Question: pathogenicity predictors of cancer mutations
1
gravatar for Bogdan
4 months ago by
Bogdan470
Palo Alto, CA, USA
Bogdan470 wrote:

Dear all,

talking about the pathogenicity predictors on cancer mutations, what algorithms or meta-predictors would you recommend to use ? Among possible choices : CADD, MutationTaster, FATHMM, CHASM, Condel CanDrA , or any other predictors/meta-predictors.

thank you,

bogdan

ADD COMMENTlink modified 4 weeks ago by onemoreuser10 • written 4 months ago by Bogdan470
1

this just came out today: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1353-5

ADD REPLYlink written 4 months ago by Jeremy Leipzig17k

Great timing!

ADD REPLYlink written 4 months ago by Kevin Blighe16k
1

thank you gentlemen !

ADD REPLYlink written 4 months ago by Bogdan470

If you are additionally interested in creating PDB files from novel/mutated amino acid sequences, and then checking how protein conformation may have changed due to the mutation, then look at the Protein Model Portal. I have added this to my list below.

ADD REPLYlink modified 4 months ago • written 4 months ago by Kevin Blighe16k

Do you know if there is a paper that assesses the performance of this approach on somatic mutations? Analyzing mutational clustering in protein structures has shown to perform well, but I'm not aware of successful methods taking a pure biophysical/protein conformation approach for cancer.

ADD REPLYlink modified 4 months ago • written 4 months ago by Collin510

please take a look here : http://bioinformatics.burnham.org/pages/publication.html;

especially : https://www.ncbi.nlm.nih.gov/pubmed/28714987

or : https://academic.oup.com/nar/article/43/D1/D968/2438384

ADD REPLYlink written 4 months ago by Bogdan470
2

I tweaked the wording of my reply so it is less ambiguous. I was actually talking about the approach Kevin suggested by analyzing protein conformation changes when the actual amino acid is substituted in the protein structure. I actually know Eduard personally (the first author on the papers you linked), and I developed HotMAPS which looks at mutational clustering in protein structures (https://www.ncbi.nlm.nih.gov/pubmed/27197156 ).

ADD REPLYlink written 4 months ago by Collin510

Thank you for the link to HotMAPS. We have been doing some whole genome sequencing analysis and we hope to link at some moment the mutation to the changes in the protein conformation.

ADD REPLYlink written 4 months ago by Bogdan470

Hi Collin - great work. I will take a read.

ADD REPLYlink written 4 months ago by Kevin Blighe16k

Are you interested in somatic mutations or germline mutations? The answer depends on your intended use.

ADD REPLYlink written 4 months ago by Collin510

Thank you Collin for your question : we would primarily be interested in somatic mutations.

ADD REPLYlink written 4 months ago by Bogdan470

The top 4 I would recommend for missense mutations would be CHASM, CanDrA (version "plus", with "cancer-in-general"), FATHMM cancer, or ParsSNP. From examining prior benchmarks and my own benchmarks, these seem to perform better. Some methods which are designed for germline mutations are decent (eg., VEST3 and REVEL), but generally the cancer focused methods are better.

ADD REPLYlink written 4 months ago by Collin510

thank you Collin. For Cancer Somatic mutations, could we also use some pathogenicity predictors like CADD and MCAP ? (that initially have been designed for germline mutations) .

ADD REPLYlink written 4 months ago by Bogdan470

I've personally aggregated a set of 8 benchmarks for missense mutations comprising in vitro experiments, in vivo experiments, and literature curated databases (OncoKB). CADD and MCAP didn't perform as well.

ADD REPLYlink written 4 months ago by Collin510

Thank you. I will look into : CHASM, CanDrA, FATHMM cancer, or ParsSNP. Talking about REVEL -- does it do a good work on somatic mutations ?

ADD REPLYlink written 4 months ago by Bogdan470

It does the best that I've seen for methods not tailored to cancer/somatic mutations. I'd recommend to stick with the cancer specific predictors unless you need to assess some other type of alteration that is not missense.

ADD REPLYlink written 4 months ago by Collin510

Is it correct to use SurfR to analyze intronic variants (from an exome sequencing)?

ADD REPLYlink written 4 weeks ago by onemoreuser10

Yes, I believe you can use it for these

ADD REPLYlink written 4 weeks ago by Kevin Blighe16k
8
gravatar for Kevin Blighe
4 months ago by
Kevin Blighe16k
University College London Cancer Institute
Kevin Blighe16k wrote:

Take your pick...

This is not a complete listing, as there are many more.

Missense predictions

Splice predictions

Protein modelling (from amino acid sequence)

[uses various modelling algorithms and produces PDB files, which can be loaded into protein viewers like Jmol]

Non-coding (i.e. regulatory)

  • CADD (germline variants)
  • DANN (germline variants)
  • FATHMM-MKL (germline variants)
  • GWAVA (germline variants | somatic mutations)
  • Funseq2 (somatic mutations)
  • SurfR (rare variants | complex disease variants | all other variants)

-------------------------

For further reading:

ADD COMMENTlink modified 10 weeks ago • written 4 months ago by Kevin Blighe16k
1

thank you very much ;)

ADD REPLYlink written 4 months ago by Bogdan470

I have updated this with a new section on non-coding (i.e. regulatory) variants, based on recent work that I have been doing. These tools allow one to get predictions for any non-coding variant, in addition to coding variants using the other tools;

ADD REPLYlink written 10 weeks ago by Kevin Blighe16k
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.3.0
Traffic: 756 users visited in the last hour