作者
Yulan Deng,Liang Xia,Jian Zhang,Senyi Deng,Mengyao Wang,Shiyou Wei,Kaixiu Li,Hongjin Lai,Yunhao Yang,Yuquan Bai,Yongcheng Liu,Lanzhi Luo,Zhenyu Yang,Yaohui Chen,Ran Kang,Fanyi Gan,Qiang Pu,Jiandong Mei,Lin Ma,Lin Feng,Chenglin Guo,Hu Liao,Yunke Zhu,Zheng Liu,Chengwu Liu,Yang Hu,Yong Yuan,Zhengyu Zha,Gang Yuan,Gao Zhang,Luonan Chen,Qing Cheng,Shensi Shen,Lunxu Liu
摘要
Summary
Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8+ T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8+ T cells, driven by specific stromal cells such as CTHCR1+ fibroblasts. This study also highlights EGFR(L858R) neoantigens in GGO samples, suggesting potential CD8+ T cell activation. Our findings offer valuable insights into lung adenocarcinoma heterogeneity, suggesting avenues for targeted therapies in early-stage disease.