Detecting differential allelic expression using high-resolution melting curve analysis: application to the breast cancer susceptibility gene CHEK2

<p>Abstract</p> <p>Background</p> <p>The gene <it>CHEK2 </it>encodes a checkpoint kinase playing a key role in the DNA damage pathway. Though <it>CHEK2 </it>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 <it>CHEK2 </it>was subject to DAE in lymphoblastoid cell lines (LCLs) from high-risk breast cancer patients for whom no mutation in <it>BRCA1</it> or <it>BRCA2</it> had been identified.</p> <p>Methods</p> <p>We implemented an assay based on high-resolution melting (HRM) curve analysis and developed an analysis tool for DAE assessment.</p> <p>Results</p> <p>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 <it>CHEK2 </it>that would lead to DAE in the transcriptional regulatory milieu of freely proliferating LCLs.</p> <p>Conclusions</p> <p>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.</p

Genetic susceptibility factors in familial nasopharyngeal carcinoma

Nasopharyngeal carcinoma is common in Southern China, Hong Kong, Taiwan and Southeast Asian countries. The current study was performed on an exceptionally large multiplex NPC family from Sarawak, Malaysia (denoted as JAQBAB family). We aimed to explore the segregation of deleterious mutation(s) or NPC risk- associated allele within the genomic segment shared identical by descent (IBD) between the affected individuals. Genome-wide SNP-array covering all somatic chromosomes were performed on 11 of the NPC patients and their first-degree family members. The kinship was assessed and pairwise IBD estimation between target individuals were performed. Haplotype phasing was also performed on certain region of interests. Whole-exome sequencing was performed to identify deleterious mutations. The highest IBD score was detected within chromosome 10. Analysis of the exome sequencing data, however, did not find any deleterious mutation within this region that was shared between individuals in the JAQBAB family. Highest IBD score at chromosome 6 was found within the major histocompatibility complex (MHC) region, supporting strong HLA-EBV-NPC association that has been reported in an earlier study. HLA deep sequence-based typing (SBT) and gene sequencing on the 11 NPC patient samples revealed that the segregation of HLA-A*27:07 allele was consistent with the haplotype phasing pattern. Our results suggest that HLA-A*24:07 may harbour the risk allele for NPC among the subjects. However, further analysis is required before any definitive conclusion can be made. Further studies on the HLA-A*24:07 sequence may reveal more specific variation within this gene that can be associated with NPC risk in this family. In addition, further studies on HLA-A and risk of NPC other Southeast-Asian populations may shed more understanding about the association between HLA-A*24:07 and sporadic NPC in this region of the world

Integrative Genome-Wide Gene Expression Profiling of Clear Cell Renal Cell Carcinoma in Czech Republic and in the United States

<div><p>Gene expression microarray and next generation sequencing efforts on conventional, clear cell renal cell carcinoma (ccRCC) have been mostly performed in North American and Western European populations, while the highest incidence rates are found in Central/Eastern Europe. We conducted whole-genome expression profiling on 101 pairs of ccRCC tumours and adjacent non-tumour renal tissue from Czech patients recruited within the “K2 Study”, using the Illumina HumanHT-12 v4 Expression BeadChips to explore the molecular variations underlying the biological and clinical heterogeneity of this cancer. Differential expression analysis identified 1650 significant probes (fold change ≥2 and false discovery rate <0.05) mapping to 630 up- and 720 down-regulated unique genes. We performed similar statistical analysis on the RNA sequencing data of 65 ccRCC cases from the Cancer Genome Atlas (TCGA) project and identified 60% (402) of the downregulated and 74% (469) of the upregulated genes found in the K2 series. The biological characterization of the significantly deregulated genes demonstrated involvement of downregulated genes in metabolic and catabolic processes, excretion, oxidation reduction, ion transport and response to chemical stimulus, while simultaneously upregulated genes were associated with immune and inflammatory responses, response to hypoxia, stress, wounding, vasculature development and cell activation. Furthermore, genome-wide DNA methylation analysis of 317 TCGA ccRCC/adjacent non-tumour renal tissue pairs indicated that deregulation of approximately 7% of genes could be explained by epigenetic changes. Finally, survival analysis conducted on 89 K2 and 464 TCGA cases identified 8 genes associated with differential prognostic outcomes. In conclusion, a large proportion of ccRCC molecular characteristics were common to the two populations and several may have clinical implications when validated further through large clinical cohorts.</p> </div

Venn diagrams showing the intersection of significant genes differentially downregulated (A) and upregulated (B) in the whole – genome expression profiling microarray dataset (K2) vs. RNA-Sequencing dataset from the Cancer Genome Atlas (TCGA).

<p>Venn diagrams showing the intersection of significant genes differentially downregulated (A) and upregulated (B) in the whole – genome expression profiling microarray dataset (K2) vs. RNA-Sequencing dataset from the Cancer Genome Atlas (TCGA).</p

Characteristics of the 101 ccRCC patients from the “K2 study” (Czech Republic) included in the whole-genome gene expression microarray study.

*<p>p value calculated using Pearson χ² testing for categorical variables and t-test for continuous variables.</p>**<p>The two younger categories were grouped.</p>***<p>All stage IV patients had distant metastasis at diagnosis, and by definition none of stage I, II or III patients had distant metastasis. Missing stages were due to the lack of lymph nodes and/or metastasis evaluation. Out of 19 cases with missing stage, 9 were pT1a, 7 were pT1b, 1 was pT2a, and 1 was pT3a.</p

Venn diagrams representing relationship between ccRCC grades and the number of differentially expressed probes.

<p>Downregulated (A) and upregulated (B) probes with fold-change (FC) ≥2 and FDR-adjusted p-value (BH) <0.05 using paired sample information are presented.</p

Hierarchical Classification of 100 significant differentially expressed genes in K2 series.

<p>Heatmap representing the 50 significant upregulated (in red) and 50 downregulated (in green) probes with the highest fold change following differential expression in ccRCC compared with non-tumour tissue (FDR-adjusted p-value (BH) <0.05, FC ≥2).</p

Characteristics of ccRCC patients in the K2 (Czech Republic) and TCGA (US) series included in survival analysis.

*<p>p value calculated using Pearson χ² testing for categorical variables and t-test for continuous variables.</p>**<p>excluding missing category.</p

Characteristics of the 65 ccRCC patients included in the TCGA series (US) using RNA-Sequencing technology.

<p>Data from all paired tumour/non-tumour sets available on April 19, 2012 were retrieved from TCGA data portal.</p>*<p>p value calculated using Pearson χ² testing for categorical variables and t-test for continuous variables.</p>**<p>the younger two categories were grouped.</p>***<p>excluding missing category.</p>□<p>Of stage IV patients 15 had distant metastasis at diagnosis, and by definition none of stage I, II or III patients had distant metastasis. Missing stages were due to the lack of lymph nodes and/or metastasis evaluation>. These four patients were pT1aNXMX, pT3aNXM0, pT1bNXMX and pT3aNXMX, respectively.</p