Project description:BackgroundRecent work led to recognize sessile serrated adenomas (SSA) as precursor to many of the sporadic colorectal cancers with microsatellite instability (MSI). However, comprehensive analyses of DNA methylation in SSA and MSI cancer have not been conducted.MethodsWith an array-based methylation sensitive amplified fragment length polymorphism (MS-AFLP) method we analyzed 8 tubular (TA) and 19 serrated (SSA) adenomas, and 14 carcinomas with (MSI) and 12 without (MSS) microsatellite instability. MS-AFLP array can survey relative differences in methylation between normal and tumor tissues of 9,654 DNA fragments containing all NotI sequences in the human genome.ResultsUnsupervised clustering analysis of the genome-wide hypermethylation alterations revealed no major differences between or within these groups of benign and malignant tumors regardless of their location in intergenic, intragenic, promoter, or 3' end regions. Hypomethylation was less frequent in SSAs compared with MSI or MSS carcinomas. Analysis of variance of DNA methylation between these four subgroups identified 56 probes differentially altered. The hierarchical tree of this subset of probes revealed two distinct clusters: Group 1, mostly composed by TAs and MSS cancers with KRAS mutations; and Group 2 with BRAF mutations, which consisted of cancers with MSI and MLH1 methylation (Group 2A), and SSAs without MLH1 methylation (Group 2B). AXIN2, which cooperates with APC and ?-catenin in Wnt signaling, had more methylation alterations in Group 2, and its expression levels negatively correlated with methylation determined by bisulfite sequencing. Within group 2B, low and high AXIN2 expression levels correlated significantly with differences in size (P = 0.01) location (P = 0.05) and crypt architecture (P = 0.01).ConclusionsSomatic methylation alterations of AXIN2, associated with changes in its expression, stratify SSAs according to some clinico-pathological differences. We conclude that hypermethylation of MLH1, when occurs in an adenoma cell with BRAF oncogenic mutational activation, drives the pathway for MSI cancer by providing the cells with a mutator phenotype. AXIN2 inactivation may contribute to this tumorigenic pathway either by mutator phenotype driven frameshift mutations or by epigenetic deregulation contemporary with the unfolding of the mutator phenotype.

Project description:BackgroundObservations of recurrent somatic mutations in tumors have led to identification and definition of signaling and other pathways that are important for cancer progression and therapeutic targeting. As tumor cells contain both an individual's inherited genetic variants and somatic mutations, challenges arise in distinguishing these events in massively parallel sequencing datasets. Typically, both a tumor sample and a "normal" sample from the same individual are sequenced and compared; variants observed only in the tumor are considered to be somatic mutations. However, this approach requires two samples for each individual.ResultsWe evaluate a method of detecting somatic mutations in tumor samples for which only a subset of normal samples are available. We describe tuning of the method for detection of mutations in tumors, filtering to remove inherited variants, and comparison of detected mutations to several matched tumor/normal analysis methods. Filtering steps include the use of population variation datasets to remove inherited variants as well a subset of normal samples to remove technical artifacts. We then directly compare mutation detection with tumor-only and tumor-normal approaches using the same sets of samples. Comparisons are performed using an internal targeted gene sequencing dataset (n = 3380) as well as whole exome sequencing data from The Cancer Genome Atlas project (n = 250). Tumor-only mutation detection shows similar recall (43-60%) but lesser precision (20-21%) to current matched tumor/normal approaches (recall 43-73%, precision 30-82%) when compared to a "gold-standard" tumor/normal approach. The inclusion of a small pool of normal samples improves precision, although many variants are still uniquely detected in the tumor-only analysis.ConclusionsA detailed method for somatic mutation detection without matched normal samples enables study of larger numbers of tumor samples, as well as tumor samples for which a matched normal is not available. As sensitivity/recall is similar to tumor/normal mutation detection but precision is lower, tumor-only detection is more appropriate for classification of samples based on known mutations. Although matched tumor-normal analysis is preferred due to higher precision, we demonstrate that mutation detection without matched normal samples is possible for certain applications.

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Tumor-Normal matched colon adenomas and carcinomas

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