Comparison of Prediction Models for Lynch Syndrome Among Individuals With Colorectal Cancer.

BACKGROUND Recent guidelines recommend the Lynch Syndrome prediction models MMRPredict, MMRPro, and PREMM1,2,6 for the identification of MMR gene mutation carriers. We compared the predictive performance and clinical usefulness of these prediction models to identify mutation carriers. METHODS Pedigree data from CRC patients in 11 North American, European, and Australian cohorts (6 clinic- and 5 population-based sites) were used to calculate predicted probabilities of pathogenic MLH1, MSH2, or MSH6 gene mutations by each model and gene-specific predictions by MMRPro and PREMM1,2,6. We examined discrimination with area under the receiver operating characteristic curve (AUC), calibration with observed to expected (O/E) ratio, and clinical usefulness using decision curve analysis to select patients for further evaluation. All statistical tests were two-sided. RESULTS Mutations were detected in 539 of 2304 (23%) individuals from the clinic-based cohorts (237 MLH1, 251 MSH2, 51 MSH6) and 150 of 3451 (4.4%) individuals from the population-based cohorts (47 MLH1, 71 MSH2, 32 MSH6). Discrimination was similar for clinic- and population-based cohorts: AUCs of 0.76 vs 0.77 for MMRPredict, 0.82 vs 0.85 for MMRPro, and 0.85 vs 0.88 for PREMM1,2,6. For clinic- and population-based cohorts, O/E deviated from 1 for MMRPredict (0.38 and 0.31, respectively) and MMRPro (0.62 and 0.36) but were more satisfactory for PREMM1,2,6 (1.0 and 0.70). MMRPro or PREMM1,2,6 predictions were clinically useful at thresholds of 5% or greater and in particular at greater than 15%. CONCLUSIONS MMRPro and PREMM1,2,6 can well be used to select CRC patients from genetics clinics or population-based settings for tumor and/or germline testing at a 5% or higher risk. If no MMR deficiency is detected and risk exceeds 15%, we suggest considering additional genetic etiologies for the cause of cancer in the family.

[1]  C. Boland,et al.  A National Cancer Institute Workshop on Hereditary Nonpolyposis Colorectal Cancer Syndrome: meeting highlights and Bethesda guidelines. , 1997, Journal of the National Cancer Institute.

[2]  P. Loehrer Accuracy of Revised Bethesda Guidelines, Microsatellite Instability, and Immunohistochemistry for the Identification of Patients With Hereditary Nonpolyposis Colorectal Cancer , 2006 .

[3]  Monica R McClain,et al.  EGAPP supplementary evidence review: DNA testing strategies aimed at reducing morbidity and mortality from Lynch syndrome , 2009, Genetics in Medicine.

[4]  J. Stockman,et al.  Prediction of MLH1 and MSH2 Mutations in Lynch Syndrome , 2008 .

[5]  W. Foulkes,et al.  A comparison of models used to predict MLH1, MSH2 and MSH6 mutation carriers. , 2009, Annals of oncology : official journal of the European Society for Medical Oncology.

[6]  H. Hampel Point: justification for Lynch syndrome screening among all patients with newly diagnosed colorectal cancer. , 2010, Journal of the National Comprehensive Cancer Network : JNCCN.

[7]  J. Stockman,et al.  Prediction of Germline Mutations and Cancer Risk in the Lynch Syndrome , 2008 .

[8]  E. Steyerberg,et al.  Comparison of predictive models, clinical criteria and molecular tumour screening for the identification of patients with Lynch syndrome in a population-based cohort of colorectal cancer patients , 2008, Journal of Medical Genetics.

[9]  Yvonne Vergouwe,et al.  Towards better clinical prediction models: seven steps for development and an ABCD for validation. , 2014, European heart journal.

[10]  Patrick M M Bossuyt,et al.  Diagnostic test accuracy may vary with prevalence: implications for evidence-based diagnosis. , 2009, Journal of clinical epidemiology.

[11]  N. Obuchowski,et al.  Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures , 2010, Epidemiology.

[12]  John D Potter,et al.  Colon Cancer Family Registry: An International Resource for Studies of the Genetic Epidemiology of Colon Cancer , 2007, Cancer Epidemiology Biomarkers & Prevention.

[13]  E. Kuipers,et al.  Mutation prediction models in Lynch syndrome: evaluation in a clinical genetic setting , 2009, Journal of Medical Genetics.

