TY - JOUR
T1 - Immunotyping the Tumor Microenvironment Reveals Molecular Heterogeneity for Personalized Immunotherapy in Cancer
AU - Zeng, Dongqiang
AU - Yu, Yunfang
AU - Qiu, Wenjun
AU - Ou, Qiyun
AU - Mao, Qianqian
AU - Jiang, Luyang
AU - Wu, Jianhua
AU - Wu, Jiani
AU - Luo, Huiyan
AU - Luo, Peng
AU - Gu, Wenchao
AU - Huang, Na
AU - Zheng, Siting
AU - Li, Shaowei
AU - Lai, Yonghong
AU - Huang, Xiatong
AU - Fang, Yiran
AU - Zhao, Qiongzhi
AU - Zhou, Rui
AU - Sun, Huiying
AU - Zhang, Wei
AU - Bin, Jianping
AU - Liao, Yulin
AU - Yamamoto, Masami
AU - Tsukamoto, Tetsuya
AU - Nomura, Sachiyo
AU - Shi, Min
AU - Liao, Wangjun
N1 - Publisher Copyright:
© 2025 The Author(s). Advanced Science published by Wiley-VCH GmbH.
PY - 2025/7/3
Y1 - 2025/7/3
N2 - The tumor microenvironment (TME) significantly influences cancer prognosis and therapeutic outcomes, yet its composition remains highly heterogeneous, and currently, no highly accessible, high-throughput method exists to define it. To address this complexity, the TMEclassifier, a machine-learning tool that classifies cancers into three distinct subtypes: immune Exclusive (IE), immune Suppressive (IS), and immune Activated (IA), is developed. Bulk RNA sequencing categorizes patient samples by TME subtype, and in vivo mouse model validates TME subtype differences and differential responses to immunotherapy. The IE subtype is marked by high stromal cell abundance, associated with aggressive cancer phenotypes. The IS subtype features myeloid-derived suppressor cell infiltration, intensifying immunosuppression. In contrast, the IA subtype, often linked to EBV/MSI, exhibits robust T-cell presence and improved immunotherapy response. Single-cell RNA sequencing is applied to explore TME cellular heterogeneity, and in vivo experiments demonstrate that targeting IL-1 counteracts immunosuppression of IS subtype and markedly improves its responsiveness to immunotherapy. TMEclassifier predictions are validated in this prospective gastric cancer cohort (TIMES-001) and other diverse cohorts. This classifier could effectively stratify patients, guiding personalized immunotherapeutic strategies to enhance precision and overcome resistance.
AB - The tumor microenvironment (TME) significantly influences cancer prognosis and therapeutic outcomes, yet its composition remains highly heterogeneous, and currently, no highly accessible, high-throughput method exists to define it. To address this complexity, the TMEclassifier, a machine-learning tool that classifies cancers into three distinct subtypes: immune Exclusive (IE), immune Suppressive (IS), and immune Activated (IA), is developed. Bulk RNA sequencing categorizes patient samples by TME subtype, and in vivo mouse model validates TME subtype differences and differential responses to immunotherapy. The IE subtype is marked by high stromal cell abundance, associated with aggressive cancer phenotypes. The IS subtype features myeloid-derived suppressor cell infiltration, intensifying immunosuppression. In contrast, the IA subtype, often linked to EBV/MSI, exhibits robust T-cell presence and improved immunotherapy response. Single-cell RNA sequencing is applied to explore TME cellular heterogeneity, and in vivo experiments demonstrate that targeting IL-1 counteracts immunosuppression of IS subtype and markedly improves its responsiveness to immunotherapy. TMEclassifier predictions are validated in this prospective gastric cancer cohort (TIMES-001) and other diverse cohorts. This classifier could effectively stratify patients, guiding personalized immunotherapeutic strategies to enhance precision and overcome resistance.
KW - IL-1
KW - cancer
KW - immunotherapy
KW - immunotyping
KW - tumor microenvironment
UR - https://www.scopus.com/pages/publications/105006693797
UR - https://www.scopus.com/pages/publications/105006693797#tab=citedBy
U2 - 10.1002/advs.202417593
DO - 10.1002/advs.202417593
M3 - Article
C2 - 40433880
AN - SCOPUS:105006693797
SN - 2198-3844
VL - 12
JO - Advanced Science
JF - Advanced Science
IS - 25
M1 - 2417593
ER -
