Characterization of CDKN2A(p16) methylation and impact in colorectal cancer: systematic analysis using pyrosequencing

Background The aim of this study is to analyse CDKN2A methylation using pyrosequencing on a large cohort of colorectal cancers and corresponding non-neoplastic tissues. In a second step, the effect of methylation on clinical outcome is addressed. Methods Primary colorectal cancers and matched non-neoplastic tissues from 432 patients underwent CDKN2A methylation analysis by pyrosequencing (PyroMarkQ96). Methylation was then related to clinical outcome, microsatellite instability (MSI), and BRAF and KRAS mutation. Different amplification conditions (35 to 50 PCR cycles) using a range of 0-100% methylated DNA were tested. Results Background methylation was at most 10% with ≥35 PCR cycles. Correlation of observed and expected values was high, even at low methylation levels (0.02%, 0.6%, 2%). Accuracy of detection was optimal with 45 PCR cycles. Methylation in normal mucosa ranged from 0 to >90% in some cases. Based on the maximum value of 10% background, positivity was defined as a ≥20% difference in methylation between tumor and normal tissue, which occurred in 87 cases. CDKN2A methylation positivity was associated with MSI (p = 0.025), BRAF mutation (p < 0.0001), higher tumor grade (p < 0.0001), mucinous histology (p = 0.0209) but not with KRAS mutation. CDKN2A methylation had an independent adverse effect (p = 0.0058) on prognosis. Conclusion The non-negligible CDKN2A methylation of normal colorectal mucosa may confound the assessment of tumor-specific hypermethylation, suggesting that corresponding non-neoplastic tissue should be used as a control. CDKN2A methylation is robustly detected by pyrosequencing, even at low levels, suggesting that this unfavorable prognostic biomarker warrants investigation in prospective studies

Stratification and Prognostic Relevance of Jass’s Molecular Classification of Colorectal Cancer

Background: The current proposed model of colorectal tumorigenesis is based primarily on CpG island methylator phenotype (CIMP), microsatellite instability (MSI), KRAS, BRAF, and methylation status of 0-6-Methylguanine DNA Methyltransferase (MGMT) and classifies tumors into five subgroups. The aim of this study is to validate this molecular classification and test its prognostic relevance. Methods: Three hundred two patients were included in this study. Molecular analysis was performed for five CIMP-related promoters (CRABP1, MLH1, p16INK4a, CACNA1G, NEUROG1), MGMT, MSI, KRAS, and BRAF. Methylation in at least 4 promoters or in one to three promoters was considered CIMP-high and CIMP-low (CIMP-H/L), respectively. Results: CIMP-H, CIMP-L, and CIMP-negative were found in 7.1, 43, and 49.9% cases, respectively. One hundred twenty-three tumors (41%) could not be classified into any one of the proposed molecular subgroups, including 107 CIMP-L, 14 CIMP-H, and two CIMP-negative cases. The 10 year survival rate for CIMP-high patients [22.6% (95%CI: 7–43)] was significantly lower than for CIMP-L or CIMP-negative (p = 0.0295). Only the combined analysis of BRAF and CIMP (negative versus L/H) led to distinct prognostic subgroups. Conclusion: Although CIMP status has an effect on outcome, our results underline the need for standardized definitions of low- and high-level CIMP, which clearly hinders an effective prognostic and molecular classification of colorectal cancer

Somatic mutations activating STAT3 in human inflammatory hepatocellular adenomas

Somatic STAT3 mutations present in a subset of inflammatory hepatocellular adenomas result in the generation of constitutively active STAT3 proteins that homodimerize independently of IL-6 stimulation

Simultaneous detection of lung fusions using a multiplex RT-PCR next generation sequencing-based approach:A multi-institutional research study

Contains fulltext : 195300.pdf (publisher's version ) (Open Access

The impact of CpG island methylator phenotype and microsatellite instability on tumour budding in colorectal cancer

