Download - Bob Brown: New challenges in using biological endpoints for epigenetic therapies in clinical trials
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 coliDAPK = death-associated protein kinaseMGMT = O-6-methylguanine-DNA methyltransferase
1. Jones & Baylin. Cell 2007;128:683–922. Teodoridis JM, et al. Drug Resistance Updates
2004;7:267–78
MBD protein
Me Me Me
Acetyl Acetyl
DNAHistone Histone
HDAC
MBD protein
Me Me Me
Acetyl
DNAHistone Histone
HDAC
MBD protein
Me Me Me
DNAHistone Histone
HMT MeMe+ Me
MBD protein
Me Me Me
DNA
HMTMe 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?
CR-UK Phase I dose escalation trial of decitabine and carboplatin in
patients with advanced solid tumours (Appleton et al 2007)
Ratio 5-methylcytosine: cytosinein PBMC DNA (CRUK Phase I trial of Decitabine &
carboplatin in advanced solid tumours)
1.5
2.0
2.5
3.0
3.5
4.0
4.5
0 5 10 15 20 25Days
Rat
io45mg/m2
90mg/m2
135mg/m2Decitabine induces
demethylation several days after treatment that reverses over time
Decitabine PK vs PD in PBCs
0
20
40
60
80
100
120
140
5 10 15 20
Methycytosine AAC
Decit
ab
ine A
UCPeak plasma levels of
decitabine correlate with demethylation in PBMCs
Proof of Mechanism: Do they do what they say on the tin?
Proof of Concept:Gene expression
(but not for all genes)
HbF
Actin
CP
70
K5
62
Da
y 1
Da
y 8
Da
y 1
0
Da
y 1
2
Da
y 1
5
Cycle 1
222018161412108642050
100
150
200 4590135non-epithelial
Day
CK
18
(%
of
da
y 1
)Proof of Concept:
Apoptosis(normal or tumour?)
Proof of Concept: Do they biologically do what they should?
Combination of DNMT and HDAC inhibitor enhances gene re-expression and chemosensitisation
65432100
1
2
3
4
5ControlCisplatinDAC
DAC+CisplatinPXD101DAC+PXD101+Cisplatin
Time (Days)
Rela
tive t
umou
r vol
ume
DAC
Day 6 Day 9 Day 12 Day 16
DAC+PXD101
Steele et al 2010
Origin of Cancer – Role of Cancer Stem Cells (CSC)?
Ovarian cancer cell lines & primary ascites contain Side Population cells
Specimen_001_23 Verapamil.fcs
HOECHST RED-A
HO
EC
HS
T B
LUE
-A
0 256 512 768 10240
256
512
768
1024
SP
Specimen_001_23.fcs
HOECHST RED-A
HOEC
HST
BLUE
-A
0 256 512 768 10240
256
512
768
1024
SP
Ascites010509_CD45 FITC.fcs
670nmLP (L3)-A42
4/44
nm (L
3)-A
0 256 512 768 10240
256
512
768
1024
SP 45neg live
Patient AscitesSP 0.021%
PEO23:
SP 6.90%
PEO23 +verapamil:
SP 0.00%
Rizzo et al, 2011
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 (b) Spheroid growth
(d) Repopulation(c) 2D colony formation
Rizzo et al, 2011
Group Gene setGene set
namep value FDR Source
ES Expressed Es exp2 ES2 0.39 0.7313overexpressed in hES cells according
to a meta-analysis
NOS targets
Nanog targets
nanog 0.128 0.32ChIP 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 0.32ChIP array of Sox2 in hES cells;
activated genes
NOS targets
Nos <10-6 <10-4 overlap of above three sets
polycomb targets
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 0.645ChIP 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
polycomb complex
PRC1polycomb complex1
0.258 0.645 polycomb complex1 genes
PRC2polycomb complex2
<10-6 <10-4 polycomb complex2 genes
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)
24 48 72 96 24 48 72 96 24 48 72 96 24 48 72 96
IGROV1
Control siRNA
PEO14
H3K27me3
EZH2 siRNA EZH2 siRNA
h.
Control siRNA
Histone H3
(b) EZH2 compounds (Chapman-Rothe, Shasaei, Rizzo, Cherblanc, Fuchter)
(a) EZH2 knock-down using SiRNA (Rizzo et al., 2011)
Does targeting EZH2 reduce sustaining/ stem cells?
Beta-actin
EZH2
H3K27me3
untre
ated
TG3-
178-
2TG
3-17
8-1
TG3-
213-
2TG
3-21
4-1
TG3-
179-
1
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)
23
GOG 218 and ICON-7: results• both trials are positive, with highly significant
improvements in progression-free survival
• overall survival analysis immature (too few events)
• no new safety concerns (hypertension in >20%; bowel perforation in <2%)
• and yet ....
• both trials are positive, with highly significant improvements in progression-free 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.
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.
→
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 cohortBlue: SGCTG cohort onlyOrange: Absent in TCGA cohort
Dai et al 2011
Systematic analysis of other pathways
Pathway/ family
genes
Multivariate PFS analysis (n=111)
HR 95% CI adjusted p value
AKT/mTOR
VEGFA 13.8 (0.9, 210.8) 0.06+
AKT1 27.2 (2.2, 329.1) 0.009**
VEGFB||DNAJC4 16.2 (1.6, 162.1) 0.018*
p53
BAI1 33.8 (1.3, 866.5) 0.033*BAX 1.7 (0.7, 4.3) 0.234
LRDD 21.2 (1.8, 250.5) 0.016*CCND1 4.6 (0.5, 37.9) 0.161
BRCA1/2HDAC4 4.7 (0.7, 32) 0.110HDAC11 7 (1, 47.8) 0.048*
RedoxPRDX2 2.8 (1.5, 5.5) 0.002**TR2IT1 28.4 (2.3, 352.4) 0.009**
MMRLIG1 1.8 (1.0, 3.5) 0.644MLH3 218.6 (7.7, 6.2x103) 0.002***
HRLRRC14||RECQL 45.4 (0.4, 4.6x103) 0.105
Table 3: Multivariate progression free survival analysis of loci significantly associated with Progression-free survival in univariate analysis
Dai, Zeller, et al
Epigenetics Unit Teams & support
Imperial College:
Tumour DNA Methylation Profiling
• Constanze Zeller
• Elizabeth Evans
• Jenny Quinn
• Jens Teodoridis
• Janet Graham
• James Flanagan
Chromatin targets
• Nadine Chapman-Rothe
• Ely Shamsaei
• Fanny Cherblanc
• Matt Fuchter
Bioinformatics• Wei Dai
Tissue collection• Sadaf Ghaem-Maghami, Nona
Rama, Amy Ford, Nicole Martin
Institute of Cancer Research:
Epigenetics (stem cell) team
• Sian Rizzo
• Alessandra Silva
• Jenny Quinn
• Louisa Luk
• Prof. Stan Kaye
• Gary Box, Sue Eccles
• Ian Titley, Gowri Vijayaraghavan
• Craig Carden, Debbie Tandy