Discrimination Between Germline And Somatic Mutations In Tumor Without The Availability Of The Normal Paired Sample

Question

[Fred Fleche]

Hi,

Let's say that I get whole-exome-sequencing data file that has been created without the availability of a normal sample related to the tumor sample sequenced. Is there a way to make the disctinction bewteen germline and somatic variants. I was thinking of comparing the variants against the COSMIC (Catalog Of Somatic Mutations In Cancer) database.

So I was wondering if some people have some suggestions of a nice accurate workflow with other sources than COSMIC.

Thanks,

Fred

Answer 1

[Christof Winter]

Here is what I do:

1. Flag known germline variants by looking in dbSNP. I use a subset of dbSNP (> 1% minor allele frequency, mapping only once to reference assembly, and not flagged as "clinically associated"). You can get such a file for ANNOVAR (database name is snp137NonFlagged for the current dbSNP build), see http://www.openbioinformatics.org/annovar/annovar_download.html

2. Flag known somatic variants by looking in COSMIC. This usually finds well-described hotspot mutations (such as activating KRAS mutations), but overall will not find most of your true somatic variants (my guess). I usually take the whole of COSMIC, irrespective of tumor type.

3. Add other cancer sequencing studies (e.g. TCGA), as many of these are not yet in COSMIC currently. For TCGA, I use the MAF files available at https://tcga-data.nci.nih.gov/tcgafiles/ftp_auth/distro_ftpusers/anonymous/tumor/. Level 3 MAF files contain experimentally validated somatic mutations only. Level 2 MAF files contain also the unvalidated ones (and can contain germline variants).

4. Look at the variant allele frequency. If it's 100%, i.e. all reads show the variant, it's very likely germline (unless your tumor sample is 100% tumor cells and all tumor cells have the mutation). If it's below 10%, it can well be an artifact, see e.g. http://www.ncbi.nlm.nih.gov/pubmed/23303777

5. Check how all of the mismatches in your data (non-reference bases in the alignment) are distributed along the reads from 5' to 3'. If you have a much higher mismatch rate at the first/last bases of your reads, you might want to exclude these read positions.

6. Filter your variant list further, as it will likely contain a considerable amount of false positives. Table 1 of the VarScan paper http://www.ncbi.nlm.nih.gov/pubmed/22300766 is a good start (read pos, strand, variant read number and frequency, distance to 3', homopolymer, map quality and read length difference).

[fo3c]

A small addition to point number 1: You could also look for known variants in the 1000 genomes project data.

[Fred Fleche]

Thanks a lot for sharing your experience. Really appreciate. Fred

Answer 2

[Stefano Berri]

Looking at already known cancer mutation is fine, but you can tell only about what it is already known.

Personally, I would look at frequency of mutations. If it is germline it is either 100% or 50% (clearly, not exactly 50%, but around there).

If it is a somatic mutation and your samples are from clinical samples (not cell lines), then infiltration with normal cells is inevitable and your mutations will be at 30-40%

If coverage is enough, you might confidently distinguish between the two.

To better understan what I mean, I suggest you this great paper