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Updated: Oct 30, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Higher TLR7 Gene Expression Predicts Poor Clinical Outcome in Advanced NSCLC Patients Treated with Immunotherapy
Sara Baglivo1, Fortunato Bianconi2, Giulio Metro1
1Medical Oncology Division, Santa Maria della Misericordia Hospital, Piazzale Menghini 8/9, 06132 Perugia, PG, Italy.
Abstract:
Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of lung cancer. However, their clinical benefit is limited to a minority of patients. To unravel immune-related factors that are predictive of sensitivity or resistance to immunotherapy, we performed a gene expression analysis by RNA-Seq using the Oncomine Immuno Response Assay (OIRRA) on a total of 33 advanced NSCLC patients treated with ICI evaluating the expression levels of 365 immune-related genes. We found four genes (CD1C, HLA-DPA1, MMP2, and TLR7) downregulated (p < 0.05) and two genes (IFNB1 and MKI67) upregulated (p < 0.05) in ICI-Responders compared to ICI-Non-Responders. The Bayesian enrichment computational analysis showed a more complex interaction network that involved 10 other genes (IFNA1, TLR4, CD40, TLR2, IL12A, IL12B, TLR9, CD1E, IFNG, and HLA-DPB1) correlated with different functional groups. Five main pathways were identified (FDR < 0.0001). High TLR7 expression levels were significantly associated with a lack of response to immunotherapy (p < 0.0001) and worse outcome in terms of both PFS (p < 0.001) and OS (p = 0.03). The multivariate analysis confirmed TLR7 RNA expression as an independent predictor for both poor PFS (HR = 2.97, 95% CI, 1.16-7.6, p = 0.023) and OS (HR = 2.2, 95% CI, 1-5.08, p = 0.049). In conclusion, a high TLR7 gene expression level was identified as an independent predictor for poor clinical benefits from ICI. These data could have important implications for the development of novel single/combinatorial strategies TLR-mediated for an efficient selection of "individualized" treatments for NSCLC in the era of immunotherapy.
Insights
High TLR7 gene expression predicts poor response to immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC). This finding may guide personalized immunotherapy strategies for better patient selection and treatment outcomes.
Area of Science:
- Immunogenomics
- Oncology
- Molecular Biology
Background:
- Immune checkpoint inhibitors (ICIs) have transformed lung cancer treatment but benefit only a subset of patients.
- Identifying predictive biomarkers is crucial for optimizing ICI therapy in non-small cell lung cancer (NSCLC).
- Understanding immune-related gene expression can reveal mechanisms of ICI resistance.
Purpose of the Study:
- To identify immune-related genes and pathways associated with response to ICI therapy in advanced NSCLC.
- To investigate the predictive value of specific gene expression profiles for ICI treatment outcomes.
- To explore novel therapeutic strategies based on identified biomarkers.
Main Methods:
- RNA sequencing (RNA-Seq) using the Oncomine Immuno Response Assay (OIRRA) on 33 advanced NSCLC patients treated with ICI.
- Analysis of 365 immune-related genes to compare expression levels between ICI-Responders and ICI-Non-Responders.
- Bayesian enrichment analysis to identify gene interaction networks and pathways.
Main Results:
- Four genes (CD1C, HLA-DPA1, MMP2, TLR7) were downregulated, and two genes (IFNB1, MKI67) were upregulated in ICI-Responders versus Non-Responders.
- High TLR7 expression was significantly associated with lack of response to immunotherapy (p < 0.0001) and poorer progression-free survival (PFS) and overall survival (OS).
- TLR7 RNA expression independently predicted poor PFS (HR=2.97) and OS (HR=2.2) in multivariate analysis.
Conclusions:
- High TLR7 gene expression is an independent predictor of poor clinical benefit from ICI therapy in NSCLC.
- These findings suggest TLR7 as a potential biomarker for patient selection in ICI treatment.
- The results support the development of TLR7-mediated therapeutic strategies for personalized NSCLC treatment.
