bioRxivCancer BiologyPreprint
Spatially Organized Tertiary Lymphoid Structures Emerge in Small Cell Lung Cancer and Associate with Improved Survival
Yingying CaoiD, Michael NirulaiD, Paul W. MalloryiD, Sarthak SahooiD, Kanak ParmariD, Christopher A. Febres‐AldanaiD, Anish ThomasiD
Abstract
Tertiary lymphoid structures (TLS) are ectopic immune aggregates associated with improved prognosis and response to immunotherapy in multiple solid tumors. However, their presence, spatial organization, and functional relevance in small cell lung cancer (SCLC), a malignancy characterized by profound immune evasion, remain poorly understood. Using imaging mass cytometry (IMC) across 320 regions of interest spanning primary lung tumor, tumor-adjacent lung, liver and lymph node metastasis, complemented by Visium HD spatial transcriptomics, we characterized the cellular architecture and molecular programs of TLS-like niches in SCLC. TLS-like niches were identified in a subset of SCLC samples, predominantly primary lung tumor tissues and adjacent lung, spanning a continuum from loose lymphoid aggregates to compact follicle-like immune structures. Organized TLS-like niches contained CD20+ B-cell cores, closely associated with CD4+ and CD8A+ T cells, proliferating lymphocytes, HLA-DR+ antigen-presenting compartments, and SMA+ stromal scaffolds, and were enriched for canonical TLS organizer signals (CXCL13, LTB, FDCSP). Patients with TLS-positive tumors demonstrated improved overall survival, and core TLS-associated transcriptional programs were associated with favorable survival in an independent bulk RNA-seq cohort. To our knowledge, this represents one of the first spatially resolved analyses of TLS-like immune architecture in SCLC, demonstrating that organized lymphoid immunity can emerge in this classically immune-evasive disease and is associated with improved survival.
- organism: human
- assay: spatial transcriptomics
- assay: imaging
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: microscopy & imaging analysis
- approach: statistical model
- Citations
- 0
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