Impact of epigenetic variations on breast cancer metastasis risk and therapy resistance

1. Impact of epigenetic variations on breast cancer metastasis risk and therapy resistance e:Med Meeting Heidelberg, 4.12.2012 Division Epigenomicsand Cancer RiskFactors Christoph Plass Dieter Weichenhan Clarissa Gerhäuser

2. DNA Chromosom Chromatin Histones Me Me Me Me Mechanisms of Epigenetic Regulation 1. DNA methylation 2. Histone tail modifications 3. non-coding RNAs (microRNAs) mRNA Transcription Translation - Inhibition of translation Protein - Degradation of mRNA

3. DNA Methylation meC • Epigenetic event • Methyl-CpG • Control of gene expression 5 Promoter CpG islands Repeats Normal cell DNA Exon unmethylated mRNA methylated  meC CpG island hypermethylation global hypomethylation Carcinogenesis genomic instability  transcription

4. DNA Methylation Profiling Projects SpecificAims: 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

5. 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. DNA Methylation Profiling Projects • Step 4:Functional analysesin 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 kioderko cell lines in xenograft models • Dilution experiments to determine stem cell characteristics • transgenic mouse model for basal BC: C3(1) SV40 TAg

7. 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., genebody, 3‘UTR…) • Input: bisulfite-converted DNA, compatiblewith FFPE tissue • Advantage: quantitative data (betavalues 0-1), hypo- and hypermethylation • Sample requirement: 0.25 µg DNA (FFPE), 1 µg (freshfrozen) • Whole genome bisulfite sequencing (WGBS) • Interrogates all CpG sites • Input: traditional 5 µg, with tagmentation modification 10-50 ng (fresh fr.) • quantitative • same input DNA can be used for genome-seq • Disadvantage: high costs Transposase complex

8. Genome Wide Methylation: Resolution 450k Tissuespecific Methylation diff. Liver hESC WGBS PCa PCa Prostate Prostate MCIp- Seq Differentially methylated region (DMR)

9. Quantitative high-throughputdetermination 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

10. Breast Cancer Methylation Profiling MCIp-CGI array on 10 ER/PR pos. low grade BC/unmatched normal breasttissue Identification of 214 CGIs hypermethylated in 6/10 BC Validation of 11 candidatesbyMassARRAy in twoindependent sample sets Correlationwithclinicaldata

11. 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) Invasive breastcancer (32) Carcinoma in situ (13) Faryna et al., FASEB J 2012 Normal tissue (11) 0 20 40 60 80 100 STD • Significant hypermethylation already in preinvasivetumors • Definition of cutoff methylation levelsallowscorrectclassification of tumors

12. DNA methylation as potential prognostic markers Median methylation ≤ 0.2 Validation Set 2: 43 ER+/PR+ IBC (Dr. J. Rom, Uni HD) Median methylation > 0.2 Methylation of KLF11 CpG5 Methylation of CPNE7 Metastasis-freesurvival p-value = 0.009 p-value = 0.0112 Years • Confirmationwith 45 IBC w/wo metastases (Dr. Rom and NCT HD) ongoing • So farnoinformation on genefunction

13. Planned project Aim 1: Identificationof epigeneticallyderegulated genes withprognosticorfunctionalrelevance for metastaticrisk (WP1: in silico; WP2: experimentally) Aim 2: Analysis of genefunctionstoidentify potential targets for intervention (combinationtherapy?) (WP3) Aim 3: Demonstration of functionalrelevancein vivo (genefunction / intervention) (WP4)

14. TCGA, 2012 Subgroupdefinition (progn. 15 M1, 73 Mx, 97 dead) 802 / ~80 5 mets IlluminInfinium 27k IlluminaInfinium 450k ICGC 6 projects on BC hundreds