In colorectal cancer, tumour budding, a process likened to epithelial mesenchymal transition, is an adverse prognostic factor which is rarely found in tumours with high-level microsatellite instability (MSI-H). Cases with MSI-H or high-level CpG island methylator phenotype (CIMP-H) have similar histomorphological features, yet seemingly opposite prognosis. We hypothesized that tumour budding is related to CIMP, thus partially explaining this prognostic difference

Frequency, phenotype, and genotype of minute gastrointestinal stromal tumors in the stomach : an autopsy study

Gastrointestinal stromal tumors are the most common mesenchymal tumors of the human digestive tract. Up to 85% of these tumors show somatic gain-of-function mutation of the receptor tyrosine kinase c-KIT gene. A recent study has shown a high frequency (22.5%) of minute gastrointestinal stromal tumors in stomachs examined during routine autopsies. The aims of our study were to confirm the previously reported incidence of gastric gastrointestinal stromal tumors in routine autopsies and to investigate their molecular alterations. Gastrointestinal stromal tumors were collected prospectively from 578 autopsies over an 18-month period. After recording the size and location of each lesion, representative tissue samples were processed for hematoxylin and eosin staining and immunohistochemically stained for CD117 and CD34. Microdissected DNA from all identified gastrointestinal stromal tumors was studied for c-KIT and platelet-derived growth factor receptor ? mutations. We identified 17 gastrointestinal stromal tumors in 578 consecutive autopsies (2.9%) located in the gastric body (47%) and fundus (47%). One tumor location was not recorded. All tumors were immunohistochemically positive for CD117 and CD34. DNA analysis showed c-KIT mutations in 11 cases. One platelet-derived growth factor receptor ? mutation was found. The incidence of gastric minute gastrointestinal stromal tumors (2.9%) is higher than the reported clinical incidence. All are benign tumors, and most, including minute tumors, contain c-KIT mutations. This finding highlights the fact that c-KIT mutations are an early event in the evolution of gastrointestinal stromal tumors but are not sufficient per se for clinically relevant disease

Optimized between-group classification: a new jackknife-based gene selection procedure for genome-wide expression data

<p>Abstract</p> <p>Background</p> <p>A recent publication described a supervised classification method for microarray data: Between Group Analysis (BGA). This method which is based on performing multivariate ordination of groups proved to be very efficient for both classification of samples into pre-defined groups and disease class prediction of new unknown samples. Classification and prediction with BGA are classically performed using the whole set of genes and no variable selection is required. We hypothesize that an optimized selection of highly discriminating genes might improve the prediction power of BGA.</p> <p>Results</p> <p>We propose an optimized between-group classification (OBC) which uses a jackknife-based gene selection procedure. OBC emphasizes classification accuracy rather than feature selection. OBC is a backward optimization procedure that maximizes the percentage of between group inertia by removing the least influential genes one by one from the analysis. This selects a subset of highly discriminative genes which optimize disease class prediction. We apply OBC to four datasets and compared it to other classification methods.</p> <p>Conclusion</p> <p>OBC considerably improved the classification and predictive accuracy of BGA, when assessed using independent data sets and leave-one-out cross-validation.</p> <p>Availability</p> <p>The R code is freely available [see <supplr sid="S1">Additional file 1</supplr>] as well as supplementary information [see <supplr sid="S2">Additional file 2</supplr>].</p> <suppl id="S1"> <title> <p>Additional File 1</p> </title> <text> <p>R code of the OBC algorithm.</p> </text> <file name="1471-2105-6-239-S1.R"> <p>Click here for file</p> </file> </suppl> <suppl id="S2"> <title> <p>Additional File 2</p> </title> <text> <p>Further description of the sarcoidosis and tumour data. This files gives details about the optimal subset of genes obtained after OBC.</p> </text> <file name="1471-2105-6-239-S2.pdf"> <p>Click here for file</p> </file> </suppl