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ETV6 (ETS translocation-variant gene 6, located on chromosome 12p), also known as TEL, encodes a transcription repressor belonging to the E26 transforming specific (ETS) family of DNA-binding proteins. ETV6 is known as a proto-oncogene involved in translocation with over 40 partners.1 In acute myeloid leukemia (AML) only a few rare translocations result in transforming fusion proteins,1 indicating that the oncogenic role of ETV6 does not play a major part in AML. However, abnormalities of the short arm of chromosome 12 (12p) are found in about 5% of AML and myelodysplastic syndromes. Most abnormalities consist of total or partial loss of 12p usually affecting ETV6 and CDKN1B, implicating these genes as tumor-suppressor genes.1, 2, 3, 4 Recently, heterozygous mutations of ETV6, resulting in loss of repressor activity, were found in AML, adding to the view that ETV6 might have tumor-suppressor characteristics.5

Functional analysis of data from genome-scale experiments, such as microarrays, requires an extensive selection of differentially expressed genes. Under many conditions, the proportion of differentially expressed genes is considerable, making the selection criteria a balance between the inclusion of false positives and the exclusion of false negatives. We developed an analytical method to determine a p-value threshold from a microarray experiment that is dependent on the quality and design of the data set. To this aim, populations of p-values are modeled as mathematical functions in which the parameters to describe these functions are estimated in an unsupervised manner. The strength of the method is exemplified by its application to a published gene expression data set of sporadic and familial breast tumors with BRCA1 or BRCA2 mutations. We present an objective and unsupervised way to set thresholds adapted to the quality and design of the experiment. The resulting mathematical description of the data sets of genome-scale experiments enables a probabilistic approach in systems biology.