What the VAF? A guide to the interpretation of variant allele fraction, percent mosaicism, and copy number in cancer.

The evolution of techniques used to identify structural variants (SVs) and copy number variants (CNVs) in genomes have seen significant development in the last decade. With the growing use of more technologies including chromosomal microarray, genome sequencing and genome mapping in clinical cytogenetics laboratories, reporting the frequency of SVs and CNVs has increased the complexity of genomic results. In conventional testing (e.g. karyotype or FISH) individual cells are analyzed and abnormalities are reported at the single cell level directly as a proportion of the analyzed cells. Whereas for bulk genome assays structural and sequence changes are often reported as variant allele frequencies and fractional copy number states. The International System of Cytogenomic Nomenclature (ISCN) recommends converting these values into a "proportion of the sample", which requires different calculations and underlying assumptions based on the data type. This review illustrates how the different methods of interpreting and reporting data are performed and identifies challenges in the conversion of these values to a proportion of the sample. We stress the need for careful interpretation of data with consideration for factors that may alter how proportions are reported including overlapping SVs and CNVs or regions with acquired homozygosity. We also demonstrate, using validation data of SVs and CNVs tested by multiple techniques how results are largely consistent across methodologies, but can show dramatic differences in rare circumstances. This review focuses on illustrating many of the challenges with aligning reporting using different techniques and their underlying assumptions. As hematologic disease classifications start to incorporate numeric limits (e.g. VAF defining thresholds), it is important for laboratory geneticists, pathologists and clinicians to appreciate the differences in methodologies, potential pitfalls and the nuances when comparing bulk genome analyses to the more conventional single cell techniques.