Global profiling of methylation in the cancer genome

Transcription

1 Global profiling of methylation in the cancer genome Andy Feber UCL Cancer Institute University College London, UK Illumina, Manchester, 7 th September 2010

2 What determines a cancer phenotype? genetic factors non-genetic factors epigenetics environment

3 Epigenetics and Cancer How are epigenetics changes involved in cancer? Definition The study of heritable changes in gene expression that occur independent of changes in the primary DNA sequence

4 Background - epigenetics DNA methylation Histone modifications, e.g., - acetylation - methylation Non-coding RNA (ncrna) & Micro RNA (mirna)

5 One Genome..Many Methylomes

6 DNA Methylation DNA methylation is the addition of methyl group to cytosine generally in CpG dinucleotides 28.6 million CpG sites in the human genome, 70% of which are methylated CpG rich regions known CpG Islands (CGIs) are generally located near to the start of genes and associate with promoters Previously thought to key site of epigenetic regulation of gene expression, and have been the main focus of epigenetic research Recently changes in methylation at regions out side CGIs, known as CGI sores have been shown to more significantly associated with gene regulation Only 7% of CpGs reside within CGIs, many CpGs remain un-analyzed by conventional approaches, microarray, PCR, bisulphite sequencing Next generation sequencing now allows the profiling of over 100x10 6 loci at one time Combined with enrichment strategies, such as MEDIP (Methylated DNA immunoprecipitation) (MeDIP-Seq), allows whole genome methylation (methylome) to be assessed in a single experiment

7 MeDIP Seq Reference Sequence.CGTGATGTCGCGCCTCACTCCGGTGG T G A T G T G C C C C A C G T G G G T G C T A C TCCGGTGG CCTCACTCCGG CGCGCCTCAC TGATGTCGCG GCTGATGTCG TGTCGCGCC TCGCGCCTC CCTCACTCCG CTCCGGTGG

8 Determining methylation from read count How to determine absolute methylation levels both within a genome and between genomes?

9 Bioinformatic challenge Within a given genomic region, MeDIP enrichment is proportional to the number of methylated CpG sites. Simple enrichment ratios/read counts do not accurately reflect the absolute methylation levels within a particular region of interest. Hypothetical Genomic Region MeDIP Enrichment Absolute Methylation Value A. A. 0% B. C. Read Count A B C B. 50% C. 100%

10 Batman : Bayesian Tool for Methylation Analysis Enrichment bias means absolute methylation levels are difficult to quantitate Batman : Bayesian Tool for Methylation Analysis f A m p G A p Abase r Ccpmc v, c 1

11 MPNST Methylome Aim To define the methylome (methylated genome) associated with a malignant phenotype Using Medip-Seq to identify tumor specific differential methylation which correlates with tumor progression/development Samples Pools of ten cases per sample cohort Malignant Peripheral Nerve Sheath Tumors (MPNST) Benign neurofibromas (NF) Normal cultured Schwann Cells (SC) Age and gender matched MPNST 6 Female, 4 Male, median age 30.7 (range 12 to 58) NF 6 Female, 4 Male, median age 27.7 (range 15 to 54)

12 Malignant Peripheral Nerve Sheath Tumors (MPNST) Familial (Germ line mutation in NF1) Neurofibromatosis type 1 (NF1) 3000 cases/year Benign Disease Sporadic (often with alterations in NF1, eg LOH) Plexiform Dermal 10-15% develop malignant disease Malignant Peripheral Nerve Sheath Tumours Only 20% of patients disease free after 5 years

13 Read Stats Sample Total number of reads Total Mapped Reads Total Unique Mapped Reads MPNST NF SC * Those with a Maq score of >10 and both paired reads mapping uniquely Covering ~68% of CpGs in each of the three genomes.

14 Copy Number Correction

15 Medip-Seq Verification Verification of medip-seq initially using the Infinium 27K Human BeadChips, Illumina. Interrogate ~27500 CpG sites across the genome. Comparison of Medip-seq data with arrays showed a high degree of correlation Similar to correlations observed between:- BeadArray v bisulphite sequencing BATMAN v bisulphite sequencing Sample Batman V Infinium Pearson correlation MPNST 0.78 NF 0.80 SC 0.77

16 Global changes in methylation What are the global changes involved in MPNST development? To assess changes in global methylation, the methylation status of each CpG site was bind into 3 methylation states Low (<40%), High (>60%), intermediate (40-60%) Global analysis of revealed a small change in global methylation (1%), compared to other tumours which show global loss of methylation ranging from 10-20%. Analysis of regulatory features of CGIs, CGI shores and promoters, show similar levels of global methylation between MPNST and Schwann cell controls Low methylation Intermediate methylation High methylation

17 Global repeat methylation One of the most commonly cited features of the cancer methylome is hypomethylation of repeats Methylation over LINE and SINE repeats, changes slightly, interestingly LINE repeats appear to lose low methylated CpGs Largest changes in global methylation seen in Satellite repeats, with a 25% change in methylation between MPNST and non-neoplastic Schwann cells Low methylation Intermediate methylation High methylation

