Expressed gene clusters with distinct prognostic properties of primary breast tumors

K Iwao a,b, R Matoba, PhD a, S Noguchi, MD, PhD b and K Kato, MD, PhD a

a Taisho Laboratory of Functional Genomics, Nara Institute of Science and Technology, Ikoma, and b Department of Surgical Oncology, Osaka University Medical School, Osaka, Japan

AIM. The natural progression of breast cancer differs greatly between patients; precise prediction of this disease course will improve the efficacy of therapeutics. We tried to identify expressed genes useful for breast cancer prognosis by gene expression profiling. METOHDS. The expression levels of 2412 genes, derived from 98 cancer and 10 normal samples, were precisely recorded by a high throughput RT-PCR technique, adapter-tagged competitive PCR (ATAC-PCR). ATAC-PCR is an advanced form of quantitative competitive PCR, which is characterized by addition of adapters with different spacer lengths to different cDNA samples. RESULTS. Cluster analysis with selected genes revealed a molecular profile correlating with estrogen receptor levels and the presence of lymph node metastases. Based on this cluster model, 21 genes discriminating estrogen receptor status were selected. Cluster analysis using these genes with original 98 samples and additional 203 samples yielded classification correlating with early recurrence, in addition to estrogen receptor and lymph node metastasis. The classification consisted of three major groups, which was verified by principal component analysis (PCA). Comparison with other diagnostic parameters revealed that this classification system provided the highest risk assessment. CONCLUSION. We have identified 21 genes that can predict prognosis of breast cancer. They are strong candidates for advanced diagnosis as well as potential therapeutic targets.

KEY WORDS: microarray, gene expression profiling, cluster analysis.

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Paper presented at the International Symposium on Predictive Oncology and Intervention Strategies; Paris, France; February 9 - 12, 2002; in the section on Prognostic Markers.