Discovery of two identities of neuroblastoma cells via the analysis of super-enhancer landscapes

Transcription

1 Discovery of two identities of neuroblastoma cells via the analysis of super-enhancer landscapes Valentina BOEVA Computational (Epi-)Genetics of Cancer Institut Cochin, Inserm U1016 / CNRS UMR 8104 / Université Paris Descartes UMR-S1016

2 Introduction in neuroblastoma NB = Pediatric cancer (avg. age 18 months) Neuroblastoma may be found in the adrenal glands and paraspinal nerve tissue from the neck to the pelvis MYCN amplification Acetylation H3K27 modifications NB cells with MYCN may be sensitive to epigenetic drugs: CDK7 inhibitor (THZ1) MYCN amplified tumors BRD4 inhibitors (I-Bet726, I-Bet151, JQ1)

3 Initial aim: Profile super-enhancers in neuroblastoma cell lines and discover core transcriptional regulatory circuitries Neuroblastoma: 25 cell lines & 6 patient-derived xenografts Normal control: Neural crest cells Model for aggressive neuroblastoma ChIP-seq data H3K27ac Active promoters, enhancers and super-enhancers Gene expression: RNA-seq data Collaboration with the team of Isabelle Janoueix Caroline Louis Simon Durand Agathe Peltier 3

4 Bioinformatics methods to work with histone modification data: Peak calling from ChIP-seq data Calling of super-enhancers based on H3K27ac peaks Copy number ROSE LILY HMCan SICER MACS Without copy number correction With copy number correction H. Ashoor V. Boeva, Bioinformatics, 2013 V. Boeva* I. Janoueix-Lerosey*, Nature Genetics, 2017

5 Principal component analysis (PCA) based on the SE signal determines 2 groups of cell lines Group I Group II V. Boeva* I. Janoueix-Lerosey*, Nature Genetics, 2017

6 The two groups of neuroblastoma are driven by different transcriptional master regulators Group I cells are more sensitive to chemotherapy Analysis: Motif enrichment, core-regulatory circuitries, gene expression correlation analysis in cell lines and 498 primary tumors, experimental ChIP-seq validation Group I SE SE SE PHOX2B GATA3 HAND2 PHOX2B GATA3 HAND2 drive Super-enhancers of MYCN, ALK, RET, LMO1, PRKCE, EYA1, BCL11A, etc. SE FOSL1 FOSL1 SE FOSL2 FOSL2 Group II SE SE SE RUNX1 RUNX2 PRRX1 RUNX1 RUNX2 PRRX1 drive Super-enhancers of MYC, BCOR, MECOM, PTPRJ, etc. SE IRF2 IRF2 SE V. Boeva* I. Janoueix-Lerosey*, Nature Genetics, 2017

7 Intermediate cell lines contain cells of both types Group I Cell ID 1 Group II 2 V. Boeva* I. Janoueix-Lerosey*, Nature Genetics, 2017 SK-N-AS (intermediate) cell line

8 The two cell types can co-exist within the same tumor Module 1 cells Module 2 cells stage 4 neuroblastoma tumor IHC for MAML3 (blue) and PRRX1 (red) in a stage 4 neuroblastoma. MAML3=the pan-neuroblastoma marker PRRX1=marker of module 2 van Groningen et al, Nat Genetics, 2017

9 More details in: Boeva et al, Nature Genetics, 2017 Poster #4!

10 Acknowledgements Institut Curie, Paris Isabelle Janoueix-Lerosey Caroline Louis Simon Durand Tatiana Popova Olivier Delattre Gudrun Schleiermacher Emmanuel Barillot Alban Lermine Amira Kramdi Institut Cochin, Paris Irina Medvedeva KAUST, Saudi Arabia Vladimir Bajic Haitham Ashoor 10

11 Computational methods used in this presentation

12 1. Detection of regions enriched in H3K27ac (peak calling) Hidden Markov Model H. Ashoor et al, Bioinformatics,

13 Peaks predicted by HMCan do not show copy number bias Copy number HMCan SICER MACS H. Ashoor et al, Bioinformatics,

14 2. Detection of Super-Enhancers in cancer cells: correction for GC-content bias and variation in copy number Without copy number correction

15 2. Detection of Super-Enhancers in cancer cells: correction for GC-content bias and variation in copy number LILY: Without copy number correction With copy number correction

16 3. Motif detection in Super-enhancers Super-enhancers are too large to look for enriched motifs

17 3. Motif detection in Super-enhancers Super-enhancers are too large to look for enriched motifs NB cell line Better approach: Discovery of enriched motifs in valley regions of H3K27ac peaks in super-enhancers H3K27ac Valleys Motif hits

