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USING NEXT GENERATION SEQUENCING TECHNOLOGY IN THE CANCER GENOME PROJECT ELLI PAPAEMMANUIL CANCER GENOME PROJECT
Cancer is driven by muta1on
Evolu1on
Greaves M 2010, Seminars in Cancer Biology
• All cancers can be attributed to: mutations in DNA
Cancer is driven by muta1on
• All cancers can be attributed to: mutations in DNA
• Germline Inherited from parent Present in all cells of the body
Cancer is driven by muta1on
• All cancers can be attributed to: mutations in DNA
• Germline Inherited from parent Present in all cells of the body
BRCA1/2
Cancer is driven by muta1on
• All cancers can be attributed to: mutations in DNA
• Somatic Acquired Present in distinct cells within the body
Base Subs1tu1ons Inser1ons dele1ons
Copy number changes Rearrangements
Mul1ple classes of muta1on
Cancer genome research-‐ key aims:
1. To define gene muta1ons that drive cancer. 2. To study the mechanisms that underpin oncogenesis. 3. To evaluate cancer genomes for novel therapeu1c targets.
4. To understand cancer clonal evolu1on. 5. To iden1fy clinical biomarkers for diagnosis, prognosis and disease
monitoring.
Cancer genome research-‐ key aims:
1. To define gene muta1ons that drive cancer. 2. To study the mechanisms that underpin oncogenesis. 3. To evaluate cancer genomes for novel therapeu1c targets.
4. To understand cancer clonal evolu1on. 5. To iden1fy clinical biomarkers for diagnosis, prognosis and disease
monitoring.
1.To define gene muta1ons that drive cancer. Pilot screen: • 15 Cancer cell lines ( 6 breast, 1 small cell lung cancer, 6 non-‐small cell
lung cancers, 1 mesothelioma and 1 melanoma) over 20 genes.
BRAF mutaSons: 60% melanomas, 3% lung cancer, 5% CRC, 100% HCL
BRAF inhibi1on in malignant melanoma BRAF p.V600E
Standard Chemotherapy response rate : 10%
Wagle et al, JCO 2011
Pre-‐treatment
BRAF V600E molecular target
BRAF inhibi1on in malignant melanoma BRAF p.V600E
Wagle et al, JCO 2011
Treatment with vemurafenib 2011
BRAF inhibi1on in malignant melanoma
Pre-‐treatment @15 weeks
70% response rate
Treatment with vemurafenib
Wagle et al, JCO 2011
BRAF p.V600E
2011
BRAF inhibi1on in malignant melanoma
BRAF p.V600E MEK1 p.C121S RET p.K710N
Pre-‐treatment @15 weeks @23 weeks
Treatment with vemurafenib
Wagle et al, JCO 2011
BRAF p.V600E
Cancer genomes using NGS
Cancer genomes using NGS
Cancer genome research-‐ key aims:
1. To define gene muta1ons that drive cancer. 2. To study the mechanisms that underpin oncogenesis. 3. To evaluate cancer genomes for novel therapeu1c targets.
4. To understand cancer clonal evolu1on. 5. To iden1fy clinical biomarkers for diagnosis, prognosis and disease
monitoring.
Mechanisms that underpin oncogenesis
Study of mutaSonal signatures
“Kataegis” Localised hypermuta1on C>Ts in TpC
ICGC Map – March 2012 “A systema1c characteriza1on of most common cancers-‐ 47 projects launched”
Cancer genome research-‐ key aims:
1. To define gene muta1ons that drive cancer. 2. To study the mechanisms that underpin oncogenesis. 3. To evaluate cancer genomes for novel therapeu1c targets.
4. To understand cancer clonal evolu1on. 5. To iden1fy clinical biomarkers for diagnosis, prognosis and disease
monitoring.
To iden1fy clinical biomarkers for diagnosis, prognosis and disease monitoring.
Myelodysplas1c syndromes
Bone Marrow
Red Blood Cells Platelets
White Blood Cells
Oxygen Blot Cloang InfecSon
Most common form of bone marrow failure in the over 60s
• MDS can be challenging to diagnose.
• High risk groups suffer from a poor prognosis.
• 50% of MDS sufferers do not receive a diagnosis.
