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Zhao L, Xi L, Liu Y, Wang G, Zong M, Xue P, Zhu S. The Impact of Tertiary Lymphoid Structures on Tumor Prognosis and the Immune Microenvironment in Colorectal Cancer. Biomedicines 2025; 13:539. [PMID: 40149517 PMCID: PMC11940631 DOI: 10.3390/biomedicines13030539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2024] [Revised: 02/13/2025] [Accepted: 02/17/2025] [Indexed: 03/29/2025] Open
Abstract
Background: Colorectal cancer (CRC) ranks as the third most common cancer worldwide. Tertiary lymphoid structures (TLSs), organized immune cell aggregates in non-lymphoid tissues, are linked to chronic inflammation and tumorigenesis. However, the precise relationship between TLSs and CRC prognosis remains unclear. This study aimed to develop a TLS-associated genetic signature to predict CRC prognosis and support clinical applications. Methods: Utilizing the TCGA database, we analyzed TLS-related gene expression in CRC versus normal tissues. Prognostic models were constructed using Cox and Kaplan-Meier analyses. CRC samples were stratified into high and low TLS groups via ssGSEA, with validation in the GSE75500 dataset. We identified clinical characteristics associated with TLS scores, created prognostic nomograms, analyzed the top 50 differential genes, assessed tumor mutations, estimated immune infiltration using CIBERSORT, and examined correlations between TLS scores and immune checkpoints. Results: A 13-gene TLS-associated prognostic model for CRC was developed, emphasizing immune response genes. Survival analysis indicated significantly better outcomes for the TLS-high group. Cox regression identified stage IV and M1 as independent factors influencing TLS scores. Nomogram analysis demonstrated that combining TLS scores with clinical features enhances prognostic accuracy. TLS scores were closely associated with immune checkpoint genes, suggesting potential immunotherapy benefits for TLS-high patients. Conclusions: This study developed and validated a TLS-based prognostic model for CRC, exploring relevant immune cells. The model holds promise for predicting clinical prognosis and treatment responsiveness in CRC patients.
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Affiliation(s)
- Leyi Zhao
- School of Clinical Medicine, Beijing University of Chinese Medicine, Beijing 100029, China; (L.Z.); (L.X.); (G.W.); (M.Z.)
| | - Lingze Xi
- School of Clinical Medicine, Beijing University of Chinese Medicine, Beijing 100029, China; (L.Z.); (L.X.); (G.W.); (M.Z.)
| | - Yani Liu
- College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China;
| | - Guoliang Wang
- School of Clinical Medicine, Beijing University of Chinese Medicine, Beijing 100029, China; (L.Z.); (L.X.); (G.W.); (M.Z.)
| | - Mingtong Zong
- School of Clinical Medicine, Beijing University of Chinese Medicine, Beijing 100029, China; (L.Z.); (L.X.); (G.W.); (M.Z.)
| | - Peng Xue
- Oncology Department, Wangjing Hospital of Chinese Academy of Traditional Chinese Medicine, Beijing 100102, China
| | - Shijie Zhu
- Oncology Department, Wangjing Hospital of Chinese Academy of Traditional Chinese Medicine, Beijing 100102, China
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Niu L, Chen T, Yang A, Yan X, Jin F, Zheng A, Song X. Macrophages and tertiary lymphoid structures as indicators of prognosis and therapeutic response in cancer patients. Biochim Biophys Acta Rev Cancer 2024; 1879:189125. [PMID: 38851437 DOI: 10.1016/j.bbcan.2024.189125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/24/2024] [Accepted: 06/02/2024] [Indexed: 06/10/2024]
Abstract
Tertiary lymphoid structures (TLS) can reflect cancer prognosis and clinical outcomes in various tumour tissues. Tumour-associated macrophages (TAMs) are indispensable components of the tumour microenvironment and play crucial roles in tumour development and immunotherapy. TAMs are associated with TLS induction via the modulation of the T cell response, which is a major component of the TLS. Despite their important roles in cancer immunology, the subtypes of TAMs that influence TLS and their correlation with prognosis are not completely understood. Here, we provide novel insights into the role of TAMs in regulating TLS formation. Furthermore, we discuss the prognostic value of these TAM subtypes and TLS, as well as the current antitumour therapies for inducing TLS. This study highlights an entirely new field of TLS regulation that may lead to the development of an innovative perspective on immunotherapy for cancer treatment.
