
The interplay between tumor cell characteristics and the immune microenvironment determines the effectiveness of immune checkpoint inhibitor (ICI) therapy in patients with advanced non-small cell lung cancer (NSCLC).


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This study investigates the variability among patients with non-small cell lung cancer (NSCLC) in their responses to immune checkpoint inhibitors (ICI). Recognizing that patients with advanced-stage NSCLC rarely qualify for surgical interventions, it becomes crucial to identify biomarkers that influence responses to ICI therapy. We conducted an analysis of single-cell transcriptomes from 33 lung cancer biopsy samples, with a particular focus on 14 core samples taken before the initiation of palliative ICI treatment. Our objective was to link tumor and immune cell profiles with patient responses to ICI. We discovered that ICI non-responders exhibited a higher presence of CD4+ regulatory T cells, resident memory T cells, and TH17 cells. This contrasts with the diverse activated CD8+ T cells found in responders. Furthermore, tumor cells in non-responders frequently showed heightened transcriptional activity in the NF-kB and STAT3 pathways, suggesting a potential inherent resistance to ICI therapy. Through the integration of immune cell profiles and tumor molecular signatures, we achieved an accuracy rate exceeding 95% in predicting patient responses to ICI treatment. These results underscore the crucial importance of the interplay between tumor and immune microenvironment, including within metastatic sites, in affecting the effectiveness of ICIs in NSCLC.

### Competing Interest Statement

The authors have declared no competing interest.

Raw single-cell RNA sequencing data generated during the current study are available in the European Genome-phenome Archive (EGA) database (accession code EGAD00001008703), and processed data can be accessed from the NCBI Gene Expression Omnibus (GEO) database (accession code GSE205335; temporary token ‘khatsauybfcjlgb’). Single-cell RNA sequencing data for LUAD patients analyzed in this study are available in the EGA database at EGAD00001005054. Bulk RNA sequencing data analyzed in this study were obtained from GEO at GSE91061 and database of Genotypes and Phenotypes (dbGap) at phs000452.v2.p1.

Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer

unveiling-the-interplay-between-tumor-and-immune-signatures-in-predicting-response-to-immune-checkpoint-therapy-in-advanced-lung-cancer


Unveiling the Interplay Between Tumor and Immune Signatures in Predicting Response to Immune Checkpoint Therapy in Advanced Lung Cancer
