scholarly journals Genome-wide ChIP-seq analysis of EZH2-mediated H3K27me3 target gene profile highlights differences between low- and high-grade astrocytic tumors

2016 ◽  
pp. bgw126 ◽  
Author(s):  
Vikas Sharma ◽  
Prit Benny Malgulwar ◽  
Suvendu Purkait ◽  
Vikas Patil ◽  
Pankaj Pathak ◽  
...  
2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi5-vi5
Author(s):  
Wies Vallentgoed ◽  
Anneke Niers ◽  
Karin van Garderen ◽  
Martin van den Bent ◽  
Kaspar Draaisma ◽  
...  

Abstract The GLASS-NL consortium, was initiated to gain insight into the molecular mechanisms underlying glioma evolution and to identify markers of progression in IDH-mutant astrocytomas. Here, we present the first results of genome-wide DNA-methylation profiling of GLASS-NL samples. 110 adult patients were identified with an IDH-mutant astrocytoma at first diagnosis. All patients underwent a surgical resection of the tumor at least twice, separated by at least 6 months (median 40.9 months (IQR: 24.0, 64.7). In 37% and 18% of the cases, patients were treated with radiotherapy or chemotherapy respectively, before surgical resection of the recurrent tumor. DNA-methylation profiling was done on 235 samples from 103 patients (102 1st, 101 2nd, 29 3rd, and 3 4th resection). Copy number variations were also extracted from these data. Methylation classes were determined according to Capper et al. Overall survival (OS) was measured from date of first surgery. Of all primary tumors, the methylation-classifier assigned 85 (87%) to the low grade subclass and 10 (10%) to the high grade subclass. The relative proportion of high grade tumors increased ~three-fold at tumor recurrence (32/101, 32%) and even further in the second recurrence (15/29, 52%). Methylation classes were prognostic, both in primary and recurrent tumors. The overall DNA-methylation levels of recurrent samples was lower than that of primary samples. This difference is explained by the increased number of high grade samples at recurrence, since near identical DNA-methylation levels were observed in samples that remained low grade. In an unsupervised analysis, DNA-methylation data derived from primary and first recurrence samples of individual patients mostly (79%) cluster together. Recurrent samples that do not cluster with their primary tumor, form a separate group with relatively low genome-wide DNA-methylation. Our data demonstrate that methylation profiling identifies a shift towards a higher grade at tumor progression coinciding with reduced genome-wide DNA-methylation levels.


2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii351-iii351
Author(s):  
Frank Dubois ◽  
Ofer Shapira ◽  
Noah Greenwald ◽  
Travis Zack ◽  
Jessica W Tsai ◽  
...  

Abstract BACKGROUND Driver single nucleotide variants (SNV) and somatic copy number aberrations (SCNA) of pediatric high-grade glioma (pHGGs), including Diffuse Midline Gliomas (DMGs) are characterized. However, structural variants (SVs) in pHGGs and the mechanisms through which they contribute to glioma formation have not been systematically analyzed genome-wide. METHODS Using SvABA for SVs as well as the latest pipelines for SCNAs and SNVs we analyzed whole-genome sequencing from 174 patients. This includes 60 previously unpublished samples, 43 of which are DMGs. Signature analysis allowed us to define pHGG groups with shared SV characteristics. Significantly recurring SV breakpoints and juxtapositions were identified with algorithms we recently developed and the findings were correlated with RNAseq and H3K27ac ChIPseq. RESULTS The SV characteristics in pHGG showed three groups defined by either complex, intermediate or simple signature activities. These associated with distinct combinations of known driver oncogenes. Our statistical analysis revealed recurring SVs in the topologically associating domains of MYCN, MYC, EGFR, PDGFRA & MET. These correlated with increased mRNA expression and amplification of H3K27ac peaks. Complex recurring amplifications showed characteristics of extrachromosomal amplicons and were enriched in coding SVs splitting protein regulatory from effector domains. Integrative analysis of all SCNAs, SNVs & SVs revealed patterns of characteristic combinations between potential drivers and signatures. This included two distinct groups of H3K27M DMGs with either complex or simple signatures and different combinations of associated variants. CONCLUSION Recurrent SVs associate with signatures shaped by an underlying process, which can lead to distinct mechanisms to activate the same oncogene.


2018 ◽  
Vol 20 (suppl_6) ◽  
pp. vi177-vi177
Author(s):  
Na Tosha Gatson ◽  
Joseph Vadakara ◽  
Erika Leese ◽  
Tierney McGee ◽  
T Linda Chi ◽  
...  
Keyword(s):  

2011 ◽  
Author(s):  
Shani A. Mulholland ◽  
Rifat A. Hamoudi ◽  
Deborah S. Malley ◽  
V. Peter Collins ◽  
Koichi Ichimura

2014 ◽  
Author(s):  
Howard C. Shen ◽  
Simon Coetzee ◽  
Dennis J. Hazelett ◽  
Gerhard A. Coetzee ◽  
Houtan Noushmehr ◽  
...  

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Chong Wang ◽  
Luyao Zhang ◽  
Liangru Ke ◽  
Weiyue Ding ◽  
Sizun Jiang ◽  
...  

AbstractPrimary effusion lymphoma (PEL) has a very poor prognosis. To evaluate the contributions of enhancers/promoters interactions to PEL cell growth and survival, here we produce H3K27ac HiChIP datasets in PEL cells. This allows us to generate the PEL enhancer connectome, which links enhancers and promoters in PEL genome-wide. We identify more than 8000 genomic interactions in each PEL cell line. By incorporating HiChIP data with H3K27ac ChIP-seq data, we identify interactions between enhancers/enhancers, enhancers/promoters, and promoters/promoters. HiChIP further links PEL super-enhancers to PEL dependency factors MYC, IRF4, MCL1, CCND2, MDM2, and CFLAR. CRISPR knock out of MEF2C and IRF4 significantly reduces MYC and IRF4 super-enhancer H3K27ac signal. Knock out also reduces MYC and IRF4 expression. CRISPRi perturbation of these super-enhancers by tethering transcription repressors to enhancers significantly reduces target gene expression and reduces PEL cell growth. These data provide insights into PEL molecular pathogenesis.


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