I am interested in understanding if it is possible to do the following:
Given a dataset of sequenced genomes (mutations, CNAs, etc.) with associated epigenetics data (over/under-expression, hyper/hypo-methyletion), select the epigenetic changes (e.g., met/HGF overxpression) that are likely persistent during tumor origination and development.
Crossing epigenetic data with mutations/CNAs is the easy part of the solution. I would be mostly interesting in selecting such epigenetic modifications for non-mutated genes.
Hi Giulio
if you provide me more details, I could help you. Are you looking for epigenetic mechanisms for cancer progression prevention? according to "autonomous epigenetic gene silencing mechanisms"?
Take a look at this recent work about pharmacological approach for epigenetic modulation, it might be inspiring!
Let me know.
Ciao Giulio,
in my opinion you can't: subsequent genetic mutations altering epigenetic modulators such as EZH2, TET2, KMT2A will likely change the original epigenetic pattern. Of course, if the first epigenetically regulated gene is a driver one, there will be a selective pressure mitigating this phenomenon (the subclone with the second epigenetic modification will be likely selected out due to poor fitting) however you cannot be 100% sure.
Hi Davide and Rocco,
Thank you very much for the pointers (that I will be go through asap), and the general comment. I agree that it is tricky, and that subsequent genetic mutations will likely alter epigenetics...well, this detection can not be done in fully automatic form...
G
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