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Open Access Highly Accessed Methodology article

A simple and reproducible breast cancer prognostic test

Luigi Marchionni1, Bahman Afsari4, Donald Geman34* and Jeffrey T Leek25*

Author Affiliations

1 The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 1550 Orleans Street, Baltimore, MD 21231, USA

2 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA

3 Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA

4 Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA

5 Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21205, USA

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BMC Genomics 2013, 14:336  doi:10.1186/1471-2164-14-336

Published: 17 May 2013

Abstract

Background

A small number of prognostic and predictive tests based on gene expression are currently offered as reference laboratory tests. In contrast to such success stories, a number of flaws and errors have recently been identified in other genomic-based predictors and the success rate for developing clinically useful genomic signatures is low. These errors have led to widespread concerns about the protocols for conducting and reporting of computational research. As a result, a need has emerged for a template for reproducible development of genomic signatures that incorporates full transparency, data sharing and statistical robustness.

Results

Here we present the first fully reproducible analysis of the data used to train and test MammaPrint, an FDA-cleared prognostic test for breast cancer based on a 70-gene expression signature. We provide all the software and documentation necessary for researchers to build and evaluate genomic classifiers based on these data. As an example of the utility of this reproducible research resource, we develop a simple prognostic classifier that uses only 16 genes from the MammaPrint signature and is equally accurate in predicting 5-year disease free survival.

Conclusions

Our study provides a prototypic example for reproducible development of computational algorithms for learning prognostic biomarkers in the era of personalized medicine.

Keywords:
Reproducible research; Gene expression analysis; Biomarkers; Top scoring pair; Prediction; Genomics; Personalized medicine; Breast cancer; MammaPrint