Using Genetics to Save Lives:
a new paradigm for breast and
colorectal cancer genetics
services
John L. Hopper
Mark A. Jenkins
Centre for Molecular, Environmental, Genetic, and Analytic (MEGA)
Epidemiology
The University of Melbourne
Carlton, Victoria, Australia
from tumour-type to genotype
Australian Breast Cancer Family Study
Population-based sampling
- 1,600 Cases (Vic & NSW Cancer Registries)
- 1,000 Controls (Government Electoral Rolls)
Cases
- unselected for family history
- over-sampled for early onset (50% < age 40)
Epidemiological data (by questionnaire)
Genetic and molecular (blood, tissue)
Funded by NHMRC, VicHealth, and since 1995,
by NIH as part of Cancer Family Registries
(see e.g. John, Hopper, et al., Br Cancer Res 2004)
Australasian Colorectal Cancer Family Study
Population-based sampling
- 1,000 Cases (Vic Cancer Registries)
- 800 Controls (Spouses and Government Electoral Rolls)
Clinic-based families
- selected for family history
- 500 from across Australia & New Zealand
Epidemiological data (by questionnaire)
Genetic and molecular (blood, tissue)
Funded by NHMRC, VicHealth, and since 1997,
by NIH as part of Cancer Family Registries
(see e.g. Newcombe et al., )
Summary of this talk
•
People vary greatly in disease risk
•
We know some genes for breast & bowel
cancer
•
But why would you want to know?
•
How high are the risks?
•
What can you do about your risk?
•
How to best find carriers?
•
New paradigm - tumours tell the story
•
Change?
Are all (wo)men really created equal?
For most common diseases,
people vary greatly in the familial
component of their risk
How do we know this?
• The unaffected relatives of people with the
disease are at increased risk of the disease
A feature of most common diseases
Increase risk typically on average about 2-fold
Increased risk is associated with:
• Closer relationship to affected(s)
• Having more affected relatives
• Younger age at diagnosis of affected relative(s)
• Relatives with bilateral or multiple related diseases
e.g. breast & ovarian cancer
• Ethnicity e.g. Ashkenazi Jews & breast cancer
Lifetime
Risk
Individual
Risk
30
20
10
Population
Risk
1
Q1
Median
Q3
Familial Causes
Breast cancer
Most
women
Average = 11%
Women with a
strong family
history/earlyonset disease
Colorectal cancer
18
16
Relative Frequency
14
Unaffected people
(population)
(average risk = 5%)
12
10
Unaffected people with
strong and/or early onset
family history
8
6
4
2
0
0 8.05
0%
5%
0.2
20%
0.4
40%
0.6
60%
0.8
80%
Lifetim e Risk
1001
%
People in the highest 25% risk group are 20 times more likely
to get CRC than the lowest 25% risk group
90% of the population are below the median risk
Genetic risk
• Very wide spectrum
• Most at very low risk
• Some at very high risk
• What influences, or influenced, those
at one end of the spectrum may differ
from what influences the average!
Known Breast Cancer Genes
TP53
PTEN
Relative Risk
10
BRCA1
Don’t exist!
BRCA2
High-risk
2
BRIP1
PALB2
ATM
CHEK2
Rare moderate risk
Common low risk
1.5
FGFR2
TNRC9
5q
1.1
Too hard to find!
0.1%
1%
2q
MAP3K1
8q
LSP1
10%
Minor allele frequency
30%
CASP8
Known Colorectal Cancer Genes
100
MUTYH
(biallelic)
High-risk
APC
Don’t exist!
Relative Risk
MSH2
30
MSH6
MLH1
PSH2
Rare moderate risk
2
MUTHY
(monoallelic)
Common low risk
MLH1 93
1.5
1.1
Too hard to find!
