Mutational processes in the human genome...DIGGING DEEP INTO DATA: LOCALIZED MUTATION SIGNATURES...

Mutational processes

in the human genome

Serena Nik-Zainal

CRUK Advanced Clinician Scientist

Honorary Consultant in Clinical Genetics

Cambridge Society for the Application of Research Talk, June 19th 2017

normal cell

normal cell

normal cell

normal cell

normal cell

normal cell

normal cell

100% cells

26,700 mutations

10 CN changes

PIK3CA, TP53, GATA3,

SMAD4, NCOR1 muts

normal cell

cancer cell

Gel-based

Kb/day/machine

Massively-parallel sequencing

Gel-basedCapillary

Kb/day/machine

Massively-parallel sequencing

Gel-basedCapillary

Kb/day/machine

Massively-parallel sequencing

Gel-basedCapillary

Massively parallel sequencing

Kb/day/machine

Massively-parallel sequencing

Massively-parallel sequencing:

Sequencing information from individual DNA molecules

Massively-parallel sequencing:

Sequencing information from individual DNA molecules

Massively-parallel sequencing: Gives us unprecedented access to the entire human genome

• Human genome ~ 3,000,000,000

--> Whole genome sequencing

• ~ 20,000 genes encompassing ~ 1-2% of human genome

--> Whole exome sequencing

Massively-parallel sequencing: Gives us unprecedented access to the entire human genome

• Human genome ~ 3,000,000,000

--> Whole genome sequencing

• ~ 20,000 genes encompassing ~ 1-2% of human genome

--> Whole exome sequencing

Massively-parallel sequencing: Gives us unprecedented access to the entire human genome

