Epithelial-to-Pericyte Transition in Cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Lu J
PROVIDER: S-EPMC5532613 | biostudies-other | 2017 Jul
REPOSITORIES: biostudies-other
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Project description:Epithelial-mesenchymal transition (EMT), a switch of polarized epithelial cells to a migratory, fibroblastoid phenotype, is considered a key process driving tumor cell invasiveness and metastasis. Using breast cancer cell lines as a model system, we sought to discover gene-expression signatures of EMT with clinical and mechanistic relevance. A supervised comparison of epithelial and mesenchymal breast cancer lines defined a 200-gene EMT signature that was prognostic across multiple breast cancer cohorts. Immunostaining of LYN, a top-ranked EMT signature gene and Src-family tyrosine kinase, was associated with significantly shorter overall survival (P=0.02), and correlated with the basal-like (“triple-negative”) phenotype. In mesenchymal breast cancer lines, RNAi-mediated knockdown of LYN inhibited cell migration and invasion, but not proliferation. Dasatinib, a dual-specificity tyrosine kinase inhibitor, also blocked invasion (but not proliferation) at nanomolar concentrations that inhibit LYN kinase activity, suggesting that LYN is a likely target and invasion a relevant endpoint for dasatinib therapy. Our findings define a prognostically-relevant EMT signature in breast cancer, and identify LYN as a mediator of invasion and possible new therapeutic target (and theranostic marker for dasatinib response), with particular relevance to clinically-aggressive basal-like breast cancer. Cell Line: cell line(epithelial-like/fibroblast-like/normal breast fibroblasts) Keywords: Logical Set Set of arrays organized by shared biological context, such as organism, tumors types, processes, etc. HEEBO oligonucleotide microarrays from the Stanford Functional Genomics Facility were used to perform gene expression profiling of 20 human breast cell lines, in comparison to a universal RNA reference. Expression data were analyzed by Significance Analysis of Microarrays to identify a 200-gene signature characteristic of EMT.
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