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Mosaic Li Fraumeni Syndrome Not Identified in Germinal Tissue.

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Mosaic Li Fraumeni syndrome (LFS) can be challenging to diagnose due to low variant allele frequencies. This case highlights the importance of multi-tissue testing for accurate TP53 variant interpretation in LFS.

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Area of Science:

  • Genetics
  • Oncology
  • Molecular Biology

Background:

  • Li Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome linked to TP53 gene alterations.
  • Next-generation sequencing (NGS) enables detection of TP53 variants at low variant allele frequencies (VAFs).
  • TP53 variants with VAF < 50% can indicate mosaic LFS, aberrant clonal expansion (ACE), or circulating tumor DNA, necessitating careful differentiation for patient management.

Purpose of the Study:

  • To investigate a complex case of potential mosaic Li Fraumeni syndrome.
  • To differentiate between mosaic LFS, aberrant clonal expansion, and other conditions presenting with low VAF TP53 variants.
  • To propose a diagnostic approach for interpreting mosaic TP53 variants in the context of cancer and reproductive risks.

Main Methods:

  • Genetic testing using a custom gene panel with high average read depth (350×).
  • DNA sequencing from four distinct tissue types: blood, saliva, cultured skin fibroblasts, and colon.
  • Analysis of TP53 variant allele frequencies across different tissues to assess for mosaicism.

Main Results:

  • An adult female with a history of adrenocortical neoplasm and osteosarcoma was found to have a pathogenic TP53 variant (c.733G>A, p.(Gly245Ser)) in peripheral blood.
  • Preimplantation genetic testing for monogenic disorders (PGT-M) did not detect the TP53 variant in any of nine embryos.
  • Variable VAFs of the TP53 variant were observed across four tissues (saliva: 44%, blood: 31%, fibroblasts: 18%, colon: 9%), supporting a post-zygotic event consistent with mosaic LFS.

Conclusions:

  • Interpreting mosaic TP53 variants presents diagnostic complexities, particularly in LFS.
  • Multi-tissue analysis is crucial for accurately diagnosing and characterizing mosaicism.
  • A proposed testing algorithm can aid in delineating mosaic phenomena and informing cancer and reproductive risk assessment.