How many genes are needed for early detection of breast cancer, based on gene expression patterns in peripheral blood cells?

Excerpt

In their recent report [1], Sharma and coworkers explore the early detection of breast cancer. They analyzed a gene expression data set (1368 genes in 62 normal and 40 tumour samples, including sample duplication in different batches) using the nearest shrunken centroid method. They identified a panel of 37 genes that permitted early detection, with the classification accuracy being about 82%. This is a typical problem with sample classification based on gene expression profiling. The objective is to achieve high prediction accuracy with as few genes as possible, and so feature selection plays an important role; examination of a large number of genes will increase the dimensionality, computational complexity, and clinical cost. According to our previous study of data sets from patients with colon cancer, leukaemia and breast cancer [2], we estimated that five or six genes – rather than 37 -would be sufficient for the early detection of beast cancer [1]. So how many genes are indeed needed? In order to address this question, we evaluated the data presented by Sharma and coworkers using the Tclass system [2]. …