signatures+mutaonnelles +et évolu-on+ clonaledes tumeurs++ · aca acc acg act cca ccc ccg cct gca...

Signatures muta-onnelles et évolu-on clonale des tumeurs

Rencontres scien-fiques des Grands Causses

Eric Letouzé Equipe "Génomique Fonc-onnelle des Tumeurs

Solides", INSERM U1162

29 septembre 2015

PLAN

1. Signatures muta-onnelles

2. Evolu-on clonale des tumeurs 2.1) Introduc-on à l’évolu-on clonale des tumeurs 2.2) Approche 1 : plusieurs tumeurs d’un même pa-ent 2.3) Approche 2 : plusieurs prélèvements d’une même tumeur 2.4) Approche 3 : au sein d’un même échan-llon tumoral

1.1) Principe des analyses de signatures muta-onnelles 1.2) Applica-on aux cancers du foie




PLAN

Les spectres muta-onnels diffèrent en fonc-on du type tumoral (version 2002)

Pfeifer et al. 2002

A G C

T C G

A G C

T A G

A T C

T A G

DNA mispairing

DNA replica-on

Benzo[a]pyrene Benzo[a]pyrene adduct on guanine

G>T (or C>A) muta-on

TP53 muta-ons

!  Different cancers have different muta-on rates:

Alexandrov et al., Nature 2013

Lawrence et al., Nature 2013

!  Different cancers display different types of muta-ons:

Les spectres muta-onnels diffèrent en fonc-on du type tumoral (version 2015)

Le spectre muta-onnel d’une tumeur reflète l’accumula-on de plusieurs processus mutagènes

Helleday et al., Nat Rev Genet 2014

MÉTHODE : Extraire les signatures muta-onnelles à par-r de données de séquençage à haut débit

!  The muta-onal landscape of a tumor results from the combina-on of several muta/onal processes opera-ve with different intensi/es.

Alexandrov et al., Cell Rep 2013

!  The problem is to find the muta/onal processes (P) and the exposures (E) while only knowing the muta/onal pa:ern of each sample (M).

M

P

E

R

!  This is similar to the cocktail party problem, and can be solved with the non-‐nega-ve matrix factoriza-on (NMF) algorithm.

M = P x E + R Alexandrov et al., Cell Rep 2013

!  Applica-on of this method to >7,000 cancers iden-fies 21 signatures, associated with known or new oncogenic mechanisms.

Signature 10 : DNA polymerase E deficiency

Signature 4: Tobacco

Signature 16 : New mutagenic mechanism?

Alexandrov et al., Nature 2013

!  Associa-on of signatures with tumor type and e-ology:

-‐> Most signatures remain to be explained!




PLAN

Le spectre muta-onnel des carcinomes hépatocellulaires

!  Average muta-onal spectrum over 243 HCC analyzed by WES:

Spontaneous deamina-on of methylated CpGs

T>C muta-ons in an AT[AGT] context, characteris-c of liver

tumors

C>A muta-ons enriched in a subset of HCC

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

0.0

0.1

0.2C>A C>G C>T T>A T>C T>G

Signature 1A

•  Ubiquitous in human cancers •  Reflects the spontaneous deamina-on of methylated Cs at CpG sites •  Associated with age

Signature 4

•  Mostly found in lung and liver cancers •  Not associated with smoking in our series.

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

0.00

0.05


!  Using the Wellcome Trust Sanger Ins-tute method, we iden-fied 8 dis/nct signatures in our HCC series:

Signature 16

•  T>C muta-ons at AT[AGT] trinucleo-des •  Liver-‐specific signature

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

0.00

0.05


G G T A T T

C C A T A A

5' 3'

3' 5'

transcribed strand

non-‐transcribed strand G G T A T T

C C A T A A

5' 3'

3' 5'

Transcrip-on coupled repair (TCR) G G T A T T

C C A T A A

5' 3'

3' 5'

T>C muta-on on the transcribed strand

Less efficient TCR

G G T A C T

C C A T A A

5' 3'

3' 5'

•  Very strong transcrip-onal strand bias:

G G T A T T

C C A T A A

5' 3'

3' 5'

Bulky DNA adduct

Signature 23

•  Found in a single tumor with > 6000 muta-ons •  Non-‐cirrho-c liver but with black mineral deposits

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

0.0

0.1


Throrotrast?

