Identification of prognostic signatures in breast cancer microarray data using Bayesian techniques

Abstract

We apply a new Bayesian data analysis technique (latent process decomposition) to four recent microarray datasets for breast cancer. Compared to hierarchical cluster analysis, for example, this technique has advantages such as objective assessment of the optimal number of sample or gene clusters in the data, penalization of overcomplex models fitting to noise in the data and a common latent space of explanatory variables for samples and genes. Our analysis provides a clearer insight into these datasets, enabling assignment of patients to one of four principal processes, each with a distinct clinical outcome. One process is indolent and associated with under-expression across a number of genes associated with tumour growth. One process is associated with over expression of GRB7 and ERBB2. The most aggressive process is associated with abnormal expression of transcription factor genes, including members of the FOX family of transcription factor genes.