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Affiliation(s)
- Li Niu
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang, China
| | - Ting Chen
- Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Aodan Yang
- Department of Breast Surgery, the First Hospital of China Medical University, Shenyang, China
| | - Xiwen Yan
- Department of Breast Surgery, the First Hospital of China Medical University, Shenyang, China
| | - Feng Jin
- Department of Breast Surgery, the First Hospital of China Medical University, Shenyang, China
| | - Ang Zheng
- Department of Breast Surgery, the First Hospital of China Medical University, Shenyang, China.
| | - Xinyue Song
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang, China.
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Yang F, Yang J, Wu M, Chen C, Chu X. Tertiary lymphoid structures: new immunotherapy biomarker. Front Immunol 2024; 15:1394505. [PMID: 39026662 PMCID: PMC11254617 DOI: 10.3389/fimmu.2024.1394505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/13/2024] [Indexed: 07/20/2024] Open
Abstract
Immunotherapy shows substantial advancement in cancer and is becoming widely used in clinical practice. A variety of biomarkers have been proposed to predict the efficacy of immunotherapy, but most of them have low predictive ability. Tertiary lymphoid structures (TLSs), the aggregation of multiple lymphocytes, have been found to exist in various tumor tissues. TLSs have been shown to correlate with patient prognosis and immunotherapy response. This review summarizes the characteristics of TLSs and the inducing factors of TLS formation, presents available evidence on the role of TLSs in predicting immunotherapy response in different cancers, and lastly emphasizes their predictive potential for neoadjuvant immunotherapy efficacy.
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Affiliation(s)
- Fangyuan Yang
- Department of Medical Oncology, Jinling Hospital, Nanjing Medical University, Nanjing, China
| | - Jiahe Yang
- Department of Medical Oncology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Meijuan Wu
- Department of Medical Oncology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Cheng Chen
- Department of Medical Oncology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Xiaoyuan Chu
- Department of Medical Oncology, Jinling Hospital, Nanjing Medical University, Nanjing, China
- Department of Medical Oncology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
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Li L, Guo Y, Gong B, Wang S, Wang MM, Sun P, Jiang S, Yang L. Association between tertiary lymphoid structures and clinical outcomes in cancer patients treated with immune checkpoint inhibitors: an updated meta-analysis. Front Immunol 2024; 15:1385802. [PMID: 38994363 PMCID: PMC11236553 DOI: 10.3389/fimmu.2024.1385802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 06/12/2024] [Indexed: 07/13/2024] Open
Abstract
Background Although numerous studies have reported the association between tertiary lymphoid structures (TLSs) and clinical outcomes in cancer patients treated with immune checkpoint inhibitors (ICIs), there remains a lack of a newer and more comprehensive meta-analysis. The main objective of this study is to explore prognostic biomarkers in immunotherapy-related patients, through analyzing the associations between tertiary lymphoid structures (TLSs) and clinical outcomes in cancer patients treated with ICIs, so as to investigate their prognostic value in cancer patients treated with ICIs. Methods A comprehensive search was conducted until February 2024 across PubMed, Embase, Web of Science, and the Cochrane Library databases to identify relevant studies evaluating the association between tertiary lymphoid structures and clinical outcomes in cancer patients treated with ICIs. The clinical outcomes were overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). Results Thirteen studies were incorporated in this meta-analysis, among which nine evaluated the prognostic value of TLSs. The results showed the high levels of TLSs predicted a significantly prolonged OS (pooled HR = 0.35, 95% CI: 0.24-0.53, p < 0.001) and PFS (pooled HR = 0.47, 95% CI: 0.31-0.72, p < 0.001), while lower ORR (pooled OR = 3.78, 95% CI: 2.26-6.33, p < 0.001) in cancer patients treated with ICIs. Conclusion Our results indicated that high levels of TLSs could predict a favorable prognosis for cancer patients treated with ICIs and have the potential to become a prognostic biomarker of immunotherapy-related patients.