Schulze, Imbeaud, Letouzé et al., Nat Genet 2015

Signature 24

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ACA

ACC

ACG

ACT

CCA

CCC

CCG

CCT

GCA

GCC

GCG

GCT

TCA

TCC

TCG

TCT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

0.0

0.1


•  5 pa-ents, all from African countries •  3/5 pa-ents present the R249S muta-on of TP53

Aflatoxin B1

Deux nouvelles signatures iden-fiées dans les CHC

U-lité clinique des signatures muta-onnelles

!  Example 1 : Iden-fy a mutagenic exposure

!  Example 2 : Iden-fy a predisposing muta-on

Signature of a known DNA repair defect

Perc

enta

ge o

f mut

atio

ns

ACA

ACC

ACG

ACT

CC

AC

CC

CC

GC

CT

GC

AG

CC

GC

GG

CT

TCA

TCC

TCG

TCT

ACA

ACC

ACG

ACT

CC

AC

CC

CC

GC

CT

GC

AG

CC

GC

GG

CT

TCA

TCC

TCG

TCT

ACA

ACC

ACG

ACT

CC

AC

CC

CC

GC

CT

GC

AG

CC

GC

GG

CT

TCA

TCC

TCG

TCT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

ATA

ATC

ATG

ATT

CTA

CTC

CTG CTT GTA

GTC

GTG GTT TTA

TTC

TTG

TTT

0.000

0.105

0.210

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

Adrenocor-cal carcinoma ACC39

Aflatoxine B1




PLAN

Le modèle d’expansion clonale

Nik-‐Zainal et al., Cell 2012

U-liser l'évolu-on clonale pour reconstruire l'ordre d'appari-on des altéra-ons géné-ques

!  Comparing the profiles of different clones can help reconstruc-ng the order in which genomic altera-ons occured:

Normal'cell'Common'precursor'1'

Common'precursor'2'

Clone'1'

Clone'2'

Clone'3'

≠ tumors

≠ parts of a same tumor

≠ cell popula-ons within a same sample

Approche 1 : plusieurs tumeurs d’un même pa-ent

Primary tumor

Recurrence

Chromosome aberra-ons

Letouzé et al., Genome Biol 2010

!  Common breakpoints in recurrent bladder cancers can be used to reconstruct the -ming of CNAs in cancer progression:

Analyse de couples adénome -‐> carcinome hépatocellulaire par séquençage exome

!  Comparing the muta-ons in HCA and HCC from the same pa-ents reveals the importance of TERT muta-ons for the transforma-on of CTNNB1-‐mutated adenomas:

Adenomas Carcinomas

Adenoma Carcinoma

!  Common sequence in HCA -‐> HCC progression:

Normal liver

CTNNB1 muta-on

TERT muta-on

Pila- et al., Cancer Cell 2014

Approche 2 : plusieurs prélèvements d’une même tumeur

!  Mul--‐region sequencing reveals intra-‐tumor heterogeneity in 8/12 breast cancers:

-‐> Tumor heterogeneity should be considered when designing targeted therapy trials.

