Faculty of Medicine, The University of Queensland, Herston, QLD, Australia.
QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.
Breast Cancer Res Treat. 2018 Jan;167(1):289-301. doi: 10.1007/s10549-017-4496-x. Epub 2017 Sep 9.
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.
细胞系在乳腺癌研究中是非常有用的工具。它们的主要优点包括对实验变量的高度控制和可重复性。然而，这些优点必须与在体外模拟如此复杂疾病的局限性相平衡。现在，为给定的实验选择细胞系需要对培养皿中分子和表型背景有基本的了解。
我们对 36 种广泛使用的乳腺癌细胞系进行了多维分析，这些细胞系在标准化条件下培养。使用流式细胞术和数字免疫组织化学比较了 14 种与内在分子特征和分化状态相关的经典乳腺癌生物标志物的表达：EpCAM、CD24、CD49f、CD44、ER、AR、HER2、EGFR、E-钙黏蛋白、p53、波形蛋白和细胞角蛋白 5、8/18 和 19。
这种逐个细胞的分析揭示了单个细胞系培养物中的显著异质性，如果分析细胞匀浆，这种异质性会被掩盖，尤其是在三阴性细胞系中。高水平的 p53 蛋白，但不是 RNA，与体细胞突变有关（p=0.008）。我们还使用 nanoString PanCancer 通路面板（代表 13 个经典癌症通路的 730 个转录本）确定了新的亚组。无监督聚类确定了五个亚组：luminal/HER2、永生化（“正常”）、claudin-low 和两个基底亚组，主要通过 TGF-β和 PI3 激酶通路基因的基线表达来区分。
这些特征与其他已发表的基因型和表型信息在用户友好的参考表中进行了比较，以帮助指导选择最适合体外和体内研究的模型，并作为分类新的患者来源的癌细胞系和异种移植物的框架。
