Integrative analysis of pan-cancer single-cell data reveals a tumor ecosystem subtype predicting immunotherapy response

Tumor ecosystem shapes cancer biology and potentially influence the response to immunotherapy, but there is a lack of direct clinical evidence. In this study, we utilized EcoTyper and publicly available scRNA-Seq cohorts from ICI-treated patients. We found a ecosystem subtype (ecotype) was linked to...

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Bibliographic Details
Published in:NPJ precision oncology 2024-09, Vol.8 (1), p.205-13, Article 205
Main Authors: Zeng, Shengjie, Chen, Liuxun, Tian, Jinyu, Liu, Zhengxin, Liu, Xudong, Tang, Haibin, Wu, Hao, Liu, Chuan
Format: Article
Language:English
Subjects:
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Cancer Research
Cancer therapies
Cells
CRISPR
Datasets
Ecosystems
Gene Therapy
Genes
Genomics
Human Genetics
Immunotherapy
Internal Medicine
Machine learning
Medical prognosis
Medicine
Medicine & Public Health
Oncology
Quality control
Standard scores
Tumors
Urology
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Summary:Tumor ecosystem shapes cancer biology and potentially influence the response to immunotherapy, but there is a lack of direct clinical evidence. In this study, we utilized EcoTyper and publicly available scRNA-Seq cohorts from ICI-treated patients. We found a ecosystem subtype (ecotype) was linked to improved responses to immunotherapy. Then, a novel immunotherapy-responsive ecotype signature (IRE.Sig) was established and validated through the analysis of pan-cancer data. Utilizing IRE.Sig, machine learning models successfully predicted ICI responses in both validation and testing cohorts, achieving area under the curve (AUC) values of 0.72 and 0.71, respectively. Furthermore, using 5 CRISPR screening cohorts, we identified several potential drugs that may augment the efficacy of ICI. We also elucidated the candidate cellular biomarkers of response to the combined treatment of pembrolizumab plus eribulin in breast cancer. This signature has the potential to serve as a valuable tool for patients in selecting appropriate immunotherapy treatments.
ISSN:2397-768X
2397-768X
DOI:10.1038/s41698-024-00703-w