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

●

●●

●

●

●

●

●

●●●●

●

●●

●●

● ●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●●

●

●

●●

●●●

●

●

●

● ●●

●●

*+

−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