Mutation-predicting models can be useful when deciding on the genetic testing of individuals at risk and in determining the cost effectiveness of screening strategies at the population level. The aim of this study was to evaluate the performance of a newly developed genetic model that incorporates tumor microsatellite instability (MSI) information, called the AIFEG model, and in predicting the presence of mutations in MSH2 and MLH1 in probands with suspected hereditary non-polyposis colorectal cancer. The AIFEG model is based on published estimates of mutation frequencies and cancer penetrances in carriers and non-carriers and employs the program MLINK of the FASTLINK package to calculate the proband's carrier probability. Model performance is evaluated in a series of 219 families screened for mutations in both MSH2 and MLH1, in which 68 disease-causing mutations were identified. Predictions are first obtained using family history only and then converted into posterior probabilities using information on MSI. This improves predictions substantially. Using a probability threshold of 10\% for mutation analysis, the AIFEG model applied to our series has 100\% sensitivity and 71\% specificity.
A genetic model for determining MSH2 and MLH1 carrier probabilities based on family history and tumor microsatellite instability
pastrello, chiara;
2006
Abstract
Mutation-predicting models can be useful when deciding on the genetic testing of individuals at risk and in determining the cost effectiveness of screening strategies at the population level. The aim of this study was to evaluate the performance of a newly developed genetic model that incorporates tumor microsatellite instability (MSI) information, called the AIFEG model, and in predicting the presence of mutations in MSH2 and MLH1 in probands with suspected hereditary non-polyposis colorectal cancer. The AIFEG model is based on published estimates of mutation frequencies and cancer penetrances in carriers and non-carriers and employs the program MLINK of the FASTLINK package to calculate the proband's carrier probability. Model performance is evaluated in a series of 219 families screened for mutations in both MSH2 and MLH1, in which 68 disease-causing mutations were identified. Predictions are first obtained using family history only and then converted into posterior probabilities using information on MSI. This improves predictions substantially. Using a probability threshold of 10\% for mutation analysis, the AIFEG model applied to our series has 100\% sensitivity and 71\% specificity.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
