Nyasha Chambwe

Variability in DNA methylation defines novel epigenetic subgroups of DLBCL associated with different clinical outcomes

Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive form of non-Hodgkin lymphoma with variable biology and clinical behavior. The current classification does not fully explain the biological and clinical heterogeneity of DLBCLs. In this study, we carried out genomewide DNA methylation profiling of 140 DLBCL samples and 10 normal germinal center B cells using the HpaII tiny fragment enrichment by ligation-mediated polymerase chain reaction assay and hybridization to a custom Roche NimbleGen promoter array. We defined methylation disruption as a main epigenetic event in DLBCLs and designed a method for measuring the methylation variability of individual cases. We then used a novel approach for unsupervised hierarchical clustering based on the extent of DNA methylation variability. This approach identified 6 clusters (A-F). The extent of methylation variability was associated with survival outcomes, with significant differences in overall and progression-free survival. The novel clusters are characterized by disruption of specific biological pathways such as cytokine-mediated signaling, ephrin signaling, and pathways associated with apoptosis and cell-cycle regulation. In a subset of patients, we profiled gene expression and genomic variation to investigate their interplay with methylation changes. This study is the first to identify novel epigenetic clusters of DLBCLs and their aberrantly methylated genes, molecular associations, and survival.

Differential gene body methylation and reduced expression of cell adhesion and neurotransmitter receptor genes in adverse maternal environment.

Early life adversity, including adverse gestational and postpartum maternal environment, is a contributing factor in the development of autism, attention deficit hyperactivity disorder (ADHD), anxiety and depression but little is known about the underlying molecular mechanism. In a model of gestational maternal adversity that leads to innate anxiety, increased stress reactivity and impaired vocal communication in the offspring, we asked if a specific DNA methylation signature is associated with the emergence of the behavioral phenotype. Genome-wide DNA methylation analyses identified 2.3% of CpGs as differentially methylated (that is, differentially methylated sites, DMSs) by the adverse environment in ventral-hippocampal granule cells, neurons that can be linked to the anxiety phenotype. DMSs were typically clustered and these clusters were preferentially located at gene bodies. Although CpGs are typically either highly methylated or unmethylated, DMSs had an intermediate (20–80%) methylation level that may contribute to their sensitivity to environmental adversity. The adverse maternal environment resulted in either hyper or hypomethylation at DMSs. Clusters of DMSs were enriched in genes that encode cell adhesion molecules and neurotransmitter receptors; some of which were also downregulated, indicating multiple functional deficits at the synapse in adversity. Pharmacological and genetic evidence links many of these genes to anxiety.

Compression of Structured High-Throughput Sequencing Data

Large biological datasets are being produced at a rapid pace and create substantial storage challenges, particularly in the domain of high-throughput sequencing (HTS). Most approaches currently used to store HTS data are either unable to quickly adapt to the requirements of new sequencing or analysis methods (because they do not support schema evolution), or fail to provide state of the art compression of the datasets. We have devised new approaches to store HTS data that support seamless data schema evolution and compress datasets substantially better than existing approaches. Building on these new approaches, we discuss and demonstrate how a multi-tier data organization can dramatically reduce the storage, computational and network burden of collecting, analyzing, and archiving large sequencing datasets. For instance, we show that spliced RNA-Seq alignments can be stored in less than 4% the size of a BAM file with perfect data fidelity. Compared to the previous compression state of the art, these methods reduce dataset size more than 40% when storing exome, gene expression or DNA methylation datasets. The approaches have been integrated in a comprehensive suite of software tools (http://goby.campagnelab.org) that support common analyses for a range of high-throughput sequencing assays.

GobyWeb: Simplified Management and Analysis of Gene Expression and DNA Methylation Sequencing Data

We present GobyWeb, a web-based system that facilitates the management and analysis of high-throughput sequencing (HTS) projects. The software provides integrated support for a broad set of HTS analyses and offers a simple plugin extension mechanism. Analyses currently supported include quantification of gene expression for messenger and small RNA sequencing, estimation of DNA methylation (i.e., reduced bisulfite sequencing and whole genome methyl-seq), or the detection of pathogens in sequenced data. In contrast to previous analysis pipelines developed for analysis of HTS data, GobyWeb requires significantly less storage space, runs analyses efficiently on a parallel grid, scales gracefully to process tens or hundreds of multi-gigabyte samples, yet can be used effectively by researchers who are comfortable using a web browser. We conducted performance evaluations of the software and found it to either outperform or have similar performance to analysis programs developed for specialized analyses of HTS data. We found that most biologists who took a one-hour GobyWeb training session were readily able to analyze RNA-Seq data with state of the art analysis tools. GobyWeb can be obtained at http://gobyweb.campagnelab.org and is freely available for non-commercial use. GobyWeb plugins are distributed in source code and licensed under the open source LGPL3 license to facilitate code inspection, reuse and independent extensions http://github.com/CampagneLaboratory/gobyweb2-plugins.

