Login / Signup

An evaluation of the challenges to developing tumor BRCA1 and BRCA2 testing methodologies for clinical practice.

Gillian EllisonMiika AhdesmäkiSally LukePaul M WaringAndrew WallaceRonnie WrightBenno RöthlisbergerKatja LudinSabine Merkelbach-BruseCarina HeydtMarjolijn J L LigtenbergArjen R MensenkampDavid Gonzalez de CastroThomas JonesAna VivancosOlga KondrashovaPatrick PauwelsChristine WeynEric HahnenJan HaukeRichie SoongZhongwu LaiBrian DoughertyT Hedley CarrJustin JohnsonJohn MillsJ Carl Barrett
Published in: Human mutation (2017)
Ovarian cancer patients with germline or somatic pathogenic variants benefit from treatment with poly ADP ribose polymerase (PARP) inhibitors. Tumor BRCA1/2 testing is more challenging than germline testing as the majority of samples are formalin-fixed paraffin embedded (FFPE), the tumor genome is complex, and the allelic fraction of somatic variants can be low. We collaborated with 10 laboratories testing BRCA1/2 in tumors to compare different approaches to identify clinically important variants within FFPE tumor DNA samples. This was not a proficiency study but an inter-laboratory comparison to identify common issues. Each laboratory received the same tumor DNA samples ranging in genotype, quantity, quality, and variant allele frequency (VAF). Each laboratory performed their preferred next-generation sequencing method to report on the variants. No false positive results were reported in this small study and the majority of methods detected the low VAF variants. A number of variants were not detected due to the bioinformatics analysis, variant classification, or insufficient DNA. The use of hybridization capture or short amplicon methods are recommended based on a bioinformatic assessment of the data. The study highlights the importance of establishing standards and standardization for tBRCA testing particularly when the test results dictate clinical decisions regarding life extending therapies.
Keyphrases
  • copy number
  • circulating tumor
  • clinical practice
  • cell free
  • genome wide
  • gene expression
  • oxidative stress
  • nucleic acid
  • bioinformatics analysis
  • big data