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I'm working on cancer exome and transcriptome sequencing project. Sequencing data on tumor samples are derived from a mixed population of cells. Is there any good way to handle this issue?
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MuTect (and many others) published to deal with this
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For SNV calling, I would also highly recommend VarScan2 (http://www.ncbi.nlm.nih.gov/pubmed/22300766). Others include SomaticSniper (http://www.ncbi.nlm.nih.gov/pubmed/22155872) and Strelka (http://www.ncbi.nlm.nih.gov/pubmed/22581179). In all cases, a matched reference tissue is required.
It is also important to be aware of/accomodate varying sources of contamination. For example, contamination levels (normal sample in the tumour sample and vice-versa) vary significantly between liquid and solid tumours, and the sampling schema. Unfortunately though there is no magic bullet, yet.Comment
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Magic bullet might be single cell sequencing. The results of edge tumor vs. core samples would be interesting as would, of course, differences between adjacent malignant cells. After talking with leukemia researchers, I'm under the impression that they do , somehow, sort the cells into "evil" vs. (presumably) "not so evil" and harvest the evil ones.Comment
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Yes, with leukemias that might actually be feasible. For solid tumors, really challenging -- the "good" and "evil" are so intertwined it is hard to pick them apart. Probably will need single-cell sequencing that is cheap enough that you can just sequence many & then decide downstream which are "good" and "evil".
If you look closely at the original Mardis lab publication on leukemia WGS (if not first, one of the early ones), they actually encountered the converse problem -- because the patient had a very severe blast crisis, the normal samples were significantly contaminated with leukemic cells!Comment
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...06-18-2026, 07:11 AM -
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
...06-02-2026, 10:05 AM
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