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. 2011 May;13(3):297-304.
doi: 10.1016/j.jmoldx.2010.12.003. Epub 2011 Mar 31.

Design and Multiseries Validation of a Web-Based Gene Expression Assay for Predicting Breast Cancer Recurrence and Patient Survival

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Free PMC article

Design and Multiseries Validation of a Web-Based Gene Expression Assay for Predicting Breast Cancer Recurrence and Patient Survival

Ryan K Van Laar. J Mol Diagn. .
Free PMC article

Abstract

Gene expression analysis is a valuable tool for determining the risk of disease recurrence and overall survival of an individual patient with breast cancer. The purpose of this study was to create and validate a robust prognostic algorithm and implement it within an online analysis environment. Genomic and clinical data from 477 clinically diverse patients with breast cancer were analyzed with Cox regression models to identify genes associated with outcome, independent of standard prognostic factors. Percentile-ranked expression data were used to train a "metagene" algorithm to stratify patients as having a high or low risk of recurrence. The classifier was applied to 1016 patients from five independent series. The 200-gene algorithm stratifies patients into risk groups with statistically and clinically significant differences in recurrence-free and overall survival. Multivariate analysis revealed the classifier to be the strongest predictor of outcome in each validation series. In untreated node-negative patients, 88% sensitivity and 44% specificity for 10-year recurrence-free survival was observed, with positive and negative predictive values of 32% and 92%, respectively. High-risk patients appear to significantly benefit from systemic adjuvant therapy. A 200-gene prognosis signature has been developed and validated using genomic and clinical data representing a range of breast cancer clinicopathological subtypes. It is a strong independent predictor of patient outcome and is available for research use.

Figures

Figure 1
Figure 1
Visualization of the 200-gene signature in the training series of patients with breast cancer. Patients are represented horizontally, and genes are represented vertically. Rows are ordered by increasing prognostic index (range, –2 to 2), and those individuals experiencing disease relapse within 10 years are indicated with a black bar adjacent to the gene-expression matrix. Patients without disease recurrence but less than a 3-year follow-up are excluded. Gene expression: red, up-regulated; green, down-regulated; and black, no change or absent.
Figure 2
Figure 2
Kaplan-Meier analysis of risk-group–stratified training and validation series. P values are from log-rank testing of the high- versus the low-risk group. Validation series 5 was analyzed using a 99-gene subset classifier, generated using 99 of 200 genes available on the NKI custom microarray used (see Supplemental Figure S1 at http://jmd.amjpathol.org).

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