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A validation of models for prediction of pathogenic variants in mismatch repair genes

Cathy Shyr1, Amanda L Blackford2, Theodore Huang1

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA.

Genetics in Medicine : Official Journal of the American College of Medical Genetics
|August 23, 2022
PubMed
Abstract

Insights

MMRpro+ and PREMM5 models effectively predict pathogenic variants in mismatch repair genes. These tools aid in identifying individuals who could benefit from enhanced cancer screening and prevention strategies.

Area of Science:

  • Genetics
  • Oncology
  • Bioinformatics

Background:

  • Predictive models like MMRpro are crucial for identifying individuals at risk of pathogenic variants in mismatch repair (MMR) genes.
  • Recent updates to MMRpro incorporated new colorectal cancer (CRC) penetrance estimates, necessitating re-evaluation of its performance.

Purpose of the Study:

  • To evaluate the predictive performance of MMRpro and other models in individuals with a family history of colorectal cancer.
  • To compare the accuracy, calibration, and discrimination of various predictive models for MMR gene variants.

Main Methods:

  • A validation study was conducted on 784 members of US clinic-based families.
  • Four models (Leiden, MMRpredict, PREMM5, and MMRpro) were assessed against germline testing results.
  • Model combination strategies were analyzed to potentially improve predictive performance.

Main Results:

  • MMRpro with additional tumor information (MMRpro+) and PREMM5 demonstrated superior discrimination and predictive accuracy compared to other models.
  • MMRpro+ exhibited the best calibration, with an observed to expected ratio of 0.98 (95% CI = 0.89-1.08).
  • Combined models showed improved performance over PREMM5 and were comparable to MMRpro+.

Conclusions:

  • MMRpro+ and PREMM5 are effective tools for predicting pathogenic variants in MMR genes.
  • These models can assist clinicians in identifying individuals who would benefit from targeted screening and prevention strategies for hereditary cancer syndromes.
Keywords:
Colorectal cancerLynch syndromeMismatch repairModel combinationModel validation

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