Cancer Research • 2024-04-07

Comparative analysis of a predictive transcriptomic model and functional gene expression signatures (FGES) for tertiary lymphoid structure (TLS) detection in lung adenocarcinoma (LUAD)


Nadezhda Lukashevich, Daniil Dymov, Aleksandr Sarachakov, Sofya Kust, Anna Love, Ivan Valiev, Dmitry Ivchenkov, Alexander Bagaev, Viktor Svekolkin, Nikita Kotlov, Vladimir Kushnarev
  1. BostonGene, Corp., Waltham, MA, US


Background: TLS are recognized as significant biomarkers for patient prognosis and therapy selection. In current clinical practice, TLS are often detected by manual assessment of H&E slides, which has been associated with reproducibility issues. Here, we developed a transcriptomic model using TLS density to stratify LUAD patients into TLS-high and -low groups and compared it with 5 published TLS FGES to assess the predictive value for patient outcomes.

Design: A regression model to predict TLS density (units(u)/mm2) was created using RNA expression data and histological mapping of 415 TCGA-LUAD samples. Features were chosen based on differential gene expression analysis of sample groups with low (Q3, 2.04 u/mm2) TLS density. The model was trained on 269 samples and independently validated on 146 TCGA and 9 internal samples. The RNA model and five previously reported TLS FGES were compared by Wilcoxon paired test to Spearman’s ρ and F1-scores from bootstrapped validation set. The resulting predictions stratified immunotherapy-treated samples from SU2C-MARK (n=58) and GSE182328 (n=28) into TLS-high and TLS-low groups by Q2 threshold for TLS density (0.78 u/mm2) for further univariate (Kaplan-Meier) and multivariate (Cox model, adjusted for age and sex) analyses for overall survival (OS) with log-rank tests. P-values were adjusted with Bonferroni correction, and 95% confidence intervals (CIs) were provided.

Results: Statistically significant upregulated genes in samples with high TLS density included FDCSP, MS4A1, and CXCL13 (logFC ≥ 0.95, FDR<0.01). The transcriptomic model showed confident superiority in correlation with TLS density compared to the best FGES in TCGA samples (ρ=0.53±0.03 vs 0.41±0.03, p<0.001). The TLS model also outperformed the highest performing FGES when stratifying TCGA samples into TLS-high and TLS-low groups (F1=0.73±0.02 vs 0.69±0.02, p<0.005). In the internal cohort, the model demonstrated robust performance (F1=0.76±0.05, ρ=0.81±0.04) and outperformed all FGES except one (FGES F1=0.78±0.05, ρ=0.86±0.05). The TLS-high group defined by the TLS RNA model had a significantly better prognosis on immunotherapy cohorts (p=0.01; 1 year OS 49% for TLS-low vs 70% for TLS-high), whereas no discernible difference in OS was identified for the 5 TLS FGES. The TLS model had a log hazard ratio (logHR) of -3.11 (CI:-5.31,-0.91; p<0.05, Harrell's C-index=0.64), indicative of a stronger association with OS than of the TLS FGES (logHR=-2.11; CI:-4.1,-0.08; p=ns; C- index=0.59).

Conclusions: Comparative analysis of the proposed transcriptomic model for TLS detection in LUAD samples demonstrated improved performance metrics and prognostic value compared to previously reported TLS FGES, affirming its potential for guiding therapeutic decision-making.
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