Molecular predictors of 3D morphogenesis by breast cancer cell lines in 3D culture

Ju Han, Hang Chang, Orsi Giricz, Genee Y. Lee, Frederick L. Baehner, Joe W. Gray, Mina J. Bissell, Paraic A. Kenny, Bahram Parvin

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

Correlative analysis of molecular markers with phenotypic signatures is the simplest model for hypothesis generation. In this paper, a panel of 24 breast cell lines was grown in 3D culture, their morphology was imaged through phase contrast microscopy, and computational methods were developed to segment and represent each colony at multiple dimensions. Subsequently, subpopulations from these morphological responses were identified through consensus clustering to reveal three clusters of round, grape-like, and stellate phenotypes. In some cases, cell lines with particular pathobiological phenotypes clustered together (e.g., ERBB2 amplified cell lines sharing the same morphometric properties as the grape-like phenotype). Next, associations with molecular features were realized through (i) differential analysis within each morphological cluster, and (ii) regression analysis across the entire panel of cell lines. In both cases, the dominant genes that are predictive of the morphological signatures were identified. Specifically, PPARc has been associated with the invasive stellate morphological phenotype, which corresponds to triple-negative pathobiology. PPARγ has been validated through two supporting biological assays.

Original languageEnglish (US)
Article numbere1000684
JournalPLoS Computational Biology
Volume6
Issue number2
DOIs
StatePublished - Feb 2010

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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