0.1%
1%
11q23
8q23
19q13
10%
Minor allele frequency
16q22
30%
20p12
14q22
18q21
Contribution of known genes to
explaining familial aggregation
of breast cancer
BRCA1
BRCA2
TP53
PTEN
ATM
CHEK2,BRIP1,PALB2
CASP8
8 WGA SNPs
Other familial risk factors
(genes, environment)
Contribution of known genes to
explaining familial aggregation
of colorectal cancer
MLH1
MSH2
PMS2
MSH6
MUTHY
8+ WGA SNPs
Other familial risk factors
(genes, environment)
䇾… quite contrary to the opinion
I started with …䇿
• BRCA1 discovered in 1995
• We carried out extensive 䇾manual䇿 sequencing
for ~90 early-onset cases:
– half with a family history
– half with no family history
• Found 3 carriers: 1 with family history and 2 with
no family history!
• Family history was not related to the mutation!!
BRCA2 2bp del 6503 (TT)
64
57
75
6
58
0
Breast(55)
(+)
44
+
41
+
12
60
67
59
76
6
78
Breast(38)
Bowel(58) Breast(62)
?Bowel(67)
+
54
52
_
_
50
+
41
38
_
Breast(38)
+
12
12
10
8
3172
That was the 䇾outlier䇿
It does not represent a
䇾typical BRCA1/2 family䇿!
BRCA1 1bp del 1876 (C)
81
70
75
88
?Breast(42)
47
65
61 57
Prostate(65)
63
74
Breast(48)
_
38
40
73
53
69
?Breast(53)
41
42
60
?Larynx(60)
37
64
50
61
58
?Lung(50)
34
Breast(38)
+
3159
BRCA1 2bp del 3888 / BRCA2 1bp del 6174 (T)
53
86
50
_56 _
/
33
90
_61
/+
31
69
3
64
29
Breast(33)
+/+
3104
Breast cancer diagnosed < age 40
• ~10% have mutation in BRCA1 or BRCA2
• 60% of carriers have no family history
(first- & second degree)
Majority of Hereditary Breast Cancer
is Sporadic
Hereditary: strong family history
High-risk genes
Familial:
Sporadic:
no family history
No genes
modest family
history
Low risk
genes
Hereditary: strong family history
High-risk genes
Familial:
Sporadic:
no family history
No genes
modest family
history
Low risk
genes
>2 affected
2 affected
1 affected
Familial
Sporadic
0 affected
Breast Ca < 40
1 in 2
1 in 8
1 in 16
1 in 20
Hereditary
<1%
5%
35%
60%
Family history is NOT a good predictor
of BRCA1 and BRCA2 mutations!
Family history does better for MMR genes
(at least when applied to early-onset colorectal
cancers)
but still not all that good
Amsterdam II
1 in 5
10%
2 affected
1 in 10
15%
1 affected
1 in 15
50%
0 affected
1 in 100
25%
Familial
Sporadic
CRC all ages
Hereditary
Amsterdam II
1 in 1.5
50%
2 affected
1 in 8
33%
1 affected
1 in 8
11%
0 affected
1 in 50
Familial
Sporadic
CRC < 45
Hereditary
6%
Why would you want to know your
genetic risk status?
• Your disease risk is high
• You can do something about it
• Benefits outweigh the negatives
How high are the risks?
Clinic-based, population-based, and
population estimates of penetrance
from families
• Penetrance: age-specific cumulative risk
• Average, over the …
mutations specific to sampled population
& weighted by their observed frequency
• Based on risks to carrier relatives
• Therefore, influenced by the genetic and
environmental background of the families
Hopper et al., CEBP 1999
Average Cumulative risk of cancer
Males
Females
70
70
60
60
CRC for MLH1 or MSH2
BC for BRCA1
50
Cumulativerisk%
CumulativeRisk%
50
40
30
20
30
CRC for MLH1
or MSH2
20
CRC for MSH6
CRC for
PMS2
10
BC for BRCA2
40
0
CRC for MSH6
CRC for PMS2
10
0
30
40
50
60
70
Age(years)
30
40
50
60
70
Age(years)
CRC for
population
BC for
population
CRC for
population
What can you do about your risk?
… otherwise why would you want to know?
