Research

Bayesian profiling of molecular signatures to predict event times

Dabao Zhang and Min Zhang^*

• * Corresponding author: Min Zhang minzhang@purdue.edu

Author Affiliations

Department of Statistics, Purdue University, 150 N. University Street, West Lafayette, Indiana 47907-2067, USA

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Theoretical Biology and Medical Modelling 2007, 4:3 doi:10.1186/1742-4682-4-3

Published: 19 January 2007

Abstract

Background

It is of particular interest to identify cancer-specific molecular signatures for early diagnosis, monitoring effects of treatment and predicting patient survival time. Molecular information about patients is usually generated from high throughput technologies such as microarray and mass spectrometry. Statistically, we are challenged by the large number of candidates but only a small number of patients in the study, and the right-censored clinical data further complicate the analysis.

Results

We present a two-stage procedure to profile molecular signatures for survival outcomes. Firstly, we group closely-related molecular features into linkage clusters, each portraying either similar or opposite functions and playing similar roles in prognosis; secondly, a Bayesian approach is developed to rank the centroids of these linkage clusters and provide a list of the main molecular features closely related to the outcome of interest. A simulation study showed the superior performance of our approach. When it was applied to data on diffuse large B-cell lymphoma (DLBCL), we were able to identify some new candidate signatures for disease prognosis.

Conclusion

This multivariate approach provides researchers with a more reliable list of molecular features profiled in terms of their prognostic relationship to the event times, and generates dependable information for subsequent identification of prognostic molecular signatures through either biological procedures or further data analysis.