• Human genome ~ 3,000,000,000

--> Whole genome sequencing

• ~ 20,000 genes encompassing ~ 1-2% of human genome

--> Whole exome sequencing

Genomic abnormalities

exon intron UTR

Genomic abnormalities

exon intron UTR

TTCACG

Genomic abnormalities

exon intron UTR

TTCACG

TTTACG

substitution

Genomic abnormalities

exon intron UTR

TTCACG

TTTACG TTT-CG

substitution deletion/

insertion

base pair resolution

Genomic abnormalities

exon intron UTR

TTCACG

TTTACG TTT-CG

substitution deletion/

insertion

base pair resolution

Genomic abnormalities

exon intron UTR

TTCACG

TTTACG TTT-CG

substitution deletion/

insertion

duplication

base pair resolution

Genomic abnormalities

exon intron UTR

TTCACG

TTTACG TTT-CG

substitution deletion/

insertion

duplication

deletion

base pair resolution

Genomic abnormalities

exon intron UTR

TTCACG

TTTACG TTT-CG

substitution deletion/

insertion

duplication

deletion

inversion

base pair resolution

Genomic abnormalities

exon intron UTR

TTCACG

TTTACG TTT-CG

substitution deletion/

insertion

duplication

deletion

inversion

translocation

Genomic abnormalities

exon intron UTR

TTCACG

TTTACG TTT-CG

substitution deletion/

insertion

duplication

deletion

inversion

translocation

base pair resolution

chromosomal scale

Driver mutations in cancer genes

• Genomic scenario

• ERBB2 - breast cancer

• BCR-ABL – leukaemia

• EGFR – lung cancer

• BRAF - metastatic melanoma

Driver mutations in cancer genes

• Genomic scenario

• ERBB2 - breast cancer

• BCR-ABL – leukaemia

• EGFR – lung cancer

• BRAF - metastatic melanoma

• Targeted drug

• Herceptin, Lapatinib

• Imatinib

• Erlotinib, Gefitinib

• Vemurafenib

Driver mutations in cancer genes

ER positive ER negative

Driver mutations in cancer genes

ER positive ER negative

Driver mutations in cancer genes

ER positive ER negative

Driver mutations in cancer genes

Wagle et al. JCO, 2011

Pre-treatment 15 Weeks 23 Weeks

Driver mutations in cancer genes

Many thousands of passenger mutations

Mutation signatures in human cells

Extracting mutation signatures

Extracting mutation signatures

Extracting mutation signatures

Extracting mutation signatures

C>T = G>A

C>A = G>T

C>G = G>C

T>A = A>T

T>C = A>G

T>G = A>C

6 mutation classes

Classification of base substitution mutations

C>T

C>A

C>G

T>A

T>C

T>G

6 mutation classes

Classification of base substitution mutations

C>T

C>A

C>G

T>A

T>C

T>G

ACA>ATA

ACC>ATC

ACG>ATG

ACT>ATT

CCA>CTA

CCC>CTC

CCG>CTG

CCT>CTT

GCA>GTA

GCC>GTC

GCG>GTG

GCT>GTT

TCA>TTA

TCC>TTC

TCG>TTG

TCT>TTT

6 mutation classes

Classification of base substitution mutations

C>T

C>A

C>G

T>A

T>C

T>G

ACA>ATA

ACC>ATC

ACG>ATG

ACT>ATT

CCA>CTA

CCC>CTC

CCG>CTG

CCT>CTT

GCA>GTA

GCC>GTC

GCG>GTG

GCT>GTT

TCA>TTA

TCC>TTC

TCG>TTG

TCT>TTT

6 mutation classes

Classification of base substitution mutations

C>T

C>A

C>G

T>A

T>C

T>G

ACA>ATA

ACC>ATC

ACG>ATG

ACT>ATT

CCA>CTA

CCC>CTC

CCG>CTG

CCT>CTT

GCA>GTA

GCC>GTC

GCG>GTG

GCT>GTT

TCA>TTA

TCC>TTC

TCG>TTG

TCT>TTT

6 mutation classes

Classification of base substitution mutations

C>T

C>A

C>G

T>A

T>C

T>G

ACA>ATA

ACC>ATC

ACG>ATG

ACT>ATT

CCA>CTA

CCC>CTC

CCG>CTG

CCT>CTT

GCA>GTA

GCC>GTC

GCG>GTG

GCT>GTT

TCA>TTA

TCC>TTC

TCG>TTG

TCT>TTT

6 mutation classes

Classification of base substitution mutations

C>T

C>A

C>G

T>A

T>C

T>G

ACA>ATA

ACC>ATC

ACG>ATG

ACT>ATT

CCA>CTA

CCC>CTC

CCG>CTG

CCT>CTT

GCA>GTA

GCC>GTC

GCG>GTG

GCT>GTT

TCA>TTA

TCC>TTC

TCG>TTG

TCT>TTT

6 mutation classes

Classification of base substitution mutations

C>T

C>A

C>G

T>A

T>C

T>G

ACA>ATA

ACC>ATC

ACG>ATG

ACT>ATT

CCA>CTA

CCC>CTC

CCG>CTG

CCT>CTT

GCA>GTA

GCC>GTC

GCG>GTG

GCT>GTT

TCA>TTA

TCC>TTC

TCG>TTG

TCT>TTT

6 mutation classes

Classification of base substitution mutations

C>T

C>A

C>G

T>A

T>C

T>G

ACA>ATA

ACC>ATC

ACG>ATG

ACT>ATT

CCA>CTA

CCC>CTC

CCG>CTG

CCT>CTT

GCA>GTA

GCC>GTC

GCG>GTG

GCT>GTT

TCA>TTA

TCC>TTC

TCG>TTG

TCT>TTT

6 mutation classes

Classification of base substitution mutations

C>T

C>A

C>G

T>A

T>C

T>G

ACA>ATA

ACC>ATC

ACG>ATG

ACT>ATT