18 3. Motif detection in Super-enhancers Super-enhancers are too large to look for enriched motifs NB cell line Better approach: Discovery of enriched motifs in valley regions of H3K27ac peaks in super-enhancers H3K27ac Valleys Motif hits TF binding (ChIP-seq) LILY:

19 Results on neuroblastoma cell lines and tumors

20 Some maths for SE score normalization 1. Real SE in a diploid region: ChIP signal: X1 reads Corresponding input signal for this diploid region: X2 reads. ROSE score: X1-X2 our score: ~(X1/1-X2/1)=(X1-X2)/1 = X1-X2 The same with values X1=400, X2=40 and k=4: 1. ROSE: 360 Our score: 360 correction of 1 corresponds to a diploid region (copy number is equal to the main ploidy) 2. an enhancer in a diploid region (here I suppose that there are k times less signal compared to SE: ChIP signal: X1/k reads Corresponding input signal for this diploid region: X2 reads. ROSE score: X1/k -X2 our score: ~( X1/3/1 -X2/1)= X1/k -X2 3. SE in the MYCN region present in 100 copies instead of 2: ChIP signal: X1*50 reads Corresponding input signal for this diploid region: X2*50 reads. 2. ROSE: 60 Our score: ROSE: Our score: ROSE: 3000 Our score: 60 ROSE score: X1*50 -X2*50 = 50*(X1-X2) our score: ~(X1*50/50-X2*50/50)= X1-X2 4. No SE/enhancer in the MYCN region present in 100 copies instead of 2: ChIP signal: X1*50/k reads Corresponding input signal for this diploid region: X2*50 reads. ROSE score: X1*50/k -X2*50 = 50*(X1/k-X2) Our score: ~(X1*50/50/k-X2*50/50)= X1/k-X2

21 Percentage Neuroblastoma Super-Enhancers defined by H3K27ac peaks are occupied by PHOX2B, HAND2 and GATA3 Top SE sorted according to the average SE score Intersection with TF binding sites defined in CLB-GA Binding by Top super-enhancers 21

22 HAND2, PHOX2B and GATA3 bind closely located regions within enhancers and SEs 10,000 strongest HAND2 binding sites (ChIP-seq) 22

23 TF peaks correspond to H3K27ac peaks in the ALK Super- Enhancer HAND2 PHOX2B GATA3 H3K27ac ALK 23

24 TF peaks correspond to H3K27ac peaks in the TBX2 Super- Enhancer HAND2 PHOX2B GATA3 H3K27ac TBX2 24

25 HAND2, PHOX2B and GATA3 bind to a MYCN enhancer HAND2 PHOX2B GATA3 H3K27ac enhancer MYCN FANTOM5 MYCN and DDX1 enhancer

26 Gene expression linearly correlates with SE score (in Log scale): examples NB cell lines Control samples Other cancer cell lines 26

27 DNMT expression can be a CIMP driver in ACC DNMT1 and DNMT3A expression is increased in CIMP-high patients

28 DNMT1 and DNMT3A expression correlated with poor survival

29 DNMT1, but not DNMT3A expression, is correlated with proliferation Gene expression TCGA Cochin Proliferation score

30 We are hiring post-docs Cancer epigenetics research projects Method development High-throughput data analysis and data mining Experimental validation

31 NB genes with super-enhancers tend to associate with neuronal differentiation Functional annotation of neuroblastoma Super-Enhancers: GO: neuron differentiation GO: neurogenesis GO: autonomic nervous system development GO: regulation of neuron differentiation GO: synapse 31

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33 Gene expression linearly correlates with SE score (in Log scale): examples NB cell lines Control samples Other cancer cell lines 33

34 Gene expression linearly correlates with SE score (in Log scale): for 1003 SE regions detected in at least 2 NB samples P-value<0.05 Pearson correlation test on 20 NB cell lines + 2 hncc

35 Is there any difference in CRCs in MYCN amplified NB? Cancer cells with a specific SV have a specific epigenetic profile (super-enhancers) These cells are sensitive to a specific drug (CDK7-enhibitor) Chipumuro et al, Cell,

36 SH-SY5Y Kelly Normalization by HMCan does not suggest any significant difference in SEs between MYCN-amplified and MYCN non-amplified NBs MYCN-amplified (top) vs MYCN non-amplified (bottom) cell lines: SE in GATA2 36

37 Normalization by HMCan does not suggest any significant difference in SEs between MYCN-amplified and MYCN non-amplified NBs SE score 37

