Classification of PTEN germline non-truncating variants: a new approach to interpretation

Henri Margot1, Natalie Jones2, Thibaut Matis2

  • 1Medical Genetics Departement, CHU de Bordeaux, Bordeaux, Nouvelle-Aquitaine, France.

PubMed

Insights

Classifying PTEN variants is challenging. A new method considers protein stability, clinical data, and allelic frequency, improving PTEN hamartoma tumour syndrome (PHTS) variant classification.

Area of Science:

  • Genetics and Genomics
  • Molecular Biology
  • Clinical Genetics

Background:

  • PTEN hamartoma tumour syndrome (PHTS) includes Cowden syndrome, caused by PTEN pathogenic variants.
  • Missense PTEN variants are challenging to classify, representing 30% of PHTS cases.
  • Existing guidelines from the Clinical Genome Resource PTEN Variant Curation Expert Panel aim to address classification difficulties.

Purpose of the Study:

  • To develop and evaluate a novel classification method for non-truncating PTEN variants.
  • To improve the accuracy and discriminative power in classifying PTEN variants associated with PHTS.
  • To propose a revision of current PTEN variant classification criteria.

Main Methods:

  • Identified 76 unique non-truncating PTEN variants in 166 patients between 2010-2020.
  • Applied current classification guidelines and developed a new method based on functional exploration, phenotypic features, familial segregation, in silico modelling, and allelic frequency.
  • Classified 17 previously unreported PTEN variants.

Main Results:

  • The new classification method demonstrated improved discriminative ability.
  • 25 PTEN variants were reclassified using the novel method.
  • 8 variants of unknown significance were reclassified.

Conclusions:

  • A revised PTEN variant classification is proposed, incorporating protein stability, clinical/segregation data, and allelic frequency.
  • The new criteria enhance the classification of non-truncating PTEN variants beyond solely assessing phosphatase activity.
  • Prospective validation of this novel classification method is recommended.
Abstract

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