Breast cancer
• Mastectomy – 90% reduction in risk
• Oophorectomy – 50% reduction
• Chemoprevention, e.g. Tamoxifen – 50% reduction
• Smoking - ?
• OC use - ?
• HRT use - ?
Colorectal cancer
• Annual colonoscopy & polypectomy > 65%
reduction in risk
• Weight reduction - ? 30% reduction
(overweight to normal weight)
• Smoking – 60% increased risk
• OC and HRT use - ?
• Exercise, aspirin, alcohol - ?
How to best find carriers?
• Family history is not all that good,
especially for breast cancer,
except if very strong
• Affecteds are good place to start,
especially if you can analyse their tumours
• Age at onset is important
LAMBDA
Log Odds of Carrying an Ancestral Mutation in BRCA1 or BRCA2 for a
Defined Personal and Family History in an Ashkenazi Jewish Woman
• Studied Ashkenazi Jewish women from
Australia & UK
• Worked out what aspects of personal and
family history best predicted carriers
• Simplified the output to make a tool clinicians
and women could use – without a computer
• Validated using large North American studies
Apicella et al., 2002, 2007; Lindor et al., 2006
LAMBDA score
Age at diagnosis / Age if unaffected
<40
Affected
Self
Breast
Bilateral
Ovarian
First-degree
Breast
Ovarian
Second-degree
Breast
Ovarian
Not affected
40-49
50-59
3
1
3
2
1
3
1
1
3
1.5
1.5
1.0
1.5
0.5
1.5
0.5
1.0
0
1.0
0
1.0
-0.5
-1.0
-1.5
Sum
scores
Table
Probability
>2 affected
2 affected
Familial
Sporadic
1 affected
0 affected
1 in 2
1 in 8
1 in 16
1 in 20
Hereditary
<1%
5%
35%
60%
New paradigm for cancer genetic
screening
… tumours have tell-tale signs of their
causes
Amsterdam II
1 in 1.5
50%
2 affected
1 in 8
33%
1 affected
1 in 8
11%
0 affected
1 in 50
Familial
Sporadic
CRC < 45
Hereditary
6%
Amsterdam II
1 in 1.5
50%
2 affected
1 in 8
33%
1 affected
1 in 8
11%
0 affected
1 in 50
Familial
Sporadic
CRC < 45
Hereditary
6%
Victorian Colorectal Cancer Family Study
•
Population-based sample of 131 incident cases
of primary adenocarcinoma of colon or rectum
•
Identified through Victorian Cancer Registry
•
< 45 years at diagnosis (1993-97)
•
•
•
Recruit first- & second-degree adult relatives
Epidemiology and family history q䇻aires
Blood and tumour samples sought
Mismatch repair loss (e.g. MLH1) and carcinogenesis
MLH1
MLH1
Germline or
Somatic mutation
Normal mismatch repair
No mismatch
MLH1
MLH1
If mismatch
affects a gene
in cancer pathway
Somatic mutation
Tumour with microsatellite instability (MSI) – a.k.a MSI-High
and lacking MLH1 protein expression (immunohistochemistry)
Immunohistochemistry measures mismatch
repair deficiency phenotype
Protein present in tumour
Protein absent in tumour
Family History Alone
MSH2
MLH1
Amsterdam II
PMS2
MSH6
MSI Testing Alone
hMLH 1
hMSH2
hMSH6
MSI High
MSI Low
IHC Testing Alone
MLH1
&/or
PSM2
MSH2
MLH2
MLH1
MSH6
MSH6 only
MSH2
Family History Alone
hMLH1
hMSH2
Amsterdam II
hMSH6
Family History Alone
hMSH2
hMLH1
Amsterdam II
hMSH6
MSI Testing Alone
hMLH 1
hMSH2
hMSH6
MSI High
MSI Low
What if diagnosis after age 45?
