Mutational Analysis of Hedgehog Signaling Pathway Genes in Human Malignant Mesothelioma

Background

The Hedgehog (HH) signaling pathway is critical for embryonic development and adult homeostasis. Recent studies have identified regulatory roles for this pathway in certain cancers with mutations in the HH pathway genes. The extent to which mutations of the HH pathway genes are involved in the pathogenesis of malignant mesothelioma (MMe) is unknown.

Methodology/Principal Findings

Real-time PCR analysis of HH pathway genes PTCH1, GLI1 and GLI2 were performed on 7 human MMe cell lines. Exon sequencing of 13 HH pathway genes was also performed in cell lines and human MMe tumors. In silico programs were used to predict the likelihood that an amino-acid substitution would have a functional effect. GLI1, GLI2 and PTCH1 were highly expressed in MMe cells, indicative of active HH signaling. PTCH1, SMO and SUFU mutations were found in 2 of 11 MMe cell lines examined. A non-synonymous missense SUFU mutation (p.T411M) was identified in LO68 cells. In silico characterization of the SUFU mutant suggested that the p.T411M mutation might alter protein function. However, we were unable to demonstrate any functional effect of this mutation on Gli activity. Deletion of exons of the PTCH1 gene was found in JU77 cells, resulting in loss of one of two extracellular loops implicated in HH ligand binding and the intracellular C-terminal domain. A 3-bp insertion (69_70insCTG) in SMO, predicting an additional leucine residue in the signal peptide segment of SMO protein was also identified in LO68 cells and a MMe tumour.

Conclusions/Significance

We identified the first novel mutations in PTCH1, SUFU and SMO associated with MMe. Although HH pathway mutations are relatively rare in MMe, these data suggest a possible role for dysfunctional HH pathway in the pathogenesis of a subgroup of MMe and help rationalize the exploration of HH pathway inhibitors for MMe therapy.     

Comparison of CpG Island Methylator Phenotype (CIMP) Frequency in Colon Cancer Using Different Probe- and Gene-Specific Scoring Alternatives on Recommended Multi-Gene Panels

Background

In colorectal cancer a distinct subgroup of tumours demonstrate the CpG island methylator phenotype (CIMP). However, a consensus of how to score CIMP is not reached, and variation in definition may influence the reported CIMP prevalence in tumours. Thus, we sought to compare currently suggested definitions and cut-offs for methylation markers and how they influence CIMP classification in colon cancer.

Methods

Methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA), with subsequent fragment analysis, was used to investigate methylation of tumour samples. In total, 31 CpG sites, located in 8 different genes (RUNX3, MLH1, NEUROG1, CDKN2A, IGF2, CRABP1, SOCS1 and CACNA1G) were investigated in 64 distinct colon cancers and 2 colon cancer cell lines. The Ogino gene panel includes all 8 genes, in addition to the Weisenberger panel of which only 5 of the 8 genes included were investigated. In total, 18 alternative combinations of scoring of CIMP positivity on probe-, gene-, and panel-level were analysed and compared.

Results

For 47 samples (71%), the CIMP status was constant and independent of criteria used for scoring; 34 samples were constantly scored as CIMP negative, and 13 (20%) consistently scored as CIMP positive. Only four of 31 probes (13%) investigated showed no difference in the numbers of positive samples using the different cut-offs. Within the panels a trend was observed that increasing the gene-level stringency resulted in a larger difference in CIMP positive samples than increasing the probe-level stringency. A significant difference between positive samples using ‘the most stringent’ as compared to ‘the least stringent’ criteria (20% vs 46%, respectively; p<0.005) was demonstrated.

Conclusions

A statistical significant variation in the frequency of CIMP depending on the cut-offs and genes included in a panel was found, with twice as many positives samples by least compared to most stringent definition used.     

Activin Signaling in Microsatellite Stable Colon Cancers Is Disrupted by a Combination of Genetic and Epigenetic Mechanisms

Background

Activin receptor 2 (ACVR2) is commonly mutated in microsatellite unstable (MSI) colon cancers, leading to protein loss, signaling disruption, and larger tumors. Here, we examined activin signaling disruption in microsatellite stable (MSS) colon cancers.

