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Sebastian

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Glioma Subtype

一篇讲胶质瘤分型的meeting课件

pez一篇讲胶质瘤分型的meeting课件Identification of Novel Glioma-Associated Genomic Alterations Using MutSigCV Identification of New Somatically Altered Glioma Genes Using MutComFocal INTRODUCTION 80% of brain tumors are diffuse gliomas represent  Unsupervised Clustering of Gliomas Identifies  Six Methylation Groups  and  Four RNA Expression Groups Associated with  IDH Status An IDH-Wild-Type Subgroup of Histologically Defined Diffuse Glioma Is Associated with Favorable Survival and Shares Epigenomic and Genomic Features with Pilocytic Astrocytoma Activation of Cell Cycle/Proliferation and Invasion/ Microenvironmental Changes Marks Progression of LGG to GBM Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma 1122 adult diffuse sample, grade Ⅱ,Ⅲ,Ⅳ. Molecular profiling of Diffuse Glioma Present by TX.Gu Cell January 28, 2016 Telomere length DNA methylation profiling Molecular analysis of progression from low-grade to high-grade disease. Highligthts Provides Insight into Molecular Classifcation Telomere Maintenance Mechanisms Disease Progression Therapeutic Options Results Telomere maintenance defined by Somatic Alterations DNA methylation reveals subtypes of IDH mutant and IDH WT Glioma Integrated Molecular Analysis of Progression from low to high grade disease Oligodendroglioma Oligoastrocytoma Astrocytoma Glioblastoma Grade II to IV 590 56% 174 17% 114 11% 169 16% Total 1122 Data Sources Gene Expression 1045 DNA Copy Number 1084 DNA Methylation 932 Exome Sequencing 820 Protein Expression 473 From TCGA and others Analysis DNA Copy Number and Gene Fusion Using GISTIC2  LGGs GBMs Significantly Mutated Genes  Point Mutations and Indels  Using Mutect ,Indelocator ,Varscan2 ,RADIA Algorithms. Using MutSigCV Algorithms identification 75 SMGs (significantly mutated genes) 10 of which had beed reported in GBMs ,12 in LGGs. 8 of which both in GBMs and LGGs 45 of which have not been reported. Using 1084 samples 513LGGs 571GBMs Identified 162 significantly altered DNA copy number segment PRADA ,deFuse Identified 1144 Gene fusion from RNA-seq profile 37 in-frame receptor tyrosine kinases Collectively These analyses recover all known glioma driving event Including in  IDH1 TP53 ATRX EGFR PTEN CIC FUBP1 Newly predicted SETD2 ARID2 DNMT3A KRAS/NRAS Chromatin Organization Related Oncogen Overlaped copy number ,mutation ,fusion transcript profiles into pathways Ras-Raf-MEK-ERK p53/apoptosis PI3K/AKT/mTOR Chromatin Modification Cell cycle  In  Ras-Raf-MEK-ERK  pathway ,alterations showed in  109 of 119 members . and most of occurring in  IDH-WT group. n=327 of 357 A Set of  Chromatin Modification  Related Genes A set of 32 genes involved in  Chromatin Modification . and most of which belongs to   IDH-Mutant-non-codel  group n=230 87% Candidates altered by mutation and DNA copy number NIPBL STAG2 A crucial adherin subunit that is essential for loading cohesins on chromatin cohesin complex gene 16% LGGs/GBMs showed alteration in  cohesin complex relate genes. Telomere Length TERT Promoter ATRX Gene Mutations in the TERT promoter have been reported in 80% of GBM ,but nearly mutually  exclusive with mutations in ATRX. (A) Heatmap of relative tumor/normal telomere lengths of 119 gliomas, grouped by TERTp and ATRX mutation status.  (B) Telomere length decreases with increasing age (measured in years at diagnosis) in blood normal control samples (n = 137).  (C) Quantitative telomere length estimates of tumors and blood normal, grouped by TERTp mutant (n = 67, 56%), ATRX mutant (n = 40, 33%), and double negative (n = 13, 11%) status. ∗∗∗ = p < 0.0001; ∗∗ = p < 0.001. Telomere Length Is Positively Correlated with ATRX, but Not TERT Promoter Mutations (A) RNaseq TERT expression is upregulated in TERTp mutant cases, but not in ATRX and double negative cases (p < 0.0001). (B) TERT expression as quantified by RNA sequencing is a highly sensitive and specific marker for the absence or presence of the TERTp mutation (C) Telomeres gradually shorten with increasing age in tumor samples A ATPase/Helicase A) Heatmap of DNA methylation data. Columns represent 932 TCGA glioma samples grouped according to unsupervised cluster analysis; rows represent DNAmethylation probes sorted by hierarchical clustering. Non-neoplastic samples are represented on the left of the heatmap.  (B) Heatmap of RNA sequencing data. Unsupervised clustering analysis for 667 TCGA glioma samples profiled using RNA sequencing are plotted in the heatmap using 2,275 most variant genes. (C) Tumor Map based on mRNA expression and DNA methylation data. Each data point is a TCGA sample colored coded according to their identified status. Unsupervised Cluster Analysis A) Heatmap of probes differentially methylated between the two IDH mutant-non-codel DNA methylation clusters allowed the identification of a low-methylation subgroup named G-CIMP-low. Non-tumor brain samples (n = 12) are represented on the left of the heatmap. (B) Heatmap of genes differentially expressed between the two IDH mutant-non-codel DNA methylation clusters. (C) Kaplan-Meier survival curves of IDH mutant methylation subtypes. Ticks represent censored values. (D) Distribution of genomic alterations in genes frequently altered in IDH mutant glioma. (E) Genomic distribution of 633 CpG probes differentially demethylated between co-clustered G-CIMP-low and G-CIMP-high. CpG probes are grouped by UCSC genome browser-defined CpG Islands, shores flanking CpG island ± 2 kb and open seas (regions not in CpG islands or shores). (F) DNA methylation heatmap of TCGA glioma samples ordered per Figure 2A and the epigenetically regulated (EReg) gene signatures defined for G-CIMP-low, G-CIMP-high, and Codel subtypes. The mean RNA sequencing counts for each gene matched to the promoter of the identified cgID across each cluster are plotted to the right. (G) Heatmap of the validation set classified using the random forest method applying the 1,300 probes defined in Figure 2A. (H) Heatmap of probes differentially methylated between G-CIMP-low and G-CIMP-high in longitudinally matched tumor samples. Figure 4. A Distinct Subgroup of IDH-Wild-Type Diffuse Glioma with Molecular Features of Pilocytic Astrocytoma (A) Kaplan-Meier survival curves for the IDH-wild-type glioma subtypes. Ticks represent censorship. (B) Distribution of previous published DNA methylation subtypes in the validation set, across the TCGA IDH-wild-type-specific DNA methylation clusters. (C) Distribution of genomic alterations in genes frequently altered in IDH-wild-type glioma. (D) Heatmap of TCGA glioma samples ordered according to Figure 2A and two EReg gene signatures defined for the IDH-wild-type DNA methylation clusters. Mean RNA sequencing counts for each gene matched to the promoter of the identified cgID across each cluster are plotted to the right. (E) Heatmap of the validation set classified using the random forest method using the 1,300 probes defined in Figure 2A. Discussion
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