Spatial signatures for predicting immunotherapy outcomes using multi-omics in non-small cell lung cancer

Aung, TN; Monkman, J; Warrell, J; Vathiotis, I; Bates, KM; Gavrielatou, N; Trontzas, IP; Tan, CW; Fernandez, AI; Moutafi, M; O&#39; Byrne K; Schalper, KA; Syrigos, K; Herbst, RS; Kulasinghe, A; Rimm, DL

Author(s): Aung, TN; Monkman, J; Warrell, J; Vathiotis, I; Bates, KM; Gavrielatou, N; Trontzas, IP; Tan, CW; Fernandez, AI; Moutafi, M; O' Byrne K; Schalper, KA; Syrigos, K; Herbst, RS; Kulasinghe, A; Rimm, DL;
Details: Publication Year 2025-10,Volume 57,Issue #10,Page 2482-2493
Journal Title: Nature Genetics
Publication Type: Oct 10
Abstract: 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.
Publisher: Springer Nature
Keywords: Humans; *Carcinoma, Non-Small-Cell Lung/genetics/therapy/immunology/pathology; *Lung Neoplasms/genetics/therapy/immunology/pathology/drug therapy; *Immunotherapy/methods; Tumor Microenvironment/immunology/genetics; Biomarkers, Tumor/genetics; Proteomics/methods; Transcriptome; Female; Male; Treatment Outcome; Prognosis; Gene Expression Profiling; Middle Aged; Aged; Multiomics
Research Division(s): Bioinformatics and Computational Biology
PubMed ID: 41073787
Publisher's Version: https://doi.org/10.1038/s41588-025-02351-7
Open Access at Publisher's Site: https://doi.org/10.1038/s41588-025-02351-7
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2025-10-20 01:57:42

Last Modified: 2025-10-20 01:57:58