skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: 3D culture models of normal and malignant breast epithelialcells

Authors:
; ; ;
Publication Date:
Research Org.:
Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, CA (US)
Sponsoring Org.:
USDOE Director. Office of Science. Biological andEnvironmental Research
OSTI Identifier:
927028
Report Number(s):
LBNL-62309
R&D Project: 443180; BnR: KP1104010
DOE Contract Number:
DE-AC02-05CH11231
Resource Type:
Journal Article
Resource Relation:
Journal Name: Nature Methods; Journal Volume: 4; Related Information: Journal Publication Date: 2007
Country of Publication:
United States
Language:
English
Subject:
60

Citation Formats

Lee, Genee Y., Kenny, Paraic A., Lee, Eva H., and Bissell, Mina J. 3D culture models of normal and malignant breast epithelialcells. United States: N. p., 2006. Web.
Lee, Genee Y., Kenny, Paraic A., Lee, Eva H., & Bissell, Mina J. 3D culture models of normal and malignant breast epithelialcells. United States.
Lee, Genee Y., Kenny, Paraic A., Lee, Eva H., and Bissell, Mina J. Fri . "3D culture models of normal and malignant breast epithelialcells". United States. doi:. https://www.osti.gov/servlets/purl/927028.
@article{osti_927028,
title = {3D culture models of normal and malignant breast epithelialcells},
author = {Lee, Genee Y. and Kenny, Paraic A. and Lee, Eva H. and Bissell, Mina J.},
abstractNote = {},
doi = {},
journal = {Nature Methods},
number = ,
volume = 4,
place = {United States},
year = {Fri Dec 29 00:00:00 EST 2006},
month = {Fri Dec 29 00:00:00 EST 2006}
}
  • 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, associationsmore » 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, PPAR? has been associated with the invasive stellate morphological phenotype, which corresponds to triple-negative pathobiology. PPAR? has been validated through two supporting biological assays.« less
  • Described are cytological techniques to differentiate malignant and normal cells in culture. Emphasis is placed upon cell function and gene expression for determinative procedures. (DLS)
  • No abstract prepared.
  • One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasetsmore » having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic value for both ER-positive and ER-negative breast cancer. The signature was selected using a novel biological approach and hence holds promise to represent the key biological processes of breast cancer.« less
  • No abstract prepared.