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Breast Cancer Research and Treatment
Published in: Breast Cancer Research and Treatment 1/2018

Open Access 01-01-2018 | Brief Report

Multidimensional phenotyping of breast cancer cell lines to guide preclinical research

Authors: Jodi M. Saunus, Chanel E. Smart, Jamie R. Kutasovic, Rebecca L. Johnston, Priyakshi Kalita-de Croft, Mariska Miranda, Esdy N. Rozali, Ana Cristina Vargas, Lynne E. Reid, Eva Lorsy, Sibylle Cocciardi, Tatjana Seidens, Amy E. McCart Reed, Andrew J. Dalley, Leesa F. Wockner, Julie Johnson, Debina Sarkar, Marjan E. Askarian-Amiri, Peter T. Simpson, Kum Kum Khanna, Georgia Chenevix-Trench, Fares Al-Ejeh, Sunil R. Lakhani

Published in: Breast Cancer Research and Treatment | Issue 1/2018

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Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusion

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.
Appendix
Available only for authorised users
Footnotes
1
Cell line nomenclature has been simplified in this paper for ease of analysis, by removing dashes and spaces (e.g. MDA-MB-231 to MDAMB231).
 
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Metadata
Title
Multidimensional phenotyping of breast cancer cell lines to guide preclinical research
Authors
Jodi M. Saunus
Chanel E. Smart
Jamie R. Kutasovic
Rebecca L. Johnston
Priyakshi Kalita-de Croft
Mariska Miranda
Esdy N. Rozali
Ana Cristina Vargas
Lynne E. Reid
Eva Lorsy
Sibylle Cocciardi
Tatjana Seidens
Amy E. McCart Reed
Andrew J. Dalley
Leesa F. Wockner
Julie Johnson
Debina Sarkar
Marjan E. Askarian-Amiri
Peter T. Simpson
Kum Kum Khanna
Georgia Chenevix-Trench
Fares Al-Ejeh
Sunil R. Lakhani
Publication date
01-01-2018
Publisher
Springer US
Published in
Breast Cancer Research and Treatment / Issue 1/2018
Print ISSN: 0167-6806
Electronic ISSN: 1573-7217
DOI
https://doi.org/10.1007/s10549-017-4496-x

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