Closed jharenza closed 3 years ago
I think this is a good idea, but I would keep it as a separate analysis from the general consensus. In other words, I would not "scavenge" back mutations into the consensus, but rather include an entirely separate analysis that evaluates known mutations. This would keep the standards clear and separate de novo analysis from analysis with outside influence.
I think this is a good idea, but I would keep it as a separate analysis from the general consensus. In other words, I would not "scavenge" back mutations into the consensus, but rather include an entirely separate analysis that evaluates known mutations. This would keep the standards clear and separate de novo analysis from analysis with outside influence.
Ok - yeah I went back and forth on that. Thanks!
@kgaonkar6, after our internal discussion, I think we need to first determine our hotspot list: 1) Look into using both versions of the hotspot table linked above. There are 470 hotspots in V1, 1110 in V2, and 221 of them from V1 are not in V2. I initially was thinking we would want a union, but I don't really recognize the list of V1 only, so maybe they were removed because they were FP. So, we may go with V2. I was thinking we could first assess how many of those V1 only hotspots are being missed in our dataset and if it makes sense to keep them or not. 2) Add the TERT promoter mutations above. 3) Download the latest version of COSMIC mutations and determine whether we are missing any of these from V2 - these could also possibly be added.
If that makes sense, I think that can be the first PR for this series. Thanks@
We are having a call on Thursday Jan 28 with David Wheeler (St Jude, formerly BCM) who has done this sort of thing while leading the BCM Genomics Lab. We might also want to add pediatric0-specific genes such as those from Ma, 2018 and Grobner, 2018
Don't think there are annotations in maf format to filter using the information in the paper describing the TERT promoter variant, should I use other filtering the exact genomic site to capture?
I believe chr5 | 1295113 | 1295113 which is also annotated as existing_variant rs1242535815,COSM1716563,COSM1716558 which is 66bp away from TSS is what we are looking for corresponding to C228T.
and chr5 | 1295135 | 1295135 | is 88 bp away from TSS is the COSM1716559 variant which corresponds to C250T promoter variant.
From my google searches :D https://www.slideshare.net/ThermoFisher/taqman-dpcr-liquid-biopsy-assays-targeting-the-tert-promoter-region https://assets.thermofisher.com/TFS-Assets/LSG/posters/taqman-dpcr-tert-promoter-poster.pdf
I checked strelka for upstream variants as a check and we have both these sites (along with others) : | Chromosome | Start_Position | End_Position | Reference_Allele | Tumor_Seq_Allele2 | Hugo_Symbol | Variant_Classification | IMPACT | Tumor_Sample_Barcode | Protein_position | Existing_variation | DISTANCE |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | chr5 | 1295113 | 1295113 | G | A | TERT | 5'Flank | MODIFIER | BS_KAZENYZE | NA | rs1242535815,COSM1716563,COSM1716558 | 66 |
2 | chr5 | 1299855 | 1299855 | A | C | TERT | 5'Flank | MODIFIER | BS_1RF75MK2 | NA | NA | 4808 |
3 | chr5 | 1299236 | 1299237 | - | C | TERT | 5'Flank | MODIFIER | BS_S0T3CQ97 | NA | NA | 4189 |
4 | chr5 | 1295088 | 1295088 | A | C | TERT | 5'Flank | MODIFIER | BS_8Q8CAY84 | NA | NA | 41 |
5 | chr5 | 1299748 | 1299748 | T | C | TERT | 5'Flank | MODIFIER | BS_4QFSH7C4 | NA | NA | 4701 |
6 | chr5 | 1295677 | 1295677 | T | G | TERT | 5'Flank | MODIFIER | BS_4ZKN0WGS | NA | NA | 630 |
7 | chr5 | 1295442 | 1295442 | C | T | TERT | 5'Flank | MODIFIER | BS_02YBZSBY | NA | NA | 395 |
8 | chr5 | 1295135 | 1295135 | G | A | TERT | 5'Flank | MODIFIER | BS_F8K4VQMF | NA | COSM1716559 | 88 |
9 | chr5 | 1295997 | 1295997 | A | C | TERT | 5'Flank | MODIFIER | BS_WH8KWW5J | NA | NA | 950 |
10 | chr5 | 1298925 | 1298925 | A | C | TERT | 5'Flank | MODIFIER | BS_VW4XN9Y7 | NA | NA | 3878 |
11 | chr5 | 1295113 | 1295113 | G | A | TERT | 5'Flank | MODIFIER | BS_1S2BHJ8K | NA | rs1242535815,COSM1716563,COSM1716558 | 66 |
12 | chr5 | 1295146 | 1295146 | A | C | TERT | 5'Flank | MODIFIER | BS_BM95DGCQ | NA | NA | 99 |
13 | chr5 | 1295407 | 1295407 | A | C | TERT | 5'Flank | MODIFIER | BS_9ZFXXJPK | NA | NA | 360 |
