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Development of a genetic model
predicting the carrier status for
mmr-gene mutations
Fabio Marroni1, Piero Benatti2, Mariapina Montera3,
Daniela Barana4, Monica Pedroni2, Margherita Torrini3,
Luca Roncucci2, Cristina Oliani4, Cristina Mareni3,
Maurizio Ponz de Leon2, Generoso Bevilacqua1 and
Silvano Presciuttini1
1Università
di Pisa 2Università di Modena 3Università di
Genova 4Ospedale Maggiore - Verona
Introduction




A relevant proportion of HNPCC families is attributable to
germline mutations in MLH1 and MSH2
Lifetime risk of developing a CRC is more than 70% in
mutation carriers. Female carriers also have a 30% lifetime
risk of developing endometrial cancer
Early detection of mmr-gene mutations can substantially
reduce the lifetime risk of developing cancer
Accurate evaluation of the probability that an individual
carries a germline pathogenic mutation at MLH1 or MSH2 is
therefore essential to help counselors and counselands
decide whether testing is appropriate.
“State of the art”



The only predictive method currently available has been
developed by Wijnen et al.
This model was based on logistic regression of 184 families, 47 of
which carried a MLH1 or MSH2 mutation.
The equation found by these Authors is as follows:
log(p/1-p)=1.4-0.1*V1+1.7*V2+2.4*V3
–
–
–
V1 is the mean age at diagnosis of CRC in all members
V2 is 1 if at least one endometrial cancer is reported 0
otherwise
V3 is 1 if the family meet Amsterdam criteria and 0 otherwise
An example: Family F093
I-1
Mean
age of colon
cancer(40+60+27+50)/4:
I-2
p(mut)=0.35
V1 =44.25
II-1
No
endometrial
cancers: V2=0
Amsterdam
met: V3 =1
criteria III-5
II-2
Colon 40
III-6
Colon 60
III-7
Colon 27
IV-1
III-4
Colon 50
Validation of Wijnen model



Validation of a model requires comparing its predictions
in large data sets of families screened for mutations with
the results of genetic tests
Wijnen model was found to underestimate the risk of
carrying a mmr-gene mutation in 509 Finnish families
(Loukola et al 1999)
We applied Wijnen model to a first set of 105 Italian
families, and found the same result (21.4 expected
mutations vs 28 observed)
Empirical Models vs Genetic Models

Empirical models (e.g. based on logistic
regression):
–
–

Strongly dependent on the particular data sets that
is being analyzed
Unable to cover all special cases with sufficient data
Genetic models:
–
Based on
parameters.
knowledge
of
relevant
genetic
Development of a genetic model to
be implemented in Fastlink


In analogy with our previous experience with
BRCA genes, we intend to develop a full
genetic model for predicting carrier probability
of MSH2 and MLH1 mutations
This requires knowledge of the following
genetic parameters:
–
–
Frequency of mutated alleles
Cancer penetrance in carriers and non-carriers
A preliminary version


Gene frequency: 1/3139 (0.00032) as reported
by Dunlop et al (2000)
Penetrance: as reported by Aarnio et al (1999)
Endometrial
Colorectal Male
Colorectal Female
Colorectal Either
Mut+ (MSH1, MLH2)
60%
100%
54%
82%
General population
1.3%
1.6%
1.6%
1.6%
Results and perspectives


Our model predicted 28.71 mutations, compared to
28 actually found.
Performance will be further improved by taking into
account additional variables:
–
–

Molecular data about MSI
Possible differences of expressivity between MLH1 and
MSH2
A crucial requirement for the improvement of our
model is increasing the number of screened families.