Figure 1b. Probability of carrier given MSI+
1.00
0.90
0.80
0.88
0.76
0.75
Probability
0.70
0.78
0.60
0.50
0.40
0.40
0.30
0.20
0.10
0.09
0.00
0-34
35-39
40-44
45-49
Age at diagnosis group
MSI+ = MSI at BAT26
50-54
55-59
Hampel et al. 2008 Supplementary data
Figure 1b. Probability of carrier given MSI+
1.00
0.90
0.80
Probability
0.70
0.67
0.60
0.50
0.44
0.40
0.30
0.28
0.20
0.10
0.07
0.04
0.00
0-40
40-49
50-59
60-69
70-79
Age at diagnosis group
MSI+ = MSI-H or MSI-L
0.00
80+
Molecular screening of all tumours < 50,
followed by genetic testing,
will identify most carriers
• ~80% of MSI-H cases will be carriers
• Constitutes ~75% of all Lynch cases <60
• Extending to 50-59: triples molecular testing
• Few carriers diagnosed after age 60
Early Onset Case
Informed Consent
Tumour
Clinical management
IHC
MSI
Future Cases
Genetic counselling
Mutation testing
Cascade testing
Carriers
Non carriers
Clinical surveillance
Population surveillance
Early Onset Case
Informed Consent
Tumour
Clinical management
IHC
MSI
Future Cases
Genetic counselling
Mutation testing
Cascade testing
Carriers
Non carriers
Clinical surveillance
Population surveillance
Could even increase efficiency even more
using additional pathology features
Pathologists
Jass JR (McGill),
Hayashi S (McGill),
O’Shea A-M (Toronto),
Burgart LJ (Mayo),
Smyrk TC (Mayo),
Shimizu D (Hawaii),
Waring PM (PeterMac),
Ruszkiewicz AR (Adelaide),
Pollett AF (Ontario),
Redston M (Harvard),
Molecular biologists
Thibodeau SN (Mayo),
Barker MA (QIMR),
Casey G (Clevlenad Clinic),
Walsh MD (QIMR)
Young JP (QIMR),
Epidemiologists/statisticians
Jenkins MA (UoM)
Dowty JG (UoM),
Baron JA (Dartmouth),
Giles GG (Victorian Cancer Registry),
Newcomb P (Fred Hutchinson)
Principal investigators
Lindor NM (Mayo),
Haile RW (Uni Southern California),
LeMarchand L (Uni Hawaii),
Gallinger S (Mt.Sinai, Toronto),
Potter JD (Fred Hutchnson),
Hopper JL (UoM_
Population-based recruitment – Colon Cancer Family Registry (NIH)
Seattle
Ontario
Mayo
Hawaii
Melbourne
Pathology
review
Pathology
review
Pathology
review
Pathology
review
Pathology
review
MSI
testing
MSI
testing
MSI
testing
MSI
testing
MSI
testing
Colorectal tumors diagnosed before age 60
n = 1,098 colorectal tumors
Association with microsatellite instability
FEATURE
Odds ratio*
(95% CI)
p-value
Tumor infil. lymphocytes
9.1
(5.9 – 14.1)
<0.001
Right sided
4.7
(3.1 – 7.3)
<0.001
Mucinous histology
2.8
(1.7 – 4.8)
<0.001
Poorly differentiated
1.9
(1.2 – 3.1)
0.005
Crohn-like lymph. reaction
1.9
(1.2 – 2.9)
0.004
Age at diagnosis <50 yrs
1.9
(1.3 – 2.9)
0002
Any one feature
21.6
(5.3 – 87.9)
<0.001
*Adjusted for all other features
Percentage of tumours
3 cases =
no instability in
mononucleotide
markers and no
loss of
mismatch repair
proteins
Percentage of tumours
Test for loss of mismatch
repair function
Diagnosis under 50 yrs
MSI-H
MSI-L/MSS
Diagnosis 50-59 yrs
MsPath score
Features of BRCA1 tumours
mitotic index greater than 50/10HPF
nuclear pleomorphism
low tubule formation
syncytial growth pattern
pushing margin
circumscribed
lymphocytic infiltrate (moderate or intense)
growth pattern (primary or secondary) trabecular
necrosis
ER IHC
PR IHC
Atypicalmedullarycarcinomaina
womanwithagermlineBRCA1
mutation
H&E,x2
Circumscribed,bosselated
appearanceobservedatlowpower.