14 | chr5 | 1295113 | 1295113 | G | A | TERT | 5'Flank | MODIFIER | BS_BFDEZK1C | NA | rs1242535815,COSM1716563,COSM1716558 | 66 |
15 | chr5 | 1297846 | 1297846 | G | T | TERT | 5'Flank | MODIFIER | BS_VF099E8S | NA | NA | 2799 |
16 | chr5 | 1296053 | 1296053 | C | A | TERT | 5'Flank | MODIFIER | BS_0FQKT8EY | NA | NA | 1006 |
17 | chr5 | 1295113 | 1295113 | G | A | TERT | 5'Flank | MODIFIER | BS_JSNJZERZ | NA | rs1242535815,COSM1716563,COSM1716558 | 66 |
18 | chr5 | 1295113 | 1295113 | G | A | TERT | 5'Flank | MODIFIER | BS_T7WMJ08W | NA | rs1242535815,COSM1716563,COSM1716558 | 66 |
19 | chr5 | 1295136 | 1295136 | A | C | TERT | 5'Flank | MODIFIER | BS_QX754ADQ | NA | NA | 89 |
20 | chr5 | 1295113 | 1295113 | G | A | TERT | 5'Flank | MODIFIER | BS_MJJZJMTK | NA | rs1242535815,COSM1716563,COSM1716558 | 66 |
21 | chr5 | 1295113 | 1295113 | G | A | TERT | 5'Flank | MODIFIER | BS_SK4H5MJQ | NA | rs1242535815,COSM1716563,COSM1716558 | 66 |
22 | chr5 | 1295112 | 1295112 | A | C | TERT | 5'Flank | MODIFIER | BS_K3PPH522 | NA | NA | 65 |
23 | chr5 | 1295113 | 1295113 | G | A | TERT | 5'Flank | MODIFIER | BS_KAD49R68 | NA | rs1242535815,COSM1716563,COSM1716558 | 66 |
24 | chr5 | 1298190 | 1298190 | G | A | TERT | 5'Flank | MODIFIER | BS_AF5D41PD | NA | rs929384767 | 3143 |
We still want to filter by IMPACT == 'HIGH|MODERATE|MODIFIER' to remove any LOW impact mutations ( like silent mutations) in the given amino acid position in hotspot database, right?
We still want to filter by IMPACT == 'HIGH|MODERATE|MODIFIER' to remove any LOW impact mutations ( like silent mutations) in the given amino acid position in hotspot database, right?
Are you saying there are low impact mutations on the MSK list? I would assume they would not be low.
I believe chr5 | 1295113 | 1295113 which is also annotated as existing_variant rs1242535815,COSM1716563,COSM1716558 which is 66bp away from TSS is what we are looking for corresponding to C228T.
and chr5 | 1295135 | 1295135 | is 88 bp away from TSS is the COSM1716559 variant which corresponds to C250T promoter variant.
This looks right to me, and nucleotides are reversed because TERT is on the reverse strand. So, I think we should use the genomic coordinates here + nucleotides.
We still want to filter by IMPACT == 'HIGH|MODERATE|MODIFIER' to remove any LOW impact mutations ( like silent mutations) in the given amino acid position in hotspot database, right?
Are you saying there are low impact mutations on the MSK list? I would assume they would not be low.
There were a few instances that the hotspot amino acid site had silent mutation
for example we have 644 in SDHA is a hotspot in MSKCC but if we have p.V644=
in our dataset we should remove it right? Only if it is a high if the hotspot is actually high impact mutations like p.V644M we will keep them.
Closed with #819
What analysis are you proposing and why?
Create a new MAF which contains consensus SNV calls from consensus snv calling and cancer hotspot calls missed by consensus, as noted below.
We previously noticed that by taking a 3/3 approach for consensus calls, we are inevitably missing some cancer hotspot mutations. We got around that for one specific cancer (DMGs) because we have clinical reports containing histone variant calls that we can add into molecular subtyping pathology module (#735 and #751). However, we are likely still missing some cancer hotspot mutations and I propose that we add a final step in which we scavenge back cancer hotspot mutations using a well-curated and downloadable list of these.
What changes need to be made? Please provide enough detail for another participant to make the update.
The next step would be to assess if any of these hotspot mutations are being missed using a 3/3 method and then determining a set of rules for adding these mutations back to the consensus SNV file. For example:
Perhaps the new file can be called
pbta-consensus-snvs-plus-hotspot.maf.gz
What input data should be used? Which data were used in the version being updated?
Cancer hotspots table, downloadable here: https://www.cancerhotspots.org/#/download plus TERT promoter mutations, noted from this paper.
When do you expect the analysis will be completed?
not sure
Who will complete the updated analysis?
s>@migbro</s @kgaonkar6