Bob Brown: New challenges in using biological
endpoints for epigenetic therapies in clinical trials
Same genotype, different phenotype
Same genome, different epigenome
Same genotype, different phenotype
High Grade Serous Ovarian Cancer: Similar
genome, very different epigenome (TCGA)
Many important genes are epigenetically
silenced in malignant cells
• Cell cycle: Rb, p16INK4a, p15INK4a, p14ARF
• Signal transduction: RASSF1, APC
• Apoptosis: DAPK, Caspase 8
• DNA repair: MLH1, MGMT, BRCA1
• Senescence: TERT, TERC
• Invasion/metastasis: TIMP-3, E-cadherin
APC = adenomatous polyposis coli
DAPK = death-associated protein kinase
MGMT = O-6-methylguanine-DNA methyltransferase
1. Jones & Baylin. Cell 2007;128:683–92
2. Teodoridis JM, et al. Drug Resistance Updates
2004;7:267–78
HDAC
Acetyl
Acetyl
MBD protein
Me Me Me
DNA
Histone
Histone
Acetyl
HDAC
MBD protein
Me Me Me
DNA
Histone
Histone
HMT
+
Me
Me
Me
MBD protein
Me Me Me
DNA
Histone
Histone
HMT
MBD protein
Me Me Me
DNA
Me
Me
Me
Epigenetic Therapies
• DNA Methyltransferase (DNMT) Inhibitors
– Azacytidine: approved in the EU for the treatment of
patients with higher-risk MDS, CMML and AML
– Decitabine: approved in the USA for the treatment of
patients with all FAB classifications of MDS
• Histone Deacetylase (HDAC) Inhibitors
– Vorinostat: approval in US for treatment of advanced
cutaneous T-cell lymphoma
Challenges with current epigenetic
therapies
• Toxicity
• Delivery
• Short plasma half-life (although long pharmacodynamic half-life)
• Lack of targeting
• Do they work by epigenetic mechanism?
• What are the chemotherapeutic epigenetic target?
• Lack of predictive biomarkers
• Do they target subpopulations of tumour stem cells?
• How to design early clinical trials if only targeting subpopulation
• Is the maximum biological dose the same as the maximum tolerated
dose?
Proof of Mechanism: Do they do what they say on the tin?
Decitabine PK vs PD in PBCs
140
120
Decitabine AUC
Peak plasma levels of
decitabine correlate with
demethylation in PBMCs
100
80
60
40
20
0
5
10
15
20
Methycytosine AAC
CR-UK Phase I dose escalation trial
of decitabine and carboplatin in
patients with advanced solid
tumours (Appleton et al 2007)
Ratio 5-methylcytosine: cytosine
in PBMC DNA (CRUK Phase I trial of Decitabine &
carboplatin in advanced solid tumours)
4.5
4.0
Ratio
Decitabine induces
demethylation several days after
treatment that reverses over time
45mg/m2
3.5
90mg/m2
3.0
135mg/m2
2.5
2.0
1.5
0
5
10
15
Days
20
25
Proof of Concept: Do they biologically do what they should?
HbF
Day 15
Day 12
Day 10
Day 8
K562
Day 1
aActin
CP70
Proof of Concept:
Gene expression
(but not for all genes)
Cycle 1
45
90
135
non-epithelial
Proof of Concept:
Apoptosis
(normal or tumour?)
CK18 (% of day 1)
200
150
100
50
0
2
4
6
8
10 12 14 16 18 20 22
Day
Combination of DNMT and HDAC inhibitor enhances
gene re-expression and chemosensitisation
DAC
DAC+
PXD101
Day 12
5
Day 16
Relati ve tumour vol ume
Day 6
Day 9
4
3
2
1
0
Steele et al 2010
Control
Cisplatin
DAC
DAC+Cisplatin
PXD101
DAC+PXD101+Cisplatin
0
1
2
3
Time (Days)
4
5
6
Origin of Cancer – Role of Cancer Stem Cells (CSC)?
Ovarian cancer cell lines & primary ascites
contain Side Population cells
PEO23:
SP 6.90%
PEO23 +verapamil:
SP 0.00%
Specimen_001_23.fcs
512
768
512
SP
256
256
0
0
256
424/44nm (L3)-A
HOECHST BLUE-A
768
512
768
HOECHST RED-A
Rizzo et al, 2011
SP 45neg live
Ascites010509_CD45 FITC.fcs
1024
Specimen_001_23 Verapamil.fcs
1024
1024
1024
Patient Ascites
SP 0.021%
768
512
SP
256
0
0
0
256
512
768
1024
HOECHST RED-A
0
256
512
768 1024
670nmLP (L3)-A
Patient ascites SP: increases following
treatment
Cell lines derived
from matched
patient ascites
Primary patient
ascites
Rizzo et al, 2011
IGROV1 SP cells have tumour stem cell like properties
(a) Tumour Initiation
(c) 2D colony formation
Rizzo et al, 2011
(b) Spheroid growth
(d) Repopulation
Group
Gene set
Gene set
name
p value
FDR
Source
ES Expressed
Es exp2
ES2
0.39
0.7313
overexpressed in hES cells according to
a meta-analysis
Nanog
targets
nanog
0.128
0.32
ChIP array of Nanog in hES cells:
activated genes
Oct4
targets
oct4
<10-6
<10-4
ChIP array of Oct4 in hES cells;
activated genes
Sox2
targets
sox2
0.128
0.32
ChIP array of Sox2 in hES cells;
activated genes
NOS
targets
Nos
<10-6
<10-4
overlap of above three sets
Suz12
targets
suz12
0.128
0.64
ChIP array of Suz12 in hES cells
Eed
targets
eed
0.388
0.7313
ChIP array of Eed in hES cells
H3K27
bound
h3k27
0.254
0.645
ChIP array of trimethylated H3K27 in
hES cells
PRC2
targets
prc2_targets1
0.128
0.64
overlap of three above sets
PRC2
targets
prc2_targets2
<10-6
<10-4
PRC2 repressed targets
transcriptionally reactivated by DZNep
PRC1
polycomb
complex1
0.258
0.645
polycomb complex1 genes
PRC2
polycomb
complex2
<10-6
<10-4
polycomb complex2 genes
NOS targets
polycomb
targets
polycomb
complex
EZH2 and ABCB1 expression is increased in Side
Population from patient ascites
Patient Ascites
sample number
Ratio of expression of
ABCB1 mRNA SP:non-SP
Ratio of expression of
EZH2 mRNA SP:non-SP
6
12.9
14.5*
7
3.1
4.2*
9
8.4
1.3
10
16.9
1.3
14
2.9
2.1*
16
3.7
5.9*
17
57.6
8.6*
18
10.3
1.6*
19
51.8
1.1
21
36.8
3.4*
PRC2 is a protein complex that catalyses the protein
methylation of lysines on histones (H3K27me3)
IGROV1
PEO14
H3K27me3
Histone H3
h. 24
48
72
96 24
Control siRNA
48
72 96
EZH2 siRNA
24
48
72 96 24
Control siRNA
48
72 96
EZH2 siRNA
Does targeting EZH2 reduce sustaining/ stem cells?