CCA>CTA

CCC>CTC

CCG>CTG

CCT>CTT

GCA>GTA

GCC>GTC

GCG>GTG

GCT>GTT

TCA>TTA

TCC>TTC

TCG>TTG

TCT>TTT

6 mutation classes

Classification of base substitution mutations

C>T

C>A

C>G

T>A

T>C

T>G

ACA>ATA

ACC>ATC

ACG>ATG

ACT>ATT

CCA>CTA

CCC>CTC

CCG>CTG

CCT>CTT

GCA>GTA

GCC>GTC

GCG>GTG

GCT>GTT

TCA>TTA

TCC>TTC

TCG>TTG

TCT>TTT

6 mutation classes

Classification of base substitution mutations

C>T

C>A

C>G

T>A

T>C

T>G

ACA>ATA

ACC>ATC

ACG>ATG

ACT>ATT

CCA>CTA

CCC>CTC

CCG>CTG

CCT>CTT

GCA>GTA

GCC>GTC

GCG>GTG

GCT>GTT

TCA>TTA

TCC>TTC

TCG>TTG

TCT>TTT

6 mutation classesATA>AGA

ATC>AGC

ATG>AGG

ATT>AGT

CTA>CGA

CTC>CGC

CTG>CGG

CTT>CGT

GTA>GGA

GTC>GGC

GTG>GGG

GTT>GGT

TTA>TGA

TTC>TGC

TTG>TGG

TTT>TGT

ATA>ACA

ATC>ACC

ATG>ACG

ATT>ACT

CTA>CCA

CTC>CCC

CTG>CCG

CTT>CCT

GTA>GCA

GTC>GCC

GTG>GCG

GTT>GCT

TTA>TCA

TTC>TCC

TTG>TCG

TTT>TCT

ATA>AAA

ATC>AAC

ATG>AAG

ATT>AAT

CTA>CAA

CTC>CAC

CTG>CAG

CTT>CAT

GTA>GAA

GTC>GAC

GTG>GAG

GTT>GAT

TTA>TAA

TTC>TAC

TTG>TAG

TTT>TAT

ACA>AAA

ACC>AAC

ACG>AAG

ACT>AAT

CCA>CAA

CCC>CAC

CCG>CAG

CCT>CAT

GCA>GAA

GCC>GAC

GCG>GAG

GCT>GAT

TCA>TAA

TCC>TAC

TCG>TAG

TCT>TAT

ACA>AGA

ACC>AGC

ACG>AGG

ACT>AGT

CCA>CGA

CCC>CGC

CCG>CGG

CCT>CGT

GCA>GGA

GCC>GGC

GCG>GGG

GCT>GGT

TCA>TGA

TCC>TGC

TCG>TGG

TCT>TGT

96 mutation classes

Classification of base substitution mutations

Mutational signatures in human cancer

Mutational signatures in human cancer

Mutational signatures in human cancer

ApCpGTpCpG

GpCpG

CpCpG

Mutational signatures in human cancer

Mutational signatures in human cancer

Tobacco

Mutational signatures in human cancer

UV light

associated with exposure to UV light

c

cancer

Mutational signatures in human cancer

associated with exposure to UV light

Simulated sunlight

c

in vitro

cancer

Mutational signatures in human cancer

Mutational signatures in human cancer

Aristolochic acid

Aristolochic acid

c

in vitro

cancer

Mutational signatures in human cancer

Mutational signatures in human cancer

HR deficiency

Mutational signatures in human cancer

MMR deficiency

Mutational signatures in human cancer

Unknown aetiology

It’s early days

• Still in the infancy of understanding mutational signatures

• This is not dogma : mutational signatures will change

• There are improvements in the mathematical frameworks

to be made

DIGGING DEEP INTO DATA:

LOCALIZED MUTATION SIGNATURES

PART II

Panoramic view of whole-genome sequenced cancers

Panoramic view of whole-genome sequenced cancers

p < 0.001Mutation number

Localised hypermutation or kataegis

Coordinate (bp)

Genomic coordinate

(bp)

Localised hypermutation or kataegis

Localised hypermutation or kataegis

Generalised hypermutation of signatures 2/13

PD4120a

ER +ve HER2 -ve

Kataegis and Signatures 2/13 share similar characteristics

TpCpATpCpG

TpCpT

TpCpC

Kataegis and Signatures 2/13 share similar characteristics

TpCpATpCpG

TpCpT

TpCpC

Kataegis and Signatures 2/13 share similar characteristics

What is the biological basis for these mutational

signatures?

• Deamination of cytosine by one of the family

of AID/APOBEC enzymes?

• The family includes AID

APOBEC1

APOBEC2

APOBEC3A-H

APOBEC4

What is the biological basis for these mutational

signatures?

Double strand

break and

rearrangement

DNA editing by the AID/APOBEC family of

cytidine deaminasesWhat is the biological basis for these mutational

signatures?

Double strand

break and

rearrangement

AID /

APOBEC

What is the biological basis for these mutational

signatures?

Double strand

break and

rearrangement

AID /

APOBEC

What is the biological basis for these mutational

signatures?

Double strand

break and

rearrangement

AID /

APOBEC

What is the biological basis for these mutational

signatures?

Double strand

break and

rearrangement

AID /

APOBEC

What is the biological basis for these mutational

signatures?

Double strand

break and

rearrangement

AID /

APOBEC

What is the biological basis for these mutational

signatures?

Double strand

break and

rearrangement

AID /

APOBEC

What is the biological basis for these mutational

signatures?

