Cancer Medicine (Aug 2022)

Tertiary lymphoid structures in lung adenocarcinoma: characteristics and related factors

  • Fangping Ren,
  • Mei Xie,
  • Jie Gao,
  • Chongchong Wu,
  • Yang Xu,
  • Xuelei Zang,
  • Xidong Ma,
  • Hui Deng,
  • Jialin Song,
  • Aiben Huang,
  • Li Pang,
  • Jin Qian,
  • Zhaofeng Yu,
  • Guanglei Zhuang,
  • Sanhong Liu,
  • Lei Pan,
  • Xinying Xue

DOI
https://doi.org/10.1002/cam4.4796
Journal volume & issue
Vol. 11, no. 15
pp. 2969 – 2977

Abstract

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Abstract Objective Tertiary lymphoid structures (TLSs) are found in a variety of malignancies and affect the growth of tumors, but few studies have addressed their role in lung adenocarcinoma (LAC). We aimed to evaluate clinical features associated with TLSs in patients with LAC. Methods and Materials A collection of resected pulmonary nodules in patients with LAC was retrospectively analyzed. TLSs were quantified by their number per square millimeter tumor area (density) and by the degree of lymphocyte aggregation (maturity) in each case. The correlation between TLS density and maturity and clinical features was calculated. Results A total of 243 patients were selected, of whom 219 exhibited TLSs. The occurrence of TLSs was correlated with computed tomography (CT) features as follows: pure ground‐glass nodules (pGGNs) (n = 43) was associated with a lower occurrence rate than part‐solid nodules (PSNs) (n = 112) and solid nodules (SNs) were (n = 88) (p = 0.037). TLS density was correlated with age and CT features. Poisson regression showed higher TLS density in PSNs and SNs than in pGGNs (incidence rate ratio [IRR]: 3.137; 95% confidence interval [CI]: 1.35–7.27; p = 0.008 and IRR: 2.44; 95% CI: 1.02–5.85; p = 0.046, respectively). In addition, TLS density was higher in patients aged under 60 years than in those aged over 60 years (IRR: 0.605; 95% CI: 0.4–0.92; p = 0.018). The maturity of TLSs was higher in patients with higher tumor stages (p = 0.026). Conclusions We demonstrated distinct profiles of TLSs in early LAC and their correlations with CT features, age, and tumor stages, which could help understand tumor progression and management.

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