The aim of this project was to use gene expression profiling to identify patients whose tumors have a low malignant potential, making adjuvant therapy unnecessary and potentially harmful, and to identify patients in need of more effective adjuvant therapies. Furthermore, authors wanted to show that the expression profile worked irrespective of primary adjuvant therapy or not and provided independent information to the established clinical factors.

The hypothesis of this study was that gene expression signatures can discriminate between breast cancer patients that benefit from adjuvant therapy and patients that fail to benefit from therapy.

This dataset consists of breast cancer patients that were operated at the Karolinska Hospital from 1 January 1994 to 31 December 1996 (n=524), identified from the population-based Stockholm–Gotland breast cancer registry established in 1976. Exclusion criteria were inavailability of frozen tumours, insufficient quality of tumour material, neoadjuvant treatment, in situ cancer, stage IV disease and active refusion to participate, leading to a final 159 patients for analysis. Mean age at breast cancer diagnosis was 58. The ethical committee at the Karolinska Hospital approved this microarray expression project.

RNA extraction was performed according to the RNeasy mini protocol (Qiagen, Hilden, Germany). In brief, a portion of the deep frozen tumor was cut into minute pieces and transferred into test tubes (maximum 40 mg/tube) with RLT buffer (RNeasy lysis Buffer, Qiagen, Hilden, Germany), followed by homogenization for around 30–40 s. Proteinase K was then added and the samples were treated for 10 min at 55°C. Total RNA was then isolated using Qiagen's microspin technology. DNase was also added to some samples to further increase the RNA quality. The quality of the RNA was assessed by measuring the 28S:18S ribosomal RNA ratio using an Agilent 2100 bioanalyzer (Agilent Technologies, Rockville, MD, USA). All samples with RNA of high quality were then stored at -70°C until microarray analyses.

Preparation of in vitro transcription products and oligonucleotide array hybridization and scanning were performed according to the protocol of Affymetrix (Santa Clara, CA, USA). In brief, the amount of starting total RNA for each probe preparation varied between 2 and 5 ?g. The in vitro transcription reactions were performed in batches to generate biotinylated cRNA targets, which were subsequently chemically fragmented at 95°C for 35 min. Fragmented and biotinylated cRNA (10 ?g) was hybridized at 45°C for 16 hours to Affymetrix high-density oligonucleotide array human HG-U133 set chips. The arrays were washed, and were then stained with streptavidin–phycoerythrin (final concentration, 10 ?g/ml). The array was then scanned according to the manufacturer's instructions (Affymetrix Genechip® Technical Manual, 2001; Affymetrix). The scanned images were inspected for the presence of obvious defects (artifacts or scratches) on the array. In the case of visible microarray artifacts, the sample was rehybridized and rescanned on new chips using the same fragmented probe. The raw expression data were normalized using the global mean method (Ploner et al).

A statistical data filter was applied to reduce noise and to obtain a useful and relevant probe set to identify markers that were highly correlated to clinical parameters. This led to 6573 final probe sets for analysis, consisting of 3393 probe sets from U133A and 3180 probe sets from U133B. All analyses were performed using natural-log-transformed expression values.

Pawitan, Yudi, Judith Bjöhle, Lukas Amler, Anna-Lena Borg, Suzanne Egyhazi, Per Hall, Xia Han, et al. “Gene Expression Profiling Spares Early Breast Cancer Patients from Adjuvant Therapy: Derived and Validated in Two Population-Based Cohorts.” Breast Cancer Research: BCR 7, no. 6 (2005): R953–64. doi:10.1186/bcr1325.

Ploner, Alexander, Lance D. Miller, Per Hall, Jonas Bergh, and Yudi Pawitan. “Correlation Test to Assess Low-Level Processing of High-Density Oligonucleotide Microarray Data.” BMC Bioinformatics 6 (2005): 80. doi:10.1186/1471-2105-6-80.