• Increased risk of disease progression to AML (25-‐30%).
MyelodysplasSc syndromes RCUD
Refractory cytopenia with unilineage dysplasia
RARS Refractory Anemia with Ringed Sideroblasts
RAEB1 & RAEB2 Refractory Anemia with Excess of Blasts 1 & 2
RCMD Refractory cytopenia with MulClineage Dysplasia
MDS del 5q-‐
Vardiman et al 2008; Greenberg et al. 1997; Malcova1 et al; 2007
PaSent risk assessment
Clinical decision making
Cytopenias Cell lineage
% Bone marrow blasts >15% Ringed sideroblasts
Prognos1c evalua1on of MDS
Cazzola, et al ; Hematologica 2011; Rolison, et al; Blood 2008
US SEER 2001 -‐ 2004
SF3B1, a new driver gene in MDS
DNA sequencing of 9 MDS PaSents
IdenSficaSon of 64 new mutaCons
Papaemmanuil et al. NEJM 2011
IdenSficaSon of somaSc variants Tumour DNA Normal DNA
SF3B1, a new driver gene in MDS
DNA sequencing of 9 MDS PaSents
IdenSficaSon of 64 new mutaCons Sample ID SF3B1
1 PD4800a
2 PD4174a p.H662Q
3 PD4175a p.K700E
4 PD4176a p.H662Q
5 PD4179a p.K700E
6 PD4180a
7 PD4181a p.K700E
8 PD4171a p.K700E
9 PD4182a
SF3B1 mutaCons in 6 of 9 paSents
Papaemmanuil et al. NEJM 2011
Diagnos1c biomarkers and predictors of clinical outcome Novel gene findings are valuable diagnosCc biomarkers with direct impact in acceleraCng and improving paCent diagnosis as well as inform on paCent clinical course and overall outcome.
Median survival 90 (SF3B1+ve) Vs 50 months (SF3B1 wt), P=0.001
Systema1c gene1c profiling of cancers
Genomic study findings 10000 neoplasms
Molecular classificaSon Phenotype and clinical correlates
Follow up analysis
Molecular classifica1on
Prognos1c profiles
WHO subtypes SF
3B1
TET2
SFRS2
ASXL1
DNMT3A
RUNX1
U2AF1
TP53
EZH2
IDH2
STAG
2ZR
SR2
CBL
NRAS
BCOR
JAK2
CUX1
IDH1
KRAS
NPM
1EP
300
PHF6
PTPN
11CREB
BP KIT
MLL2
MPL NF1
WT1
CDKN
2AIRF1
ATRX
ETV6
KDM6A
SH2B3
CEB
PAFLT3
GNAS
PTEN
BRAF
CTN
NA1
0
50
100
150RARARSRARS−TRCMDRCMD−RSRAEB5q−CMMLMDS−MPNMDS−UMDS−AML
Genotype-‐ clinical correla1ons
0.20
0.25
0.30
0.35
0.40plt
gen.
