A simple and reproducible breast cancer prognostic test

BMC Genomics

Methodology Article

Bahman Afsari¹  Jeffrey T Leek²  Donald Geman³  Luigi Marchionni⁴ 

[1] Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, 21218, Baltimore, MD, USA;Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, 21205, Baltimore, MD, USA;Center for Computational Biology, Johns Hopkins University, 21205, Baltimore, MD, USA;Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, 21218, Baltimore, MD, USA;Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, 21218, Baltimore, MD, USA;The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 1550 Orleans Street, 21231, Baltimore, MD, USA;

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

DOI  :  10.1186/1471-2164-14-336

 received in 2012-12-18, accepted in 2013-05-04,  发布年份 2013

来源: Springer

【 摘 要 】

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

【 授权许可】

Unknown   
© Marchionni et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.