Apredominantpushingmargin
H&E,x25
Thetumorhasasyncytialgrowth
pattern,highmitoticrate,high
nucleargrade,andanareaof
necrosis.
H&E,x100
Armesetal.,1999
Atypicalmedullarycarcinomaina
womanwithagermlineBRCA1
mutation
H&Ex10
Apredominantlypushingmargin
Highgradenuclearpleomorphism,
asyncytialgrowthpattern
H&Ex25
Highmitoticrateathighpower
(H&E,x100
Alsoknowntohighhighfrequencyof
p53somaticmutations.
Armesetal.,1999
Diagnosed <40 years (ABCFS)
140
number of tumours
120
100
80
27/29 carriers
60
27/107 tumours were carriers
40
20
0
0
1
2
3
4
SCORE
score
BRCA1 germline mutation carrier
5
6
7
5+ Criteria
8
9
Diagnosed <40 years (ABCFS)
Strong family history
number of tumours
20
15
8/8 carriers
10
8/18 tumours were carriers
5
0
0
1
2
3
4
score
BRCA1 germline mutation carrier
5
6
7
8
9
Family History Alone
BRCA1
BRCA2
Familial
Breast-Ovary
Syndrome
Pathology “5+” Criteria Alone
BRCA1
BRCA2
?
Predictors of BRCA1 carriers
Mitotic index > 50 mitoses per HPF:
OR = 10 (95% CI 3-38; p<0.001)
Trabecular growth pattern:
OR = 14 (3-78; p<0.001)
Each first degree relative with breast cancer
diagnosed before the age of 60 years:
OR = 2.9 (1.4-6.7; p=0.007)
Area under the Receiver Operator Characteristic
(ROC) curve: 0.91 (95% CI: 0.63-0.98)
3-variable model for BRCA1 carriers
Early Onset Case
Informed Consent
Tumour
Clinical management
Pathology
Future Cases
Genetic counselling
Mutation testing
Cascade testing
Carriers
Non carriers
Clinical surveillance
Population surveillance
Change
How to achieve it?
Colorectal cancer
The Cancer Council Victoria (TCCV) has endorsed IHC
for all cases diagnosed before the age of 50 yrs
Happening in other states,
e.g. South Sydney, Perth (using MSI BAT26 only)
One specialised pathology service could handle all of
Australia䇻s work load
1,000 case < 50 per year in Australia
IHC/MSI would target <150 for genetic testing
~100 carrier families identified, therefore
~ 400 carriers identified (case + 3 rels) per year
Could be done retrospectively as well
Breast cancer
䇾Rapid䇿 testing beginning to happen in ad hoc manner
BOADICEA soon to include ER and PR
Developing local version that includes the 2 pathology
features above
Not to replace current practice of seeing 䇾worried well䇿,
but make breast cancer services more cost-effective
by shifting emphasis to early-onset cases
We have the evidence
to do things better
Mutation carriers better identified by studying
tumours of early-onset cases
Even independently of family history
Implementation will depend on local issues (ethics,
resources, skills, cost-benefit, turf)
May not apply (as well) to later onset cases
Should become standard practice for
Victorian cancer family genetics services
Acknowledgements
Epidemiology and Statistical Analyses
Margaret McCredie, Graham Giles, Mark Jenkins, Roger
Milne, Gillian Dite, Carmel Apicella, James Dowty,
Graham Byrnes, Maggie Angelakos, and many others
Genetic Analyses
Melissa Southey, Andrea Tesoriero, Letitia Smith, Fleur
Hammet, Fabrice Odefrey, Caroline Chatfield, Leeanne
Mead,and many others
Co-ordination and Interviewing
Judi Maskiell and many, many others
Study participants and their relatives
Funding
National Health & Medical Research Council, Vic
Health, VBCRC, NBCF, NSW Cancer Council, National
Institutes of Health (USA)