Epigenetic programming in chronic lymphocytic leukemia
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1 Epigenetic programming in chronic lymphocytic leukemia Christopher Oakes 10 th Canadian CLL Research Meeting September th, 2014
2 Epigenetics and DNA methylation programming in normal and tumor cells: Epigenetic = a modification of DNA or chromatin that alters its function that is heritable Epigenetic states are programmed in early cell lineage development and become fixed in mature, differentiated cells and thus define cellular identity enhancer promoter Programming CpG methylation Cancer cells acquire aberrant epigenetic marks that are important to the malignant phenotype
3 Remarkable stability of DNA methylation in CLL revealed by 450K array profiling: 450K array (Illumina) analysis of 29 CLLs at 2 timepoints: Pearson correlation matrix, 450K CpGs 50x CpGs samples samples (Oakes et al., Cancer Discovery, 2014)
4 Summary I High longitudinal stability of DNA methylation patterns in CLL: CLL cell population: Time = Years Instability/ Evolution High Stability Co-evolution of genetic & epigenetic alterations
5 Low methylation heterogeneity reveals clonal orgins of epigenetic patterns in CLL: Oakes et al., Cancer Discovery, 2014
6 Low methylation heterogeneity reveals clonal orgins of epigenetic patterns in CLL: Targeted bisulfite sequencing: CLL21 CLL86 CLL44 CpG: Global Heterogeneity assessment (450K): Allele A Allele G Sequence reads CLL32 CLL36 Healthy B cells CpG: Methylation heterogeneity (arbitrary units) Allele A Allele G Sequence reads 0 methylated unmethylated B (PB) B (LN) T healthy donor CLL CLL AML (ICGC) Glioblastoma Renal Colon Lung Oakes et al., Cancer Discovery, 2014
7 Summary II High longitudinal stability of DNA methylation patterns in CLL: Time = Years to Decades CLL founder cell: Instability/ Evolution MBL: CLL: Low Methylation Heterogeneity, High Methylation Stability High epigenetic stability in CLL permits the elucidation of the methylation patterns present in the founder cell at the time of malignant transformation Co-evolution of genetic & epigenetic alterations
8 CLL patients cluster into three distinct epigenetic subgroups: 450k array (Illumina) analysis of 249 CLLs Unsupervised analysis displaying the 500 most discriminating CpGs methylation % cluster 1 cluster 2 cluster CLL profiles downloaded from the ICGC Data Portal (
9 DNA methylation clusters versus IGHV mutation status: cluster 1 cluster 2 cluster 3 Number of cases IGHV mutation status: % mutation of IGHV sequence
10 DNA methylation clusters versus clinical outcome: Independent, clinically-annotated sample set (n = 349): cluster 1 cluster 2 cluster 3
11 Summary III Three epigenetic subgroups derive from distinct cell poulations: CLL founder cell: cluster 1 cluster 2 cluster 3 MBL: Time = Years to Decades CLL: Low Methylation Heterogeneity, High Methylation Stability IGHV (% germline): 100 ~ <95 Clinical outcome: poor intermediate favorable Mutation spectrum: adverse mixed low risk How/why 3 clusters? Are methylation clusters related to B cell maturation?
12 Phylo(epi)genetic analysis of the development of blood cell types: Illumina 450K analysis: (10,000 most variable probes) B lymphocytes HSCs Myeloid T/NK lymphocytes Profiles downloaded from: ICGC Data Portal ( GEO Database (
13 Example of DNA methylation programming during B cell maturation: EBF1: (chromosome 5) %GC CpG islands Gene (EBF1) CD5- NBC CD5 NBC CD5- MBC CD5 MBC TFs DNaseH H3K4me3 H3K27ac H3K4me1 Conservation EBF1
14 Isolation of B cells at various stages of maturation (naïve memory):
15 DNA methylation programming during B cell maturation:
16 DNA methylation programming during B cell maturation: Top 1000 programmed CpGs (naïve memory): hypermethylated hypomethylated
17 Genomic features associated with B cell methylation programming: NBC csmbc: Chromatin state segmentation analysis: Transcription factor motif & association analysis: :
18 Summary IV - Normal B cell DNA methylation programming: Hypomethylation of enhancers & promoters Hypermethylation of PRC-marked regions Ag (αigm) DC (IL15) (CD40L) AP-1 EBF Oct NF-kB MYC HOXA9 NBC GCF encsmbc ncsmbc MGZ csmbc T H How do the CLL methylation clusters relate to normal B cells at various stages of maturation?
