Detecting differential allelic expression using high-resolution melting curve analysis: application to the breast cancer susceptibility gene CHEK2
1 Genetic Cancer Susceptibility Group, IARC, 69372 Lyon, France
2 INSERM U590, Université Lyon 1, Lyon, France
3 Peter MacCallum Cancer Center, East Melbourne 2, VIC 3002, Australia
4 Unité Mixte de Génétique Constitutionnelle des Cancers Fréquents, Hospices Civils de Lyon, Centre Léon Bérard, 69373 Lyon, France
5 Department of Pathology, The University of Melbourne, VIC 3010, Australia
6 Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
BMC Medical Genomics 2011, 4:39 doi:10.1186/1755-8794-4-39Published: 11 May 2011
The gene CHEK2 encodes a checkpoint kinase playing a key role in the DNA damage pathway. Though CHEK2 has been identified as an intermediate breast cancer susceptibility gene, only a small proportion of high-risk families have been explained by genetic variants located in its coding region. Alteration in gene expression regulation provides a potential mechanism for generating disease susceptibility. The detection of differential allelic expression (DAE) represents a sensitive assay to direct the search for a functional sequence variant within the transcriptional regulatory elements of a candidate gene. We aimed to assess whether CHEK2 was subject to DAE in lymphoblastoid cell lines (LCLs) from high-risk breast cancer patients for whom no mutation in BRCA1 or BRCA2 had been identified.
We implemented an assay based on high-resolution melting (HRM) curve analysis and developed an analysis tool for DAE assessment.
We observed allelic expression imbalance in 4 of the 41 LCLs examined. All four were carriers of the truncating mutation 1100delC. We confirmed previous findings that this mutation induces non-sense mediated mRNA decay. In our series, we ruled out the possibility of a functional sequence variant located in the promoter region or in a regulatory element of CHEK2 that would lead to DAE in the transcriptional regulatory milieu of freely proliferating LCLs.
Our results support that HRM is a sensitive and accurate method for DAE assessment. This approach would be of great interest for high-throughput mutation screening projects aiming to identify genes carrying functional regulatory polymorphisms.