Molecular Portrait of Breast-Cancer-Derived Cell Lines Reveals Poor Similarity with Tumors
datasetposted on 02.07.2015 by Paolo Cifani, Ufuk Kirik, Sofia Waldemarson, Peter James
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Breast-cancer-derived cell lines are an important sample source for cancer proteomics and can be classified on the basis of transcriptomic analysis into subgroups corresponding to the molecular subtypes observed in mammary tumors. This study describes a tridimensional fractionation method that allows high sequence coverage and proteome-wide estimation of protein expression levels. This workflow has been used to conduct an in-depth quantitative proteomic survey of five breast cancer cell lines matching all major cancer subgroups and shows that despite their different classification, these cell lines display a very high level of similarity. A proteome-wide comparison with the RNA levels observed in the same samples showed very little to no correlation. Finally, we demonstrate that the proteomes of in vitro models of breast cancer display surprisingly little overlap with those of clinical samples.