Development of a genetic model predicting the carrier status for mmr-gene mutations Fabio Marroni 1, Piero Benatti 2, Mariapina Montera 3, Daniela Barana 4, Monica Pedroni 2, Margherita Torrini 3, Luca Roncucci 2, Cristina Oliani 4, Cristina Mareni 3, Maurizio Ponz de Leon 2, Generoso Bevilacqua 1 and Silvano Presciuttini 1 1 Università di Pisa 2 Università di Modena 3 Università di Genova 4 Ospedale 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)= *V *V *V 3 – V 1 is the mean age at diagnosis of CRC in all members – V 2 is 1 if at least one endometrial cancer is reported 0 otherwise – V 3 is 1 if the family meet Amsterdam criteria and 0 otherwise

An example: Family F093 Mean age of colon cancer( )/4: V 1 =44.25 No endometrial cancers: V 2 =0 Amsterdam criteria met: V 3 =1 p(mut)=0.35

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 knowledge of relevant genetic parameters.

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 ( ) as reported by Dunlop et al (2000) Penetrance: as reported by Aarnio et al (1999)

Results and perspectives Our model predicted 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.