Microarrays and breast cancer clinical studies: forgetting what we have not yet learnt

By the time that a breast cancer is clinically apparent it has undergone multiple genetic and epigenetic primary carcinogenic events and further secondary molecular changes that ensure the adaptation of its cells to the changing micro-environment. The diversity of these genetic changes has made it difficult to classify breast cancer molecularly, and as a consequence there has been great enthusiasm for using genome-wide profiling methods to acquire a better understanding of the disease. This has led to an increasing number of studies using expression array profiling to improve the prediction of cancer prognosis [1-7]. Great things have been promised by exponents of these technologies [8]. How should we view the impact of current work?Irrespective of the questions being addressed in a profiling study, microarray techniques have inherent problems that lead to considerable data variability. Major sources of variability can arise from methods of RNA extraction [9,10], different types of probe preparation [9,11], probe labelling [12,13] and hybridisation [14,15]. It is also clear that varying the microarray platform, reference sample or segmentation method used for microarray image analysis leads to significant differences in data repeatability and gene discovery [16-18]. Although the MIAME (minimum information about a microarray experiment) report defines standards for information needed for reporting microarray experiments [19], it does not describe or quantify variabilities in the experiments. More studies addressing these experimental issues are urgently needed [20,21] along with efforts to define common standards for expression measurement controls. Guidelines are already emerging for best practice in using expression profiling for clinical trials [22].The aim of supervised classification of microarray data is to detect genes that might prospectively predict defined outcomes. Existing studies in breast cancer have involved three steps: identifying a set of genes that