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An Accurate Genetic Model for Predicting the Carrier Status of mmr-gene Mutations

An Accurate Genetic Model for Predicting the Carrier Status of mmr-gene Mutations.

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An Accurate Genetic Model for Predicting the Carrier Status of mmr-gene Mutations

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  1. An Accurate Genetic Model for Predicting the Carrier Status of 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

  2. 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 MLH1or MSH2 is therefore essential to help counselors and counselands decide whether testing is appropriate.

  3. Aim of the study • To refine a genetic model developed previously, evaluating the probability of carrying a germline mutation in MSH2 or MLH1 • To use information on MSI for the calculation of carrier probability • To compare the performance of this predictive model with that of Wijnen (Wijnen et al, NEJM, 1998) on our dataset, including 105 families screened for mmr-genes, in which 28 mutations were identified

  4. Model Parameter Values • Colorectal and endometrial cancer penetrances in carriers of mmr-genes mutations were obtained from published data (Vasen et al , JCO, 2001) • Penetrances in the general population were obtained by national cancer registries • Frequency of mmr-genes mutations was set to 0.00032 (Dunlop et al, BJC, 2000)

  5. Genetic model Penetrances

  6. Microsatellite Instability (MSI) • Knowledge of microsatellite status (stability or instability) can help predicting carrier status. • MSI is an indicator of increased mutation risk. The increase in risk was measured in our data as the increased fraction of mutations detected in MSI probands. • MSS tumors are rarely observed in carriers of mmr-gene mutations: we therefore set the posterior probability of being carrier given MS stable phenotype to 1/10th of the prior probability.

  7. MSI and mutations in families stratified by risk

  8. Mutation probabilities corrected by MS status The interpolated quadratic curve is used to correct prior probabilities

  9. Results • In our dataset of 105 families, 28 mutations were observed. The use of microsatellite data increased performance of both models. (Chi-square value in bracket)

  10. Results • Area under ROC curves significantly increased in both models after the inclusion of microsatellite data.

  11. Conclusions • The genetic model performed better than Wijnen model in term of observed/expected ratio, while the AUC was similar. • Both models show an important increase in performance when taking into account data on MSI.

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