PT - JOURNAL ARTICLE AU - Loukola, Anu AU - de la Chapelle, Albert AU - Aaltonen, Lauri A TI - Strategies for screening for hereditary non-polyposis colorectal cancer AID - 10.1136/jmg.36.11.819 DP - 1999 Nov 01 TA - Journal of Medical Genetics PG - 819--822 VI - 36 IP - 11 4099 - http://jmg.bmj.com/content/36/11/819.short 4100 - http://jmg.bmj.com/content/36/11/819.full SO - J Med Genet1999 Nov 01; 36 AB - Germline mutations in DNA mismatch repair genes (MLH1, MSH2,PMS1, PMS2, andMSH6) predispose to hereditary non-polyposis colorectal cancer (HNPCC). In the absence of pathognomonic clinical features, diagnosis of HNPCC is often based on family history. Microsatellite instability (MSI) analysis has successfully been used for screening colorectal cancer patients for HNPCC. The aim of this study was to evaluate the feasibility of a recently introduced logistical model based on family history data in detecting HNPCC patients with germline mutations. A series of 509 kindreds with a proband with colorectal cancer was studied. MSI analysis and subsequent germline mutation analysis (MLH1and MSH2) in MSI positive patients had been performed previously. Of the 509 patients, 63 (12%) were MSI positive and 10 (2%) had a germline mutation in MLH1or MSH2. The power of the logistical model was tested to determine its value in predicting the probability of a germline mutation. The model proposed a high probability in three out of 10 mutation positive cases when data on cancer in first degree relatives were considered (typically three generation pedigrees, consisting, on average, of eight people). Using extended pedigrees and family cancer data in the 10 mutation positive kindreds (an average of 38 family members available), the model suggested high probabilities in seven out of 10 mutation positive cases. We conclude that for the model to predict germline mutation cases, extensive pedigrees and family history data are required. When screening colorectal cancer patients for HNPCC, a model using a combination of family information and MSI has optimal specificity and sensitivity.