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Genomic Prediction of Locoregional Recurrence After Mastectomy in Breast Cancer

Skye H. Cheng, Cheng-Fang Horng, Mike West, Erich Huang, Jennifer Pittman, Mei-Hua Tsou, Holly Dressman, Chii-Ming Chen, Stella Y. Tsai, James J. Jian, Mei-Chin Liu, Joseph R. Nevins, Andrew T. Huang

From the Departments of Radiation Oncology, Research, Laboratory and Pathology, Surgery and Medical Oncology, Koo Foundation Sun Yat-Sen Cancer Center, Taipei, Taiwan; Departments of Radiation Oncology, Surgery, Medicine, and Biostatistics and Bioinformatics, Duke University Medical Center; and the Institute of Statistics and Decision Sciences, and the Institute for Genome Sciences and Policy, Duke University, Durham, NC

Address reprint requests to Skye H. Cheng, MD, Department of Radiation Oncology, Koo Foundation Sun Yat-Sen Cancer Center, No. 125, Lih-Der Road, Pei-Tou District, Taipei, Taiwan; e-mail: skye{at}mail.kfcc.org.tw

PURPOSE: This study aims to explore gene expression profiles that are associated with locoregional (LR) recurrence in breast cancer after mastectomy.

PATIENTS AND METHODS: A total of 94 breast cancer patients who underwent mastectomy between 1990 and 2001 and had DNA microarray study on the primary tumor tissues were chosen for this study. Eligible patient should have no evidence of LR recurrence without postmastectomy radiotherapy (PMRT) after a minimum of 3-year follow-up (n = 67) and any LR recurrence (n = 27). They were randomly split into training and validation sets. Statistical classification tree analysis and proportional hazards models were developed to identify and validate gene expression profiles that relate to LR recurrence.

RESULTS: Our study demonstrates two sets of gene expression profiles (one with 258 genes and the other 34 genes) to be of predictive value with respect to LR recurrence. The overall accuracy of the prediction tree model in validation sets is estimated 75% to 78%. Of patients in validation data set, the 3-year LR control rate with predictive index more than 0.8 derived from 34-gene prediction models is 91%, and predictive index 0.8 or less is 40% (P = .008). Multivariate analysis of all patients reveals that estrogen receptor and genomic predictive index are independent prognostic factors that affect LR control.

CONCLUSION: Using gene expression profiles to develop prediction tree models effectively identifies breast cancer patients who are at higher risk for LR recurrence. This gene expression–based predictive index can be used to select patients for PMRT.

Supported by Synpac; the Koo Foundation Sun Yat-Sen Cancer Center Research Fund; and by National Science Foundation (US) Grants No. NSF DMS-0102227 and 0112340.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

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