Transcript Folie 1 - uni

Impact of epigenetic variations on breast cancer
metastasis risk and therapy resistance
e:Med Meeting Heidelberg, 4.12.2012
Division Epigenomics and Cancer Risk Factors
Christoph
Plass
6 July 2015
Dieter
Weichenhan
Clarissa Gerhauser, German Cancer Research Center
Clarissa
Gerhäuser
Mechanisms of Epigenetic Regulation
DNA
Chromosom
1. DNA methylation
Histones
Chromatin
2. Histone tail modifications
3. non-coding RNAs (microRNAs)
Me
Me
Me
mRNA
Transcription
Translation
Me
- Inhibition of
translation
- Degradation
of mRNA
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Protein
DNA Methylation
meC
NH2
5
N
O
CH3
 Epigenetic event
 Methyl-CpG
 Control of gene expression
N
Promoter
CpG islands
Normal cell
DNA
Repeats
Exon
unmethylated
methylated
mRNA
CpG island
hypermethylation
 meC
global
hypomethylation
Carcinogenesis
 transcription
6 July 2015
genomic
instability
Clarissa Gerhauser, German Cancer Research Center
DNA Methylation Profiling Projects
Specific Aims:
Identification of differentially methylated genes with critical
roles during cancer development, recurrence, radiation
sensitivity
Leukemia (DFG-SPP)
Prostate (ICGC)
Breast (FRONTIER)
Lung (DZL)
Glioblastoma
Colon
Head&Neck
Pancreas
Cholangiocarcinoma…
 Epigenetic markers for early detection, prognosis, and as
potential targets for intervention and cancer prevention
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
DNA Methylation Profiling Projects
Step 1: Genome-wide methylation profiling
• Methyl-CpG Immunoprecipitation (MCIp)/CpG island array - NGS
• Illumina 450k technology
• Whole genome bisulfite sequencing (WGBS) (Tagmentation)
Step 2: Technical validation/confirmation in independent sample sets
• High-throughput quantification of DNA methylation
• Sequenom MassARRAY technology 384-well format
Step 3: Selection of candidate genes
• Correlation with clinical data
• Correlation with gene expression (RT-PCR, or published data)
• Correlation with protein expression/TMA
• Demethylation analyses in cell culture to confirm epigenetic
regulation
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
DNA Methylation Profiling Projects
Step 4: Functional analyses in vitro
• Reporter gene assays for promoter/enhancer methylation
• Gene overexpression and knockdown by si- and sh-RNA
 Effects on proliferation, colony formation, DNA repair, cell cycle
regulation, migration/invasion
• Reporter construct panel for key transcription factor pathways
(Chris Oakes, HEK cells)
• ChIP-Seq: Histone marks, TF-binding
Step 5: Confirmation of gene function in vivo
• Testing of gene ki oder ko cell lines in xenograft models
• Dilution experiments to determine stem cell characteristics
• transgenic mouse model for basal BC: C3(1) SV40 TAg
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Genome Wide Methylation Screens
MCIp-Seq
Enrichment of highly methylated DNA with MBD2 protein
Sample req. ~3 µg gDNA (fresh frozen tissue)
Not quantitative
Limited analysis of hypomethylation events
Illumina 450k technology
Further development of 27k array (27.000 CpG sites, 14.000 promoters)
Interrogates >480.000 distinct CpG sites (CGIs, prom., gene body, 3‘UTR…)
Input: bisulfite-converted DNA, compatible with FFPE tissue
Advantage: quantitative data (beta values 0-1), hypo- and hypermethylation
Sample requirement: 0.25 µg DNA (FFPE), 1 µg (fresh frozen)
Whole genome bisulfite sequencing (WGBS)
Interrogates all CpG sites
Input: traditional 5 µg, with tagmentation modification 10-50 ng (fresh fr.)
quantitative
Transposase
same input DNA can be used for genome-seq
complex
Disadvantage: high costs
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Genome Wide Methylation: Resolution
450k
Tissue specific
Methylation diff.
