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CDAC 2018 Boeva discovery

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Presentation at the Workshop and School on Cancer Development and Complexity 2018
http://cdac2018.lakecomoschool.org

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CDAC 2018 Boeva discovery

  1. 1. Discovery of two states of neuroblastoma cells via the analysis of super-enhancer landscape Valentina BOEVA Computational (Epi-)Genetics of Cancer Institut Cochin, Inserm U1016 / CNRS UMR 8104 / Université Paris Descartes UMR-S1016
  2. 2. Epigenetic profiles = combination of CpG methylation of DNA and histone modifications M. S. Yan et al, J. Appl. Physiol., 2010 -CH3 + Information about the 3D structure of chromatin 2
  3. 3. Relationship between histone modifications and chromatin states • Histone modifications Bhaumik et al, Nat Str & Mol Biol, 2007 Li et al, Cell, 2007 3
  4. 4. ChIP-seq for histone marks allows identification of enhancer regions in normal cells and in cancer CLB-GA neuroblastoma cell line ZMYZ1 H3K27ac H3K27ac peaks H3K4me3 H3K4me3 peaks Active promoter Active enhancer ~70kb 3Kb
  5. 5. ChIP-seq for histone marks allows identification of super-enhancer regions CLB-GA neuroblastoma cell line H3K27ac H3K27ac peaks H3K4me3 H3K4me3 peaks Active Super-enhancer 240Kb log2(super-enhancer score) Log(geneexpression) Correlation of gene expression with super- enhancer score: GATA2 super-enhancer
  6. 6. Definition of super-enhancers using H3K27ac read counts ROSE (Rick Young & James Bradner) Enhancer rank H3K27acreadcount(ChIP-Input) Super-enhancers Enhancers For cancer genomes: LILY: https://github.com/BoevaLab/LILY
  7. 7. Super-enhancer regions are occupied by hundreds of proteins and point to cell identity genes Super-enhancer 7Hnisz et al., Cell 2013 in addition to high H3K27ac Whyte et al., Cell 2013
  8. 8. De novo cancer specific super-enhancers 8 Colorectal cancer Hnisz et al., Cell 2013
  9. 9. De novo super-enhancer creation via a genetic mutation • T-cell acute lymphoblastic leukemia: somatic mutations => binding motifs for MYB => a super-enhancer upstream of the TAL1 oncogene Mansour et al, Science, 2014 9
  10. 10. Rewiring of transcriptional networks in cancer 10 Normal cell Cancer cell ? TF network rewiring?
  11. 11. Rewiring of core regulatory circuitries (CRCs) in cancer • CRCs = set of TFs that autoregulate themselves and determine cell identity in normal cells 11 Saint-André et al, Genome Research, 2016
  12. 12. Rewiring of core regulatory circuitries (CRCs) in cancer • CRCs = set of TFs that autoregulate themselves and determine cell identity in normal cells 12Saint-André et al, Genome Research, 2016
  13. 13. Rewiring of core regulatory circuitries (CRCs) in cancer In cancer: 13 Normal cell Cancer cell TFs gain/lose SEs (+ Number of gene copies change and affect expression) Cell identity changeCRCs change
  14. 14. Part 1 Computational strategies for the analysis of epigenetic landscape in cancer 14
  15. 15. 1. Peak calling: detection of regions enriched in a given histone mark H3K27ac signal H3K27ac peaks Sequenced reads (.BAM) 1. 2.
  16. 16. Standard methods for signal detection can miss signal in regions of genomic loss Copy number profile MACS SICER H3K27me3peaks Position along chr8 Peaks predicted by tools: Zhang,Y. et al. (2008) Genome Biol., 9, R137 Zang,C. et al. (2009) Bioinformatics, 25, 1952–1958. chr8 16
  17. 17. Solution: explicit normalization for copy number status H. Ashoor et al, Bioinformatics, 2013 www.cbrc.kaust.edu.sa/hmcan or https://bitbucket.org/pyminer/hmcan 17
  18. 18. Solution: explicit normalization for copy number status 18 H. Ashoor et al, Bioinformatics, 2013 Hidden Markov Model
  19. 19. HMCan uses “Input” (control) data to annotate copy number alterations Copy number profile for Hela-S3 cell line obtained using the ENCODE Input Boeva et al. Bioinformatics, 2012, 28(3):423-5. Boeva et al. Bioinformatics, 2011, 27(2):268-9.
