Question: Calling variants in blood tumor samples without matched normal samples
2
gravatar for umn_bist
3.2 years ago by
umn_bist320
umn_bist320 wrote:

I am attempting to call mutations of raw RNA-Seq of single prostate circulating tumor cells (paper) without matching normals.

From cursory research I found that tumor only without a matched normal calls 117x more variants than matched tumor/normal pairs (Appistry). I also found an interesting study that (successfully) discriminated somatic and germline mutations without matching normals but using virtual normal (i.e., normal samples from unrelated individuals) (paper).

My question is, are blood tumor samples treated any differently than tissue tumor samples in respect to calling variants? If there are no normal blood samples available, is filtering against the EVS dataset my best option? Thank you for your time and help.

unmatched-normal ctc tcga • 1.9k views
ADD COMMENTlink modified 3.2 years ago by Chris Miller20k • written 3.2 years ago by umn_bist320
2

Also, to be clear, you may want to avoid using the "blood-tumor" terminology here. There are heme malignancies derived from the blood cells - AML, CML, MDS, etc.  Your samples may be circulating in the blood, but they're still derived from solid tumors

ADD REPLYlink written 3.2 years ago by Chris Miller20k
3
gravatar for Chris Miller
3.2 years ago by
Chris Miller20k
Washington University in St. Louis, MO
Chris Miller20k wrote:

Some of this has been covered in previous questions on here (dig around a little), but the gist is this

- you will never get rid of all germline mutations without a matched normal. 

- make sure your variant caller is appropriate and doesn't bias you towards mutations at 50%/100% VAF, as some germline callers can

- a panel of normals can be very helpful in filtering out both common population variants and sequencing artifacts. Every individual contains private rare mutations that will ultimately be indistinguishable from a somatic hit, though.

- yes, use EVS (or maybe even the non-TCGA-derived part of ExAC) to remove common variants.

- RNAseq complicates things even further, because you're dealing with an enhanced error rate.

 

ADD COMMENTlink written 3.2 years ago by Chris Miller20k
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