Transcript Genetic Risk Assessment for Breast Cancer

Genetic Risk Assessment for
Breast Cancer
A Comparison of Different Methods
Jonathan Berg
© J. Berg 2000
Why assess risk to an individual of
developing breast cancer ?
• Clinical decision making
• Patient decision making
eg.
Is screening justified ?
Is prophylactic treatment justified ?
Estimation of Risk
When there is no known BRCA1 or BRCA2 mutation
• Empirical risk estimation
• Statistical model to estimate risk
• Computer based systems
– Cyrillic 2 / Cyrillic 3
Presentation of Risk
Relative Risk
Your lifetime risk of dying is
1 X the population risk
Presentation of Risk
Absolute Risk
Your lifetime risk of dying is
100%
Presentation of Risk
Absolute Risk Over Time
Your risk of dying in the next 10 years is
2%
Conversion of Relative Risk to Absolute Risk
• Assuming relative risk is constant over time
• Using population morbidity and mortality data
(ftp://ftp.vanderbilt.edu/pub/biostat/absrisk.zip)
Using Absolute risks:
10 year risk estimates
Relative
Risk
1
2
3
5
10
20
0.1
0.1
0.2
0.3
0.6
30
0.5
0.9
1.4
2.3
4.6
40
1.5
3.0
4.5
7.4
14
50
2.5
5.0
7.4
12
23
60
2.5
4.9
7.3
12
22
70
2.4
4.7
7.0
11
21
Age
Age-specific Relative vs. Absolute Risk
Data based on mortality and incidence statistics for Scotland for 1996 provided by ISD
An evidence based treatment
decision should be derived from:
Absolute risk (calculated how ?)
Sensitivity and specificity of screening (age dependant)
Or efficacy of treatment
Benefit of early tumour detection
Meta-Analysis (Empiric)
Pharoah et.al. 1997
Meta-Analysis of 74 Studies
• Family history
(up to two 1st or one 2nd degree)
• Age at which relatives affected
(before age 50 or after age 50)
CASH Data (Empiric)
4,730 Individuals with breast cancer age 20-54
• Family history
• Age at which relatives affected
(10 year intervals)
CASH Model
4,730 Individuals with breast cancer age 20-54
Model based on autosomal dominant inheritance
single major locus with high penetrance
•
Family history (1st and 2nd degree)
• Age at which relatives affected
(10 year intervals)
Gail Model
2,852 Individuals with breast cancer
Logistic regression model
Giving 10, 20 and 30 year risks
•
Family history (1st degree relatives)
• Hormonal factors + clinical history
Cyrillic
TM
3
Assumes model of single major locus
Generally uses CASH penetrance figures
• Family history (any)
• Unaffected relatives
So Which is Best ?
20 Year Risk of Breast Cancer for 40 year old proband
MetaCASH
Analysis Empiric
CASH
Model
Gail Cyrillic
Model
Mother Affected
age 45
9.8
-
5.3
7.6
4.6
Mother and Aunt Affected
age 45 and 60
9.8*
-
10.7
7.6*
7.3
Mother and Aunt Affected
age 60 and 70
6.4*
-
4.9
7.6*
4.2
3 Affected Relatives
age 40, 45 and 60
13.8*
32.1*
17.0*
12.8*
15.8
Sister Affected
age 40
12.7
10.5
5.3
7.6
4.1
Mother and Sister Affected
age 45 and 40
13.8
32.1
17.0
12.8
11.5
Paternal Aunt Affected
age 45
6.4
-
4.9
7.6*
4.2
20 Year Risk of Breast Cancer
MetaCASH
Analysis Empiric
CASH
Model
Gail Cyrillic
Model
Mother Affected
age 45
9.8
-
5.3
7.6
4.6
Mother and Aunt Affected
age 45 and 60
9.8*
-
10.7
7.6*
7.3
Mother and Aunt Affected
age 60 and 70
6.4*
-
4.9
7.6*
4.2
3 Affected Relatives
age 40, 45 and 60
13.8*
32.1*
17.0*
12.8*
15.8
Sister Affected
age 40
12.7
10.5
5.3
7.6
4.1
Mother and Sister Affected
age 45 and 40
13.8
32.1
17.0
12.8
11.5
Paternal Aunt Affected
age 45
6.8
-
3.8
-
3.8
Conclusions
• Risks estimated depend on method used.
• These differences in risk estimation can be
large enough to affect clinical decision making.
• Cyrillic gives a lower risk in many situations,
particularly where there are a small number of
affected individuals in the pedigree.
• More complete validation of risk estimation
systems is required. This may require new large
data sets.
Acknowledgments
Mary Porteous
Susan Holloway
Gus Ferguson