Heritability in the Efficiency of Nonsense-Mediated mRNA Decay in Humans

BACKGROUND: In eukaryotes mRNA transcripts of protein-coding genes in which an intron has been retained in the coding region normally result in premature stop codons and are therefore degraded through the nonsense-mediated mRNA decay (NMD) pathway. There is evidence in the form of selective pressure for in-frame stop codons in introns and a depletion of length three introns that this is an important and conserved quality-control mechanism. Yet recent reports have revealed that the efficiency of NMD varies across tissues and between individuals, with important clinical consequences. PRINCIPAL FINDINGS: Using previously published Affymetrix exon microarray data from cell lines genotyped as part of the International HapMap project, we investigated whether there are heritable, inter-individual differences in the abundance of intron-containing transcripts, potentially reflecting differences in the efficiency of NMD. We identified intronic probesets using EST data and report evidence of heritability in the extent of intron expression in 56 HapMap trios. We also used a genome-wide association approach to identify genetic markers associated with intron expression. Among the top candidates was a SNP in the DCP1A gene, which forms part of the decapping complex, involved in NMD. CONCLUSIONS: While we caution that some of the apparent inter-individual difference in intron expression may be attributable to different handling or treatments of cell lines, we hypothesize that there is significant polymorphism in the process of NMD, resulting in heritable differences in the abundance of intronic mRNA. Part of this phenotype is likely to be due to a polymorphism in a decapping enzyme on human chromosome 3

Analytical methods for inferring functional effects of single base pair substitutions in human cancers

Cancer is a genetic disease that results from a variety of genomic alterations. Identification of some of these causal genetic events has enabled the development of targeted therapeutics and spurred efforts to discover the key genes that drive cancer formation. Rapidly improving sequencing and genotyping technology continues to generate increasingly large datasets that require analytical methods to identify functional alterations that deserve additional investigation. This review examines statistical and computational approaches for the identification of functional changes among sets of single-nucleotide substitutions. Frequency-based methods identify the most highly mutated genes in large-scale cancer sequencing efforts while bioinformatics approaches are effective for independent evaluation of both non-synonymous mutations and polymorphisms. We also review current knowledge and tools that can be utilized for analysis of alterations in non-protein-coding genomic sequence

Prognostic significance of nucleolar assessment in invasive breast cancer

Aims: Nucleolar morphometric features have a potential role in the assessment of aggressiveness of many cancers. However, the role of nucleoli in invasive breast cancer (IBC) is still unclear. This study aimed to investigate the optimal scoring method of nucleoli in IBC and their prognostic significance, and refine the grading of BC by incorporating the nucleolar score.Methods and results: Digital images acquired from hematoxylin and eosin (H&E) stained sections from a large IBC cohort were divided into training (n=400) and validation (n=1200) sets were used in this study. Four different assessment methods including 1) modified Helpap’s method, and counting prominent nucleoli (size ≥2.5μm) in 2) 10 field views (10 FVs), 3) 5 FVs and 4) 1 FV were evaluated in the training set to identify the optimal method associated with the best performance and significant prognostic value. The optimal method was applied to the validation set and to an external validation set the Cancer Genome Atlas (TCGA) data (n=743). Scoring prominent nucleoli in 5 FVs, showed the highest inter-observer concordance rate (intraclass correlation coefficient = 0.8) and significant association with breast cancer specific survival (BCSS) (