Open Access Open Badges Research article

Data integration from two microarray platforms identifies bi-allelic genetic inactivation of RIC8A in a breast cancer cell line

Aslaug Aamodt Muggerud12, Henrik Edgren34, Maija Wolf34, Kristine Kleivi14, Emelyne Dejeux5, Jörg Tost5, Therese Sørlie16* and Olli Kallioniemi34*

Author Affiliations

1 Department of Genetics, Institute for Cancer Research, Norwegian Radium Hospital, Oslo University Hospital, 0310 Oslo, Norway

2 Faculty of Medicine, Division The Norwegian Radium Hospital, University of Oslo, 0316 Oslo, Norway

3 Institute for Molecular Medicine (FIMM), University of Helsinki, Biomedicum Helsinki 2 U, Tukholmankatu 8, FIN-00290 Helsinki, Finland

4 Medical Biotechnology, VTT Technical Research Centre of Finland and Centre for Biotechnology, University of Turku, FIN-20520 Turku, Finland

5 CEA-Institut de Génomique, Centre National de Génotypage, Laboratory for Epigenetics, 2 rue Gaston Crèmieux, 91000 Evry, France

6 Department of Informatics, University of Oslo, Oslo, Norway

For all author emails, please log on.

BMC Medical Genomics 2009, 2:26  doi:10.1186/1755-8794-2-26

Published: 11 May 2009



Using array comparative genomic hybridization (aCGH), a large number of deleted genomic regions have been identified in human cancers. However, subsequent efforts to identify target genes selected for inactivation in these regions have often been challenging.


We integrated here genome-wide copy number data with gene expression data and non-sense mediated mRNA decay rates in breast cancer cell lines to prioritize gene candidates that are likely to be tumour suppressor genes inactivated by bi-allelic genetic events. The candidates were sequenced to identify potential mutations.


This integrated genomic approach led to the identification of RIC8A at 11p15 as a putative candidate target gene for the genomic deletion in the ZR-75-1 breast cancer cell line. We identified a truncating mutation in this cell line, leading to loss of expression and rapid decay of the transcript. We screened 127 breast cancers for RIC8A mutations, but did not find any pathogenic mutations. No promoter hypermethylation in these tumours was detected either. However, analysis of gene expression data from breast tumours identified a small group of aggressive tumours that displayed low levels of RIC8A transcripts. qRT-PCR analysis of 38 breast tumours showed a strong association between low RIC8A expression and the presence of TP53 mutations (P = 0.006).


We demonstrate a data integration strategy leading to the identification of RIC8A as a gene undergoing a classical double-hit genetic inactivation in a breast cancer cell line, as well as in vivo evidence of loss of RIC8A expression in a subgroup of aggressive TP53 mutant breast cancers.