Biomarkers are measurable biological indicators—such as proteins, genetic sequences, or imaging signals—that reflect physiological or pathological processes. They are widely used in diagnostics, prognostics, and therapeutic monitoring, particularly in oncology and neurology. Advances in AI and machine learning have improved the identification of complex biomarker patterns from large datasets. NEC Laboratories America explores computational biomarker discovery to enhance predictive healthcare analytics. This research supports precision medicine by linking molecular data to patient outcomes.

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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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