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Immune characteristics associated with lymph node metastasis in early-stage NSCLC

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Abstract

Purpose

Tumor metastasis significantly impacts the prognosis of non-small cell lung cancer (NSCLC) patients, with lymph node (LN) metastasis being the most common and early form of spread. With the development of adjuvant immunotherapy, increasing attention has been paid to the tumor-draining lymph nodes（TDLN) in early-stage NSCLC, especially tumor-metastatic lymph nodes, which provides poor prognostic information but has potential benefits in adjuvant treatment.

Methods

We showed the remodeled immune environment in TDLNs through using TCR-seq to analyse 24 primary lung cancer tissues and 134 LNs from 24 lung cancer patients with or without LN metastasis. Additionally, we characterized the spatial profiling of immunocytes and tumor cells in TDLNs and primary tumor sites through using multi-IHC.

Results

We found the remodeled immune environment in TDLNs through analyzing primary lung cancer tissues and LNs from NSCLC patients with or without LN metastasis. Considering the intricate communication between tumor and immunocytes, we further subdivided TDLNs, revealing that metastasis-negative LNs from LN-metastatic patients (MNLN) exhibited greater immune activation, exhaustion, and memory in comparison to both metastasis-positive LNs (MPLN) and TDLNs from non-LN-metastatic patients (NMLN).

Conclusions

Our data indicate that LN metastasis facilitated tumor-specific antigen presentation in TDLNs and induces T cell priming, while existing tumor cells generate an immune-suppressive environment in MPLNs through multiple mechanisms. These findings contribute to a comprehensive understanding of the immunological mechanisms through which LN metastasis influences tumor progression and plays a role in immunotherapy for NSCLC patients.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This investigation was supported by the Natural Science Foundation of China (81903020), the Natural Science Foundation of Hunan Province (2022JJ30072), the National Multidisciplinary Cooperative Diagnosis and Treatment Capacity Building Project for Major Diseases (Lung Cancer).

Author information

Author notes

  1. Ziyu Zhang, Li Li and Yang Gao contributed equally to this work.

Authors and Affiliations

  1. Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China

    Ziyu Zhang, Li Li, Juan Jiang, Shiqing Liu, Jian An, Pengbo Deng, NanNan Du, Chengping Hu & Min Li

  2. Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha, China

    Yang Gao & Xiaoxiong Xiao

  3. Geneplus-Beijing Institute, Beijing, China

    Liyan Ji, Zhipeng Zhou, Pansong Li & Xuefeng Xia

  4. Xiangya Lung Cancer Center, Xiangya Hospital, Central South University, Changsha, China

    Min Li

  5. Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China

    Min Li

  6. Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China

    Min Li

  7. Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, Hunan, China

    Min Li

  8. National Clinical Research Center for Geriatric Disorders, Changsha, China

    Min Li

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  1. Ziyu Zhang
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Contributions

Ziyu Zhang: The main manuscript text writing, data curation, formal analysis, investigation, visualization, methodology, writing–original draft, writing–review and editing. Li Li: Fig. 4–5 preparation, data curation, formal analysis, investigation, visualization. Yang Gao: Resource, formal analysis, investigation. Xiaoxiong Xiao: Resource, formal analysis, investigation. Liyan Ji: Fig. 1–3 preparation, data curation, formal analysis, investigation, methodology. Zhipeng Zhou: Fig. 1–3 preparation, data curation, formal analysis, investigation, methodology. Juan Jiang: Resources, data curation, formal analysis, investigation. Shiqing Liu: Data curation, formal analysis, investigation. Jian An: Resources, data curation, formal analysis, investigation. Pengbo Deng: Resources, data curation, formal analysis, investigation. NanNan Du: Data curation, formal analysis, investigation. Pansong Li: Data curation, formal analysis, investigation. Xuefeng Xia: Investigation, methodology. Chengping Hu: Conceptualization, formal analysis, supervision, funding acquisition, methodology, writing–review, and editing. Min Li: Conceptualization, resources, formal analysis, supervision, funding acquisition, visualization, writing–original draft, project administration, writing–review and editing. All authors reviewed the manuscript.

Corresponding author

Correspondence to Min Li.

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Ethics approval

This study was approved by the Institutional Review Board (IRB) of Xiangya Hospital. The study was conducted in accordance with the Declaration of Helsinki.

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Written informed content was obtained from every patient.

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Not applicable.

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The authors have no relevant financial or non-financial interests to disclose.

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Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 40 KB)

13402_2023_873_MOESM2_ESM.tif

Supplementary file2 Figure S1 Correlation and comparison of TCR diversity among tumor, LNs, and blood. (A) The association of Shannon and clonality between tumor and age using Pearson coefficiency. (B) The correlation of Shannon and clonality between blood and age using Pearson coefficiency. Comparison of clonality (C) and Shannon (D) at different tumor stages. (E) TCR diversity on different pathological tumors. (F) Pearson coefficiency of Shannon between tumor and blood at metastasis and non-metastatic patients. (G) The correlation of Shannon between LNs and tumor or PBMC (TIF 5500 KB)

13402_2023_873_MOESM3_ESM.tif

Supplementary file3 Figure S2 Comparison of Shannon and richness among LNs, tumor, and blood. The violin plots showing Shannon index (A) and richness (B) among blood, LNs and tumor. Two sided Mann-Whitney U test was performed to test the significance between groups. The box plots depicting Shannon index (C) and richness (D) of tumor, LNs, and blood between metastatic and non-metastatic patients (TIF 3000 KB)

13402_2023_873_MOESM4_ESM.jpg

Supplementary file4 Figure S3 T cell subsets and TCR diversity/characterization in LNs. (A) The Pearson coefficiency of clonality between tumor and LNs at the presence (left panel) or absence (middle panel) of metastasis in metastatic patients and non-metastatic patients (right panel). (B) Comparison of clonality among MPLN, MNLN, and NMLN at different lymph node staging. (C) The density of CD4+ and CD8+cells among MPLN, MNLN, and NMLN. (D) The ratio of viral-associated TCRs to non-viral TCRs among MPLN, MNLN, and NMLN. (E) The bar plot illustrates the length of CDR3 amino acid between metastasis-positive LNs and metastasis-negative LNs (JPG 1115 KB)

13402_2023_873_MOESM5_ESM.tif

Supplementary file5 Figure S4 Box plots depicting the frequency of V gene and J genes among MPLN, MNLN, and NMLN (n =134). Two-sided Man-Whitney U test (TIF 2733 KB)

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Zhang, Z., Li, L., Gao, Y. et al. Immune characteristics associated with lymph node metastasis in early-stage NSCLC. Cell Oncol. 47, 447–461 (2024). https://doi.org/10.1007/s13402-023-00873-y

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