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

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. 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. Relationship between histone modifications and chromatin
states
• Histone modifications
Bhaumik et al, Nat Str & Mol Biol, 2007
Li et al, Cell, 2007
3

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. 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. 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. 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. De novo cancer specific super-enhancers
8
Colorectal
cancer
Hnisz et al., Cell 2013

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. Rewiring of transcriptional networks in cancer
10
Normal cell
Cancer cell
?
TF network rewiring?

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. 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. 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. Part 1
Computational strategies for the analysis of epigenetic
landscape in cancer
14

15. 1. Peak calling: detection of regions enriched in a given histone
mark
H3K27ac signal
H3K27ac peaks
Sequenced reads (.BAM)
1.
2.

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. 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. Solution: explicit normalization for copy number status
18
H. Ashoor et al, Bioinformatics, 2013
Hidden Markov Model

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. Peaks predicted by HMCan do not show copy number bias
20H. Ashoor et al, Bioinformatics, 2013
Copy number
HMCan
MACS
SICER

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

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

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. 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. 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. Part 2
Application to neuroblastoma

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. 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. 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. 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. 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. Principal component analysis (PCA) based on the SE signal
determines 2 groups of cell lines
36

37. Principal component analysis (PCA) based on the SE signal
determines 2 groups of cell lines
37
Normal controls

38. Normal controls
Principal component analysis (PCA) based on the SE signal
determines 2 groups of cell lines
38

39. Principal component analysis (PCA) based on the SE signal
determines 2 groups of cell lines
39
Group II
Group I

40. We identify super-enhancers (SEs) in group I and group II

41. TBX2 locus is gained in ~82% of aggressive NBs
• 765 high risk NBs:
41
Position along chr17
Fractionofpatientsw/gainorloss

42. We identify super-enhancers (SEs) in group I and group II

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. Enrichment of neuroblastoma super-enhancers in PHOX2B and
AP-1 binding motifs
• i-cisTarget (Herrmann et al, 2012) 44
Group I

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. 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
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. 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. 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. 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. 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. 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. 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. 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. 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. 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. TF modules I (PHOX2B-driven) and II (AP-1-driven) determine
NBs of distinct identity
NB primary tumors

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. 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. 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. Treatment may change (temporary?) transcriptional and
epigenetic landscape of neuroblastoma tumors
(Module 1)
(Module2)
(Module 1)
(Module2)

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. 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