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June 7, 2026Cancer Discovery0 citations

Spatial Atlas Decodes Tumor Immune Hubs Across Cancer Types

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Key Points

  • This research aims to create a comprehensive spatial atlas of tertiary lymphoid structures (TLSs) across multiple cancer types.
  • Constructed a spatial atlas using spatial transcriptomic profiling from 340 tumor samples across 12 cancer types.
  • Developed an AI framework to predict TLS maturation states from standard pathology slides.
  • Derived a composite TLS score reflecting the distribution of maturation states.
  • Identified distinct TLS maturation states associated with enhanced immune activation and tissue remodeling.
  • Demonstrated that TLS spatial positioning correlates with tumor-intrinsic signaling gradients.
  • The composite TLS score accurately stratified survival and treatment response across different cancer types, outperforming conventional measures.

Abstract

Tertiary lymphoid structures (TLS) are organized immune cell aggregates that assemble within tumors and function as local antitumor immune hubs. Although TLSs have emerged as promising biomarkers of prognosis and immunotherapy response, their cellular and molecular heterogeneity across cancer types has remained poorly characterized at scale. Cho and colleagues now report a pan-cancer spatial atlas of TLSs, constructed from spatial transcriptomic profiling of 340 tumor samples spanning 12 cancer types, that systematically maps TLS abundance, spatial distribution, maturation state, and cellular composition in their native tissue context. The team defined distinct TLS maturation states and showed that progression through these states is accompanied by coordinated remodeling of immune, stromal, and vascular niche populations, as well as enhanced immune activation. The spatial positioning of TLSs within tumors was not incidental: Intratumoral TLSs were associated with distance-dependent gradients in tumor-intrinsic signaling, with immune activation programs enriched in tumor regions closest to TLSs and proliferative and invasive programs predominating in more distal regions. To translate these spatial insights into a clinically deployable tool, an artificial intelligence (AI) framework was developed to predict TLS maturation states directly from routine hematoxylin and eosin–stained pathology slides, validating performance across The Cancer Genome Atlas and multiple independent therapy cohorts, encompassing more than 3,000 whole-slide images and 25,088 individual TLSs. Leveraging this classifier, the authors derived a maturation-aware composite TLS score capturing not merely TLS presence but the full distribution of maturation states within a given tumor. This composite score robustly stratified patients by survival and treatment response across cancer types and therapeutic contexts, outperforming conventional TLS measures. Together, these findings establish a spatially defined framework of TLS biology, advance AI-driven TLS classification as a scalable precision immuno-oncology tool, and motivate therapeutic strategies aimed at promoting TLS maturation to amplify antitumor immunity.Cho KS, Liu Y, Pei G, Chen J, Dai Y, Liu Y, et al. Pan-cancer spatial atlas of tertiary lymphoid structures. Science 2026;392:eadz2742.Note: Research Watch is written by Cancer Discovery editorial staff. Readers are encouraged to consult the original articles for full details. For more Research Watch, visit Cancer Discovery online at https://aacrjournals.org/cdnews.

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A 2026 study studied this question.

synapsesocial.com/papers/6a250ce97def13d035e1d18fhttps://doi.org/10.1158/2159-8290.cd-rw2026-058
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