Pan-cancer spatial atlas of tertiary lymphoid structures.
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| Title: | Pan-cancer spatial atlas of tertiary lymphoid structures. |
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| Authors: | Cho, Kyung Serk (AUTHOR), Liu, Yunhe (AUTHOR), Pei, Guangsheng (AUTHOR), Chen, Jianfeng (AUTHOR), Dai, Yibo (AUTHOR), Liu, Yang (AUTHOR), Zhou, Tieling (AUTHOR), Bougouin, Antoine (AUTHOR), Serrano, Alejandra (AUTHOR), Wani, Khalida (AUTHOR), Jadhav, Akshaya (AUTHOR), Min, Jimin (AUTHOR), Hernandez, Sharia (AUTHOR), Lu, Wei (AUTHOR), Zhang, Daiwei (AUTHOR), Jiang, Jiahui (AUTHOR), Shamsutdinova, Diana (AUTHOR), Dai, Enyu (AUTHOR), Peng, Fuduan (AUTHOR), Sinjab, Ansam (AUTHOR) |
| Source: | Science. 5/28/2026, Vol. 392 Issue 6801, p1-23. 23p. |
| Subjects: | Tumor microenvironment, Histopathology, RNA sequencing, Pathological anatomy, Immune checkpoint inhibitors |
| Abstract: | Tertiary lymphoid structures (TLSs) are critical regulators of antitumor immunity, yet their spatial organization, maturation, and clinical relevance remain incompletely defined across cancers. We analyzed spatial transcriptomics spanning 12 cancer types to construct a pan-cancer TLS atlas and characterized TLS spatial architecture and maturation states. TLS maturation was accompanied by coordinated remodeling of distinct niche cell populations and distance-dependent gradients in tumor programs, orthogonally supported by ultrahigh-plex single-cell spatial profiling. To enable scalable TLS profiling, we trained an artificial intelligence framework that predicts TLS maturation states directly from hematoxylin and eosin–stained images and evaluated it across TCGA and independent therapy cohorts. We further derived a maturation-aware composite score capturing intratumoral TLS state composition, which robustly stratifies patients across cancer and treatment contexts, outperforming conventional TLS metrics. Editor's summary: Tertiary lymphoid structures (TLSs) form local immune hubs inside tumors, but they are diverse and not all are equally functional. Cho et al. built a pan-cancer atlas and developed an artificial intelligence (AI)–based framework to detect and characterize TLSs in human tumors. TLSs varied in maturation state, spatial location, cellular composition and organization. Intratumoral TLSs were associated with spatial gradients in tumor-intrinsic signaling. A scalable model was trained to detect and classify TLSs on standard pathology slides, and a composition-based TLS score was designed to stratify patients based upon survival outcomes. The study provides insights into TLS biology and may offer a path toward integrating TLS features into future clinical trials. —Priscilla N. Kelly INTRODUCTION: Tertiary lymphoid structures (TLSs) are ectopic immune aggregates that arise within tumors and regulate antitumor immunity. Although TLS presence has been associated with enhanced immune activity and improved outcomes in several settings, their maturation states, spatial locations relative to tumors, and context-dependent associations have not been systematically characterized at a pan-cancer scale, limiting a unified view of TLS biology and clinical utility. We asked how TLS maturation and spatial context relate to local immune programs and tumor cell states and whether these features can be quantified from routine histopathology. To address this, we combined spatial transcriptomics (ST) with deep learning applied to hematoxylin and eosin (H&E) whole-slide images (WSIs), building an atlas of TLS diversity across human cancers and a deployable framework that translates TLS spatial features into patient stratification. RATIONALE: Whole-transcriptome spatial profiling across entire tissue sections enables unbiased identification of TLS niches, maturation trajectories, and spatial context relative to tumor regions [intratumoral (IT), peritumoral (PT), distal-tumoral (DT)], avoiding marker-limited and region of interest–constrained analyses. We uniformly processed 340 ST sections and paired histology, then extended to 3071 WSIs using an artificial intelligence (AI)–enabled framework to detect and classify TLSs at scale. We hypothesized that TLS maturation state and spatial context would be associated with distinct local immune programs and TLS proximity–linked tumor pathway patterns and that a composition-based score (capturing early, primary, or secondary TLS states and their relative proportions) would outperform binary "TLS present or absent" or dominant-state heuristics for prognosis. Cross-cohort analyses, orthogonal validations (including ultrahigh-plex imaging and single-cell spatial profiling), and sensitivity tests were used to assess robustness and generalizability. RESULTS: Across 12 cancer types, we profiled TLSs including TLSs resolved by ST and identified from WSIs. TLSs