Atlas of clinically distinct cell states and ecosystems across human solid tumors.
Cell
; 184(21): 5482-5496.e28, 2021 10 14.
Article
en En
| MEDLINE
| ID: mdl-34597583
ABSTRACT
Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell-state co-occurrence patterns, we discovered ten clinically distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Microambiente Tumoral
/
Neoplasias
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Cell
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos