Abstract

Gene panel sequencing has become a common diagnostic tool for detecting somatically acquired mutations in myeloid neoplasms. However, many panels have restricted content, provide insufficient sensitivity levels, or lack clinically validated workflows. We here describe the development and validation of the Genomic Medicine Sweden myeloid gene panel (GMS-MGP), a capture-based 191 gene panel including mandatory genes in contemporary guidelines as well as emerging candidates. The GMS-MGP displayed uniform coverage across all targets, including recognized difficult GC-rich areas. The validation of 117 previously described somatic variants showed a 100% concordance with a limit-of-detection of a 0.5% variant allele frequency (VAF), achieved by utilizing error correction and filtering against a panel-of-normals. A national interlaboratory comparison investigating 56 somatic variants demonstrated highly concordant results in both detection rate and reported VAFs. In addition, prospective analysis of 323 patients analyzed with the GMS-MGP as part of standard-of-care identified clinically significant genes as well as recurrent mutations in less well-studied genes. In conclusion, the GMS-MGP workflow supports sensitive detection of all clinically relevant genes, facilitates novel findings, and is, based on the capture-based design, easy to update once new guidelines become available. The GMS-MGP provides an important step toward nationally harmonized precision diagnostics of myeloid malignancies.

Original languageEnglish
Article numbere23257
JournalGenes Chromosomes and Cancer
Volume63
Issue number7
DOIs
Publication statusPublished - 2024 Jul

Bibliographical note

Publisher Copyright:
© 2024 The Author(s). Genes, Chromosomes and Cancer published by Wiley Periodicals LLC.

Subject classification (UKÄ)

  • Medical Genetics
  • Cancer and Oncology

Free keywords

  • capture-based gene panel
  • interlaboratory comparison
  • myeloid malignancies
  • paired tumor-normal analysis
  • precision diagnostics
  • somatic variant detection

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