varied substantially in prevalence, maturation, and spatial distribution across cancer types. TLSs spanned a maturation continuum [early (E-TLSs), primary follicle–like (P-TLSs), and secondary follicle–like (S-TLSs)] and occupied distinct spatial niches relative to tumors. Maturation was associated with coordinated immune organization: B and T cell zoning and activation, follicular dendritic cell networks, cytokine and chemokine signaling, and interferon (IFN)–related responses. Spatial analyses around intratumoral TLSs (IT-TLSs) revealed distance-dependent tumor pathway gradients: Immune activation and antigen-presentation programs (e.g., IFN-α and IFN-γ responses and MHC class II signatures) were enriched in tumor regions closest to TLSs and attenuated with distance, whereas proliferative and invasive programs (e.g., MYC targets, G2/M cell-cycle programs, epithelial-mesenchymal transition, and related oncogenic pathways) were relatively higher in TLS-distal tumor regions, highlighting spatial coupling between TLS niches and tumor programs. These findings were supported by single-cell–resolved spatial multimodal profiling. To scale beyond ST, we trained an AI framework to detect TLSs and predict maturation states directly from H&E WSIs and evaluated it across additional The Cancer Genome Atlas and independent therapy cohorts. We distilled these spatial features of TLSs into a maturation-aware composite score capturing within-tumor TLS state composition that robustly stratifies patients by survival and treatment response across cancers and treatment contexts. CONCLUSION: TLSs are spatially organized, heterogeneous immune hubs whose maturation and spatial context are linked to local immune organization, tumor pathway gradients, and patient outcomes. By uniting whole-section ST with AI on standard WSIs, we provide both a pan-cancer atlas of TLS diversity and a practical route to scalable, histology-deployable TLS profiling. The composition-based TLS composite score is positioned for prospective evaluation as a stratifier or endpoint in immuno-oncology trials and may help guide strategies aimed at promoting beneficial TLS states. Future work should incorporate longitudinal sampling under therapy, improved subclass or isotype-resolved antibody profiling as platforms mature, and functional perturbation to test causal mechanisms and refine TLS-informed clinical decision-making. Pan-cancer TLS atlas and AI-enabled patient stratification.: We built a pan-cancer TLS atlas and developed AI-enabled frameworks to detect and phenotype TLSs. Across 12 cancers, we mapped TLS location, maturation states, and tumor-signaling gradients. A maturation-aware composite score stratifies patients across clinical contexts. GCB, germinal center B cell; DC, dendritic cell; FDC, follicular dendritic cell. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Tertiary lymphoid structures (TLSs) are critical regulators of antitumor immunity, yet their spatial organization, maturation, and clinical relevance remain incompletely defined across cancers. We analyzed spatial transcriptomics spanning 12 cancer types to construct a pan-cancer TLS atlas and characterized TLS spatial architecture and maturation states. TLS maturation was accompanied by coordinated remodeling of distinct niche cell populations and distance-dependent gradients in tumor programs, orthogonally supported by ultrahigh-plex single-cell spatial profiling. To enable scalable TLS profiling, we trained an artificial intelligence framework that predicts TLS maturation states directly from hematoxylin and eosin–stained images and evaluated it across TCGA and independent therapy cohorts. We further derived a maturation-aware composite score capturing intratumoral TLS state composition, which robustly stratifies patients across cancer and treatment contexts, outperforming conventional TLS metrics. Editor's summary: Tertiary lymphoid structures (TLSs) form local immune hubs inside tumors, but they are diverse and not all are equally functional. Cho et al. built a pan-cancer atlas and developed an artificial intelligence (AI)–based framework to detect and characterize TLSs in human tumors. TLSs varied in maturation state, spatial location, cellular composition and organization. Intratumoral TLSs were associated with spatial gradients in tumor-intrinsic signaling. A scalable model was trained to detect and classify TLSs on standard pathology slides, and a composition-based TLS score was designed to stratify patients based upon survival outcomes. The study provides insights into TLS biology and may offer a path toward integrating TLS features into future clinical trials. —Priscilla N. Kelly INTRODUCTION: Tertiary lymphoid structures (TLSs) are ectopic immune aggregates that arise within tumors and regulate antitumor immunity. Although TLS presence has been associated