Spatial Decoding of Tertiary Lymphoid Structure Maturation in Non-Small Cell Lung Cancer Using Deep Neural Networks

Chen, B.; Foo, C.; Andersson, A.; Kayser, B. D.; Yang, Y.; Missarova, A.; Kulkarni, P.; Huetter, J.-C.; Chatterjee, S.; Kim, Y.; Liang, Y.; Killinbeck, E.; Murphy, S.; Fuentes, E.; Giltnane, J. M.; Jesudason, R.; Li, B.; BenTaieb, A.; Richmond, D.; Biancalani, T.; Johnston, R. J.; Lubeck, E.; Risom, T.; Rozenblatt-Rosen, O.; Wei, R.; McGinnis, L. M.

doi:10.64898/2026.01.16.697961

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bioRxiv · 10.64898/2026.01.16.697961

Spatial Decoding of Tertiary Lymphoid Structure Maturation in Non-Small Cell Lung Cancer Using Deep Neural Networks

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Abstract

Understanding the role of tertiary lymphoid structures (TLS) is crucial in non-small cell lung cancer (NSCLC), as they are associated with patient prognosis and treatment outcomes. Specific cellular ecosystems that originate anti-tumor activity or predict immunotherapy response remain poorly characterized. To this end, we developed a high-resolution, multimodal spatial atlas jointly profiling transcriptomics, proteomics, and histology to characterize TLS maturation in NSCLC alongside secondary lymph organs as a baseline. Using this atlas, we proposed a pathologist-in-the-loop framework that combines a variational graph autoencoder (VGAE) with diffusion pseudotime to refine human expert annotations and characterize TLS maturation. These spatial molecular representations were extended to H&E whole-slide images via a vision transformer-based foundation model. Next, we resolved cellular composition, spatial organization, and cell-cell interactions within these data and defined two divergent spatial ecosystems. Clinical evidence suggests that these ecosystems are associated with distinct patient outcomes: a mature germinal center niche with favorable prognoses, and a tumor-macrophage-fibroblast niche with unfavorable prognoses. In summary, our work decodes key components of TLS heterogeneity, identifies hallmark spatial patterns involved in NSCLC adaptive immunity, and provides a framework for translating spatial omics insights into clinical applications.

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Chen, B., Foo, C., Andersson, A., Kayser, B. D., Yang, Y., Missarova, A., Kulkarni, P., Huetter, J.-C., Chatterjee, S., Kim, Y., Liang, Y., Killinbeck, E., Murphy, S., Fuentes, E., Giltnane, J. M., Jesudason, R., Li, B., BenTaieb, A., Richmond, D., Biancalani, T., Johnston, R. J., Lubeck, E., Risom, T., Rozenblatt-Rosen, O., Wei, R., McGinnis, L. M.. 2026-01-20. Spatial Decoding of Tertiary Lymphoid Structure Maturation in Non-Small Cell Lung Cancer Using Deep Neural Networks. https://doi.org/10.64898/2026.01.16.697961

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