Dataset features


Application: Gene expression microarray analysis
Number of samples: 12
Release date: Mar 3 2016
Last update date: Aug 9 2018
Access: Public
Diseases: Breast Neoplasms, Neoplasms
Dataset link A Preclinical Model for ERα-Positive Breast Cancer Points to the Epithelial Microenvironment as Determinant of Luminal Phenotype and Hormone Response [BT20 & HCC1806]

Experimental Protocol

Eight- to twelve-week-old female SCID Beige mice (Charles River) were injected with 5x10e5 BT20-GFP/luc2 cells (n=3) or 5x10e5 HCC1806-GFP/luc2 cells (n=3) either into the mammary fat pad or 2x10e5 BT20-GFP/luc2 cells (n=3) or 2x10e5 HCC1806-GFP/luc2 cells intraductally (n=3). Xenografted BT20 and HCC1806 basal breast cancer cells were sorted by FACS based on GFP expression; total RNA was extracted using Trizol Reagent (Invitrogen), purified with the miRNeasy Mini Kit (Qiagen), quantity and quality were assessed by NanoDrop®ND-1000 spectrophotometer and RNA 6000 NanoChips with the Agilent 2100 Bioanalyzer (Agilent, Palo Alto, USA). Only samples with RIN score >7.0 were included. For each sample, 300 ng of total RNA were amplified using the message amp II enhanced kit (AM1791, Ambion). 12.5 μg of biotin-labelled cRNA were chemically fragmented. Affymetrix GeneChip Human Genome U133A 2.0 Arrays (Affymetrix, Santa Clara, CA, USA) were hybridized with 11μg of fragmented target, at 45°C for 17 hours, washed and stained according to Affymetrix GeneChip® Expression Analysis Manual (Fluidics protocol FS450_0007). Arrays were scanned using the GeneChip® Scanner 3000 7G (Affymetrix) and raw data was extracted from the scanned images and analyzed with the Affymetrix Power Tools software package (Affymetrix). All statistical analyses were performed using R and Bioconductor packages ( Hybridization quality was assessed using the Expression Console software (Affymetrix). Normalized expression signals were calculated from Affymetrix CEL files using RMA. Differential hybridized features were identified using Bioconductor package “limma” that implements linear models for microarray data (Smyth, 2004). P values were adjusted for multiple testing with Benjamini and Hochberg’s method to control false discovery rate (FDR) (Benjamini et al., 2001). Probe sets showing ≥2-fold change and a FDR ≤0.05 were considered significant.










George Sflomos
George A Sflomos