Methods

Fifty-one population-based MSS colon cancers were assessed for ACVR1, ACVR2 and pSMAD2 protein. Consensus mutation-prone portions of ACVR2 were sequenced in primary cancers and all exons in colon cancer cell lines. Loss of heterozygosity (LOH) was evaluated for ACVR2 and ACVR1, and ACVR2 promoter methylation by methylation-specific PCR and bisulfite sequencing and chromosomal instability (CIN) phenotype via fluorescent LOH analysis of 3 duplicate markers. ACVR2 promoter methylation and ACVR2 expression were assessed in colon cancer cell lines via qPCR and IP-Western blots. Re-expression of ACVR2 after demethylation with 5-aza-2′-deoxycytidine (5-Aza) was determined. An additional 26 MSS colon cancers were assessed for ACVR2 loss and its mechanism, and ACVR2 loss in all tested cancers correlated with clinicopathological criteria.

Results

Of 51 MSS colon tumors, 7(14%) lost ACVR2, 2 (4%) ACVR1, and 5(10%) pSMAD2 expression. No somatic ACVR2 mutations were detected. Loss of ACVR2 expression was associated with LOH at ACVR2 (p<0.001) and ACVR2 promoter hypermethylation (p<0.05). ACVR2 LOH, but not promoter hypermethylation, correlated with CIN status. In colon cancer cell lines with fully methylated ACVR2 promoter, loss of ACVR2 mRNA and protein expression was restored with 5-Aza treatment. Loss of ACVR2 was associated with an increase in primary colon cancer volume (p<0.05).

Conclusions

Only a small percentage of MSS colon cancers lose expression of activin signaling members. ACVR2 loss occurs through LOH and ACVR2 promoter hypermethylation, revealing distinct mechanisms for ACVR2 inactivation in both MSI and MSS subtypes of colon cancer.     

Oncogenic CagA promotes gastric cancer risk via activating ERK signaling pathways: a nested case-control study

Background

CagA cellular interaction via activation of the ERK signaling pathway may be a starting point in the development of gastric cancer. This study aimed to evaluate whether genes involved in ERK downstream signaling pathways activated by CagA are susceptible genetic markers for gastric cancer.

Methods

In the discovery phase, a total of 580 SNPs within +/−5 kbp of 30 candidate genes were genotyped to examine an association with gastric cancer risk in the Korean Multi-center Cancer Cohort (100 incident gastric cancer case-control sets). The most significant SNPs (raw or permutated p value<0.02) identified in the discovery analysis were re-evaluated in the extension phase using unconditional logistic regression model (400 gastric cancer case-control sets). Combined analyses including pooled- and meta-analysis were conducted to summarize all the results.

Results

24 SNPs in eight genes (ERK, Dock180, C3G, Rap1, Src, CrkL, Mek and Crk) were significantly associated with gastric cancer risk in the individual SNP analyses in the discovery phase (p<0.05). In the extension analyses, ERK rs5999749, Dock180 rs4635002 and C3G rs7853122 showed marginally significant gene-dose effects for gastric cancer. Consistently, final combined analysis presented the SNPs as significantly associated with gastric cancer risk (OR = 1.56, [95% CI: 1.19–2.06], OR = 0.61, [95% CI: 0.43–0.87], OR = 0.59, [95% CI: 0.54–0.76], respectively).

Conclusions

Our findings suggest that ERK rs5999749, Dock180 rs4635002 and C3G rs7853122 are genetic determinants in gastric carcinogenesis.     

Quantitative Proteomic Analysis of Gingival Crevicular Fluid in Different Periodontal Conditions

Aim

To quantify the proteome composition of the GCF in periodontal health (HH) and in sites with different clinical conditions in chronic periodontitis (CP) subjects.

Material and Methods

5 subjects with HH and 5 with CP were submitted to full-mouth periodontal examination, and GCF sampling. Sites in the CP group were classified and sampled as periodontitis (P, probing depth, PD>4 mm), gingivitis (G, PD≤3mm with bleeding on probing, BOP), and healthy sites (H, PD≤3mm without BOP). GCF proteins were subjected to liquid chromatography electrospray ionization mass spectrometry for identification, characterization and quantification.

Results

230 proteins were identified; 145 proteins were detected in HH, 214 in P, 154 in G, and 133 in H. Four proteins were exclusively detected at HH, 43 proteins at P, 7 proteins at G, and 1 protein at H. Compared to HH group, 35 and 6 proteins were more abundant in P and G (p<0.001), respectively; and 4, 15 and 37 proteins were less abundant in P, G and H (p≤0.01), respectively.

Conclusions

There are marked differences in the GCF proteome according to disease profile. Comprehension of the role of the identified proteins in the etiopathogenesis of periodontal disease may lead to biomarkers definition.