Genome-wide measures of gene expression can identify patterns of gene activity that subclassify tumours and might provide a better means than is currently available for individual risk assessment in patients with lymph-node-negative breast cancer. This study aimed to develop a gene-expression-based algorithm and to use it to provide quantitative predictions on disease outcome for patients with lymph-node-negative breast cancer.

The hypothesis of this study was that a gene expression profile could accurately predict distant tumour recurrence in patients with lymph-node-negative breast cancer.

The study cohort consists of patients with lymph-node-negative breast cancer who were treated during 1980–95, but who did not receive systemic neoadjuvant or adjuvant therapy. Frozen tumour samples from were selected from the tumour bank at the Erasmus Medical Center (Rotterdam, Netherlands).

Tumour samples were submitted to the reference laboratory from 25 regional hospitals for measurements of steroid-hormone receptors. Guidelines for primary treatment were similar for all hospitals. Selection of tumours aimed to avoid bias. On the assumption of a relapse rate of 25–30% in 5 years, and a substantial loss of tumours for quality-control reasons, 436 samples of invasive tumours were processed. Patients with poor, intermediate, and good clinical outcome were included. Samples were rejected on the basis of insufficient tumour content (53), poor RNA quality (77), or poor chip quality (20); thus, 286 samples were eligible for further analysis. The study was approved by institutional medical ethics committee (number 02·953).

The median age of the patients at surgery was 52 years (range 26–83). 219 had undergone breast-conserving surgery and 67 modified radical mastectomy. Radiotherapy was given to 248 patients (87%) according to our institutional protocol. The proportions of patients who underwent breast-conserving therapy and radiotherapy are normal for lymph-node-negative disease. Patients were included irrespective of radiotherapy status.

Lymph-node negativity was based on pathological examination by regional pathologists as described by Foekens et al. All 286 tumour samples were confirmed to have sufficient (>70%) tumour and uniform involvement of tumour in 5 µm frozen sections stained with haematoxylin and eosin.

Postoperative follow-up involved examinations every 3 months for 2 years, every 6 months for years 3–5, and every 12 months from year 5. The date of diagnosis of metastasis was defined as that at confirmation of metastasis after symptoms reported by the patient, detection of clinical signs, or at regular follow-up.

Total RNA was isolated from 20–40 cryostat sections of 30 µm thickness (50–100 mg) with RNAzol B (Campro Scientific, Veenendaal, Netherlands). Biotinylated targets were prepared by published methods (Affymetrix, Santa Clara, CA, USA, Lipshutz et al) and hybridised to the Affymetrix oligonucleotide microarray U133a GeneChip. Arrays were scanned by standard Affymetrix protocols. Each probe set was treated as a separate gene. Expression values were calculated by use of Affymetrix GeneChip analysis software MAS 5.0. Chips with average intensity of less than 40 or background signal of more than 100 were rejected. For chip normalisation, probe sets were scaled to a target intensity of 600, and scale mask files were not selected.

Wang, Yixin, Jan G. M. Klijn, Yi Zhang, Anieta M. Sieuwerts, Maxime P. Look, Fei Yang, Dmitri Talantov, et al. “Gene-Expression Profiles to Predict Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer.” Lancet (London, England) 365, no. 9460 (February 19, 2005): 671–79. doi:10.1016/S0140-6736(05)17947-1.

Foekens, J. A., H. Portengen, W. L. van Putten, A. M. Trapman, J. C. Reubi, J. Alexieva-Figusch, and J. G. Klijn. “Prognostic Value of Receptors for Insulin-like Growth Factor 1, Somatostatin, and Epidermal Growth Factor in Human Breast Cancer.” Cancer Research 49, no. 24 Pt 1 (December 15, 1989): 7002–9.

Lipshutz, R. J., S. P. Fodor, T. R. Gingeras, and D. J. Lockhart. “High Density Synthetic Oligonucleotide Arrays.” Nature Genetics 21, no. 1 Suppl (January 1999): 20–24. doi:10.1038/4447.