Global profiling of methylation in the cancer genome - Illumina · Global profiling of methylation in the cancer genome Andy Feber UCL Cancer Institute University College London,

Global profiling of methylation

in the cancer genome

Andy Feber

UCL Cancer Institute

University College London, UK

Illumina, Manchester, 7th September 2010

genetic

factors

What determines a cancer phenotype?

non-genetic

factors

• epigenetics

• environment

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”

Background - epigenetics

Histone modifications, e.g., - acetylation

- methylation

DNA methylation

Non-coding RNA (ncRNA)

&

Micro RNA (miRNA)

One Genome……..Many Methylomes

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 100x106 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

MeDIP Seq

T

CG

AT

C

G

A

T CG

T

C

G

G

CA

C

CTGTGG

Reference Sequence

….CGTGATGTCGCGCCTCACTCCGGTGG…

TCCGGTGG

CCTCACTCCGG

CGCGCCTCAC

TGATGTCGCG

GCTGATGTCG

TGTCGCGCC

TCGCGCCTC

CCTCACTCCG

CTCCGGTGG

Determining methylation from read count

• How to determine absolute methylation levels both within a genome and between genomes?

• 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.

Absolute Methylation ValueMeDIP EnrichmentHypothetical Genomic Region

C.

100%

C.

B.

50%

B.

A.

0%

A.

A

B CRead

Count

Bioinformatic challenge…

• Enrichment bias means absolute methylation levels are difficult to

quantitate

p

1

c

ccpbasep v,mCrA|AGm|Af

Batman : Bayesian Tool for Methylation Analysis

Batman :

Bayesian Tool for Methylation Analysis

MPNST Methylome

• 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

Aim

• 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)

Samples

Benign Disease

Familial

(Germ line

mutation in NF1)

Sporadic

(often with alterations

in NF1, eg LOH)

Plexiform Dermal

Malignant Peripheral

Nerve Sheath Tumours

10-15% develop

malignant disease

Neurofibromatosis type 1 (NF1)

3000 cases/year

Only 20% of patients disease free

after 5 years

Malignant Peripheral Nerve Sheath Tumors (MPNST)

Sample Total number

of reads

Total

Mapped

Reads

Total Unique

Mapped

Reads

MPNST 140119516 133145064 75918388

NF 140442616 134234980 81619250

SC 138120350 131484108 68697944

Read Stats

* Those with a Maq score of >10 and both paired reads mapping uniquely

• Covering ~68% of CpGs in each of the three genomes.

Copy Number Correction

Sample Batman V Infinium

Pearson correlation

MPNST 0.78

NF 0.80

SC 0.77

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

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%.

Intermediate methylationLow methylation High methylation

• Analysis of regulatory features of CGIs, CGI shores and promoters, show similar

levels of global methylation between MPNST and Schwann cell controls

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

Intermediate methylationLow methylation High methylation

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

• Increasing numbers of DMRs during

progression from non-neoplastic

schwann cell controls to MPNSTsDMRs Hypermethylated Hypomethylated

h2bDMR

(SC v NF)

45239 46587

b2mDMR

(NF v MPNST)

41886 45230

cDMR

(SC v MPNST)

48391 53075

Hypermethylated Hypomethylated

CGI 385 79

CGI shores 2119 1669

promoters 1097 1098

Non CGI

associated

promoters 293 175

exons 11858 11432

Introns 61709 57632

miRNA 22 30

Conserved regions 16535 27805

Satellite repeats 142 1398

LTR repeats 14339 12665

LINE repeats 34515 25359

SINE repeats 32661 39502


CGI 49 47

CGI shores 996 1382

promoters 484 812

Non CGI

associated

promoters

39 95

exons 7885 12104

Introns 48086 49503

miRNA 19 31






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)SC v MPNST

(cDMRs)

DMR Enrichment


• 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.

• Are DMRs enriched in specific genomic features


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

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


CGI 385 79

CGI shores 2119 1669

promoters 1097 1098

Non CGI

associated

promoters 293 175

exons 11858 11432

Introns 61709 57632

miRNA 22 30






DMRs in Genomic Features• Where to start?

• 101,466 unique cDMRs

• Do DMRs associate with candidate genes

SC v MPNST

(cDMRs)

Candidate genes

MEST

- Imprinted region, differently methylated in glioblastomas (which also have frequent NF1 mutations)

WT1 –Wilms tumor suppressor 1 gene,

MPNST

NF

SC

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+)

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 1st exon, shows a strong

inverse relationship with gene expression

• 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

Effect of methylation gene expression

CGIs and CGI shores

• 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

Does gene expression reflect methylation state?

• 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)

• Can the expression of genes associated with DMRs discriminate between disease

phenotypes?

Hyper CGI Shore

N=1056

Hyper non CGI Promoter

N=702

SOX10

MPNST

NF

CDKN2A

MPNST

NF

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

Acknowledgements

Lab:

UCL Cancer Institute, UK:Adrienne Flanagan

Andrew TeschendorffElia Stupka Nadege PresneauBernadine Idowu

Gurdon Institute, UK:Thomas Down

Barts and The London School of Medicine and Dentistry, UK:Vardman Rakyan

Illumina, USA: Gray Schroth

Zhang Lu

Funding:

SACT(Skeletal Action Cancer Trust)

Documents

Global profiling of methylation in the cancer genome - Illumina · Global profiling of methylation in the cancer genome Andy Feber UCL Cancer Institute University College London,