Question: Hi,

I have a callset from cancer NGS data (tumor/normal design, ~100 samples) and I have CNVs called there. Additional complexity is - some samples are close to tri- and tetra-ploid state.

I study impact of genomic variants on therapy response.

In some papers I've met definition "ploidy corrected CNVs" - but without clear explanation how it was done. If I understood correctly, people take average ploidy across the genome and consider everything that is below 0.5 copies below this baseline as deletion and 0.5 above the ploidy baseline as duplication. So diploid gene X in tetraploid sample is considered as largely deleted.

Questions:

1) How to correctly perform ploidy correction?

2) Should we correct for ploidy for largely deleterious tumors? E.g. where average ploidy is 1.5 (half of genome was deleted). Common sense tells me that no, but I wander if it is correct.

3) Bonus question: do you know btw how to take clonality (expressed as cancer cell fraction) of variant into account? Duplication in 10% of cells seems to affect therapy response less than duplication in 90%. But I also doubt that effect of CCF is linear.

upd: there is a related question, https://www.biostars.org/p/227207/ , however it is not exactly about this. The answer in the question is "Otherwise, I agree with you, calling relative to ploidy doesn't make a lot of sense biologically." - but I see that what is important is relative gene dosage, not absolute, and if everything is tetraploid - we may expect some sort of gene product balancing there.

Upd1: I apologize for wrong usage of terminology and I am blocked from any responses until tomorrow since I have reached 5 posts limit on this website.

Answer: Since you link an old response of mine:

1). When you say you have CNV calls, I assume segmented log2 tumor/normal coverage ratios, obtained by tools like CNVkit, Control-Freec, GATK etc.. The measured log2-ratios are a function of copy number, purity and ploidy; copy number obviously increases coverage linearly, purity also increases coverage by eliminating coverage coming from normal cells that do not have the CNA, and ploidy shifts log-ratios (https://www.biostars.org/p/381620/#381677). So ploidy adjustment needs to jointly calculates these three factors. Tools like ABSOLUTE, ASCAT, FACETS, Sequenza, Batternberg, and many others do that for you.

2). Every ploidy different from 2 will result in a shift in log2-ratios and, again, tumor purity affects the scaling, so yes.

3).The allelic fraction of a variant is a function of purity, multiplicity (number of chromosomes harboring the mutation), total copy number, CCF, a small mapping bias (non-reference alleles often have a smaller chance of passing filters) and sampling noise. The tools mentioned above will provide you the purity and allele-specific copy numbers. Getting the multiplicity is essentially just trying all likely ones and then picking the best one [for example][1]. Tools like PyClone can provide more accurate CCFs by clustering CCFs, thus reducing sampling noise.

There is a ton of literature that should help you finding the best solution for your data.

[1]: https://stm.sciencemag.org/content/7/283/283ra54.abstract