Insights from the International Cancer
Genome Consortium
Mapping all somatic mutations in cancer
Mapping all somatic mutations in cancer
IntegratedGenomicAnalysesofOvarianCarcinoma
TheCancerGenomeAtlasResearchNetwork
Nature 2011 (in press)
High frequency novel mutations in granulosa
and clear cell ovarian cancers
Shah et al (2009)
Wiegand et al (2010)
95% FOXL2
49% ARID1a
Mountains and hills in colorectal cancer
Wood et al (2007) Science
•Commonly mutated genes already found (mountains)
•Newly identified genes are at a low frequency (hills)
When a low frequency
mutation is found - is it a
passenger or a driver of cancer?
Evidence of a driver
Mutation occurs at a higher frequency than expected by chance – size
of gene, proximity to fragile sites
Mutation found in independent datasets
Mutation is biologically plausible – non-synonymous, pathogenic
Functional studies demonstrate an effect in cells/animals
Common versus rare cancers
Rare cancers – often have single (or a
few) dominant drivers
Common solid cancers – some
common mutations but also many
low frequency mutations
Few pathways to transformation
Multiple pathways to transformation
Single oncogene addicted therapeutic
targets eg. Kit in gastrointestinal
stromal tumours
Many potential targets but at a low
frequency – few new Her2’s
Extensive repertoire for development
of resistance
Novel insights into cancer biology
by resequencing
Structural change (Pleasance Nature 2010)
Patterns of metastatic spread
(Campbell et al Nature 2010)
Chromothripsis (Stephens et al Cell 2011)
Inherited mutations
Classical linkage studies
High penetrance, uncommon
Genome wide association study (GWAS)
Common and low risk
Inherited mutations
Classical linkage studies
High penetrance, uncommon
Genome wide association study (GWAS)
Common and low risk
Tumour genome
studies
Resequencing of low risk
loci in cancer patients
Novel germline
mutations eg. PALB2
Moderate-high penetrance
rare variants
BRCA1 and BRCA2 germline mutations in
ovarian cancer
Risk of ovarian cancer in germline carriers 30-80%
5-18% frequency of germline mutations in ovarian cancer
Risk of carrying a germline allele if a women presents with ovarian cancer unclear. Why is
this important?
•
Identification of at risk family members
•
Germline status influences response to conventional and targeted therapy – PARP
inhibitors
Provision of genetic testing is based on family history and age of onset
AOCS 1000 women study
Gillian Mitchell (co-PI)
Stephen Fox
Cliff Meldrum
Anna deFazio
Sian Fereday
Michael Friedlander
Penny Webb
Kathryn Alsop
Mary-Anne Young
AOCS study design
Include:
Primary ovary, peritoneal,
fallopian tube: Blood available
Serous, undifferentiated, clear
cell, high grade endometrioid
Testing:
All exons, splice sites
Sanger sequencing and MLPA
(large deletions)
NATA accredited testing lab
(Peter Mac Pathology)
High frequency germline BRCA mutations in
serous ovarian cancer
• 16% of serous cancers were positive for a germline BRCA1 or BRCA2 mutation
• 43% of mutation positive women did not have a family history suggestive of a
carrier
• Doubt that ‘clear cell’ and ‘endometrioid’ BRCA cases were as initial
histological diagnosis
• Current genetic testing guidelines should change to offer BRCA testing to all
women with invasive ovarian cancer
High response rates to second line platinum in
carriers – even those with short first line response
First Line
PLATINUM
Mutation +
Second Line
PLATINUM
RECIST
11%
Resistant
<6
months
15%
RECIST+
89%
>6
months
85%
Responsive
The ‘Standard Model’ of molecularly targeted
therapy
Find the genes driving growth
Figure out the
biology of the cancer
Make a specific drug
Treat only those patients with
the specific event
Molecularly targeted therapy in GIST and NSCLC
Gastrointestinal stromal tumour
Imatinib
Patients
alive
Years
Historicalaveragesurvival
Lynch et al (2004)
The ‘Standard Model’ of molecularly targeted
therapy
A mutation must be present in a
reasonable fraction of cases
The corresponding protein must be
drugable
The tumour must have a substantial
dependency on the target that is not
easily reverted
Adaptation by cancer genome in the face of
monotherapy
Nature Reviews Cancer 2007
Alternative pro-angiogenic
factors
BMDC recruitment
Increased pericyte coverage
Increased invasion
Increased time to relapse through inhibition of
angiogenesis in ovarian cancer
Median increased time to relapse
4.7months
N=1873
Median increased time to relapse
1.7months
N=1528
Adaptation by cancer genome in the face of
monotherapy
Nature 2008
A shift in our approach to therapy?
Molecular
nostic
diagnostic
Transient responses?
M
Monotherapy
Make a drug
g
Cancer
Identify key
components
Integrate
information to
comprehensively
‘explain’ individual
cancers in real-time
Understand
adaptive processes
that
allow resistance
t
Combination and
adaptive therapy
Durable
response?