Suggestions for further computational analysis of a rare cancer disease exome

Question

[smk]

Hi all ,

One of our collaborators has sequenced the exome of a cancer patient with a rare cancer disease (JMML).I have analysed the exome sequences using the mercury pipeline already.

We further analysed high coverage data for selected cancer genes and found some single nucleotide variants as well. I was wondering what computational/informatics aspect can I incorporate in this work. I know the question is kind of absurd but I would appreciate if someone can provide me some pointers .

Like if they have been involved in such analysis and what sort of biological questions can be asked which can be explored from computational perspective using additional public data since its only a single sample (and also a reduced genelist high coverage sample).

Thanks

[russhh]

For the genes where variants are observed, is there evidence for somatic mutations / differential expression of the genes in tumour vs normal studies of more common cancers?

[smk]

Yes there are databases like cosmic. Which are a part of Mercury analysis pipeline. It checks for such SNPs in the data.

[DG]

Is the exome sequenced just of the tumour, the germline of the patient, or both? Same goes for the additional high coverage data. Is it possible to gather information on family members? What questions are you looking to ask? The last question is really the most important. There are things you can do, although with a single sample you will always be limited in how much confidence you can put in any of the answers you extract, and finding anything interesting and novel becomes VERY difficult. But if you are looking for say, germline hereditary predisposing factors, that is very different from looking for driver mutations in the tumour for instance

Answer 1

[Sean Davis]

The smaller the dataset, the more hypothesis-driven your questions need to be, at least in my experience. In this case, JMML is known to have a few commonly mutated genes (NF1, PTPN11, RAS pathway, etc.) for which you probably have high-depth data, allowing for some measurement of clonality at those loci, particularly if you have multiple samples.

You might also consider RNA-seq to measure the expression of variants and to capture potential expressed fusion genes.

Finally, don't forget to look at the germline, not just somatic mutations, particularly for JMML.