38 Super-enhancers with differential score between MYCN-amplified and MYCN non-amplified NBs P-value <0.01 With FDR adjustment: no significant regions (Wilcoxon rank test) 38

39 ChIP-seq technique can provide information about modifications of histone tails Mains steps of ChIP-Seq technique: ChIP-seq = chromatin immunoprecipitation + sequencing bp + Control (e.g., input DNA) A cluster of reads (peak) in the UCSC genome browser 39

40 PHOX2B is critical for the growth of neuroblastoma cells of noradrenergic type PHOX2B expressed PHOX2B inhibited with shrna CLB-GA cell line Caroline Louis

41 Analysis of ChIP-seq data: density profile calculation 4 2 binned density 0.wig file density reads chromosome We calculate the density both for the ChIP and control sample 41

42 Nebula: web-service for analysis of ChIP-seq data Statistics for external connections to Curie Nebula Nebula 42

43 Proportion of peaks Proportion of genes with a peak Peak count Proportion of genes with a peak at a given distance (cumulative) Proportion of genes with a peak at a given distance (density) 2e-07 6e-07 Nebula: web-service for analysis of ChIP-seq data Peak calling Calculation of the density and cumulative distribution of peak locations relative to gene transcription start sites Annotation of peaks with genomic features and genes with peak information A B C ChIP Control down-regulated no-response up-regulated ChIP Control D ChIP Control Peak height E Distance from TSS (Kb) Distance from TSS (bp) down-regulated no-response up-regulated Control GeneDown. Enh. Imm.Down. Interg. Intrag. Prom. Some graphs produced produced by Nebula Enh. Prom. Imm.Down. Intrag. GeneDown. F.Intron Exons 2,3,etc.Introns E.I.Junctions V. Boeva, A. Lermine et al, Bioinformatics,

44 Disruption of the genomic sequence in cancer can affect epigenetic profiles Mutations and structural variants (SVs) in cancer genomes Disruption of epigenetic profiles by mutation of epigenomeregulatory proteins (readers, writers or erasers) Disruption of regulatory elements Disruption of interactions between genes and regulatory elements 44

45 What transcription factors may be involved in the formation of NB-specific super-enhancers? Likely candidates: TCF12 (that interacts with Hand2), PBX3, JUND, GATA2 and GATA3 together with many others TFs (ChIPseq experiments are ongoing) CTCF JUND PBX3 TCF12 GATA2 GATA3 H3K27ac (SK-N-SH) H3K27ac (SHSY-5Y) Super enhancer Genes ENCODE DATA 45

46 Neuroblastoma cells have been shown to be sensitive to CDK7 inhibitor CDK7 inhibitor (THZ1) MYCN amplified tumors Suggested mechanism: desactivation of super-enhancers created by MYCN Chipumuro et al., Cell, 2014

47 Neuroblastoma cells have been shown to be sensitive to CDK7 inhibitor CDK7 inhibitor (THZ1) MYCN amplified tumors HMCan normalization of the same data Suggested mechanism: desactivation of super-enhancers created by MYCN Chipumuro et al., Cell, 2014

48 Neuroblastoma cells have been shown to be sensitive to BRD4 inhibitor JQ1 MYCN high or MYCN amplified tumors MYCN low or MYCN WT tumors 650 cancer cell lines Puissant et al., Cancer Discov. 2013

49 A variety of compounds reverse epigenetic changes Molecule Mechanism Cancer JQ1, I-Bet151, I-Bet762 BRD4 multiple myeloma, Merkel cell carcinoma, castration-resistant prostate cancer, ER+ breast cancers, ovarian carcinoma, human osteosarcoma (+rapamycin) THZ CDK7 T-ALL, basal breast cancer, Pivanex, Valproate, TSA, Vorinostat, Romidepsin neuroblastoma, AML HDAC prostate, endometrial and cervical carcinomas, leukemias and lymphomas Suramin, Cambinol SIRT1 & SIRT2 lymphomas, prostate, lung and breast 5-Azacitidine, Zebularine, RG108 cancer DNMT1 hepatocellular carcinoma, breast cancer, prostate cancer, renal carcinoma (+ interferon α-2β) DZNep, EPZ-6433 GSK126, EI1 PRC2 prostate cancer, neuroblastoma

50 ChIP-seq technique can provide information about modifications of histone tails Mains steps of ChIP-Seq technique: ChIP-seq = chromatin immunoprecipitation + sequencing bp + Control (e.g., input DNA) A cluster of reads (peak) in the UCSC genome browser 50