BDVal: reproducible large-scale predictive model development and validation in high-throughput datasets

UNLABELLED High-throughput data can be used in conjunction with clinical information to develop predictive models. Automating the process of developing, evaluating and testing such predictive models on different datasets would minimize operator errors and facilitate the comparison of different modeling approaches on the same dataset. Complete automation would also yield unambiguous documentation of the process followed to develop each model. We present the BDVal suite of programs that fully automate the construction of predictive classification models from high-throughput data and generate detailed reports about the model construction process. We have used BDVal to construct models from microarray and proteomics data, as well as from DNA-methylation datasets. The programs are designed for scalability and support the construction of thousands of alternative models from a given dataset and prediction task. AVAILABILITY AND IMPLEMENTATION The BDVal programs are implemented in Java, provided under the GNU General Public License and freely available at http://bdval.campagnelab.org.

Extracellular vesicles in DLBCL provide abundant clues to aberrant transcriptional programming and genomic alterations.

The biological role of extracellular vesicles (EVs) in diffuse large B-cell lymphoma (DLBCL) initiation and progression remains largely unknown. We characterized EVs secreted by 5 DLBCL cell lines, a primary DLBCL tumor, and a normal control B-cell sample, optimized their purification, and analyzed their content. We found that DLBCLs secreted large quantities of CD63, Alix, TSG101, and CD81 EVs, which can be extracted using an ultracentrifugation-based method and traced by their cell of origin surface markers. We also showed that tumor-derived EVs can be exchanged between lymphoma cells, normal tonsillar cells, and HK stromal cells. We then examined the content of EVs, focusing on isolation of high-quality total RNA. We sequenced the total RNA and analyzed the nature of RNA species, including coding and noncoding RNAs. We compared whole-cell and EV-derived RNA composition in benign and malignant B cells and discovered that transcripts from EVs were involved in many critical cellular functions. Finally, we performed mutational analysis and found that mutations detected in EVs exquisitely represented mutations in the cell of origin. These results enhance our understanding and enable future studies of the role that EVs may play in the pathogenesis of DLBCL, particularly with regards to the exchange of genomic information. Current findings open a new strategy for liquid biopsy approaches in disease monitoring.

Abstract LB-235: IL10 autoregulatory loop in DLBCLs: New biomarker and a therapeutic target

Diffuse Large B cell Lymphoma (DLBCL) is a common aggressive lymphoma that represents 30-40% of newly diagnosed cases of non-Hodgkin Lymphomas, but accounts for up to 80% of lymphoma-related mortality. It is biologically and clinically heterogeneous disease with variable response to conventional R-CHOP chemotherapy. R-CHOP remains the standard first line therapy after decades of investigation, but is associated with frequent lack of response. It has been reported that more clinically aggressive cases of DLBCLs have constitutive activation of NF-kB and STAT3 and have higher level of circulating IL10 cytokine in patient9s peripheral. We further investigated the role of IL10 and its surface receptor in supporting the neoplastic phenotype of DLBCL. We measured and analyzed copy number changes using SNP array on a subset of 91 primary DLBCLs and identified broad regions of genomic amplification and deletion in this cohort using the GISTIC algorithm and determined that Il10RA is amplified in 17% and IL10RB in 8 % of primary DLBCLs. Gene expression for all 3 genes is markedly elevated, as determined using Affymetrix HG U133 plus 2.0 array data on 59 primary DLBCLs: up to 3 fold for IL10RA, and more than 10 fold for IL10RB and IL10 cytokine as compared to normal Germinal center B cells (NGCB)(all t-test, p We thus hypothesized that DLBCLs are dependent on the feed-forward autostimulatory loop that starts from autocrine IL10 stimulation through overexpressed receptor leading to cell proliferation and that bliocking the receptor will lead to cell death. We tested the effect of blocking the receptor using anti-IL10R Ab in a panel of 12 cell lines and 5 primary DLBCLs cultured ex-vivo. The Ab effect was dose-dependent and cell death ensued after 1-3 days of treatment. Within 3 days of treatment with 1 ug/ml most cell lines had reduced viability by more than 50%, and after treatment with 10 ug/ml all cell lines were more than 90% dead. On day 3 massive induction of apoptosis was detected in all DLBCLs using standard approaches: measuring Annexin V/ DAPI by flow cytometry and PARP-1 cleavage by western blot. We determined that blocking IL10R results in specific inhibition of signaling through JAK1/2 and loss of phosphorylation at STAT3Y705 immediately after treatment and inhibition of signaling through MAPK and phosphorylation of STAT3S727 at later treatment time points. The inhibition of signaling is sustained for days with only one drug treatment leading to induction of apoptosis. We observed downregulation of the known transcriptional targets of STAT3 that are crucial for maintaining cell cycle and proliferation like CCND1, CCND2, CMYC, JUNB. Anti-IL10R treatment resulted in significant downregulation of IL10 and IL10RA transcription, thus leading to interruption of IL10-IL10R autostimulatory loop. We thus propose that IL10R is a novel therapeutic target in DLBCLs that allows easy detection and targeting. Our findings warranty further animal studies and development of humanized antibody for clinical use in patients. Citation Format: Wendy Beguelin, Seema Sawh, Nyasha Chambwe, Huimin Geng, Yanwen Jiang, Pilar M. Dominguez, Wayne Tam, Rita Shaknovich. IL10 autoregulatory loop in DLBCLs: New biomarker and a therapeutic target. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-235. doi:10.1158/1538-7445.AM2014-LB-235