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Atlas of clinically distinct cell states and ecosystems across human solid tumors.
Luca, Bogdan A; Steen, Chloé B; Matusiak, Magdalena; Azizi, Armon; Varma, Sushama; Zhu, Chunfang; Przybyl, Joanna; Espín-Pérez, Almudena; Diehn, Maximilian; Alizadeh, Ash A; van de Rijn, Matt; Gentles, Andrew J; Newman, Aaron M.
Afiliação
  • Luca BA; Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
  • Steen CB; Division of Oncology, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
  • Matusiak M; Department of Pathology, Stanford University, Stanford, CA 94305, USA.
  • Azizi A; Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA.
  • Varma S; Department of Pathology, Stanford University, Stanford, CA 94305, USA.
  • Zhu C; Department of Pathology, Stanford University, Stanford, CA 94305, USA.
  • Przybyl J; Department of Pathology, Stanford University, Stanford, CA 94305, USA.
  • Espín-Pérez A; Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA.
  • Diehn M; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA.
  • Alizadeh AA; Division of Oncology, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Division of Hematology, Depart
  • van de Rijn M; Department of Pathology, Stanford University, Stanford, CA 94305, USA.
  • Gentles AJ; Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA. Electronic address:
  • Newman AM; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA. Electronic address: amnewman@stanford
Cell ; 184(21): 5482-5496.e28, 2021 10 14.
Article em 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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Microambiente Tumoral / Neoplasias Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Cell Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Microambiente Tumoral / Neoplasias Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Cell Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos