Pei Fen Kuan

Mutations in isocitrate dehydrogenase 1 and 2 occur frequently in intrahepatic cholangiocarcinomas and share hypermethylation targets with glioblastomas

Mutations in the genes encoding isocitrate dehydrogenase, IDH1 and IDH2, have been reported in gliomas, myeloid leukemias, chondrosarcomas and thyroid cancer. We discovered IDH1 and IDH2 mutations in 34 of 326 (10%) intrahepatic cholangiocarcinomas. Tumor with mutations in IDH1 or IDH2 had lower 5-hydroxymethylcytosine and higher 5-methylcytosine levels, as well as increased dimethylation of histone H3 lysine 79 (H3K79). Mutations in IDH1 or IDH2 were associated with longer overall survival (P=0.028) and were independently associated with a longer time to tumor recurrence after intrahepatic cholangiocarcinoma resection in multivariate analysis (P=0.021). IDH1 and IDH2 mutations were significantly associated with increased levels of p53 in intrahepatic cholangiocarcinomas, but no mutations in the p53 gene were found, suggesting that mutations in IDH1 and IDH2 may cause a stress that leads to p53 activation. We identified 2309 genes that were significantly hypermethylated in 19 cholangiocarcinomas with mutations in IDH1 or IDH2, compared with cholangiocarcinomas without these mutations. Hypermethylated CpG sites were significantly enriched in CpG shores and upstream of transcription start sites, suggesting a global regulation of transcriptional potential. Half of the hypermethylated genes overlapped with DNA hypermethylation in IDH1-mutant gliobastomas, suggesting the existence of a common set of genes whose expression may be affected by mutations in IDH1 or IDH2 in different types of tumors.

Rho directs widespread termination of intragenic and stable RNA transcription.

The transcription termination factor Rho is a global regulator of RNA polymerase (RNAP). Although individual Rho-dependent terminators have been studied extensively, less is known about the sites of RNAP regulation by Rho on a genome-wide scale. Using chromatin immunoprecipitation and microarrays (ChIP-chip), we examined changes in the distribution of Escherichia coli RNAP in response to the Rho-specific inhibitor bicyclomycin (BCM). We found ≈200 Rho-terminated loci that were divided evenly into 2 classes: intergenic (at the ends of genes) and intragenic (within genes). The intergenic class contained noncoding RNAs such as small RNAs (sRNAs) and transfer RNAs (tRNAs), establishing a previously unappreciated role of Rho in termination of stable RNA synthesis. The intragenic class of terminators included a previously uncharacterized set of short antisense transcripts, as judged by a shift in the distribution of RNAP in BCM-treated cells that was opposite to the direction of the corresponding gene. These Rho-terminated antisense transcripts point to a role of noncoding transcription in E. coli gene regulation that may resemble the ubiquitous noncoding transcription recently found to play myriad roles in eukaryotic gene regulation.

Median Absolute Deviation to Improve Hit Selection for Genome-Scale RNAi Screens

High-throughput screening (HTS) of large-scale RNA interference (RNAi) libraries has become an increasingly popular method of functional genomics in recent years. Cell-based assays used for RNAi screening often produce small dynamic ranges and significant variability because of the combination of cellular heterogeneity, transfection efficiency, and the intrinsic nature of the genes being targeted. These properties make reliable hit selection in the RNAi screen a difficult task. The use of robust methods based on median and median absolute deviation (MAD) has been suggested to improve hit selection in such cases, but mean and standard deviation (SD)—based methods are still predominantly used in many RNAi HTS. In an experimental approach to compare these 2 methods, a genome-scale small interfering RNA (siRNA) screen was performed, in which the identification of novel targets increasing the therapeutic index of the chemotherapeutic agent mitomycin C (MMC) was sought. MAD values were resistant to the presence of outliers, and the hits selected by the MAD-based method included all the hits that would be selected by SD-based method as well as a significant number of additional hits. When retested in triplicate, a similar percentage of these siRNAs were shown to genuinely sensitize cells to MMC compared with the hits shared between SD- and MAD-based methods. Confirmed hits were enriched with the genes involved in the DNA damage response and cell cycle regulation, validating the overall hit selection strategy. Finally, computer simulations showed the superiority and generality of the MAD-based method in various RNAi HTS data models. In conclusion, the authors demonstrate that the MAD-based hit selection method rescued physiologically relevant false negatives that would have been missed in the SD-based method, and they believe it to be the desirable 1st-choice hit selection method for RNAi screen results. ( Journal of Biomolecular Screening 2008:149-158)

Variation in chromatin accessibility in human kidney cancer links H3K36 methyltransferase loss with widespread RNA processing defects.

