Genomic prediction of locoregional recurrence after mastectomy in breast cancer.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Cheng, SH; Horng, C-F; West, M; Huang, E; Pittman, J; Tsou, M-H; Dressman, H; Chen, C-M; Tsai, SY; Jian, JJ; Liu, M-C; Nevins, JR; Huang, AT

Published Date

  • October 1, 2006

Published In

Volume / Issue

  • 24 / 28

Start / End Page

  • 4594 - 4602

PubMed ID

  • 17008701

Electronic International Standard Serial Number (EISSN)

  • 1527-7755

Digital Object Identifier (DOI)

  • 10.1200/JCO.2005.02.5676


  • eng

Conference Location

  • United States