Gene Signatures are specific sets of genes whose expression patterns are used to characterize biological states, disease subtypes, or treatment responses. They serve as biomarkers for diagnostics and prognostics in cancer, immunology, and other medical fields. Computational analyses identify these signatures from large transcriptomic datasets. Gene signatures guide personalized medicine by helping predict therapy outcomes. Their interpretation depends on bioinformatics, systems biology, and statistical modeling.

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Spatial Signatures for Predicting Immunotherapy Outcomes Using Multi-Omics in Non-Small Cell Lung Cancer

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Non-small cell lung cancer (NSCLC) shows variable responses to immunotherapy, highlighting the need for biomarkers to guide patient selection. We applied a spatial multi-omics approach to 234 advanced NSCLC patients treated with programmed death 1-based immunotherapy across three cohorts to identify biomarkers associated with outcome. Spatial proteomics (n?=?67) and spatial compartment-based transcriptomics (n?=?131) enabled profiling of the tumor immune microenvironment (TIME). Using spatial proteomics, we identified a resistance cell-type signature including proliferating tumor cells, granulocytes, vessels (hazard ratio (HR)?=?3.8, P?=?0.004), and a response signature, including M1/M2 macrophages and CD4 T cells (HR?=?0.4, P?=?0.019). We then generated a cell-to-gene resistance signature using spatial transcriptomics, which was predictive of poor outcomes (HR?=?5.3, 2.2, 1.7 across Yale, University of Queensland and University of Athens cohorts), while a cell-to-gene response signature predicted favorable outcomes (HR?=?0.22, 0.38 and 0.56, respectively). This framework enables robust TIME modeling and identifies biomarkers to support precision immunotherapy in NSCLC.

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