The AID / APOBEC family of cytidine

deaminases

• AID plays a central role in somatic hypermutation

and class switch recombination at the

immunoglobulin loci

• APOBEC3A-H mutate viruses and restrict

retrotransposon activity

Which member(s) of the family is

responsible for Signature 2/13?

AID

APOBEC1

APOBEC2

APOBEC3A

APOBEC3B

APOBEC3C

APOBEC3DE

APOBEC3F

APOBEC3G

APOBEC3G

APOBEC3H

APOBEC4

A T C G T C G A T G C T C G A T C A T C A G A G T C A A

T A G C A G C T A C G A G C T A G T A G T C T C A G T T

| | | | | | | | | | | | | | | | | | | | | | | | | | | |

Kataegis microclusters demonstrate processivityStrand asymmetry

A T C G T C G A T G C T C G A T C A T C A G A G T C A A

T A G C A G C T A C G A G C T A G T A G T C T C A G T T

| | | | | | | | | | | | | | | | | | | | | | | | | | | |

Kataegis microclusters demonstrate processivityStrand asymmetry

A T C G T C G A T G C T C G A T C A T C A G A G T C A A

T A G C A G C T A C G A G C T A G T A G T C T C A G T T

| | | | | | | | | | | | | | | | | | | | | | | | | | | |

Kataegis microclusters demonstrate processivityStrand asymmetry

A T C G T C G A T G C T C G A T C A T C A G A G T C A A

T A G C A G C T A C G A G C T A G T A G T C T C A G T T

| | | | | | | | | | | | | | | | | | | | | | | | | | | |

Kataegis microclusters demonstrate processivityStrand asymmetry

A T C G T C G A T G C T C G A T C A T C A G A G T C A A

T A G C A G C T A C G A G C T A G T A G T C T C A G T T

| | | | | | | | | | | | | | | | | | | | | | | | | | | |

Kataegis microclusters demonstrate processivityStrand asymmetry

Kataegis

• Cytosine mutations

• TpC sequence context

• Localised hypermutation

• Co-localising with

rearrangements

• Strand asymmetry about

rearrangement breakpoints

Kataegis

• Cytosine mutations

• TpC sequence context

• Localised hypermutation

• Co-localising with

rearrangements

• Strand asymmetry about

rearrangement breakpoints

Signature 2 & 13

• Cytosine mutations

• TpC sequence context

• Generalised hypermutation

• NOT co-localising with

rearrangements

• Strand asymmetry relating to

replication direction

Kataegis

• Cytosine mutations

• TpC sequence context

• Localised hypermutation

• Co-localising with

rearrangements

• Strand asymmetry about

rearrangement breakpoints

Signature 2 & 13

• Cytosine mutations

• TpC sequence context

• Generalised hypermutation

• NOT co-localising with

rearrangements

• Strand asymmetry relating to

replication direction

Distinct (but related) signatures in human cancers

Distinct (but related) signatures in human cancers

Deep computational analysis paired with experimental work

has started to reveal and validate biological insights into

mechanisms of how the human genome is constantly mutating

Accumulation of mutations in cancer

Accumulation of mutations in cancer

Summary II

• A remarkable phenomenon of localised hypermutation, termed

“kataegis”, was frequently observed in 21 primary breast cancers

and subsequently in many other cancer types including pancreatic

cancer, lung cancer and haematological cancers.

• Regions of kataegis usually co-localized with somatic

rearrangements.

• Base substitutions in these regions were almost exclusively of

cytosine at TpC dinucleotides. This is very similar to the genome-

wide mutagenesis of Signatures 2 and 13.

• A role for the APOBEC family of cytidine deaminases is proposed.

MUTATIONAL PROCESSES:

WHAT DOES IT MEAN FOR PATIENTS?

PART III

Mutational processes in 560 breast cancers

SNZ.2017

Correlated

with age

Mutational processes in 560 breast cancers

SNZ.2017

Associated with

cytosine

deaminases

Mutational processes in 560 breast cancers

SNZ.2017

Mismatch

repair

deficiency

Mutational processes in 560 breast cancers

SNZ.2017

Homologous

recombination

repair deficiency

Mutational processes in 560 breast cancers

SNZ.2017

Uncertain

aetiology

Mutational processes in 560 breast cancers

SNZ.2017

Mutational processes in 560 breast cancers

SNZ.2017

Mutational processes in 560 breast cancers

SNZ.2017

Driver mutations in breast cancer patients

The individuality of each tumour

“No two patients share the same set of drivers or

the same quantities of mutational signatures in

their tumors”