Comprehensive sequencing of human cancers has identified recurrent mutations in genes encoding chromatin regulatory proteins. For clear cell renal cell carcinoma (ccRCC), three of the five commonly mutated genes encode the chromatin regulators PBRM1, SETD2, and BAP1. How these mutations alter the chromatin landscape and transcriptional program in ccRCC or other cancers is not understood. Here, we identified alterations in chromatin organization and transcript profiles associated with mutations in chromatin regulators in a large cohort of primary human kidney tumors. By associating variation in chromatin organization with mutations in SETD2, which encodes the enzyme responsible for H3K36 trimethylation, we found that changes in chromatin accessibility occurred primarily within actively transcribed genes. This increase in chromatin accessibility was linked with widespread alterations in RNA processing, including intron retention and aberrant splicing, affecting ∼25% of all expressed genes. Furthermore, decreased nucleosome occupancy proximal to misspliced exons was observed in tumors lacking H3K36me3. These results directly link mutations in SETD2 to chromatin accessibility changes and RNA processing defects in cancer. Detecting the functional consequences of specific mutations in chromatin regulatory proteins in primary human samples could ultimately inform the therapeutic application of an emerging class of chromatin-targeted compounds.

DiffSplice: The genome-wide detection of differential splicing events with RNA-seq

The RNA transcriptome varies in response to cellular differentiation as well as environmental factors, and can be characterized by the diversity and abundance of transcript isoforms. Differential transcription analysis, the detection of differences between the transcriptomes of different cells, may improve understanding of cell differentiation and development and enable the identification of biomarkers that classify disease types. The availability of high-throughput short-read RNA sequencing technologies provides in-depth sampling of the transcriptome, making it possible to accurately detect the differences between transcriptomes. In this article, we present a new method for the detection and visualization of differential transcription. Our approach does not depend on transcript or gene annotations. It also circumvents the need for full transcript inference and quantification, which is a challenging problem because of short read lengths, as well as various sampling biases. Instead, our method takes a divide-and-conquer approach to localize the difference between transcriptomes in the form of alternative splicing modules (ASMs), where transcript isoforms diverge. Our approach starts with the identification of ASMs from the splice graph, constructed directly from the exons and introns predicted from RNA-seq read alignments. The abundance of alternative splicing isoforms residing in each ASM is estimated for each sample and is compared across sample groups. A non-parametric statistical test is applied to each ASM to detect significant differential transcription with a controlled false discovery rate. The sensitivity and specificity of the method have been assessed using simulated data sets and compared with other state-of-the-art approaches. Experimental validation using qRT-PCR confirmed a selected set of genes that are differentially expressed in a lung differentiation study and a breast cancer data set, demonstrating the utility of the approach applied on experimental biological data sets. The software of DiffSplice is available at http://www.netlab.uky.edu/p/bioinfo/DiffSplice.

A statistical framework for Illumina DNA methylation arrays

MOTIVATION The Illumina BeadArray is a popular platform for profiling DNA methylation, an important epigenetic event associated with gene silencing and chromosomal instability. However, current approaches rely on an arbitrary detection P-value cutoff for excluding probes and samples from subsequent analysis as a quality control step, which results in missing observations and information loss. It is desirable to have an approach that incorporates the whole data, but accounts for the different quality of individual observations. RESULTS We first investigate and propose a statistical framework for removing the source of biases in Illumina Methylation BeadArray based on several positive control samples. We then introduce a weighted model-based clustering called LumiWCluster for Illumina BeadArray that weights each observation according to the detection P-values systematically and avoids discarding subsets of the data. LumiWCluster allows for discovery of distinct methylation patterns and automatic selection of informative CpG loci. We demonstrate the advantages of LumiWCluster on two publicly available Illumina GoldenGate Methylation datasets (ovarian cancer and hepatocellular carcinoma). AVAILABILITY R package LumiWCluster can be downloaded from http://www.unc.edu/∼pfkuan/LumiWCluster.