(a) EZH2 knock-down using SiRNA (Rizzo et al., 2011)
(b) EZH2 compounds (Chapman-Rothe, Shasaei, Rizzo, Cherblanc, Fuchter)
H3K27me3
EZH2
Beta-actin
EZH2 as a potential anti-cancer target ?
•
Many genes in cancer, including tumour suppressor genes, are
epigentically silenced by mechanisms associated with H3K27me3
which can be independent of DNA methylation (Kondo et al Nat Genet,
2008; 40: 741-750)
•
H3K27me3 is somatically inherited during cell division (Margueron et al
Nature, 2009. 461: 762-7)
•
Repressive chromatin marks in tumour stem cells may make genes
vulnerable to CpG island DNA methylation (Ohm et al 2007, Nat Genet,
39; 237-242)
•
EZH2 is frequently over-expressed in a wide variety of tumour types
and is driver of metastasis (Min et al Nature Medicine 2010, 16: 286-94)
•
EZH2 is essential for Glioblastoma cancer stem cell maintenance
(Suvà et al, Cancer Res 2009 69:921)
GOG 218 and ICON-7: results
• both trials are positive, with highly
significant improvements in progressionfree survival
• overall survival analysis immature (too few
events)
• no new safety concerns (hypertension in
>20%; bowel perforation in <2%)
• and yet ....
↑
Burger et al. GOG study - presented at ASCO, Chicago, 2010.
Perren T et al. (ICON-7) – presented at ESMO, Milan 2010.
→
GOG investigator analysis used
CA125/RECIST-determined progression.
If data censored for CA125, median PFS
for Arm I and III increase to 12.0 m and
18.2 m, respectively.
23
CpG island methylation as a biomarker
Stable in vivo and ex vivo
Sensitive PCR based assays for single loci
Array based methods for genome wide patterns
Aberrant tumour methylation can be detected in
tumour DNA in accessible body fluids
DNA Methylation Prognostic Biomarkers in Wnt signalling pathway
Red: SGCTG cohort &
TCGA cohort
Blue: SGCTG cohort only
Orange: Absent in TCGA
cohort
Dai et al 2011
Systematic analysis of other pathways
Table 3: Multivariate progression free survival analysis of loci significantly
associated with Progression-free survival in univariate analysis
Pathway/
family
AKT/mTOR
p53
BRCA1/2
Redox
MMR
HR
Multivariate PFS analysis (n=111)
genes
HR
95% CI
adjusted p value
VEGFA
AKT1
13.8
27.2
(0.9, 210.8)
(2.2, 329.1)
0.06+
0.009**
VEGFB||DNAJC4
BAI1
BAX
LRDD
CCND1
HDAC4
HDAC11
PRDX2
TR2IT1
LIG1
MLH3
LRRC14||REC
QL
16.2
33.8
1.7
21.2
4.6
4.7
7
2.8
28.4
1.8
218.6
(1.6, 162.1)
(1.3, 866.5)
(0.7, 4.3)
(1.8, 250.5)
(0.5, 37.9)
(0.7, 32)
(1, 47.8)
(1.5, 5.5)
(2.3, 352.4)
(1.0, 3.5)
(7.7, 6.2x103)
0.018*
0.033*
0.234
0.016*
0.161
0.110
0.048*
0.002**
0.009**
0.644
0.002***
45.4
(0.4, 4.6x103)
0.105
Dai, Zeller, et al
Epigenetics Unit Teams & support
Imperial College:
Institute of Cancer Research:
Tumour DNA Methylation Profiling
Epigenetics (stem cell) team
• Constanze Zeller
• Sian Rizzo
• Elizabeth Evans
• Alessandra Silva
• Jenny Quinn
• Jenny Quinn
• Jens Teodoridis
• Louisa Luk
• Janet Graham
• James Flanagan
• Prof. Stan Kaye
Chromatin targets
• Nadine Chapman-Rothe
• Gary Box, Sue Eccles
• Ely Shamsaei
• Ian Titley, Gowri Vijayaraghavan
• Fanny Cherblanc
• Craig Carden, Debbie Tandy
• Matt Fuchter
Bioinformatics
• Wei Dai
Tissue collection
• Sadaf Ghaem-Maghami, Nona
Rama, Amy Ford, Nicole Martin