Multidimensional phenotyping of breast cancer cell lines to guide preclinical research

Saunus, Jodi M., Smart, Chanel E., Kutasovic, Jamie R., Johnston, Rebecca L., Kalita-de Croft, Priyakshi, Miranda, Mariska, Rozali, Esdy N., Vargas, Ana Cristina, Reid, Lynne E., Lorsy, Eva, Cocciardi, Sibylle, Seidens, Tatjana, McCart Reed, Amy E., Dalley, Andrew J., Wockner, Leesa F., Johnson, Julie, Sarkar, Debina, Askarian-Amiri, Marjan E., Simpson, Peter T., Khanna, Kum Kum, Chenevix-Trench, Georgia, Al-Ejeh, Fares and Lakhani, Sunil R. (2017) Multidimensional phenotyping of breast cancer cell lines to guide preclinical research. Breast Cancer Research and Treatment, 167 1: 1-13. doi:10.1007/s10549-017-4496-x

Author Saunus, Jodi M.
Smart, Chanel E.
Kutasovic, Jamie R.
Johnston, Rebecca L.
Kalita-de Croft, Priyakshi
Miranda, Mariska
Rozali, Esdy N.
Vargas, Ana Cristina
Reid, Lynne E.
Lorsy, Eva
Cocciardi, Sibylle
Seidens, Tatjana
McCart Reed, Amy E.
Dalley, Andrew J.
Wockner, Leesa F.
Johnson, Julie
Sarkar, Debina
Askarian-Amiri, Marjan E.
Simpson, Peter T.
Khanna, Kum Kum
Chenevix-Trench, Georgia
Al-Ejeh, Fares
Lakhani, Sunil R.
Title Multidimensional phenotyping of breast cancer cell lines to guide preclinical research
Journal name Breast Cancer Research and Treatment   Check publisher's open access policy
ISSN 0167-6806
Publication date 2017-09-09
Year available 2018
Sub-type Article (original research)
DOI 10.1007/s10549-017-4496-x
Open Access Status DOI
Volume 167
Issue 1
Start page 1
End page 13
Total pages 13
Place of publication New York, NY, United States
Publisher Springer New York LLC
Language eng
Abstract Cell lines are extremely useful tools in breast cancer research. Their key benefits include a high degree of control over experimental variables and reproducibility. However, the advantages must be balanced against the limitations of modelling such a complex disease in vitro. Informed selection of cell line(s) for a given experiment now requires essential knowledge about molecular and phenotypic context in the culture dish.

We performed multidimensional profiling of 36 widely used breast cancer cell lines that were cultured under standardised conditions. Flow cytometry and digital immunohistochemistry were used to compare the expression of 14 classical breast cancer biomarkers related to intrinsic molecular profiles and differentiation states: EpCAM, CD24, CD49f, CD44, ER, AR, HER2, EGFR, E-cadherin, p53, vimentin, and cytokeratins 5, 8/18 and 19.

This cell-by-cell analysis revealed striking heterogeneity within cultures of individual lines that would be otherwise obscured by analysing cell homogenates, particularly amongst the triple-negative lines. High levels of p53 protein, but not RNA, were associated with somatic mutations (p = 0.008). We also identified new subgroups using the nanoString PanCancer Pathways panel (730 transcripts representing 13 canonical cancer pathways). Unsupervised clustering identified five groups: luminal/HER2, immortalised ('normal'), claudin-low and two basal clusters, distinguished mostly by baseline expression of TGF-beta and PI3-kinase pathway genes.

These features are compared with other published genotype and phenotype information in a user-friendly reference table to help guide selection of the most appropriate models for in vitro and in vivo studies, and as a framework for classifying new patient-derived cancer cell lines and xenografts.
Keyword Breast cancer cell lines
Digital immunohistochemistry
In vitro model
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID APP1017028
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
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Created: Wed, 13 Sep 2017, 08:49:42 EST by Priyakshi Kalita-de Croft on behalf of UQ Centre for Clinical Research