Project description:Total RNA from 46 breast cell lines was labelled using the Illumina TotalPrep RNA Amplification kit (Ambion) following manufacturer's instructions. 1.5 µg of biotin-labelled cRNA were used for each hybridisation on Sentrix Human-6 v1 BeadChips (Illumina, San Diego, CA) following manufacturer's protocol.
Project description:In an experimental model of tumor dormancy, heat shock protein 27 (HSP27) was up-regulated in angiogenic human breast cancer cells when compared with non-angiogenic cells. Stable down-regulation of HSP27 in angiogenic tumor cells was followed by long-term tumor dormancy in vivo and associated with reduced intra-tumoral endothelial cell proliferation, decreased secretion of VEGF and bFGF from tumor cells, and increased expression of thrombospondin-1. Phosphorylation of the transcription factor STAT3 and nuclear expression of NFκB were reduced following suppression of HSP27. In contrast, tumor cell proliferation and apoptosis were not affected. By clinical validation, high HSP27 expression was associated with markers of aggressive tumors and decreased survival in breast cancer and melanoma patients. Our present findings suggest a link between HSP27 and dormancy through tumor-vascular interactions. Targeting HSP27, a multifunctional cytoprotective protein, might offer a novel strategy in cancer treatment.
Project description:Transcriptional profiling was conducted on RNA from 23 breast cancer cell lines to identify genes whose expression level correlates with sensitivity of particular drug Experiment Overall Design: Baseline gene expression profiling was performed using 23 breast cancer cell lines to identify genomic signatures highly correlated with in vitro sensitivity to a particular drug
Project description:We assessed alternative splicing in breast cancer through global profiling of transcriptomes of basal and luminal subtype cell lines using Affymetrix Human Junction Array.
Project description:We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.