  20. 20. Peaks predicted by HMCan do not show copy number bias 20H. Ashoor et al, Bioinformatics, 2013 Copy number HMCan MACS SICER
  21. 21. Peaks predicted by HMCan do not show copy number bias 0.75 0.5 0.25 0.0 0.75 0.5 0.25 0.0 Distance from TSS Density HMCan MACS H3K36me3, HeLa S3 cell line Highly expressed genes Silent genes
  22. 22. HMCan-diff: a method to detect changes in histone marks in cells with different genetic backgrounds 22 H. Ashoor et al, Nucleic Acids Res., 2017 • Library size correction • GC-content correction • Copy number correction • Variable signal-to-noise ratio correction • Iterative Hidden Markov Models
  23. 23. ROSE 2. Detection of Super-Enhancers in cancer cells: correction for GC-content bias and variation in copy number Without copy number correction
  24. 24. 2. Detection of Super-Enhancers in cancer cells: correction for GC-content bias and variation in copy number Without copy number correction With copy number correction LILY http://boevalab.com/LILY/ ROSE Boeva et al., Nature Genetics, 2017
  25. 25. 2. Detection of Super-Enhancers in cancer cells: correction for GC-content bias and variation in copy number Boeva et al., Nature Genetics, 2017 LILY allows the detection of enhancers and super-enhancers in amplification regions N206 (Kelly) CLB-PE CHP-212 SK-N-FI No MYCN amplification Super-enhancer Super-enhancer Super-enhancer With MYCN amplification
  26. 26. 3. Motif detection in Super-enhancers Super-enhancers are too large to look for enriched motifs
  27. 27. 3. Motif detection in Super-enhancers Super-enhancers are too large to look for enriched motifs Better approach: Discovery of enriched motifs in valley regions of H3K27ac peaks in super-enhancers Valleys H3K27ac Motif hits
  28. 28. 3. Motif detection in Super-enhancers Super-enhancers are too large to look for enriched motifs Better approach: Discovery of enriched motifs in valley regions of H3K27ac peaks in super-enhancers Valleys TF binding (ChIP-seq) H3K27ac Motif hits LILY: http://boevalab.com/LILY/ Boeva et al., Nature Genetics, 2017
  29. 29. Summary Part 1 • Analysis of chromatin states in cancer cells needs correction for copy number • Additionally, we propose to correct for GC-content bias, library size and differences in signal-to-noise ratio • To detect super-enhancers, we should consider copy number bias as well • Motif enrichment analysis in super-enhancer regions should be performed in valleys of H3K27ac signal
  30. 30. Part 2 Application to neuroblastoma
  31. 31. Role of epigenetic remodeling in neuroblastoma (NB) In collaboration with the groups of I. Janoueix & G. Schleiermacher (U830, Institut Curie) Common genetic events: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 22% of patients: MYCN amplification 10% of patients: activating ALK mutation at diagnosis Hereditary neuroblastomas (rare): • ALK mutation • Deletions/mutations in PHOX2B
  32. 32. Role of epigenetic remodeling in neuroblastoma (NB) In collaboration with the groups of I. Janoueix & G. Schleiermacher (U830, Institut Curie) 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 SWI/SNF complex Acetylation H3K27 NB cells may be sensitive to “epigenetic” drugs: • CDK7 inhibitor (THZ1) – MYCN amplified tumors • BRD4 inhibitors (I-Bet726, I-Bet151, JQ1) • HDAC 1/2 inhibitors Frequent mutations in ARID1A & ARID1B Genetic events with possible epigenetic consequences:
  33. 33. Profiling of super-enhancers in neuroblastoma cell lines • Cancer: 25 neuroblastoma cell lines & 6 patient-derived xenografts • Normal control: Neural crest cells • ChIP-seq data – H3K27ac • Gene expression: RNA-seq data Active promoters, enhancers and super-enhancers Model for aggressive neuroblastoma 33 Collaboration with the team of Isabelle Janoueix • Caroline Louis • Simon Durand • Agathe Peltier
  34. 34. Detection of super-enhancer (SE) regions using HMCan and LILY 34 H3K27ac profiles in NB and normal cells Example: SE in ALK in NB cell linesControls
  35. 35. Detection of super-enhancer (SE) regions using HMCan and LILY 35 H3K27ac profiles in NB and normal cells Controls Example: SE in PHOX2B in NB cell lines
  36. 36. Principal component analysis (PCA) based on the SE signal determines 2 groups of cell lines 36
  37. 37. Principal component analysis (PCA) based on the SE signal determines 2 groups of cell lines 37 Normal controls
  38. 38. Normal controls Principal component analysis (PCA) based on the SE signal determines 2 groups of cell lines 38
  39. 39. Principal component analysis (PCA) based on the SE signal determines 2 groups of cell lines 39 Group II Group I
  40. 40. We identify super-enhancers (SEs) in group I and group II
  41. 41. TBX2 locus is gained in ~82% of aggressive NBs • 765 high risk NBs: 41 Position along chr17 Fractionofpatientsw/gainorloss
