Prediction Models for Lynch Syndrome

Chapter

Abstract

Numerous strategies are currently available for the identification of individuals and families with Lynch syndrome that have evolved considerably over time. Prediction models for Lynch syndrome can quantify an individual’s risk of carrying a germline mismatch repair gene mutation and help clinicians decide who should be referred for further genetic risk assessment and/or genetic testing. In this chapter, we review the main prediction models developed for the identification of individuals at risk for Lynch syndrome with a focus on their specific features, performance measures as assessed by several validation studies, comparison with other clinical and molecular strategies for the diagnosis of Lynch syndrome, and their implementation and potential uses in clinical practice. We also introduce a new prediction model that provides prospective cancer risk estimates for individuals with MMR gene mutations based on comprehensive literature reviews. Lastly, we address the future considerations related to the use of clinical prediction models, including the impact of next-generation DNA sequencing technologies and the increased uptake of simultaneous testing of multiple genes (multigene panel testing) associated with inherited cancer susceptibility.

Keywords

Lynch syndrome prediction models MMRPredict MMRPro PREMM5 PREMM Ask2me Discrimination Calibration Decision curve analysis Clinical usefulness Molecular tumor testing Implementation of prediction models 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Fay Kastrinos
    • 1
    • 2
  • Gregory Idos
    • 3
    • 4
  • Giovanni Parmigiani
    • 5
    • 6
  1. 1.Herbert Irving Comprehensive Cancer Center, Columbia University Medical CenterNew YorkUSA
  2. 2.Division of Digestive and Liver DiseasesColumbia University Medical CenterNew YorkUSA
  3. 3.Department of Medicine, Division of Gastrointestinal and Liver DiseaseUniversity of Southern CaliforniaLos AngelesUSA
  4. 4.Norris Comprehensive Cancer Center, University of Southern CaliforniaLos AngelesUSA
  5. 5.Department of Biostatistics and Computational BiologyDana-Farber Cancer InstituteBostonUSA
  6. 6.Department of BiostatisticsHarvard Medical SchoolBostonUSA

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