Liver
WGBS
hESC
PCa
MCIpSeq
PCa
Prostate
Prostate
Differentially methylated region (DMR)
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Quantitative high-throughput determination
of DNA methylation (MassARRAY)
Bisulfite treatment of DNA
PCR amplification of regions of interest
In vitro transcription
Base-specific cleavage
16 m/z
MALDI-TOF mass spectrometry-based
MassARRAY analysis
Statistical analysis
DNA from FFPE tissue can be used
500 ng DNA sufficient for ~30 amplicons (200-500 bp)
High-throughput 384 well format
Ehrich et al., 2005
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Breast Cancer Methylation Profiling
1. MCIp-CGI array on 10 ER/PR pos. low grade BC/unmatched normal breast tissue
2. Identification of 214 CGIs hypermethylated in 6/10 BC
3. Validation of 11 candidates by MassARRAy in two independent sample sets
4. Correlation with clinical data
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
DNA hypermethylation as diagnostic biomarker
Validation set 1: ER+/PR+ low-grade IBC and DCIS (Prof. Sinn, Uni HD/NCT)
BCAN
HOXD1
KCTD8
KLF11
CPNE7
*
Distant
Metastases
(20)
Faryna et al., FASEB J 2012
Invasive
breast cancer (32)
Carcinoma
in situ (13)
Normal
tissue (11)
STD
0
20
40
60
80
100
 Significant hypermethylation already in preinvasive tumors
 Definition of cutoff methylation levels allows correct classification of tumors
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
DNA methylation as potential prognostic markers
Validation Set 2: 43 ER+/PR+ IBC (Dr. J. Rom, Uni HD)
Methylation of CPNE7
1.0
Metastasis-free survival
Metastasis-free survival
Methylation of KLF11 CpG5
0.8
0.6
0.4
p-value = 0.009
Median methylation < 0.62
Median methylation  0.62
0.2
0.0
0
2
4
6
8
p-value = 0.0112
Median methylation ≤ 0.2
Median methylation > 0.2
10
Years
Years
 Confirmation with 45 IBC w/wo metastases (Dr. Rom and NCT HD) ongoing
 So far no information on gene function
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Planned project
Aim 1:
Identification of epigenetically deregulated genes with prognostic or functional
relevance for metastatic risk (WP1: in silico; WP2: experimentally)
Aim 2:
Analysis of gene functions to identify potential targets for intervention
(combination therapy?) (WP3)
Aim 3:
Demonstration of functional relevance in vivo (gene function / intervention) (WP4)
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
TCGA, 2012
ICGC
6 July 2015
Subgroup definition
(progn. 15 M1, 73 Mx, 97 dead)
6 projects on BC
802 / ~80
5 mets
hundreds
Clarissa Gerhauser, German Cancer Research Center
Illumin Infinium 27k
Illumina Infinium 450k
Planned project
WP1: In silico screen of available datasets to identify epigenetically regulated
genes involved in metastasis and drug-resistance
Link to other WP that identify interesting gene/miRNA candidates
(Christian/Cindy/Stefan)
WP2: Genome-wide methylation analysis
2.1 Tumor-stroma interaction
Link to WP Erlangen (Samples needed)
• Normal - Tumor – Stroma from 10 patients M0 / 10 patients M1 (same subtype?)
• 450k array  100-200 ng DNA from microdissected tissue (FFPE)
• Link to WP Ulrike
2.2 Epigenetic alterations in metastases
Link to WP Erlangen (Samples needed)
• Normal – Tumor - (Stroma) – Metastases from min. 3 patients
• WGBS  10-50 ng DNA from microdissected fresh frozen tissue
• Best coverage of methylation events – Link to WP Jose TF-binding
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Planned project
2.3 Epigenetic alterations in drug resistance
Link to WP Erlangen (Samples needed)
• resistant tumors vs. not-resistant tumors (possible)? 10 each
• 450k array  100-200 ng DNA (FFPE)
• Identify DMRs
Link to WP Christian
2.4 Validation on methylation events from 2.1-2.3.
• Quantitative methylation analyses by Massarray
• FFPE material sufficient
• Link to WP Erlangen (Samples needed) (as many as possible)
• Correlation with protein expression (TMA), link to WP Erlangen
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Planned project
WP3: Functional analyses in vitro
3.1 siRNA screen/overexpression of differentially methylated genes
affecting metastasis risk (from WP1 and 2 and other WPs)
• Endpoint proliferation, migration (high throughput migration assay Stefan, which
cell lines suitable?)
• Reporter construct assay panel for key pathways affecting migration, invasion,
EMT?
