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

This study investigates the variability among patients with non-small cell lung cancer (NSCLC) in their responses to immune checkpoint inhibitors (ICIs). 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 (area under the curve [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.

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Publication Details

Journal
eLife
Published
2024-11-08
DOI
https://doi.org/10.7554/elife.98366.3
Citations
5
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
1.41

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article

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

Jung Kyoon Choi, Kyungjong Lee, Hye Hyeon Eum, Jong Ho Cho et al.
5 citations
eLife
Cancer Immunotherapy and Biomarkers
1.41
article

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

Jung Kyoon Choi, Kyungjong Lee, Hye Hyeon Eum, Jong Ho Cho, Dasom Jeong, Hyun Ae Jung, Jong‐Mu Sun, Myung‐Ju Ahn, Huiram Kang, Jung Won Choi, Minsu Na, Hong Kwan Kim, Bo Mi Ku, Se‐Hoon Lee, Jin Seok Ahn, Hae‐Ock Lee, Nayoung Kim, Jeong Yeon Kim, Areum Jo, Jung-Il Lee, Sehhoon Park
article en
5 citations

Abstract

This study investigates the variability among patients with non-small cell lung cancer (NSCLC) in their responses to immune checkpoint inhibitors (ICIs). 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 (area under the curve [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.

eLifeVol. 13
Korea Advanced Institute of Science and Technology (KR), Samsung Medical Center (KR), Sungkyunkwan University (KR), Catholic University of Korea (KR)
National Research Foundation of Korea
Reduced inequalities
Openalex Percentile: Top 15%
Cancer Immunotherapy and Biomarkers
1.41
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