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Background <p>Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality, with chemoimmunotherapy (CIT) as the first-line standard for advanced NSCLC without driver mutations. However, predictive biomarkers for CIT response are limited. Tertiary lymphoid structures (TLS) play a critical role in antitumor immunity and may serve as potential biomarkers. This study aimed to screen and validate a TLS-derived gene signature to predict responses to first-line CIT in advanced NSCLC.</p> Methods <p>Data from three randomized trials (ORIENT-11, OAK, POPLAR) and The Cancer Genome Atlas—NSCLC were analyzed. TLS scores were computed via ssGSEA based on 17 TLS-related gene signatures. Patients were stratified into TLS-high and TLS-low groups. The predictive value was assessed by survival analysis, nomograms, and receiver operating characteristic curve. Correlations with programmed cell death 1 ligand (PD-L1) and ImmuneScore were evaluated.</p> Results <p>TLS signature 3 was identified as a predictive biomarker. In ORIENT-11, high signature 3 scores correlated with longer progression-free survival (PFS) (9.92 vs 6.77&#xa0;months, <i>p</i> = 0.001) and overall survival (OS) (not reached vs 17.60&#xa0;months, <i>p</i> &lt; 0.001). Multivariate analysis confirmed signature 3 as an independent predictor for both outcomes (PFS HR = 2.14, <i>p</i> = 0.006; OS HR = 2.24, <i>p</i> = 0.002). A nomogram integrating signature 3 and clinicopathologic factors showed strong discriminative power. Signature 3 also correlated with PD-L1 expression and ‘hot’ immune phenotypes, enhancing prediction in PD-L1-negative subsets.</p> Conclusions <p>TLS signature 3 predicts CIT response independently of PD-L1, improving outcomes in PD-L1-negative patients and complementing PD-L1 testing. Integration into clinical practice may refine treatment decisions, warranting further mechanistic and clinical validation.</p>

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Tertiary lymphoid structures gene signature predicts response to immunotherapy plus chemotherapy in advanced non-small cell lung cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月13日

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  • Wei Du,
  • Bijing Xiao,
  • Xuan Yang,
  • Jianhua Zhan,
  • Haishuang Sun,
  • Yunpeng Yang,
  • Wenfeng Fang,
  • Yan Huang,
  • Dongchen Sun,
  • Shaodong Hong,
  • Li Zhang

摘要

Background

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality, with chemoimmunotherapy (CIT) as the first-line standard for advanced NSCLC without driver mutations. However, predictive biomarkers for CIT response are limited. Tertiary lymphoid structures (TLS) play a critical role in antitumor immunity and may serve as potential biomarkers. This study aimed to screen and validate a TLS-derived gene signature to predict responses to first-line CIT in advanced NSCLC.

Methods

Data from three randomized trials (ORIENT-11, OAK, POPLAR) and The Cancer Genome Atlas—NSCLC were analyzed. TLS scores were computed via ssGSEA based on 17 TLS-related gene signatures. Patients were stratified into TLS-high and TLS-low groups. The predictive value was assessed by survival analysis, nomograms, and receiver operating characteristic curve. Correlations with programmed cell death 1 ligand (PD-L1) and ImmuneScore were evaluated.

Results

TLS signature 3 was identified as a predictive biomarker. In ORIENT-11, high signature 3 scores correlated with longer progression-free survival (PFS) (9.92 vs 6.77 months, p = 0.001) and overall survival (OS) (not reached vs 17.60 months, p < 0.001). Multivariate analysis confirmed signature 3 as an independent predictor for both outcomes (PFS HR = 2.14, p = 0.006; OS HR = 2.24, p = 0.002). A nomogram integrating signature 3 and clinicopathologic factors showed strong discriminative power. Signature 3 also correlated with PD-L1 expression and ‘hot’ immune phenotypes, enhancing prediction in PD-L1-negative subsets.

Conclusions

TLS signature 3 predicts CIT response independently of PD-L1, improving outcomes in PD-L1-negative patients and complementing PD-L1 testing. Integration into clinical practice may refine treatment decisions, warranting further mechanistic and clinical validation.