Discovery and prioritization of somatic mutations in diffuse large B-cell lymphoma (DLBCL) by whole-exome sequencing

Eli and Edythe Broad Institute, Cambridge, MA 02412, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 03/2012; 109(10):3879-84. DOI: 10.1073/pnas.1121343109
Source: PubMed

ABSTRACT To gain insight into the genomic basis of diffuse large B-cell lymphoma (DLBCL), we performed massively parallel whole-exome sequencing of 55 primary tumor samples from patients with DLBCL and matched normal tissue. We identified recurrent mutations in genes that are well known to be functionally relevant in DLBCL, including MYD88, CARD11, EZH2, and CREBBP. We also identified somatic mutations in genes for which a functional role in DLBCL has not been previously suspected. These genes include MEF2B, MLL2, BTG1, GNA13, ACTB, P2RY8, PCLO, and TNFRSF14. Further, we show that BCL2 mutations commonly occur in patients with BCL2/IgH rearrangements as a result of somatic hypermutation normally occurring at the IgH locus. The BCL2 point mutations are primarily synonymous, and likely caused by activation-induced cytidine deaminase-mediated somatic hypermutation, as shown by comprehensive analysis of enrichment of mutations in WRCY target motifs. Those nonsynonymous mutations that are observed tend to be found outside of the functionally important BH domains of the protein, suggesting that strong negative selection against BCL2 loss-of-function mutations is at play. Last, by using an algorithm designed to identify likely functionally relevant but infrequent mutations, we identify KRAS, BRAF, and NOTCH1 as likely drivers of DLBCL pathogenesis in some patients. Our data provide an unbiased view of the landscape of mutations in DLBCL, and this in turn may point toward new therapeutic strategies for the disease.

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    • "Awide varietyof tumors express HVEM and, thus, have the potential to inhibit the proliferation of Vg9Vd2 þ T cells. Interestingly, several groups have reported that a high percentage of adult onset and pediatric follicular lymphoma and diffuse large B-cell lymphoma contain deletions in the gene-encoding HVEM (TNFRSF14), and are associated with worse prognosis (Cheung et al. 2010; Launay et al. 2012; Lohr et al. 2012; Bjordahl et al. 2013; Martin-Guerrero et al. 2013). We previously argued that TNFRSF14 deletions may be acquired in more aggressive tumors as an adaptation to prevent NK cell costimulation through CD160 (S ˇ edyét al. 2013). "
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    ABSTRACT: The tumor necrosis factor superfamily (TNFSF) and its corresponding receptor superfamily (TNFRSF) form communication pathways required for developmental, homeostatic, and stimulus-responsive processes in vivo. Although this receptor-ligand system operates between many different cell types and organ systems, many of these proteins play specific roles in immune system function. The TNFSF and TNFRSF proteins lymphotoxins, LIGHT (homologous to lymphotoxins, exhibits inducible expression, and competes with HSV glycoprotein D for herpes virus entry mediator [HVEM], a receptor expressed by T lymphocytes), lymphotoxin-β receptor (LT-βR), and HVEM are used by embryonic and adult innate lymphocytes to promote the development and homeostasis of lymphoid organs. Lymphotoxin-expressing innate-acting B cells construct microenvironments in lymphoid organs that restrict pathogen spread and initiate interferon defenses. Recent results illustrate how the communication networks formed among these cytokines and the coreceptors B and T lymphocyte attenuator (BTLA) and CD160 both inhibit and activate innate lymphoid cells (ILCs), innate γδ T cells, and natural killer (NK) cells. Understanding the role of TNFSF/TNFRSF and interacting proteins in innate cells will likely reveal avenues for future therapeutics for human disease. Copyright © 2014 Cold Spring Harbor Laboratory Press; all rights reserved.
    Cold Spring Harbor perspectives in biology 12/2014; 7(4). DOI:10.1101/cshperspect.a016279 · 8.68 Impact Factor
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    • "A recent study analyzing multiple tumor types reported that over 70% of 140 recurrently altered regions did not contain a known oncogene or tumor suppressor (Zack et al., 2013). As a result, most recent driver discovery efforts have focused on point mutations, which directly indicate the target genes by virtue of their precise location (Kandoth et al., 2013; Lohr et al., 2012; Wong et al., 2011), and less progress has been made with respect to SCNAs. However, the increased frequency of recurring SCNAs relative to point mutations (87 SCNA regions versus six mutated genes with >5% population frequency) (Figure 1A) highlights the need for methods to pinpoint drivers within these regions. "
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    ABSTRACT: Identifying driver genes in cancer remains a crucial bottleneck in therapeutic development and basic understanding of the disease. We developed Helios, an algorithm that integrates genomic data from primary tumors with data from functional RNAi screens to pinpoint driver genes within large recurrently amplified regions of DNA. Applying Helios to breast cancer data identified a set of candidate drivers highly enriched with known drivers (p < 10(-14)). Nine of ten top-scoring Helios genes are known drivers of breast cancer, and in vitro validation of 12 candidates predicted by Helios found ten conferred enhanced anchorage-independent growth, demonstrating Helios's exquisite sensitivity and specificity. We extensively characterized RSF-1, a driver identified by Helios whose amplification correlates with poor prognosis, and found increased tumorigenesis and metastasis in mouse models. We have demonstrated a powerful approach for identifying driver genes and how it can yield important insights into cancer. Copyright © 2014 Elsevier Inc. All rights reserved.
    Cell 11/2014; 159(6). DOI:10.1016/j.cell.2014.10.048 · 32.24 Impact Factor
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    • "We applied PatternCNV to a set of 15 germ line–tumor pairs of diffuse large B-cell lymphoma exome-seq data (Lohr et al., 2012). When comparing CNV results derived from exome-seq using PatternCNV with those calculated from SNP microarray data profiled on the same samples, the two sets of results largely correlate for large CNVs. "
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    ABSTRACT: Motivation: Exome sequencing (exome-seq) data, which are typically used for calling exonic mutations, have also been utilized in detecting DNA copy number variations (CNVs). Despite the existence of several CNV detection tools, there is still a great need for a sensitive and an accurate CNV-calling algorithm with built-in QC steps, and does not require a paired reference for each sample.Results: We developed a novel method named PatternCNV, which (i) accounts for the read coverage variations between exons while leveraging the consistencies of this variability across different samples; (ii) reduces alignment BAM files to WIG format and therefore greatly accelerates computation; (iii) incorporates multiple QC measures designed to identify outlier samples and batch effects; and (iv) provides a variety of visualization options including chromosome, gene and exon-level views of CNVs, along with a tabular summarization of the exon-level CNVs. Compared with other CNV-calling algorithms using data from a lymphoma exome-seq study, PatternCNV has higher sensitivity and specificity.Availability and implementation: The software for PatternCNV is implemented using Perl and R, and can be used in Mac or Linux environments. Software and user manual are available at, and R package at Asmann.Yan@mayo.eduSupplementary information: Supplementary data are available at Bioinformatics online.
    Bioinformatics 05/2014; 30(18). DOI:10.1093/bioinformatics/btu363 · 4.98 Impact Factor
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