Atlas of clinically distinct cell states and ecosystems across human solid tumors

Cell. 2021 Oct 14;184(21):5482-5496.e28. doi: 10.1016/j.cell.2021.09.014. Epub 2021 Sep 30.

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.

Keywords: CIBERSORTx; EcoTyper; cancer genomics; cell states; cellular communities; ecosystems; ecotypes; expression deconvolution; tumor immunology; tumor microenvironment.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cell Survival
  • Gene Expression Regulation, Neoplastic
  • Humans
  • Immunotherapy
  • Inflammation / pathology
  • Ligands
  • Neoplasms / genetics
  • Neoplasms / pathology*
  • Phenotype
  • Prognosis
  • Transcription, Genetic
  • Tumor Microenvironment*

Substances

  • Ligands