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Single-cell morphology encodes metastatic potential.
Wu, Pei-Hsun; Gilkes, Daniele M; Phillip, Jude M; Narkar, Akshay; Cheng, Thomas Wen-Tao; Marchand, Jorge; Lee, Meng-Horng; Li, Rong; Wirtz, Denis.
Afiliação
  • Wu PH; Johns Hopkins Physical Sciences-Oncology Center, The Johns Hopkins University, Baltimore, MD 21218, USA.
  • Gilkes DM; Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
  • Phillip JM; Johns Hopkins Physical Sciences-Oncology Center, The Johns Hopkins University, Baltimore, MD 21218, USA.
  • Narkar A; Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.
  • Cheng TW; Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
  • Marchand J; Department of Cell Biology, The Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.
  • Lee MH; Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
  • Li R; Division of Vascular and Endovascular Surgery, Boston Medical Center, Boston University School of Medicine, Boston, MA 02118, USA.
  • Wirtz D; Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
Sci Adv ; 6(4): eaaw6938, 2020 01.
Article em En | MEDLINE | ID: mdl-32010778
ABSTRACT
A central goal of precision medicine is to predict disease outcomes and design treatments based on multidimensional information from afflicted cells and tissues. Cell morphology is an emergent readout of the molecular underpinnings of a cell's functions and, thus, can be used as a method to define the functional state of an individual cell. We measured 216 features derived from cell and nucleus morphology for more than 30,000 breast cancer cells. We find that single cell-derived clones (SCCs) established from the same parental cells exhibit distinct and heritable morphological traits associated with genomic (ploidy) and transcriptomic phenotypes. Using unsupervised clustering analysis, we find that the morphological classes of SCCs predict distinct tumorigenic and metastatic potentials in vivo using multiple mouse models of breast cancer. These findings lay the groundwork for using quantitative morpho-profiling in vitro as a potentially convenient and economical method for phenotyping function in cancer in vivo.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Análise de Célula Única Tipo de estudo: Etiology_studies / Prognostic_studies Limite: Animals / Female / Humans Idioma: En Revista: Sci Adv Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Análise de Célula Única Tipo de estudo: Etiology_studies / Prognostic_studies Limite: Animals / Female / Humans Idioma: En Revista: Sci Adv Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos