
Single‐Cell RNA‐seq Reveals a Developmental Hierarchy Super‐Imposed Over Subclonal Evolution in the Cellular Ecosystem of Prostate Cancer
Author(s) -
Ge Guangzhe,
Han Yang,
Zhang Jianye,
Li Xinxin,
Liu Xiaodan,
Gong Yanqing,
Lei Zhentao,
Wang Jie,
Zhu Weijie,
Xu Yangyang,
Peng Yiji,
Deng Jianhua,
Zhang Bao,
Li Xuesong,
Zhou Liqun,
He Huiying,
Ci Weimin
Publication year - 2022
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202105530
Subject(s) - biology , somatic evolution in cancer , prostate cancer , tumor progression , genetic heterogeneity , cell , genetics , cancer research , cancer , rna , computational biology , chromoplexy , stem cell , transcriptional regulation , phenotype , transcription factor , gene , pca3
Prostate cancer (PCa) is a complex disease. An ongoing accumulation of mutations results in increased genetic diversity, with the tumor acquiring distinct subclones. However, non‐genetic intra‐tumoral heterogeneity, the cellular differentiation state and the interplay between subclonal evolution and transcriptional heterogeneity are poorly understood. Here, the authors perform single‐cell RNA sequencing from 14 untreated PCa patients. They create an extensive cell atlas of the PCa patients and mapped developmental states onto tumor subclonal evolution. They identify distinct subclones across PCa patients and then stratify tumor cells into four transcriptional subtypes, EMT‐like (subtype 0), luminal A‐like (subtype 1), luminal B/C‐like (subtype 2), and basal‐like (subtype 3). These subtypes are hierarchically organized into stem cell‐like and differentiated status. Strikingly, multiple subclones within a single primary tumor present with distinct combinations of preferential subtypes. In addition, subclones show different communication strengths with other cell types within the tumor ecosystem, which may modulate the distinct transcriptional subtypes of the subclones. Notably, by integrating TCGA data, they discover that both tumor cell transcriptional heterogeneity and cellular ecosystem diversity correlate with features of a poor prognosis. Collectively, their study provides the analysis of subclonal and transcriptional heterogeneity and its implication for patient prognosis.