• Confirmation by RPPA? (WP Ulrike)
• Identification of druggable targets in pathways
• Comparison of gene dose effects with drug treatment
• Link to WP Rainer: generation of stable cell lines for highly relevant candidates
for in vivo analyses (WP Karin)
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Planned project
3.2. Functional analyses related to drug resistance (link to WP Christian)
• Co-treatment of parental and resistant cell lines with anti-cancer therapeutics
and epigentic drugs (DNMT and HDAC inhibitors)
• Methylation changes (MassARRAY)
• Effect on proliferation, apoptosis induction, cell cycle regulation
3.3. Functional analyses related to TF-pathways (WP Jose)
• Reporter gene assays for promoter/enhancer methylation
• Reporter construct assay panel for key pathways (Chris Oakes)
• ChIP and ChIP-Seq for TF-DNA methylation interaction and chromatin marks
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Planned project
WP4: Functional analyses in vivo
4.1 Confirmation in vivo (WP Karin)
• Xenograft models with ki/ko cell lines (Rainer), drug intervention studies?
• transgenic mouse model for basal BC: C3(1) SV40 TAg (depending of human –
mouse correlation)
4.2 Planning for translational studies?
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Breast cancer: C3(1) SV40 TAg mouse model
Green et al, Oncogene 2001
C3(1) region
of rat PSBP
SV40 TAg
p53
100% breast cancer ~ 20 weeks
70-80% prostate cancer
pRB
Progression of mammary carcinogenesis similar to human disease
normal
atypia
pre-invasive
invasive BC
metastasis
Human
Breast
Cancer
Prevention studies:




Exercise (Murphy et al., 2011)
Green tea (Leong et al., 2008)
Green tea, black tea (Kaur et al., 2007)
VEGF-DT385 toxin (Wild et al., 2004)
6 July 2015




Celcoxib (Kavanaugh & Green, 2003)
Retinoids (Wu et al., 2002, 2000)
DFMO, DHEA (Green et al., 2001)
p21 induction (Shibata et al., 2001)
Clarissa Gerhauser, German Cancer Research Center
Kinetics of DNA methylation changes
Developmental
phases
Birth
Ablaction Puberty
w0 w1
Mammary
gland tumors
Adult
w12
w3
w22-24
wt control
Lung mets
W32??
Tissue collection
every 4 weeks
C3(1) tg
w4
w8
w12
w16
w20
w24
 Genome-wide methylation analysis by MCIp/Seq
 Quantitative analysis of methylation changes by MassARRAY
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Genome-wide analysis using Next-Generation Sequencing
Lyl1
Plekhg5
Espn
w4
transgene
w8
w12
w16
w20
w24
w4
wildtype
w8
w12
w16
w20
w24
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
K. Heilmann
Validation of novel candidate genes by MassARRAY
Age
50
80
Mab21l2
wt
Avg. Methylation (%)
Avg. Methylation (%)
60
TG
WT
Mab21l2
tg
40
30
20
10
60
wt
tg
40
20
4 w 12 w 16 w 20 w 24 w 4 w 12 w 16 w 20 w 24 w
4 w 12 w 16 w 20 w 24 w 4 w 12 w 16 w 20 w 24 w
A93
Atp
60
70
Atp6v1b1
wt
50
Avg. Methylation (%)
Avg. Methylation (%)
Lyl1
0
0
60
Lyl1
tg
40
30
20
10
50
40
A930037G
wt
tg
30
20
10
0
0
4 w 12 w 16 w 20 w 24 w 4 w 12 w 16 w 20 w 24 w
4 w 12 w 16 w 20 w 24 w 4 w 12 w 16 w 20 w 24 w
Espin
Avg. Methylation (%)
80
Espn
60
wt
tg
40
20
0
4 w 16 w 24 w 4 w 12 w 16 w 20 w 24 w
 Development-associated genes
 Sig. increase in methylation during
carcinogenesis
 Function in breast carcinogenesis
largely unknown
A. Ward/ M. Pudenz
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Genome Wide Methylation Screen
MCIp (Methyl-CpG immunoprecipitation) & NGS
3 µg genomic DNA
modified from Gebhard et al., 2006
Dieter Weichenhan
bp
600-
100-
Fragmentation by sonication
Robot-assisted binding to
MBD2-coated magnetic beads
(MBD2: Methyl binding domain protein)
Fractionation by salt gradient
Library prep,
NGS (Solid, Illumina HiSeq)
Bioinformatic analysis
Lei Gu
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Genome Wide Methylation Screen
Illumina 450k array technology
Interrogates >480.000 distinct CpG sites
(CpG islands, promoters, gene body, 3‘UTR…)
Input: bisulfite-converted DNA
Advantage: quantitative data (beta values 0-1)
Compatible with FFPE tissue
