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BMC Cancer. 2012; 12: 246.
Published online 2012 June 15. doi:  10.1186/1471-2407-12-246
PMCID: PMC3409022
The application of nonsense-mediated mRNA decay inhibition to the identification of breast cancer susceptibility genes
Julie K Johnson,corresponding author1,2,5 Nic Waddell,3 and Georgia Chenevix-Trench1, kConFab Investigators
1Queensland Institute of Medical Research, Brisbane, Australia
2School of Medicine, University of Queensland, Brisbane, Australia
3Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
4The Kathleen Cuningham Foundation for Research into Familial Breast Cancer (kConFab), Peter MacCallum Cancer Centre, Melbourne, Australia
5Queensland Institute of Medical Research, Royal Brisbane Hospital, Locked bag 2000, Herston, Brisbane, QLD, 4029, Australia
corresponding authorCorresponding author.
Julie K Johnson: Julie.Johnson/at/qimr.edu.au; Nic Waddell: n.waddell/at/imb.uq.edu.au; Georgia Chenevix-Trench: GeorgiaT/at/qimr.edu.au
Received December 6, 2011; Accepted June 15, 2012.
Abstract
Background
Identification of novel, highly penetrant, breast cancer susceptibility genes will require the application of additional strategies beyond that of traditional linkage and candidate gene approaches. Approximately one-third of inherited genetic diseases, including breast cancer susceptibility, are caused by frameshift or nonsense mutations that truncate the protein product []. Transcripts harbouring premature termination codons are selectively and rapidly degraded by the nonsense-mediated mRNA decay (NMD) pathway. Blocking the NMD pathway in any given cell will stabilise these mutant transcripts, which can then be detected using gene expression microarrays. This technique, known as gene identification by nonsense-mediated mRNA decay inhibition (GINI), has proved successful in identifying sporadic nonsense mutations involved in many different cancer types. However, the approach has not yet been applied to identify germline mutations involved in breast cancer. We therefore attempted to use GINI on lymphoblastoid cell lines (LCLs) from multiple-case, non- BRCA1/2 breast cancer families in order to identify additional high-risk breast cancer susceptibility genes.
Methods
We applied GINI to a total of 24 LCLs, established from breast-cancer affected and unaffected women from three multiple-case non-BRCA1/2 breast cancer families. We then used Illumina gene expression microarrays to identify transcripts stabilised by the NMD inhibition.
Results
The expression profiling identified a total of eight candidate genes from these three families. One gene, PPARGC1A, was a candidate in two separate families. We performed semi-quantitative real-time reverse transcriptase PCR of all candidate genes but only PPARGC1A showed successful validation by being stabilised in individuals with breast cancer but not in many unaffected members of the same family. Sanger sequencing of all coding and splice site regions of PPARGC1A did not reveal any protein truncating mutations. Haplotype analysis using short tandem repeat microsatellite markers did not indicate the presence of a haplotype around PPARGC1A which segregated with disease in the family.
Conclusions
The application of the GINI method to LCLs to identify transcripts harbouring germline truncating mutations is challenging due to a number of factors related to cell type, microarray sensitivity and variations in NMD efficiency.
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