pubmed.ncbi.nlm.nih.gov

Infiltrating characteristics and prognostic value of tertiary lymphoid structures in resected gastric neuroendocrine neoplasm patients - PubMed

  • ️Mon Jan 01 2024

. 2024 Feb 5;13(2):e1489.

doi: 10.1002/cti2.1489. eCollection 2024.

Affiliations

Infiltrating characteristics and prognostic value of tertiary lymphoid structures in resected gastric neuroendocrine neoplasm patients

Daming Cai et al. Clin Transl Immunology. 2024.

Abstract

Objectives: Tertiary lymphoid structures (TLSs) are lymphocyte aggregates that play an anti-tumor role in most solid tumors. However, the functions of TLS in gastric neuroendocrine neoplasms (GNENs) remain unknown. This study aimed to determine the characteristics and prognostic values of TLS in resected GNEN patients.

Methods: Haematoxylin-eosin, immunohistochemistry (IHC) and multiple fluorescent IHC staining were used to assess TLS to investigate the correlation between TLSs and clinicopathological characteristics and its prognostic value.

Results: Tertiary lymphoid structures were identified in 84.3% of patients with GNEN. They were located in the stromal area or outside the tumor tissue and mainly composed of B and T cells. A high density of TLSs promoted an anti-tumor immune response in GNEN. CD15+ TANs and FOXP3+ Tregs in TLSs inhibited the formation of TLSs. High TLS density was significantly associated with prolonged recurrence-free survival (RFS) and overall survival (OS) of GNENs. Univariate and multivariate Cox regression analyses revealed that TLS density, tumor size, tumor-node-metastasis (TNM) stage and World Health Organisation (WHO) classification were independent prognostic factors for OS, whereas TLS density, tumor size and TNM stage were independent prognostic factors for RFS. Finally, OS and RFS nomograms were developed and validated, which were superior to the WHO classification and the TNM stage.

Conclusion: Tertiary lymphoid structures were mainly located in the stromal area or outside the tumor area, and high TLS density was significantly associated with the good prognosis of patients with GNEN. Incorporating TLS density into a nomogram may improve survival prediction in patients with resected GNEN.

Keywords: gastric neuroendocrine neoplasms; nomogram; prognosis; tertiary lymphoid structures; tumor immune microenvironment.

© 2024 The Authors. Clinical & Translational Immunology published by John Wiley & Sons Australia, Ltd on behalf of Australian and New Zealand Society for Immunology, Inc.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1

H&E staining, immunohistochemistry and multispectral fluorescent immunohistochemistry of tertiary lymphoid structures (TLS). (a) Representative H&E‐staining images for AGG, FL‐I and FL‐II (the red dotted line represents TLS region and the yellow dotted line represents the germinal centre in TLS). (b) Immunohistochemistry images showing the cellular compositions of TLS: CD4+ T cells; CD8+ T cells; CD45RO+memory T cells; CD20+ B cells; CD11c+DC cells; NCR1+ NK cells; CD15+ TANs; and FOXP3+ Tregs. (c) Multispectral fluorescent immunohistochemistry images of TLS displaying the cellular compositions and mature forms of TLS intuitively. Magnification: 200×. Scale bars correspond to 100 μm. (TLS maturity classification: AGG/FL‐I/FL‐II. Aggregates (AGG) are almost squashed, elongated or teardrop shaped; primary mature TLS (FL‐I) appears round or oval; and secondary mature TLS (FL‐II) contains a germinal centre).

Figure 2
Figure 2

(a) The relationship between the tertiary lymphoid structures (TLS) density and WHO classification. (The WHO classifications contain G1, G2/G3 and NEC/MiNEN.) (b) The association of the distribution of TLS maturity with WHO classification (the TLS maturity is described in the main text). (c) The association of the cell components of TLS with WHO classification (the cell components of TLS are described in the main text). (d) Association of the TLS density with the AJCC 8th TNM grade. (The AJCC 8th TNM grade includes I, II, III and IV.) (e) The association of the distribution of TLS maturity with AJCC 8th TNM grade. (f) Relationship between the cell components of TLS and AJCC 8th TNM grade. ** P < 0. 01, *** P < 0.001 and **** P < 0.0001.