Yates et al., Nat Medicine 2015

Approche 3 : au sein d’un même échan-llon tumoral

!  Low-‐amplitude copy-‐number changes and muta-ons with a low allelic frac-on reveal subclonal altera-ons:

Nik-‐Zainal et al., Cell 2012

Variant allele fraction

VAF = Number of mutated reads/Total number of reads

Analyse sous-‐clonale mul--‐échan-llons : Transforma-on adénome -‐> carcinome hépatocellulaire

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CHC464T

CHC465T

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●CHC465T (HCC)

CHC464T (HCA)

-‐> There is clear evidence of mul-ple clones in the HCA. The HCC is derived from subclone 1, represen-ng around 18% of tumor cells in the HCA sample:

HCA-‐specific variants

HCC-‐specific variants

Subclone 1 -‐> clonal muta-ons

Histogram of tmp$Tumor_Varprop

tmp$Tumor_Varprop

Frequency

0.0 0.1 0.2 0.3 0.4 0.5 0.6

0200

400

600

800

Histogram of tmp$Tumor_Varprop

tmp$Tumor_Varprop

Frequency

0.0 0.1 0.2 0.3 0.4 0.5 0.6

0500

1000

1500

2000

2500

3000

Variant allele frac-on


Freq

uency

Freq

uency

clonal

subclone 1 (18%) subclone 2 (35%)

clonal

subclonal

Ordonner les duplica-ons chromosomiques à l'aide des muta-ons 6p

X

1q

14 13 8 16/17

CHC909T

5 10

0.0 0.2 0.4 0.6 0.8 1.0

−2−1

01

23

variant proportion

log

ratio

T/N

cov

erag

e

6p 6p

X

13/14

8/16/17

1q 1q

Normal copy-‐number

5 10

5 10


0.0 0.2 0.4 0.6 0.8 1.0

−10

12

3

variant proportion

log

ratio

T/N

cov

erag

e

Late

Early


0.0 0.2 0.4 0.6 0.8 1.0

−10

12

3

variant proportion

log

ratio

T/N

cov

erag

e

0.0 0.2 0.4 0.6 0.8 1.0

−10

12

3

variant proportion

log

ratio

T/N

cov

erag

e

Chr 10 (late) Chr 5 (early)

!  The ra-o of duplicated/non-‐duplicated muta-ons depends on the earliness of each duplica-on

!  ... and can be used to infer the molecular -me of each duplica-on:

0 100 Point muta-on -me

Muta-ons/chromosome copy (1309-‐16)/3 16

1309 16 Duplicated/non-‐duplicated muta-ons

Time = 16/(16+(1309-‐16)/3) = 4%

4%

Chr 5 duplica-on

0 20 40 60 80 100

+1+5 +6 +10

+17

+20 +Xloh

13loh

14loh

21

Point mutation time (%)

0 20 40 60 80 100

+1 +3+5+8 +11

+12

+15

+19 +X


0 20 40 60 80 100

+8 +X


0 20 40 60 80 100

+1+2 +8+15


!  Timing chromosome duplica-ons in hepatocellular carcinomas:

+1 +10

+11

+12

+13

+15

+17

+19 +2 +20

+21 +3 +4 +5 +6 +7 +8 +X

0

20

40

60

80

100

Recurrent CNAs

Poin

t mut

atio

n tim

e

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!  Molecular -me distribu-on for recurrent chromosome duplica-ons:

!  Comprendre l'évolu-on clonale des tumeurs est capital pour l'u-lisa-on des marqueurs géné-ques en clinique

!  Diverses approches permeyent de reconstruire l'évolu-on clonale et l'ordre d'appari-on des altéra-ons géné-ques chez un pa-ent

!  Les données de séquençage génome complet nous offrent une vision dynamique du développement tumoral.

ÉVOLUTION CLONALE DES TUMEURS

!  Les échan-llons tumoraux portent les traces des processus mutagènes auxquels ils ont été soumis

SIGNATURES MUTATIONNELLES

!  Les signatures muta-onnelles sont un formidable ou-l pour comprendre les liens entre exposi-ons environnementales, prédisposi-ons géné-ques et cancer

!  31 signatures muta-onnelles ont été iden-fiées à ce jour, dont une bonne moi-é restent inexpliquées.

Conclusions

signatures+mutaonnelles +et évolu-on+ clonaledes tumeurs++ · aca acc acg act cca ccc ccg cct gca...

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