Recently, I’ve been working to help prepare a manuscript on Vestibular Schwannomas (VS), a type of benign cancer of the myelin-forming cells along the nerves of the ear. I’ve been thinking a lot about strategies for filtering exome variant calls to feed into mutational signature analysis.
Mutational signatures are important because they describe the types of mutational processes operating on the genome of the tumor cells. Many of these processes are known (see the COSMIC database), however, some are entirely novel. The variants that are used for calculating such signatures are somatic in nature, and have to be carefully curated from the raw variant calls that you get from a pipeline like GATK.
Looking at the existing literature, I find that there is no common or “best practices” methodology for filtering variants in whole exome data. Some groups are very stringent
, others less so
. The first step in most cases is to just subtract normal variant calls from tumor in most cases. However, there are further filtering steps that should be undertaken.
If I had to describe some overall commonalities in the literature approaches to somatic variant filters, it could include:
1) removing variants that are present in dbSNP or 1000genomes or other non-cancer exome data
2) taking only variants in coding regions (exons) or splicing sites
3) variants must appear in more than X reads in the tumor, and fewer than X reads in the normal (generally ~5 and ~2, respectively)
4) subtraction of “normals” from “tumor” (either pooled normals, or paired)
5) variant position must be covered by a minimum depth (usually > 10X)
6) throwing away reads from low mapping quality (MQ) regions
Some papers only consider non-synonymous variants, but for mutational signatures, to me it makes sense to take all validated variants (especially in exome data because you are starting with fewer raw variant calls than whole genome data).
As far as actual numbers of variants that are “fed” into the mutational signature analysis, most papers do not report this directly (surprisingly). If you dig around in the SI sections, sometimes you can find it indirectly.
It looks like, generally, the number of variants is somewhere around 10,000 for papers dealing with specific tumor types (not pan-cancer analyses of public databases). Several papers end up with ~1000 variants per tumor (ranging from 1,000 up to 7,000). So with 10 tumors sequenced, that would be 10,000 filtered, high-confidence SNVs.
If you’re working on exome mutational signature analysis and you have your own filtering criteria, I’d love for you to share it in the comments.