Comparison of Prediction Models for Lynch Syndrome Among Individuals With Colorectal Cancer
Fay Kastrinos1, Rohit P Ojha2, Celine Leenen2
1Herbert Irving C omprehensive Cancer Center and Division of Digestive and Liver Diseases, Columbia University, Medical Center, New York, NY (FK); Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN (RPO); Department of Gastroenterology and Hepatology, Erasmus MC, Rotterdam, the Netherlands (CL); Statistical and Data Analysis Center, Harvard School Public Health, Boston, MA (CA); Population Sciences Division, Dana-Farber Cancer Institute, Boston, MA (RCM); Department of Oncology (JB) and Genetics Department (IV), University Hospital Vall d'Hebrón, Barcelona, Spain; Department of Gastroenterology, Hospital Clinic of Barcelona, IDIBAPS, CIBERehd, Barcelona, Spain (FB); Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John's, NL, Canada (RG); Department of Health Sciences Research, Mayo Clinic, Scottsdale, AZ (NML); Division of Molecular Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN (SNT); Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA (PN); Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia (AKW, MJ, DDB); Unit of Hereditary Digestive Tract Tumors, Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy (LB, PS); Clinical Cancer Genetics Program, Ohio State University Comprehensive Cancer Center, Columbus, OH (HH); Division of Gastroenterology, Brigham and Women's Hospital, Boston, MA (SS); Harvard Medical School, Boston, MA (SS); Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands (EWS); Oncogenomics Group, Genetic Epidemiology Laboratory, The University of Melbourne, Parkville, Victoria, Australia (DDB). fk18@columbia.edu.
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.