18 DMR - Differentially Methylated Regions Regions of differential methylation were defined by average Batman methylation scores over 1kb. Regions were called differentially methylated if they had an average difference of 33% in batman methylation score DMRs Hypermethylated Hypomethylated Increasing numbers of DMRs during progression from non-neoplastic schwann cell controls to MPNSTs h2bdmr (SC v NF) b2mdmr (NF v MPNST) cdmr (SC v MPNST)

19 Hypermethylated CGI CGI shores promoters Non CGI associated promoters exons Introns mirna Conserved regions Satellite repeats LTR repeats LINE repeats SINE repeats DMRs in Genomic Features Comparison of DMRs in different genomic features shows in which regions methylation changes during disease progression Association of features DMRs with genes allows identification of potential candidate onoc- and tumorsuppressor genes SC v NF (h2bdmr) Hypomethylated SC v MPNST (cdmrs) Hypermethylated CGI CGI shores promoters Non CGI associated promoters exons Introns mirna Conserved regions Satellite repeats LTR repeats LINE repeats SINE repeats Hypomethylated

20 Hypermethylated DMR Enrichment Are DMRs enriched in specific genomic features Relative enrichment analysis was carried out to identify those features that have a significantly (p<0.001, red bars) higher number of DMRs than would be expected by chance Significant enrichment of hypomethylated satellite and SINE repeats, also enrichment of hypermethylated LINE repeats Of those regions assumed to be functionally relevant in the regulation of gene expression, only CGI shores and promoters (not associated with a CGI) to be significantly enriched Previous studies have focused on CGI and CGI associated promoters, suggesting many possible sites important in cancer have been missed. Hypomethylated

21 Enrichment in repeats Analysis of aberrant methylation in repeats located either within or outside introns showed a distinct pattern of repeat methylation We see significant enrichment of both hypomethylated non-intronic SINEs and non-intronic satellites repeats Also significant enrichment of intronic SINE repeats in early disease Enrichment of hypermethylated intronic LINE repeats, as well as non-intronic LINES Hypermethylated Hypomethylated

22 Discrete types of satellite repeats show enrichment Satellite repeats be divided into 19 different types of repeat Enrichment analysis of sat repeat type highlighted 2 specific types of repeat which under go hypomethylation, SATR1 and ARL SATR1 appear to early events in tumourigenic progression, whereas ARL hypomethylation may be a later event Do satellite repeats undergo sequence specific methylation? Knock-out of specific DNMT family members have been shown to alter specific satellite repeat methylation What its the role of aberrant satellite repeat methylation in cancer Hypermethylated Hypomethylated

23 Where to start? DMRs in Genomic Features 101,466 unique cdmrs Do DMRs associate with candidate genes SC v MPNST (cdmrs) Hypermethylated CGI CGI shores promoters Non CGI associated promoters exons Introns mirna Conserved regions Satellite repeats LTR repeats LINE repeats SINE repeats Hypomethylated

24 Candidate genes MEST - Imprinted region, differently methylated in glioblastomas (which also have frequent NF1 mutations) WT1 Wilms tumor suppressor 1 gene, SC NF MPNST

25 Association of methylation of gene expression If aberrant methylation is a key driver of tumourigenic process? which regions of the genome show strongest correlation with gene expression? Integration of independent gene expression from MPNST (n=10) and NF (n=28) Henderson et al., 2005 (Affy U95) and Miller et al., 2009 (Affy U133+)

26 Effect of methylation on gene expression across canonical gene features Is there a difference in canonical methylation of genomic features and those genes with high expression in MPNST vs low expression in MPNST Largest difference in methylation (13%) observed in the 1 st exon, shows a strong inverse relationship with gene expression

27 Effect of methylation gene expression CGIs and CGI shores If CGI shores have a greater effect of gene expression than CGIs, is there a difference in methylation between genes with high and low expression Strong inverse relationship in both up stream and down stream shores, with no difference in CGI. Largest difference in methylation (11%) seen ~800bp-1.5kb outside the CGI suggesting these regions are important in the regulation of gene expression

28 Does gene expression reflect methylation state? Can the expression of genes associated with DMRs discriminate between disease phenotypes? Partition clustering (with 10,000 permutations) of the expression of genes associated with DMRs between NF and MPNST, show significant association between :- hypermethylation of CGI shores (p=0.0001) hypermethylated non-cgi promoters (p=0.0003) hypomethylated CGI shores (p=0.0001) Hyper CGI Shore N=1056 Hyper non CGI Promoter N=702

29 SOX10 MPNST NF

30 CDKN2A MPNST NF

31 Summary Medip-seq provides high resolution methylation profiling of the human epigenome Provides insights into the role aberrant methylation plays in regions not accessible by other technologies Whole genome methylation profiles can identify potential prognostic/diagnostic molecular markers of malignant development and progression. Still not the whole picture, other epigenetic modifications are out there Non-CpG methylation 5-hydroxymethylcytosine

32 Acknowledgements Lab: UCL Cancer Institute, UK: Adrienne Flanagan Andrew Teschendorff Elia Stupka Nadege Presneau Bernadine Idowu Gurdon Institute, UK: Thomas Down Barts and The London School of Medicine and Dentistry, UK: Vardman Rakyan Funding: SACT (Skeletal Action Cancer Trust) Illumina, USA: Gray Schroth Zhang Lu