Deep learning identifies a T-cell exhaustion-dependent transcriptional signature for predicting clinical outcomes and response to immune checkpoint blockade

doi:10.1038/s41389-023-00482-2

科研成果详情

题名	Deep learning identifies a T-cell exhaustion-dependent transcriptional signature for predicting clinical outcomes and response to immune checkpoint blockade
作者	Zhang, Zicheng; Chen, Hongyan; Yan, Dongxue; Chen, Lu; Sun, Jie; Zhou, Meng
发表日期	2023-07-11
发表期刊	ONCOGENESIS 影响因子和分区
语种	英语
原始文献类型	Article
其他关键词	RESISTANCE ; LANDSCAPE ; MECHANISM
摘要	Immune checkpoint blockade (ICB) therapies have brought unprecedented advances in cancer treatment, but responses are limited to a fraction of patients. Therefore, sustained and substantial efforts are required to advance clinical and translational investigation on managing patients receiving ICB. In this study, we investigated the dynamic changes in molecular profiles of T-cell exhaustion (TEX) during ICB treatment using single-cell and bulk transcriptome analysis, and demonstrated distinct exhaustion molecular profiles associated with ICB response. By applying an ensemble deep-learning computational framework, we identified an ICB-associated transcriptional signature consisting of 16 TEX-related genes, termed ITGs. Incorporating 16 ITGs into a machine-learning model called MLTIP achieved reliable predictive power for clinical ICB response with an average AUC of 0.778, and overall survival (pooled HR = 0.093, 95% CI, 0.031-0.28, P < 0.001) across multiple ICB-treated cohorts. Furthermore, the MLTIP consistently demonstrated superior predictive performance compared to other well-established markers and signatures, with an average increase in AUC of 21.5%. In summary, our results highlight the potential of this TEX-dependent transcriptional signature as a tool for precise patient stratification and personalized immunotherapy, with clinical translation in precision medicine.
资助项目	National Natural Science Foundation of China[61973240,62072341,62272346];
出版者	SPRINGERNATURE
ISSN	2157-9024
卷号	12 期号:1
DOI	10.1038/s41389-023-00482-2
页数	11
WOS类目	Oncology
WOS研究方向	Oncology
WOS记录号	WOS:001029783000001
收录类别	SCIE ; PUBMED ; SCOPUS
URL	查看原文
PubMed ID	37433793
SCOPUSEID	2-s2.0-85165255254
通讯作者地址	[Sun, Jie]School of Biomedical Engineering,Eye Hospital,Wenzhou Medical University,Wenzhou,325027,China ; [Zhou, Meng]School of Biomedical Engineering,Eye Hospital,Wenzhou Medical University,Wenzhou,325027,China
Scopus学科分类	Molecular Biology;Cancer Research
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/181639
专题	仁济学院_眼视光、生物医学工程学部
通讯作者	Sun, Jie; Zhou, Meng
作者单位	School of Biomedical Engineering,Eye Hospital,Wenzhou Medical University,Wenzhou,325027,China
第一作者单位	仁济学院_眼视光、生物医学工程学部
通讯作者单位	仁济学院_眼视光、生物医学工程学部
第一作者的第一单位	仁济学院_眼视光、生物医学工程学部
推荐引用方式 GB/T 7714	Zhang, Zicheng,Chen, Hongyan,Yan, Dongxue,et al. Deep learning identifies a T-cell exhaustion-dependent transcriptional signature for predicting clinical outcomes and response to immune checkpoint blockade[J]. ONCOGENESIS,2023,12(1).
APA	Zhang, Zicheng, Chen, Hongyan, Yan, Dongxue, Chen, Lu, Sun, Jie, & Zhou, Meng. (2023). Deep learning identifies a T-cell exhaustion-dependent transcriptional signature for predicting clinical outcomes and response to immune checkpoint blockade. ONCOGENESIS, 12(1).
MLA	Zhang, Zicheng,et al."Deep learning identifies a T-cell exhaustion-dependent transcriptional signature for predicting clinical outcomes and response to immune checkpoint blockade".ONCOGENESIS 12.1(2023).