eLifeBioinformaticsPeer-reviewed journal article
Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer
Nayoung KimiD, Sehhoon Park, Areum JoiD, Hye Hyeon EumiD, Hong Kwan KimiD, Kyungjong LeeiD, Jong Ho ChoiD, Bo Mi KuiD, Hyun Ae JungiD, Jong‐Mu SuniD, Se‐Hoon LeeiD, Jin Seok AhniD, Jung-Il LeeiD, Jung Won ChoiiD, Dasom Jeong, Minsu NaiD, Huiram KangiD, Jeong Yeon KimiD, Jung Kyoon ChoiiD, Hae‐Ock LeeiD, Myung‐Ju AhniD
Abstract
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 discriminative power (AUC) exceeding 95% in identifying 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.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 6
- FWCI
- 1.03
- Field percentile
- top 20%
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