Sample requirement: 0.25 to 1 µg
Data handling rel. „easy“
treated
WA 0.7 µM
treated
MDA-MB231 cells treated with demethylating agent
WA 0.175 µM
control
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
20% 
control
Tagmentation-based
whole genome bisulfite NGS
Adey & Shendure ,
Genome Res. 2012
Conventional
Tagmentation based
Fragmentation
hyperactive transposase
Polishing
Tagmentation:
all in one
A-tailing
Adaptor
ligation
seq. barcode
PCR
PCR
seq. barcode
Sample requirement: 5 µg
6 July 2015
Bisulfite treatment,
NGS
Clarissa Gerhauser, German Cancer Research Center
10-50 ng
Tagmentation-based whole genome bisulfite NGS
hyperactive transposase
in vitro assembled
transposome
free ME adaptors (hyperactive
derivatives of IS50 end sequences)
tagmentation
genomic DNA
• genomic DNA is frag-(tag)mented with end-joining
of ME adaptors to 5‘end of fragments
seq. barcode
• bisulfite treatment
• limited-cycle PCR is used to append seq-platform-specific primers
• NGS
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Flowchart of tagmentation-based WGBNGS
(i) Assembly of the transposome
(ii) Tagmentation of genomic DNA
SPRI purification
(iii) Oligonucleotide replacement
and gap repair
SPRI purification
(iv) Bisulfite treatment
Column purification
(v) Limited cycle number PCR
SPRI purification
(vi) Next generation sequencing
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Quantitative high-throughput determination
of DNA methylation (MassARRAY)
Bisulfite treatment of DNA
PCR amplification of regions of interest
In vitro transcription
Base-specific cleavage
16 m/z
MALDI-TOF mass spectrometry-based
MassARRAY analysis
Statistical analysis
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Ehrich et al., 2005
Planned project
WP1: Identification of genes with relevance for metastasis risk
1.1 In silico search, use of available datasets
• Several recent genome-wide methylation studies have identified aberrant methylation as
biomarker of poor prognosis (metastasis risk)
• Mainly relevant for ER-neg. BC
• Both hyper- and hypomethylation events
• Compile gene list, compare methylation status with expression in additional datasets
(TCGA, ICGC) - Link to miRNA WP
• Select candidates for validation and functional studies
1.2 Genome-wide methylation analysis
• Limited information for ER+ BC
• Perform 450k methylation analysis on 40 ER+ BC with known metastasis status (FFPE
samples are available)
• Select differentially methylated regions (DMRs) and proceed as above
• Validation of 10-20 hypo- and hypermethylated candidates by MassARRAY
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Planned project
1.3 Whole genome bisulfite sequencing (Tagmetation)
• So far, no genome-wide data available for methylation changes between tumor
and metastasis (only two matching N-T-M datasets out of ~650 450k datasets in
TCGA)
• Genome-wide data will facilitate TF-binding analysis – link to WP Jose
• Same samples could be used for whole genome seq (costs!)
• identify genes with aberrant methylation between samples, mRNA expression?
Sample requirement: 3-5 triplets of Normal-Tumor-Metastasis (fresh frozen,
high purity (LCM?), but ~ 50 ng DNA sufficient)
6 July 2015
Clarissa Gerhauser, German Cancer Research Center
Planned project
WP2: Identification and validation genes with relevance for aquired drug
resistance
2.1 In silico screen
• Available information mainly from comparison of parental and resistant BC cell
lines; (data Aoife?) human studies?
• Resistance mainly related to hypomethylation events
• Compile gene list, compare gene functions, expression? (Stefan)
• Select candidates for validation and functional studies
2.2 Validation of methylation changes in clinical samples
• Sample availability?
before and after therapy, or resistant tumors vs. not-resistant tumors
• Validation of up to 50 hypo- and hypermethylated candidates by MassARRAY
would require ~ 1 µg DNA (can be FFPE), more efficient to do 450k?
6 July 2015
Clarissa Gerhauser, German Cancer Research Center