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Population-Based Molecular Prognosis of Breast Cancer by Transcriptional Profiling

Authors' Affiliations: ¹ Mary Babb Randolph Cancer Center, Departments of ² Statistics, ³ Medicine and Division of Hematology/Oncology, ⁴ Microbiology, Immunology, and Cell Biology, and ⁵ Community Medicine, West Virginia University; and ⁶ The Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, West Virginia

Requests for reprints: Lan Guo, Mary Babb Randolph Cancer Center/Department of Community Medicine, West Virginia University, 1814 HSS, 1 Medical Center Drive, Morgantown, WV 26506-9300. Phone: 304-293-6455; Fax: 304-293-4667; E-mail: lguo{at}hsc.wvu.edu.

Purpose: The purpose of this study is to predict breast cancer recurrence and metastases and to identify gene signatures indicative of clinicopathologic characteristics using gene expression patterns derived from cDNA microarray.

Experimental Design: Expression profiles of 7,650 genes were investigated on an unselected group of 99 node-negative and node-positive breast cancer patients to identify prognostic gene signature of recurrence and metastases. The identified gene signature was validated on independent 78 patients with primary invasive carcinoma (T₁/T₂ and N₀) and on 58 patients with locally advanced breast cancer (T₃/T₄ and/or N₂). The gene predictors were identified using a combination of random forests and linear discriminant analysis function.

Results: This study identified a new 28-gene signature that achieved highly accurate disease-free survival and overall survival (both at P < 0.001, time-dependent receiver operating characteristic analysis) in individual breast cancer patients. Patients categorized into high-risk, intermediate-risk, and low-risk groups had distinct disease-free survival (P < 0.005, Kaplan-Meier analysis, log-rank test) in three patient cohorts. A strong association (P < 0.05) was identified between risk groups and tumor size, tumor grade, estrogen receptor and progesterone receptor status, and HER2/neu overexpression in the studied cohorts. We also identified 14-gene predictors of nodal status and 9-gene predictors of tumor grade.

Conclusions: This study has established a population-based approach to predicting breast cancer outcomes at the individual level exclusively based on gene expression patterns. The 28-gene recurrence signature has been validated as quantifying the probability of recurrence and metastases in patients with heterogeneous histology and disease stage.

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