[14]  J. Stockman Screening for the Lynch Syndrome (Hereditary Nonpolyposis Colorectal Cancer) , 2006 .

[15]  Ewout W Steyerberg,et al.  The PREMM(1,2,6) model predicts risk of MLH1, MSH2, and MSH6 germline mutations based on cancer history. , 2011, Gastroenterology.

[16]  T. Tuohy,et al.  Hereditary and familial colon cancer. , 2010, Gastroenterology.

[17]  A. Wagner,et al.  Guidelines on genetic evaluation and management of Lynch syndrome. , 2015, Gastrointestinal endoscopy.

[18]  J. Potter,et al.  Identification of Lynch syndrome among patients with colorectal cancer. , 2012, JAMA.

[19]  D. Horsman,et al.  Validation of predictive models for germline mutations in DNA mismatch repair genes in colorectal cancer , 2009, International journal of cancer.

[20]  J. Stockman Cancer Risks Associated With Germline Mutations in MLH1, MSH2, and MSH6 Genes in Lynch Syndrome , 2013 .

[21]  F. Radvanyi,et al.  Evaluation of predictive models in daily practice for the identification of patients with Lynch syndrome , 2012, International journal of cancer.

[22]  Randall W Burt,et al.  Guidelines on genetic evaluation and management of Lynch syndrome: a consensus statement by the US Multi-Society Task Force on colorectal cancer. , 2014, Gastroenterology.

[23]  Harry Campbell,et al.  Identification and survival of carriers of mutations in DNA mismatch-repair genes in colon cancer. , 2006, The New England journal of medicine.

[24]  Andrew J Vickers,et al.  Traditional statistical methods for evaluating prediction models are uninformative as to clinical value: towards a decision analytic framework. , 2010, Seminars in oncology.

[25]  S. Goodman,et al.  Beyond the Usual Prediction Accuracy Metrics: Reporting Results for Clinical Decision Making , 2012, Annals of Internal Medicine.

[26]  G. Petersen,et al.  Recommendations for the care of individuals with an inherited predisposition to Lynch syndrome: a systematic review. , 2006, JAMA.

[27]  M. Woods,et al.  Prediction of Lynch syndrome in consecutive patients with colorectal cancer. , 2009, Journal of the National Cancer Institute.

[28]  P. Austin,et al.  Comments on 'Graphical assessment of internal and external calibration of logistic regression models by using loess , 2014 .

[29]  E. Steyerberg,et al.  Validation and extension of the PREMM1,2 model in a population-based cohort of colorectal cancer patients. , 2008, Gastroenterology.

[30]  Bhramar Mukherjee,et al.  Calculation of risk of colorectal and endometrial cancer among patients with Lynch syndrome. , 2009, Gastroenterology.

[31]  E. Steyerberg,et al.  Comparison of the clinical prediction model PREMM1,2,6 and molecular testing for the systematic identification of Lynch syndrome in colorectal cancer , 2012, Gut.

[32]  E. Kuipers,et al.  Yield of routine molecular analyses in colorectal cancer patients ≤70 years to detect underlying Lynch syndrome , 2012, The Journal of pathology.

[33]  P. Bossuyt,et al.  Sources of Variation and Bias in Studies of Diagnostic Accuracy , 2004, Annals of Internal Medicine.

[34]  Aung Ko Win,et al.  Criteria and prediction models for mismatch repair gene mutations: a review , 2013, Journal of Medical Genetics.

[35]  J. Mecklin,et al.  The International Collaborative Group on Hereditary Non-Polyposis Colorectal Cancer (ICG-HNPCC) , 1991, Diseases of the colon and rectum.

[36]  Sudhir Srivastava,et al.  Revised Bethesda Guidelines for hereditary nonpolyposis colorectal cancer (Lynch syndrome) and microsatellite instability. , 2004, Journal of the National Cancer Institute.

[37]  E. Elkin,et al.  Decision Curve Analysis: A Novel Method for Evaluating Prediction Models , 2006, Medical decision making : an international journal of the Society for Medical Decision Making.

[38]  Eero Pukkala,et al.  Cancer risk in hereditary nonpolyposis colorectal cancer syndrome: later age of onset. , 2005, Gastroenterology.

[39]  O. Olopade,et al.  Performance of Lynch Syndrome Predictive Models in a Multi-Center US Referral Population , 2011, American Journal of Gastroenterology.