Identification of frequently mutated genes with relevance to nonsense mediated mRNA decay in the high microsatellite instability cancers.
ABSTRACT Frameshift mutations at coding mononucleotide repeats (cMNR) are frequent in high-microsatellite instability (MSI-H) cancers. Frameshift mutations in cMNR result in the formation of a premature termination codon (PTC) in the transcribed mRNA, and these abnormal mRNAs are generally degraded by nonsense mediated mRNA decay (NMD). We have identified novel genes that are frequently mutated at their cMNR by blocking NMD in two MSI-H cancer cell lines. After blocking NMD, we screened for differentially expressed genes using DNA microarrays, and then used database analysis to select 28 candidate genes containing cMNR with more than 9 nucleotide repeats. cMNR mutations have not been previously reported in MSI-H cancers for 15 of the 28 genes. We analyzed the cMNR mutation of each of the 15 genes in 10 MSI-H cell lines and 21 MSI-H cancers, and found frequent mutations of 12 genes in MSI-H cell lines and cancers, but not in microsatellite stable (MSS) cancers. Among these genes, the most frequently mutated in MSI-H cell lines were MLL3 (70%), PHACTR4 (70%), RUFY2 (50%) and TBC1D23 (50%). MLL3, which has already been implicated in cancer, had the highest mutation frequency in MSI-H cancers (48%). Our combined approach of NMD block, database search, and mutation analysis has identified a large number of genes mutated in their cMNR in MSI-H cancers. The identified mutations are expected to contribute to MSI-H tumorigenesis by causing an absence of gene expression or low gene dosage effects.
Article: The application of nonsense-mediated mRNA decay inhibition to the identification of breast cancer susceptibility genes.[show abstract] [hide abstract]
ABSTRACT: 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 1. 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. 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. 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. 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.BMC Cancer 06/2012; 12:246. · 3.01 Impact Factor