15. Planned project • WP1: In silicoscreenof availabledatasetstoidentifyepigeneticallyregulated genes involved in metastasis and drug-resistance • Link toother WP thatidentifyinterestinggene/miRNAcandidates (Christian/Cindy/Stefan) • WP2: Genome-widemethylation analysis • 2.1 Tumor-stromainteraction • Link toWP Erlangen (Samples needed) • Normal - Tumor – Stroma from 10 patients M0 / 10 patients M1 (same subtype?) • 450k array 100-200 ng DNA frommicrodissectedtissue (FFPE) • Link toWP Ulrike • 2.2 Epigeneticalterations in metastases • Link toWP Erlangen (Samples needed) • Normal – Tumor - (Stroma) – Metastasesfrom min. 3 patients • WGBS 10-50 ng DNA frommicrodissectedfreshfrozentissue • Best coverage of methylation events – Link toWP Jose TF-binding

16. Planned project • 2.3 Epigeneticalterations in drugresistance • Link toWP Erlangen (Samples needed) • resistanttumors vs. not-resistanttumors(possible)?10 each • 450k array 100-200 ng DNA (FFPE) • Identify DMRs • Link toWP Christian • 2.4 Validation on methylation eventsfrom 2.1-2.3. • Quantitative methylation analysesbyMassarray • FFPE material sufficient • Link toWP Erlangen (Samples needed) (asmanyaspossible) • Correlationwithproteinexpression (TMA), link toWP Erlangen

17. Planned project • WP3: Functionalanalysesin vitro • 3.1 siRNAscreen/overexpression of differentially methylated genes affectingmetastasisrisk(fromWP1 and 2 and other WPs) • Endpointproliferation, migration (highthroughputmigrationassayStefan, whichcelllinessuitable?) • Reporter construct assay panel for key pathways affecting migration, invasion, EMT? • Confirmation by RPPA? (WP Ulrike) • Identification of druggabletargets in pathways • Comparison of gene dose effectswithdrugtreatment • Link toWP Rainer: generation of stablecelllines for highly relevant candidates • for in vivo analyses(WPKarin)

18. Planned project • 3.2. Functionalanalysesrelatedtodrugresistance (link toWP Christian) • Co-treatmentof parental and resistantcelllineswithanti-cancertherapeutics and epigenticdrugs (DNMT and HDAC inhibitors) • Methylation changes (MassARRAY) • Effecton proliferation, apoptosisinduction, cellcycleregulation • 3.3. FunctionalanalysesrelatedtoTF-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

19. Planned project • WP4: Functionalanalysesin 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?

20. Breast cancer: C3(1) SV40 TAg mouse model Green et al, Oncogene 2001 C3(1) region of rat PSBP p53 100% breast cancer ~ 20 weeks 70-80% prostate cancer pRB SV40 TAg Progression of mammary carcinogenesis similar to human disease normal atypia metastasis pre-invasive invasive BC Human Breast Cancer Preventionstudies: • Exercise(Murphy et al., 2011) • Green tea(Leong et al., 2008) • Green tea, blacktea(Kaur et al., 2007) • VEGF-DT385 toxin (Wild et al., 2004) • Celcoxib(Kavanaugh & Green, 2003) • Retinoids(Wu et al., 2002, 2000) • DFMO, DHEA (Green et al., 2001) • p21 induction(Shibata et al., 2001)

21. Kinetics of DNA methylation changes Developmental phases Mammary glandtumors Birth Ablaction Puberty Lung mets Adult w12 w22-24 w0 w1 w3 W32?? wt control Tissuecollection every 4 weeks C3(1) tg w4 w8 w12 w16 w20 w24 • Genome-wide methylation analysisbyMCIp/Seq  Quantitative analysis of methylation changesby MassARRAY

22. Genome-wide analysis using Next-Generation Sequencing Lyl1 Plekhg5 Espn transgene w4 w8 w12 w16 w20 w24 wildtype w4 w8 w12 w16 w20 K. Heilmann w24