Figure 3
Figure 3

(a) Correlation between tumor‐infiltrating immune cells located at the tumor margin or tumor centre outside the tertiary lymphoid structures (TLS) zone and TLS density. (TM refers to tumor margin and TC means tumor centre). (b) The components and infiltration profile of main immune‐infiltrating cells located at the tumor areas outside TLS zone of gastric neuroendocrine neoplasms. (The purple bar indicates the tumor‐infiltrating immune cells located at the tumor margin outside TLS zone every high‐power field (HPF); and the blue bar indicates the tumor‐infiltrating immune cells located at the tumor centre outside TLS zone every HPF.) (c) The heatmap image showing the correlation of the tumor‐infiltrating immune cells in the TLS zone, the tumor‐infiltrating immune cells located at the tumor margin outside the TLS zone and the tumor‐infiltrating immune cells located at the tumor centre outside the TLS zone.

Figure 4
Figure 4

Multispectral fluorescent immunohistochemistry of tumor‐infiltrating immune cells outside the tertiary lymphoid structures (TLS) located at the tumor margin or tumor centre in gastric neuroendocrine neoplasms (GNENs) patients with high TLS density and low TLS density. Tumor margin: from left to right, it presents the representative immunofluorescence images of tumor‐infiltrating immune cells outside the TLS located at the tumor margin in GNEN patients with high TLS density and low TLS density. Tumor centre: from left to right, it presents representative immunofluorescence images of tumor‐infiltrating immune cells outside the TLS located at the tumor centre in GNEN patients with high TLS density and low TLS density. Magnification: 400×. Scale bars correspond to 100 μm.

Figure 5
Figure 5

Kaplan–Meier survival analyses for recurrence‐free survival (RFS) based on the density of tertiary lymphoid structures (TLS) in the training cohort (a). Kaplan–Meier survival analyses for the overall survival (OS) based on the density of TLS in the training cohort (b). Kaplan–Meier survival analyses for RFS and OS were conducted based on the density of TLS in the external validation cohort (c, d).

Figure 6
Figure 6

(a) A nomogram was constructed considering four factors: tertiary lymphoid structures (TLS) density, tumor size, AJCC 8th tumor–node–metastasis (TNM) stage and WHO classification to predict the probability of OS at 3 and 5 years. The probabilities were estimated as the sum of points for each variable as a function of total points. Each component was assigned points by tracing a line upwards from the corresponding values to the ‘point’ line. The total sum of points contributed by each variable was displayed on the ‘total points’ line. To determine the associated probability forecasts, a line was drawn downwards from the total points. The Bootstrap method was employed for internal validation, involving 1000 repeated samples. Calibration curves of the model in the training set (b, c) and decision curve analysis (DCA) of nomogram, AJCC 8th TNM grade and WHO classification for 3‐ and 5‐year OS in the training set (d, e). Calibration curves of the model in the external validation set (f, g) and DCA curve of nomogram, AJCC 8th TNM grade and WHO classification for 3‐ and 5‐year OS in the external validation set (h, i).

Figure 7
Figure 7

(a) A nomogram was constructed based on three factors: tertiary lymphoid structures (TLS) density, tumor size and AJCC 8th tumor–node–metastasis (TNM) stage for predicting the probability of RFS at 3 and 5 years. The probabilities were estimated as the sum of points for each variable as a function of total points. Each component was assigned points by drawing a line upwards from the matching values to the ‘point’ line. On the ‘total points’ line, the total sum of points added by each variable is shown. A line was drawn downward to read the associated probability forecasts. The Bootstrap method was used for internal validation, with 1000 repeat samples. Calibration curves for the model in the training set (b, c) and decision curve analysis (DCA) of the model in the training set (d, e). Calibration curves for the model in the external validation set (f, g) and DCA curve for the model in the external validation set (h, i).

Similar articles

References

    1. Yang Z, Wang W, Lu J et al. Gastric neuroendocrine tumors (G‐NETs): incidence, prognosis and recent trend toward improved survival. Cell Physiol Biochem 2018; 45: 389–396. - PubMed
    1. Dasari A, Shen C, Halperin D et al. Trends in the incidence, prevalence, and survival outcomes in patients with neuroendocrine tumors in the United States. JAMA Oncol 2017; 3: 1335–1342. - PMC - PubMed
    1. Masui T, Ito T, Komoto I, Uemoto S, Group JPS . Recent epidemiology of patients with gastro‐entero‐pancreatic neuroendocrine neoplasms (GEP‐NEN) in Japan: a population‐based study. BMC Cancer 2020; 20: 1104. - PMC - PubMed
    1. Zheng R, Zhao H, An L et al. Incidence and survival of neuroendocrine neoplasms in China with comparison to the United States. Chin Med J (Engl) 2023; 136: 1216–1224. - PMC - PubMed
    1. Expert Committee on Neuroendocrine Neoplasms CSoCO . Chinese expert consensus on gastroenteropancreatic neuroendocrine neoplasms (2022 edition). Zhonghua Zhong Liu Za Zhi 2022; 44: 1305–1329. - PubMed