Whole genome profiling

SNZ.2017

Whole genome profiling

SNZ.2017

Whole genome profiling

SNZ.2017

Whole genome profiling

SNZ.2017

Whole genome profiling

SNZ.2017

Whole genome profiling

SNZ.2017

Whole genome profiling

SNZ.2017SNZ.2017

Whole genome profiling

SNZ.2017SNZ.2017

Whole genome profiling

SNZ.2017SNZ.2017

CLINICAL APPLICATIONS OF

MUTATIONAL SIGNATURES

PART IV

Helen

Davies

Dominik Glodzik

Predicting BRCA1/BRCA2 deficiency

HRDetect identifies additional BRCA1/BRCA2 defective tumours

HRDetect identifies additional BRCA1/BRCA2 defective tumours

HRDetect identifies additional BRCA1/BRCA2 defective tumours

HRDetect identifies additional BRCA1/BRCA2 defective tumours

22 known

germline

HRDetect identifies additional BRCA1/BRCA2 defective tumours

22 known

germline

33 new

germline

22 somatic or

promoter

hypermethylation

47 unknown

HRDetect is superior to any individual mutational signature and the HRD index

HRDetect distinguishes clinical outcome

& is robust between samples in the same patient

HRDetect distinguishes clinical outcome

& is robust between samples in the same patient

HRDetect distinguishes clinical outcome

& is robust between samples from the same patient

Summary

• Presented a mutational-signature based algorithm called HRDetect

with excellent sensitivity and specificity for detecting germline

BRCA1/BRCA2 deficient tumours

• Identify a significant proportion of tumours that are BRCA1/BRCA2

deficient that were not previously known (and missed using typical

exome sequencing methods)

• Mutational signatures provides an opportunity to fine-tune genomic

stratification

SNZ.2017

WGS reveals MMR deficient breast cancers

SNZ.2017

WGS reveals MMR deficient breast cancers

SNZ.2017SNZ.2017

WGS reveals MMR deficient breast cancers

MLH1 MSH2

MSH6 PMS2

Immunohistochemistry of MMR proteins : PD23579a

WGS reveals subclonal MMR deficiency

WGS reveals subclonal MMR deficiency

Unpicking cancer evolution

Mutational signatures

Signatures associated with MMR

deficiency

Unpicking cancer evolution

Signatures associated with MMR

deficiency

Acquired mutations in MMR genes

Mutational signatures

Unpicking cancer evolution

Mutational signatures in 560 breast cancer patients

SNZ.2017

SNZ.2017

Mutational signatures in 560 breast cancer patients

The unmet need

SNZ.2017

560 breast cancer genomes

AMC, Netherlands

CRI, UK

DFCI, USA

Erasmus, Netherlands

Institute Curie, France

Bordet, Belgium

Bari, Italy

NCI, Netherlands

ICGC Korea

Lund, Sweden

MD Anderson, USA

MSKCC, USA

NCC, Singapore

Oslo, Norway RUNMC, Netherlands

Synergie, France

TCRU, Belgium

Bergen, Norway

Dundee, Scotland

Reykjavik, Iceland

Brisbane, Australia

acknowledgements

BASIS Consortium & ICGC Breast Cancer Working Group

Mike Stratton

Ewan Birney

Marc van der Vijver

Ake Borg

John Martens

Anne-Lise Borreson-Dale

Henk Stunnenberg

Andrea Richardson

Alastair Thompson

Jorunn Erla Eyfjord

Andy Futreal

Christos Sotiriou

Andy Tutt

Sunil Lakhani

Steven van Laere

Paul Span

Carlos Caldas

Laura van’t Veer

Gilles Thomas

Alain Viari

Gu Kong

The team

Becky Harris Project Coordinator

Sandro Morganella (industry)

Dominik Glodzik (Lund University)

Hongwei Chen (Group Leader Nanjing Uni )

Xueqing Zou

Helen Davies

Helen Davies

Andrea Degasperi

Tauanne Dias Amarante

Ilias Georgakopoulos-Soares (PhD)

Gene Koh (PhD)

HR

Johan Staaf

WTSI IT/Admin

Keiran Raine

David Jones

Andrew Menzies

Lucy Stebbings

Jon Hinton

Adam Butler

Sancha Martin

MMR

Colin Purdie (Dundee)

Elin Borgen (Oslo)

Hege Russnes (Oslo)

Se Jin Jang (Asan)