23. wt wt wt wt wt tg tg tg tg tg Validation of novel candidate genes by MassARRAY TG WT Age Lyl1 Mab21l2 A930037G Atp6v1b1 Espn •  Development-associated genes •  Sig. increase in methylation during carcinogenesis • Function in breast carcinogenesis largely unknown • Ward/ M. Pudenz

25. Genome Wide Methylation Screen MCIp (Methyl-CpG immunoprecipitation) & NGS bp 600- 100- 3 µg genomic DNA Dieter Weichenhan Fragmentation by sonication Robot-assisted binding to MBD2-coated magnetic beads (MBD2: Methyl binding domain protein) modified from Gebhard et al., 2006 Fractionation by salt gradient Library prep, NGS (Solid, Illumina HiSeq) Bioinformatic analysis Lei Gu

26. Genome Wide Methylation Screen Illumina 450k arraytechnology Interrogates >480.000 distinct CpG sites (CpG islands, promoters, genebody, 3‘UTR…) Input: bisulfite-converted DNA Advantage: quantitative data (betavalues 0-1) Compatiblewith FFPE tissue Sample requirement: 0.25 to 1 µg Data handling rel. „easy“ MDA-MB231cellstreatedwith demethylating agent WA 0.175 µM WA 0.7 µM treated treated 20%  control control

27. Tagmentation-based whole genome bisulfite NGS Adey & Shendure , Genome Res. 2012 Conventional Tagmentation based Fragmentation hyperactivetransposase Polishing Tagmentation: all in one A-tailing Adaptor ligation seq. barcode PCR PCR seq. barcode Sample requirement: 5 µg 10-50 ng Bisulfite treatment, NGS

28. Tagmentation-based whole genome bisulfite NGS hyperactivetransposase 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

29. 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

30. Quantitative high-throughputdetermination 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 Ehrich et al., 2005

31. Planned project • WP1: Identification of genes withrelevance for metastasisrisk • 1.1 In silicosearch, use of availabledatasets • Severalrecentgenome-wide methylation studieshaveidentifiedaberrant methylation asbiomarker of poorprognosis (metastasisrisk) • Mainly relevant for ER-neg. BC • Both hyper- and hypomethylationevents • Compilegenelist, compare methylation statuswithexpression in additional datasets (TCGA, ICGC) - Link tomiRNA WP • Select candidates for validation and functionalstudies • 1.2 Genome-wide methylation analysis • Limited information for ER+ BC • Perform 450k methylation analysis on 40 ER+ BC withknownmetastasisstatus (FFPEsamplesareavailable) • Select differentially methylated regions (DMRs) and proceedasabove • Validation of 10-20 hypo- and hypermethylated candidatesby MassARRAY

32. Planned project • 1.3 Wholegenome bisulfite sequencing (Tagmetation) • So far, nogenome-widedataavailable for methylation changesbetweentumor and metastasis (onlytwomatching N-T-M datasets out of ~650 450k datasets in TCGA) • Genome-widedata will facilitate TF-bindinganalysis – link to WP Jose • Same samplescouldbeused for wholegenomeseq (costs!) • identify genes withaberrant methylation betweensamples, mRNAexpression? • Sample requirement: 3-5 triplets of Normal-Tumor-Metastasis (freshfrozen, highpurity (LCM?), but ~ 50 ng DNA sufficient)

33. Planned project • WP2: Identification and validation genes withrelevance for aquireddrugresistance • 2.1 In silicoscreen • Availableinformationmainlyfromcomparison of parental and resistant BC celllines; (dataAoife?) human studies? • Resistance mainlyrelatedtohypomethylationevents • Compilegenelist, comparegenefunctions, expression? (Stefan) • Select candidates for validation and functionalstudies • 2.2 Validation of methylation changes in clinicalsamples • Sample availability? • before and after therapy, orresistanttumors vs. not-resistanttumors • Validation of upto 50 hypo- and hypermethylated candidatesby MassARRAY wouldrequire ~ 1 µg DNA (canbe FFPE), moreefficientto do 450k?