R^2
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*+
−1.5
−1
−0.5
0
0.5
1
SF3B1
RUNX
1TP
53WT1
U2AF
1de
l5q
EP300
CTNN
A1 tri8
STAG
2BC
OR
CUX1
JAK2
TET2
com
plex
NRAS
PHF6
IRF1
MPL
ZRSR
2de
l12
KRAS
othe
rMLL2
KIT
ASXL1
CEBPA
chr3
PTEN
IDH2
CDKN
2APT
PN11
GNA
SIDH1
del2
0qEZ
H2 CBL
del1
1BR
AFal
t17q
ETV6
SFRS
2CR
EBBP
DNMT3A
delY
tri19
ATRX
KDM6A NF1
NPM1
del7
_7q
FLT3
SH2B3
Mechanisms of malignant progression SF3B1
SRSF2
ASXL1
STAG2
TET2
RUNX1
CUX1
IDH2
DNMT3A
GATA2
WT1
NPM1
ZRSR2
BCOR
EZH
2KIT
JAK2
KDM6A
TP53
CREBBP
CDKN2A
NF1
CBL
IDH1
MPL
MLL2
ATRX
EP300
IRF1
RAD21
ETV
6PTE
NBRAF
GNAS
FLT3
PTP
N11
CTN
NA1
PHF6
CEBPA
KRAS
SH2B3
NRAS
U2AF1
chr3
del5
qde
l7_7
qtri
8de
l11
del1
2al
t17q
tri19
del2
0qde
lYco
mpl
ex
SF3B1SRSF2ASXL1STAG2TET2
RUNX1CUX1IDH2
DNMT3AGATA2WT1
NPM1ZRSR2BCOREZH2KIT
JAK2KDM6ATP53
CREBBPCDKN2A
NF1CBLIDH1MPLMLL2ATRXEP300IRF1
RAD21ETV6PTENBRAFGNASFLT3
PTPN11CTNNA1
PHF6CEBPAKRASSH2B3NRASU2AF1
chr3del5q
del7_7qtri8
del11del12
alt17qtri19
del20qdelY
complex
** * * * * * ** ** ** ** * ******
* *
* ** * *
* *
* * *
0.0010.010.11101001000
*P > 0.05BH < 0.1Bf. < 0.05
PD6155aEZH2
c.2069G>A0.912
SF3B1c.1873C>T0.230
RUNX1c.496C>T0.223
TET2c.4760_4761insT
0.368
STAG2c.1452_1453insGGGGA
0.027
0 50 100 150
0.0
0.2
0.4
0.6
0.8
1.0
Months
Even
t−fre
e fra
ctio
n
N onc.01234567
Prognos1c value of muta1on status
0 50 100 150
0.0
0.2
0.4
0.6
0.8
1.0ASXL1
Months
Even
t−fre
e fra
ctio
n
++++
543 wild type 1 unknown ^4 possible onc. 66 oncogenic **
Predic1ng a pa1ents clinical course
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.060 Months
FPR
TPR
all (0.78 ± 0.03)gen (0.74 ± 0.04)bm_ (0.77 ± 0.04)cli (0.74 ± 0.04)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.090 Months
FPR
TPR
all (0.84 ± 0.02)gen (0.73 ± 0.06)bm_ (0.79 ± 0.03)cli (0.76 ± 0.05)
1
10
100
1000
10000
100000
1000000
10000000
100000000
1000000000
0 12 24 36
Time (months)
Tumour cells
Risk strati!cation
Therapy duration Prediction of relapse
Personalised Haematology
To iden1fy clinical biomarkers for diagnosis, prognosis and disease monitoring.
Catalogue of Soma1c Muta1ons In Cancer Curate, Standardise, Combine
DAS
Current entries on : 700,000 tumours 233,000 mutaCons
Adam Butler Adam Shlien Alagu Jayakumar Andrew Menzies Andrew Barthorpe Angela Macharia Anne McLaren-‐Douglas Barbara Kremeyer Ben Robinson Calli LaSmer Catherine Leroy Chai Kok Charloje Dunham Claire Hardy CosS A David Beare David Jones David Wedge Elizabeth Murchison Elizabeth Anderson Elli Papaemmanuil Fiona Kogera Graham Bignell Gunes Gundem Helen Davies
Howard Lighkoot Ian Whitmore Jennifer Yen John Marshall John Gamble Jonathan Teague Jonathan Hinton Jonathan Brammeld Jorge Soares Jorge Zamora Jose Tubio Kalyan Kallepally Karl Lawrence Keiran Raine Kenric Leung Laura Mudie Laura Hirst Lucy Stebbings Lucy Yates Ludmil Alexandrov Manasa Ramakrishna Mark Maddison Mathew Garnej Mingming Jia Moritz Gerstung Niccolo Bolli
Nidhi Bindal Olivia Rochelle Patrick Tarpey Paula Barnes Peter Campbell Peter Van Loo Prasad Gunasekaran Rebecca Shepherd Sally Bamford Sam BehjaS Sancha MarSn Sarah O'Meara Serena Nik-‐Zainal Serge Dronov Simon Forbes Sonja Heidorn Stacey Price Steve Gamble SSan Knappsog Stuart McLaren Susanna Cooke TaSana Mironenko Tony Webb Wendy McLaughlin Wanjaun Yang Yilong Li
The Cancer Genome Project
Peter Campbell Andy Futreal Mike Stratton UltanMcDermot