title: Molecular Portrait of Breast-Cancer-Derived Cell Lines Reveals Poor Similarity with Tumors.
abstract: 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.
authors.affiliation: Department of Immunotechnology, CREATE Health, Lund University, House 406, Medicon Village, 223 81 Lund, Sweden.
authors.forename: Paolo
authors.initials: P
authors.lastname: Cifani
authors.affiliation: Department of Immunotechnology, CREATE Health, Lund University, House 406, Medicon Village, 223 81 Lund, Sweden.
authors.forename: Ufuk
authors.initials: U
authors.lastname: Kirik
authors.affiliation: Department of Immunotechnology, CREATE Health, Lund University, House 406, Medicon Village, 223 81 Lund, Sweden.
authors.forename: Sofia
authors.initials: S
authors.lastname: Waldemarson
authors.affiliation: Department of Immunotechnology, CREATE Health, Lund University, House 406, Medicon Village, 223 81 Lund, Sweden.
authors.forename: Peter
authors.initials: P
authors.lastname: James
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology*; D045744:Cell Line, Tumor; D005260:Female; D006801:Humans
keywords: RNA−protein correlation; SILAC; biomarkers; breast cancer; cell lines; classification; sample fractionation; transcriptomics