19 Comparison of Normal B cell and CLL DNA methylation programming: LP-CLL (Less Programmed) IP-CLL (Intermediate Programmed) HP-CLL (Highly Programmed)
20 Phylo(epi)genetic analysis of individual CLL samples with normal B cells: Top 1000 most variable CpGs in both CLL & B cells:
21 Comparison of B cell programming in CLL and global gene expression: Total RNA-seq:
22 Summary V CLL clusters derive from various stages of B cell maturation: Normal B cell DNA methylation programming Time = Hours to Days AP-1 EBF Oct NF-kB MYC HOXA9 NBC GCF encsmbc ncsmbc MGZ csmbc How/why do normal cells become malignant? Aggressive vs. indolent? Time = Years to Decades CLL founder cell: MBL: CLL: High Methylation Stability cluster: LP-CLL IP-CLL HP-CLL Global gene expression: TCL1A ZAP70 BTK mir-29 IRF4 transition from an aggressive to an indolent gene expression signature TCL1A ZAP70 BTK mir-29 IRF4
23 CLL-specific methylation reveals a transcription factor signature of aggressive disease:
24 Summary VI Deregulation of transcription factors enact malignant transformation in CLL: Normal B cell DNA methylation programming Time = Hours to Days AP-1 EBF Oct NF-kB MYC HOXA9 NBC GCF encsmbc ncsmbc MGZ csmbc CLL initiating mutation(s), BCR c-fos activation EBF1 NFAT EGR2 E2A OCT2 Time = Years to Decades CLL founder cell: MBL: CLL: High Methylation Stability Global gene expression: cluster: LP-CLL IP-CLL HP-CLL TCL1A ZAP70 BTK mir-29 IRF4 transition from an aggressive to an indolent gene expression signature TCL1A ZAP70 BTK mir-29 IRF4
25 Conclusions: All comparisons of CLL to crude CD19 B cells should be interpreted very cautiously The vast majority of differences between U vs M CLL occur between normal B cell subsets (epigenome & transcriptome) DNA methylation programming is continual and driven by signaling pathway activation and enacted by specific transcription factors. The CLL founder cell can arise from within a broad window of development and in-turn each CLL patients clone is unique. The degree of DNA methylation programming achieved strongly impacts on the clonal phenotype (gene expression & clinical course) CLL-specific changes are linked to aberrant activity of NFAT & EGR transcription factor families and loss of AP-1 and EBF1 The mechanisms governing extreme flexibility vs. stability is key to understanding the cause and evolution of the disease
26 Acknowledgements: Marc Seifert Ralf Kuppers Daniel Mertens Peter Lichter Christoph Plass Bioinformatics/statistics: Yassen Assenov Lei Gu Manuela Zucknick Olga Bogatyrova Clinical collaborations: John C. Byrd David Lucas Amy Stark (The Ohio State University) Hartmut Döhner Stephan Stilgenbauer Eugen Tausch Johannes Bloehdorn (University of Ulm) Laura Rassenti Thomas Kipps (CLL Research Consortium) Thorsten Zenz Leo Sellner Jennifer Hüllein (Nationales Centrum fur Tumorerkrankungen Heidelberg)
27
28 The distribution of genetic aberrations within CLL methylation clusters: LP-CLL IP-CLL HP-CLL 100 TP53 17p 11q SF3B1 NOTCH1 BRAF 12 MYD88 13q NOTCH1* BRAF* del 11q* del 17p* TP53* LP-CLL methylation % 50 0 IP-CLL HP-CLL events: n=7 Fisher s exact test: P=0.011 n=6 P=0.018 n=14 P=0.003 n=18 P=0.001 n=23 P=0.001 trisomy 12 SF3B1* MYD88* bidel 13q del 13q* (sole) events: Fisher s exact test: n=17 P=0.39 n=17 P=0.027 n=15 P= n=16 P=0.064 n=21 P=0.016 *P<0.05
29 Examples of DNA methylation programming during B cell maturation: EGR2: (chromosome 10) %GC CpG islands Gene (EBF1) CD5- NBC CD5 NBC CD5- MBC CD5 MBC TFs DNaseH H3K4me3 H3K27ac H3K4me1 Conservation EGR2
30
Nature Methods: doi: /nmeth.3115
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