Chao-Jen Wong

Fred Hutchinson Cancer Research Center, Seattle, Washington, United States

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Publications (6)38.52 Total impact

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    ABSTRACT: & Aims: Genetic and epigenetic alterations contribute to the pathogenesis of colorectal cancer (CRC). There is considerable molecular heterogeneity among colorectal tumors, which appears to arise as polyps progress to cancer. This heterogeneity results in different pathways to tumorigenesis. Although epigenetic and genetic alterations have been detected in conventional tubular adenomas, little is known about how these affect progression to CRC. We compared methylomes of normal colon mucosa, tubular adenomas, and colorectal cancers to determine how epigenetic alterations might contribute to cancer formation. We conducted genome-wide array-based studies and comprehensive data analyses of aberrantly methylated loci in 41 normal colon tissue, 42 colon adenomas, and 64 cancers using HumanMethylation450 arrays. We found genome-wide alterations in DNA methylation in the non-tumor colon mucosa adjacent to tubular adenomas and cancers. Three classes of cancers and 2 classes of adenomas were identified based on their DNA methylation patterns. The adenomas separated into classes of high-frequency methylation (adenoma-H), and low-frequency methylation (adenoma-L). Within the adenoma-H class a subset of adenomas had mutant KRAS. Additionally, the adenoma-H class had DNA methylation signatures similar to those of cancers with low or intermediate levels of methylation, whereas the adenoma-L class had methylation signatures similar to that of non-tumor colon tissue. The CpGs sites that were differentially methylated in these signatures are located in intragenic and intergenic regions. Genome-wide alterations in DNA methylation occur during early stages of progression of tubular adenomas to cancer. These findings reveal heterogeneity in the pathogenesis of colorectal cancer, even at the adenoma step of the process.
    Gastroenterology 04/2014; · 12.82 Impact Factor
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    ABSTRACT: Alterations in DNA methylation have been proposed to create a field cancerization state in the colon, where molecular alterations that predispose cells to transformation occur in histologically normal tissue. However, our understanding of the role of DNA methylation in field cancerization is limited by an incomplete characterization of the methylation state of the normal colon. In order to determine the colon's normal methylation state, we extracted DNA from normal colon biopsies from the rectum, sigmoid, transverse, and ascending colon and assessed the methylation status of the DNA by pyrosequencing candidate loci as well as with HumanMethylation450 arrays. We found that methylation levels of repetitive elements LINE-1 and SAT-α showed minimal variability throughout the colon in contrast to other loci. Promoter methylation of EVL was highest in the rectum and progressively lower in the proximal segments, whereas ESR1 methylation was higher in older individuals. Genome-wide methylation analysis of normal DNA revealed 8388, 82, and 93 differentially methylated loci that distinguished right from left colon, males from females, and older vs. younger individuals, respectively. Although variability in methylation between biopsies and among different colon segments was minimal for repetitive elements, analyses of specific cancer-related genes as well as a genome-wide methylation analysis demonstrated differential methylation based on colon location, individual age, and gender. These studies advance our knowledge regarding the variation of DNA methylation in the normal colon, a prerequisite for future studies aimed at understanding methylation differences indicative of a colon field effect.
    Epigenetics: official journal of the DNA Methylation Society 01/2014; 9(4). · 4.58 Impact Factor
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    ABSTRACT: Barrett's esophagus (BE) is a metaplastic process whereby the normal stratified, squamous esophageal epithelium is replaced by specialized intestinal epithelium. Barrett's is the only accepted precursor lesion for esophageal adenocarcinoma (EAC), a solid tumor that is rapidly increasing in incidence in western countries. BE evolves into EAC through intermediate steps that involve increasing degrees of dysplasia. Current histologic criteria are quite subjective and the clinical behavior of BE is highly variable and difficult to predict using these standards. It is widely believed that molecular alterations present in BE and EAC will provide more precise prognostic and predictive markers for these conditions than the current clinical and histologic features in use. In order to further define molecular alterations that can classify unique groups of BE and EAC, we utilized methylation microarrays to compare the global gene methylation status of a collection of normal squamous, BE, BE + high-grade dysplasia (HGD), and EAC cases. We found distinct global methylation signatures, as well as differential methylation of specific genes, that discriminated these histological groups. We also noted high and low methylation epigenotypes among the BE and EAC cases. Additional validation of those CpG sites that distinguished BE from BE + HGD and EAC may lead to the discovery of useful biomarkers with potential clinical applications in the diagnosis and prognosis of BE and EAC.
    Epigenetics: official journal of the DNA Methylation Society 12/2011; 6(12):1403-12. · 4.58 Impact Factor
  • Gastroenterology 01/2011; 140(5). · 12.82 Impact Factor
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    ABSTRACT: Between-sample variation in high-throughput flow cytometry data poses a significant challenge for analysis of large-scale data sets, such as those derived from multicenter clinical trials. It is often hard to match biologically relevant cell populations across samples because of technical variation in sample acquisition and instrumentation differences. Thus, normalization of data is a critical step before analysis, particularly in large-scale data sets from clinical trials, where group-specific differences may be subtle and patient-to-patient variation common. We have developed two normalization methods that remove technical between-sample variation by aligning prominent features (landmarks) in the raw data on a per-channel basis. These algorithms were tested on two independent flow cytometry data sets by comparing manually gated data, either individually for each sample or using static gating templates, before and after normalization. Our results show a marked improvement in the overlap between manual and static gating when the data are normalized, thereby facilitating the use of automated analyses on large flow cytometry data sets. Such automated analyses are essential for high-throughput flow cytometry.
    Cytometry Part A 11/2009; 77(2):121-31. · 3.71 Impact Factor
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    Martin Morgan, Chao-Jen Wong
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    ABSTRACT: 1 Structures for genomic data: ExpressionSet Genomic data can be very complex, usually consisting of a number of different bits and pieces. In Bioconductor we have taken the approach that these pieces should be stored in a single structure to easily manage the data. The package Biobase contains standardized data structures to represent genomic data. The ExpressionSet class is designed to combine several different sources of informa-tion into a single convenient structure. An ExpressionSet can be manipulated (e.g., subsetted, copied), and is the input to or output of many Bioconductor functions. The data in an ExpressionSet consist of • assayData: Expression data from microarray experiments (assayData is used to hint at the methods used to access different data components, as we show below). • metadata: A description of the samples in the experiment (phenoData), metadata about the features on the chip or technology used for the experi-ment (featureData), and further annotations for the features, for example gene annotations from biomedical databases (annotation). • experimentData: A flexible structure to describe the experiment. The ExpressionSet class coordinates all of these data, so that you do not usually have to worry about the details. However, an ExpressionSet needs to be created in the first place, because it will be the starting point for many of the analyses using Bioconductor software. ExpressionSet instances are created in one of two ways. Often, an Expres-sionSet is the output of an R function. For instance, justRMA in the affy Biocon-ductor package reads in manufacturer CEL files and outputs an ExpressionSet.