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1256 Metabolic mapping of non-small cell lung cancer reveals spatial features associated with immunotherapy resistance and response in the first-line setting

jitc · 2025-11-04 · canonical JSON source

12 visible annotations · policy: published · automated confidence ≥ 75.00%

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Background Immunotherapy has improved outcomes in a subset of non-small cell lung cancer (NSCLC) patients. However, most patients develop resistance. Understanding therapeutic efficacy requires deep, spatially resolved, single-cell characterization of the tumor microenvironment (TME). Emerging spatial omics technologies now enable such resolution across both tumoural and stromal compartments, uncovering functional and metabolic heterogeneity across tissues.Methods We employed spatially resolved transcriptomics (whole-transcriptome) alongside multiplexed protein profiling (>70 markers spanning cancer hallmarks) on pre-treatment NSCLC biopsies. Data were integrated with same-slide H&E and pathology review (ground truth) for spatial annotation of cell types, states, and metabolic activity. Spatial interaction metrics were derived using geometric profiling and feature engineering. Associations with progression-free survival (PFS) and overall survival (OS) were identified through statistical stability selection and multivariate modelling.Results Spatial metabolic heterogeneity across tissues was notable with tumour regions displaying elevated glycolytic activity, which correlated with poor clinical response. Conversely, regions enriched with inducible nitric oxide synthase (iNOS)+ immune cells and lymphocyte infiltration were linked to clinical benefit. Multivariate modelling identified a spatial-metabolic signature that predicted PFS beyond 24 months with high accuracy (AUC = 0.8), underscoring the prognostic value of spatial arrangement and metabolic state in immunotherapy response.Conclusions This study demonstrates that layered spatial and functional profiling of the NSCLC TME reveals key prognostic markers linked to immunotherapy response. We present a scalable framework for spatial biomarker discovery, with implications for precision oncology and immunotherapy therapy stratificationEthics Approval This study has Queensland University of Technology (QUT) Human Research Ethics Committee approval (UHREC #2000000494) and University of Queensland ratification.