  42. 42. We identify super-enhancers (SEs) in group I and group II
  43. 43. PHOX2B is the TF with the most differential SE score between groups I and II; contributes to both PC1 and PC2 43 Gene FC Score Group I over Group II Loadings to PC1 Loadings to PC2 FEV 36.44074275 -0.00492329 3.479539978 PHOX2B 28.63836241 2.691379249 1.77614779 TFAP2B 27.80404109 0.27051936 2.642200469 CHRNA3 27.56705746 1.984824375 2.101342469 L1CAM 27.16342924 0.549763903 2.9489563 ATP1A3 26.06634028 -0.042332076 3.422740075 Isabelle Janoueix-Lerosey & Caroline Louis
  44. 44. Enrichment of neuroblastoma super-enhancers in PHOX2B and AP-1 binding motifs • i-cisTarget (Herrmann et al, 2012) 44 Group I
  45. 45. Enrichment of neuroblastoma super-enhancers in PHOX2B and AP-1 binding motifs • i-cisTarget (Herrmann et al, 2012) 45 Group II Group I
  46. 46. TFs predicted to participate in CRCs in NB cell lines based on the SE and motif analysis include PHOX2B and AP-1 46 Saint-André et al, Genome Research, 2016
  47. 47. 47 PHOX2B XXX XXX PHOX2B XXX XXX SE SE SE FOSL1 FOSL2 XXX FOSL1 FOSL2 XXX SE SE SE 94% of these genes have significant correlation between SE strength and expression (p-value <0.05) Definition of CRCs: TFs predicted to participate in auto-regulatory loops with PHOX2B or AP1
  48. 48. We use gene expression correlation to narrow down possible modules in neuroblastoma CRCs Distance based on the correlation of gene expression over 25 NB cell lines and 6 PDX
  49. 49. In 25 NB cell lines: Gene expression correlation for the selected two TF modules for Group I (PHOX2B driven) and Group II (AP-1 driven)
  50. 50. In 25 NB cell lines: Module 1? Module 2? Gene expression correlation for the selected two TF modules for Group I (PHOX2B driven) and Group II (AP-1 driven)
  51. 51. 498 NB primary tumors (M. Fischer) Gene expression correlation for the selected two TF modules for Group I (PHOX2B driven) and Group II (AP-1 driven)
  52. 52. 498 NB primary tumors (M. Fischer) Module 1? Module 2? Gene expression correlation for the selected two TF modules for Group I (PHOX2B driven) and Group II (AP-1 driven)
  53. 53. 498 NB primary tumors (M. Fischer) Module 1 Module 2 Gene expression correlation for the selected two TF modules for Group I (PHOX2B driven) and Group II (AP-1 driven)
  54. 54. Gene expression correlation for the selected two TF modules for Group I (PHOX2B driven) and Group II (AP-1 driven) 498 NB primary tumors (M. Fischer) Module 1 Module 2
  55. 55. Module 1 was validated by ChIP-seq in the CLB-GA neuroblastoma cell line 55 PHOX2B HAND2 PHOX2B GATA3 H3K27ac PHOX2B GATA3 HAND2 PHOX2B GATA3 HAND2 SE SE SE
  56. 56. Can we characterize tumors as expressing genes of module 1 or 2? Module 1 Module 2 Average gene expression (log2) for a given sample
  57. 57. TF modules I (PHOX2B-driven) and II (AP-1-driven) determine NBs of distinct identity NB primary tumors
  58. 58. Heterogeneity at the single cell level can explain the absence of clear groups in human tumors SK-N-AS (intermediate) cell line Isabelle Janoueix-Lerosey & Simon Durand Cell ID I II
  59. 59. Heterogeneity at the single cell level can explain the absence of clear groups in human tumors van Groningen et al, Nat Genetics, 2017 IHC for MAML3 (blue) and PRRX1 (red) in a stage 4 neuroblastoma. MAML3=the pan-neuroblastoma marker PRRX1=marker of module 2 Module 1 cells Module 2 cells stage 4 neuroblastoma tumor
  60. 60. Disruption of module 1 may impair cell growth or induce a phenotype change Decreased expression of PHOX2B in CLB-GA cells also impaired tumor growth in vivo PHOX2B knockdown Control Isabelle Janoueix-Lerosey van Groningen et al, Nat Genetics, 2017 PRRX1 expression induces a transition toward NCC-like state (Module 1) (Module2)
  61. 61. Treatment may change (temporary?) transcriptional and epigenetic landscape of neuroblastoma tumors (Module 1) (Module2) (Module 1) (Module2)
  62. 62. Summary Part 2 • We propose existence of a Core Regulatory Circuitry in which PHOX2B, HAND2 and GATA3 are master TF defining transcriptional program in neuroblastoma cells of noradrenergic type • A subset neuroblastoma cells have transcriptional and epigenetic profiles similar to these of neural crest cells (NCC-like type); their Core Regulatory Circuitry includes FOSL1 and FOSL2 • The two subtypes can co-exist within the same cell line • Less differentiated (NCC-like) cells are less sensitive to chemotherapy 62 Boeva at al, Nature Genetics, published online on July 24 or tumor
  63. 63. Acknowledgements 63 Emmanuel Barillot Alban Lermine Amira Kramdi Isabelle Janoueix-Lerosey Caroline Louis Simon Durand Agathe Peltier Tatiana Popova Olivier Delattre Gudrun Schleiermacher Institut Curie, Paris Vladimir Bajic Haitham Ashoor KAUST, Saudi Arabia Irina Medvedeva Institut Cochin, Paris

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