A high percentage of potential oncology drugs fail in clinical trials, partly because preclinical models used to test them are inadequate. Breast cancer is the leading cause of cancer-related death among women worldwide but we lack appropriate in vivo models for the ER+ subtypes, which represent more than 75% of all cases. We address these issues by xenografting tumor cells to their site of origin, the milk ducts. All ER+ cell lines and patient-derived xenografts grow mimicking their clinical counterparts. Disease progresses with invasion and metastasis, which become amenable to study. The action of hormones, important in breast carcinogenesis, can now be studied in a relevant context. Importantly, these open opportunities for development and evaluation of therapies.
Overall design: Eight- to twelve-week-old female SCID Beige mice (Charles River) were injected with 2x10e5 MCF7-DsRed/luc2 cells (n=3) either into the mammary fat pad or 5x10e4 MCF7-DsRed/luc2 cells intraductally (n=3). Xenografted MCF7 were FACS sorting based on DsRed 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 (http://www.Bioconductor.org). 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.
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