputed inflamed vs non-inflamed NSCLC tumors. (B) Kaplan-Meier curves showing differences in survival probability between inflamed vs non-inflamed NSCLC tumors over time.Abstract 1347 Figure 2Summary of findings