HIF1α and HIF2α independently activate SRC to promote melanoma metastases

Malignant melanoma is characterized by a propensity for early lymphatic and hematogenous spread. The hypoxia-inducible factor (HIF) family of transcription factors is upregulated in melanoma by key oncogenic drivers. HIFs promote the activation of genes involved in cancer initiation, progression, and metastases. Hypoxia has been shown to enhance the invasiveness and metastatic potential of tumor cells by regulating the genes involved in the breakdown of the ECM as well as genes that control motility and adhesion of tumor cells. Using a Pten-deficient, Braf-mutant genetically engineered mouse model of melanoma, we demonstrated that inactivation of HIF1α or HIF2α abrogates metastasis without affecting primary tumor formation. HIF1α and HIF2α drive melanoma invasion and invadopodia formation through PDGFRα and focal adhesion kinase-mediated (FAK-mediated) activation of SRC and by coordinating ECM degradation via MT1-MMP and MMP2 expression. These results establish the importance of HIFs in melanoma progression and demonstrate that HIF1α and HIF2α activate independent transcriptional programs that promote metastasis by coordinately regulating cell invasion and ECM remodeling.

MERTK receptor tyrosine kinase is a therapeutic target in melanoma

Metastatic melanoma is one of the most aggressive forms of cutaneous cancers. Although recent therapeutic advances have prolonged patient survival, the prognosis remains dismal. C-MER proto-oncogene tyrosine kinase (MERTK) is a receptor tyrosine kinase with oncogenic properties that is often overexpressed or activated in various malignancies. Using both protein immunohistochemistry and microarray analyses, we demonstrate that MERTK expression correlates with disease progression. MERTK expression was highest in metastatic melanomas, followed by primary melanomas, while the lowest expression was observed in nevi. Additionally, over half of melanoma cell lines overexpressed MERTK compared with normal human melanocytes; however, overexpression did not correlate with mutations in BRAF or RAS. Stimulation of melanoma cells with the MERTK ligand GAS6 resulted in the activation of several downstream signaling pathways including MAPK/ERK, PI3K/AKT, and JAK/STAT. MERTK inhibition via shRNA reduced MERTK-mediated downstream signaling, reduced colony formation by up to 59%, and diminished tumor volume by 60% in a human melanoma murine xenograft model. Treatment of melanoma cells with UNC1062, a novel MERTK-selective small-molecule tyrosine kinase inhibitor, reduced activation of MERTK-mediated downstream signaling, induced apoptosis in culture, reduced colony formation in soft agar, and inhibited invasion of melanoma cells. This work establishes MERTK as a therapeutic target in melanoma and provides a rationale for the continued development of MERTK-targeted therapies.

A Statistical Framework for the Analysis of ChIP-Seq Data

Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has revolutionalized experiments for genome-wide profiling of DNA-binding proteins, histone modifications, and nucleosome occupancy. As the cost of sequencing is decreasing, many researchers are switching from microarray-based technologies (ChIP-chip) to ChIP-Seq for genome-wide study of transcriptional regulation. Despite its increasing and well-deserved popularity, there is little work that investigates and accounts for sources of biases in the ChIP-Seq technology. These biases typically arise from both the standard preprocessing protocol and the underlying DNA sequence of the generated data. We study data from a naked DNA sequencing experiment, which sequences noncross-linked DNA after deproteinizing and shearing, to understand factors affecting background distribution of data generated in a ChIP-Seq experiment. We introduce a background model that accounts for apparent sources of biases such as mappability and GC content and develop a flexible mixture model named MOSAiCS for detecting peaks in both one- and two-sample analyses of ChIP-Seq data. We illustrate that our model fits observed ChIP-Seq data well and further demonstrate advantages of MOSAiCS over commonly used tools for ChIP-Seq data analysis with several case studies. This article has supplementary material online.

DNA‐methylation profiling distinguishes malignant melanomas from benign nevi

DNA methylation, an epigenetic alteration typically occurring early in cancer development, could aid in the molecular diagnosis of melanoma. We determined technical feasibility for high‐throughput DNA‐methylation array‐based profiling using formalin‐fixed paraffin‐embedded tissues for selection of candidate DNA‐methylation differences between melanomas and nevi. Promoter methylation was evaluated in 27 common benign nevi and 22 primary invasive melanomas using a 1505 CpG site microarray. Unsupervised hierarchical clustering distinguished melanomas from nevi; 26 CpG sites in 22 genes were identified with significantly different methylation levels between melanomas and nevi after adjustment for age, sex, and multiple comparisons and with β‐value differences of ≥0.2. Prediction analysis for microarrays identified 12 CpG loci that were highly predictive of melanoma, with area under the receiver operating characteristic curves of >0.95. Of our panel of 22 genes, 14 were statistically significant in an independent sample set of 29 nevi (including dysplastic nevi) and 25 primary invasive melanomas after adjustment for age, sex, and multiple comparisons. This first report of a DNA‐methylation signature discriminating melanomas from nevi indicates that DNA methylation appears promising as an additional tool for enhancing melanoma diagnosis.