CONTEXT: Lynch syndrome is caused primarily by mutations in the mismatch repair genes MLH1 and MSH2. OBJECTIVES: To analyze MLH1/MSH2 mutation prevalence in a large cohort of patients undergoing genetic testing and to develop a clinical model to predict the likelihood of finding a mutation in at-risk patients. DESIGN, SETTING, AND PARTICIPANTS: Personal and family history were obtained for 1914 unrelated probands who submitted blood samples starting in the year 2000 for full gene sequencing of MLH1/MSH2. Genetic analysis was performed using a combination of sequence analysis and Southern blotting. A multivariable model was developed using logistic regression in an initial cohort of 898 individuals and subsequently prospectively validated in 1016 patients. The complex model that we have named PREMM(1,2) (Prediction of Mutations in MLH1 and MSH2) was developed into a Web-based tool that incorporates personal and family history of cancer and adenomas. MAIN OUTCOME MEASURE: Deleterious mutations in MLH1/MSH2 genes. RESULTS: Overall, 14.5% of the probands (130/898) carried a pathogenic mutation (MLH1, 6.5%; MSH2, 8.0%) in the development cohort and 15.3% (155/1016) in the validation cohort, with 42 (27%) of the latter being large rearrangements. Strong predictors of mutations included proband characteristics (presence of colorectal cancer, especially > or =2 separate diagnoses, or endometrial cancer) and family history (especially the number of first-degree relatives with colorectal or endometrial cancer). Age at diagnosis was particularly important for colorectal cancer. The multivariable model discriminated well at external validation, with an area under the receiver operating characteristic curve of 0.80 (95% confidence interval, 0.76-0.84). CONCLUSIONS: Personal and family history characteristics can accurately predict the outcome of genetic testing in a large population at risk of Lynch syndrome. The PREMM(1,2) model provides clinicians with an objective, easy-to-use tool to estimate the likelihood of finding mutations in the MLH1/MSH2 genes and may guide the strategy for molecular evaluation.
CONTEXT: Lynch syndrome is caused primarily by mutations in the mismatch repair genes MLH1 and MSH2. OBJECTIVES: To analyze MLH1/MSH2 mutation prevalence in a large cohort of patients undergoing genetic testing and to develop a clinical model to predict the likelihood of finding a mutation in at-risk patients. DESIGN, SETTING, AND PARTICIPANTS: Personal and family history were obtained for 1914 unrelated probands who submitted blood samples starting in the year 2000 for full gene sequencing of MLH1/MSH2. Genetic analysis was performed using a combination of sequence analysis and Southern blotting. A multivariable model was developed using logistic regression in an initial cohort of 898 individuals and subsequently prospectively validated in 1016 patients. The complex model that we have named PREMM(1,2) (Prediction of Mutations in MLH1 and MSH2) was developed into a Web-based tool that incorporates personal and family history of cancer and adenomas. MAIN OUTCOME MEASURE: Deleterious mutations in MLH1/MSH2 genes. RESULTS: Overall, 14.5% of the probands (130/898) carried a pathogenic mutation (MLH1, 6.5%; MSH2, 8.0%) in the development cohort and 15.3% (155/1016) in the validation cohort, with 42 (27%) of the latter being large rearrangements. Strong predictors of mutations included proband characteristics (presence of colorectal cancer, especially > or =2 separate diagnoses, or endometrial cancer) and family history (especially the number of first-degree relatives with colorectal or endometrial cancer). Age at diagnosis was particularly important for colorectal cancer. The multivariable model discriminated well at external validation, with an area under the receiver operating characteristic curve of 0.80 (95% confidence interval, 0.76-0.84). CONCLUSIONS: Personal and family history characteristics can accurately predict the outcome of genetic testing in a large population at risk of Lynch syndrome. The PREMM(1,2) model provides clinicians with an objective, easy-to-use tool to estimate the likelihood of finding mutations in the MLH1/MSH2 genes and may guide the strategy for molecular evaluation.
Authors: E Lastra; M García-González; B Llorente; C Bernuy; M J Barrio; L Pérez-Cabornero; M Durán; C García-Girón Journal: Clin Transl Oncol Date: 2012-04 Impact factor: 3.405
Authors: Fay Kastrinos; Ewout W Steyerberg; Rowena Mercado; Judith Balmaña; Spring Holter; Steven Gallinger; Kimberly D Siegmund; James M Church; Mark A Jenkins; Noralane M Lindor; Stephen N Thibodeau; Lynn Anne Burbidge; Richard J Wenstrup; Sapna Syngal Journal: Gastroenterology Date: 2010-08-19 Impact factor: 22.682
Authors: Miguel Serrano; Pedro Lage; Sara Belga; Bruno Filipe; Inês Francisco; Paula Rodrigues; Ricardo Fonseca; Paula Chaves; Isabel Claro; Cristina Albuquerque; António Dias Pereira Journal: Fam Cancer Date: 2012-12 Impact factor: 2.375
Authors: Elena M Stoffel; Rowena C Mercado; Wendy Kohlmann; Beth Ford; Shilpa Grover; Peggy Conrad; Amie Blanco; Kristen M Shannon; Mark Powell; Daniel C Chung; Jonathan Terdiman; Stephen B Gruber; Sapna Syngal Journal: Am J Gastroenterol Date: 2010-03-30 Impact factor: 10.864
Authors: Brindusa Truta; Yunn-Yi Chen; Amie M Blanco; Guoren Deng; Peggy G Conrad; Yong Ho Kim; Eun Taek Park; Sanjay Kakar; Young S Kim; Fernando Velayos; Marvin H Sleisenger; Jonathan P Terdiman Journal: Fam Cancer Date: 2008-02-19 Impact factor: 2.375
Authors: Leticia Moreira; Francesc Balaguer; Noralane Lindor; Albert de la Chapelle; Heather Hampel; Lauri A Aaltonen; John L Hopper; Loic Le Marchand; Steven Gallinger; Polly A Newcomb; Robert Haile; Stephen N Thibodeau; Shanaka Gunawardena; Mark A Jenkins; Daniel D Buchanan; John D Potter; John A Baron; Dennis J Ahnen; Victor Moreno; Montserrat Andreu; Maurizio Ponz de Leon; Anil K Rustgi; Antoni Castells Journal: JAMA Date: 2012-10-17 Impact factor: 56.272