with enhanced immune activity and improved outcomes in several settings, their maturation states, spatial locations relative to tumors, and context-dependent associations have not been systematically characterized at a pan-cancer scale, limiting a unified view of TLS biology and clinical utility. We asked how TLS maturation and spatial context relate to local immune programs and tumor cell states and whether these features can be quantified from routine histopathology. To address this, we combined spatial transcriptomics (ST) with deep learning applied to hematoxylin and eosin (H&E) whole-slide images (WSIs), building an atlas of TLS diversity across human cancers and a deployable framework that translates TLS spatial features into patient stratification. RATIONALE: Whole-transcriptome spatial profiling across entire tissue sections enables unbiased identification of TLS niches, maturation trajectories, and spatial context relative to tumor regions [intratumoral (IT), peritumoral (PT), distal-tumoral (DT)], avoiding marker-limited and region of interest–constrained analyses. We uniformly processed 340 ST sections and paired histology, then extended to 3071 WSIs using an artificial intelligence (AI)–enabled framework to detect and classify TLSs at scale. We hypothesized that TLS maturation state and spatial context would be associated with distinct local immune programs and TLS proximity–linked tumor pathway patterns and that a composition-based score (capturing early, primary, or secondary TLS states and their relative proportions) would outperform binary "TLS present or absent" or dominant-state heuristics for prognosis. Cross-cohort analyses, orthogonal validations (including ultrahigh-plex imaging and single-cell spatial profiling), and sensitivity tests were used to assess robustness and generalizability. RESULTS: Across 12 cancer types, we profiled TLSs including TLSs resolved by ST and identified from WSIs. TLSs varied substantially in prevalence, maturation, and spatial distribution across cancer types. TLSs spanned a maturation continuum [early (E-TLSs), primary follicle–like (P-TLSs), and secondary follicle–like (S-TLSs)] and occupied distinct spatial niches relative to tumors. Maturation was associated with coordinated immune organization: B and T cell zoning and activation, follicular dendritic cell networks, cytokine and chemokine signaling, and interferon (IFN)–related responses. Spatial analyses around intratumoral TLSs (IT-TLSs) revealed distance-dependent tumor pathway gradients: Immune activation and antigen-presentation programs (e.g., IFN-α and IFN-γ responses and MHC class II signatures) were enriched in tumor regions closest to TLSs and attenuated with distance, whereas proliferative and invasive programs (e.g., MYC targets, G2/M cell-cycle programs, epithelial-mesenchymal transition, and related oncogenic pathways) were relatively higher in TLS-distal tumor regions, highlighting spatial coupling between TLS niches and tumor programs. These findings were supported by single-cell–resolved spatial multimodal profiling. To scale beyond ST, we trained an AI framework to detect TLSs and predict maturation states directly from H&E WSIs and evaluated it across additional The Cancer Genome Atlas and independent therapy cohorts. We distilled these spatial features of TLSs into a maturation-aware composite score capturing within-tumor TLS state composition that robustly stratifies patients by survival and treatment response across cancers and treatment contexts. CONCLUSION: TLSs are spatially organized, heterogeneous immune hubs whose maturation and spatial context are linked to local immune organization, tumor pathway gradients, and patient outcomes. By uniting whole-section ST with AI on standard WSIs, we provide both a pan-cancer atlas of TLS diversity and a practical route to scalable, histology-deployable TLS profiling. The composition-based TLS composite score is positioned for prospective evaluation as a stratifier or endpoint in immuno-oncology trials and may help guide strategies aimed at promoting beneficial TLS states. Future work should incorporate longitudinal sampling under therapy, improved subclass or isotype-resolved antibody profiling as platforms mature, and functional perturbation to test causal mechanisms and refine TLS-informed clinical decision-making. Pan-cancer TLS atlas and AI-enabled patient stratification.: We built a pan-cancer TLS atlas and developed AI-enabled frameworks to detect and phenotype TLSs. Across 12 cancers, we mapped TLS location, maturation states, and tumor-signaling gradients. A maturation-aware composite score stratifies patients across clinical contexts. GCB, germinal center B cell; DC, dendritic cell; FDC, follicular dendritic cell. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00368075 |
| DOI: | 10.1126/science.adz2742 |