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Affiliation(s)
- Lingli Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yusheng Guo
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Molecular Imaging, Wuhan, China
| | - Bingxin Gong
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Molecular Imaging, Wuhan, China
| | - Sichen Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | | | - Peng Sun
- Clinical & Technical Support, Philips Healthcare, Beijing, China
| | - Shanshan Jiang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Molecular Imaging, Wuhan, China
| | - Lian Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Molecular Imaging, Wuhan, China
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Chen Z, Wang X, Jin Z, Li B, Jiang D, Wang Y, Jiang M, Zhang D, Yuan P, Zhao Y, Feng F, Lin Y, Jiang L, Wang C, Meng W, Ye W, Wang J, Qiu W, Liu H, Huang D, Hou Y, Wang X, Jiao Y, Ying J, Liu Z, Liu Y. Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response. NPJ Precis Oncol 2024; 8:73. [PMID: 38519580 PMCID: PMC10959936 DOI: 10.1038/s41698-024-00579-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/14/2024] [Indexed: 03/25/2024] Open
Abstract
Tertiary lymphoid structures (TLSs) have been associated with favorable immunotherapy responses and prognosis in various cancers. Despite their significance, their quantification using multiplex immunohistochemistry (mIHC) staining of T and B lymphocytes remains labor-intensive, limiting its clinical utility. To address this challenge, we curated a dataset from matched mIHC and H&E whole-slide images (WSIs) and developed a deep learning model for automated segmentation of TLSs. The model achieved Dice coefficients of 0.91 on the internal test set and 0.866 on the external validation set, along with intersection over union (IoU) scores of 0.819 and 0.787, respectively. The TLS ratio, defined as the segmented TLS area over the total tissue area, correlated with B lymphocyte levels and the expression of CXCL13, a chemokine associated with TLS formation, in 6140 patients spanning 16 tumor types from The Cancer Genome Atlas (TCGA). The prognostic models for overall survival indicated that the inclusion of the TLS ratio with TNM staging significantly enhanced the models' discriminative ability, outperforming the traditional models that solely incorporated TNM staging, in 10 out of 15 TCGA tumor types. Furthermore, when applied to biopsied treatment-naïve tumor samples, higher TLS ratios predicted a positive immunotherapy response across multiple cohorts, including specific therapies for esophageal squamous cell carcinoma, non-small cell lung cancer, and stomach adenocarcinoma. In conclusion, our deep learning-based approach offers an automated and reproducible method for TLS segmentation and quantification, highlighting its potential in predicting immunotherapy response and informing cancer prognosis.
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Affiliation(s)
- Ziqiang Chen
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Xiaobing Wang
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zelin Jin
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Bosen Li
- Department of General Surgery/Gastric Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Dongxian Jiang
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yanqiu Wang
- Departments of Pathology, International Peace Maternity and Child Health Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mengping Jiang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Dandan Zhang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Pei Yuan
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yahui Zhao
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feiyue Feng
- Thoracic Surgery Department, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yicheng Lin
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Liping Jiang
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chenxi Wang
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weida Meng
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Wenjing Ye
- Division of Rheumatology and Immunology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jie Wang
- Departments of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wenqing Qiu
- Shanghai Xuhui Central Hospital, Shanghai, China
| | - Houbao Liu
- Shanghai Xuhui Central Hospital, Shanghai, China
- Department of General Surgery/Biliary Tract Disease Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Dan Huang
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Institute of Pathology, Fudan University, Shanghai, China
| | - Yingyong Hou
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xuefei Wang
- Department of General Surgery/Gastric Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of General Surgery, Zhongshan Hospital (Xiamen), Fudan University, Shanghai, China
| | - Yuchen Jiao
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Jianming Ying
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Zhihua Liu
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Yun Liu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China.
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