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Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
Published on: February 8, 2018
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Tertiary Lymphoid Structures Predict Prognosis and Immune Checkpoint Inhibitor Efficacy in Lung Squamous Cell
Kuifei Chen1,2, Suna Zhou2, Pin Zhou3
1Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou, Zhejiang, China.
Immunology
|August 15, 2025
Summary
Tertiary lymphoid structures (TLSs) presence, high density, and maturity are linked to better outcomes in lung squamous cell carcinoma (LUSC). These findings highlight TLSs as a positive prognostic factor for LUSC patients, potentially predicting immunotherapy response.
Area of Science:
- Oncology
- Immunology
- Pathology
Background:
- Lung squamous cell carcinoma (LUSC) presents a significant mortality challenge.
- Tertiary lymphoid structures (TLSs) are critical microenvironmental components that modulate anti-tumour immune responses.
Purpose of the Study:
- To investigate the prognostic impact of TLSs in LUSC.
- To evaluate the predictive value of TLSs for immunotherapy efficacy in LUSC patients.
Main Methods:
- Haematoxylin and eosin staining and immunofluorescence staining were employed to assess TLSs.
- Comparative survival analyses were performed for patients stratified by TLS presence, density, and maturity (imTLS vs. mTLS).
- Multivariate Cox regression models analyzed prognostic factors in an LUSC immunotherapy cohort.
Main Results:
- TLS presence (TLS+) and high TLS density were significantly associated with better overall survival (OS).
- Mature TLS (mTLS) demonstrated a strong correlation with improved OS in LUSC patients.
- Stage and high PD-L1 expression (≥50%) were identified as predictors of TLS+ status.
Conclusions:
- TLSs, particularly when present at high density and in a mature state, represent a favorable prognostic factor in LUSC.
- TLS status is an independent predictor of overall survival in the LUSC immunotherapy cohort.
- TLS assessment may aid in predicting patient prognosis and immunotherapy response in LUSC.

