Abstract
We identified a set of genes with an unexpected bimodal distribution among breast cancer patients in multiple studies. The property of bimodality seems to be common, as these genes were found on multiple microarray platforms and in studies with different end-points and patient cohorts. Bimodal genes tend to cluster into small groups of four to six genes with synchronised expression within the group (but not between the groups), which makes them good candidates for robust conditional descriptors. The groups tend to form concise network modules underlying their function in cancerogenesis of breast neoplasms.
Highlights
Whole-genome gene expression studies primarily aim to identify conditional descriptors, i.e. subsets of genes or functional groups whose expression profiles distinguish between different biological states
[6], an “Amsterdam” signature consisting of 70 genes [7], a 76-gene “Rotterdam” signature [8] for metastasis, and a set of 21 genes associated with disease outcomes for ER+ tumors [9]
The phenomenon of bimodality of gene expression Originally, we identified a set of bimodally expressed genes within the previously published dataset of 295 early breast cancer samples run on two custom cDNA array platforms [19,28]
Summary
Whole-genome gene expression studies primarily aim to identify conditional descriptors, i.e. subsets of genes or functional groups whose expression profiles distinguish between different biological states. The traditional method consists of selecting a set of descriptor genes (gene signatures) using a variety of statistical methods [1,2,3,4,5] Using this approach, a number of gene signatures were deduced for breast cancer phenotypes, including an “intrinsic” set for clustering of breast cancers [6], an “Amsterdam” signature consisting of 70 genes [7], a 76-gene “Rotterdam” signature [8] for metastasis, and a set of 21 genes associated with disease outcomes for ER+ tumors [9]. Gene signatures have many issues as descriptors - for instance, loss of specificity in validation studies with an increased number of samples [10], generally poor cross-platform compatibility (Amsterdam and Rotterdam signatures virtually do not overlap in gene content), lack of mechanistic (functional) correlation with phenotype, etc
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