Planned Analysis: Tumor Mutation Burden

[cgreene]

We have planned to include an analysis of tumor mutation burden as well as a comparison to the tumor mutation burden of adult tumors in TCGA. This issue is for fleshing out the analysis. The manuscript issue for the methods description is: AlexsLemonade/OpenPBTA-manuscript#7

[cansavvy]

This may be a simplistic question, but in our case TMB = Median(# coding indels + # coding base substitutions) / Megabase? Second question is, I am presuming we need to do some kind of analysis that looks at differences between synonymous and nonsynonymous mutations? This perhaps may needs to be related to age of diagnosis?

[cgreene]

@PichaiRaman : I think this came from you - how were you planning to do this analysis?

[jharenza]

@cansav09 I think we are looking for something like this - Figure 1 from the Grobner, 2018 landscape, but for brain tumors:

For reference, here is the second pan-pediatric cancer landscape from 2018:
Ma, 2018

[cansavvy]

Now that I've gotten somewhat familiar with the SNV data, I can do this analysis as well. It looks like Grobner says they used Number of SNV of coding mutations per Mb? Just to try to clarify, does this mean we include synonymous and non-synonymous mutations alike in this number? They don't mention distinguishing between this in their Methods, but I've seen other papers that look into it.

Here's my tentative plan, let me know what you think:

Step 1) Start with Mutect2 results only. (Once I make the script for this, and then later after #30 is determined, I can easily go back and change it).
Step 2) Calculate total number of mutations per Mb
Step 3) Graph this in a jitterplot like above with the disease type as the x axis.
Step 4) Split this out into a jitterplot that examines nonsynonymous vs synonymous.

[jharenza]

Hi @cansav09! I think @afarrel may have some input here, as he has worked with a lot of TMB data. People do this many different ways. I had previously used only non-synonymous mutations to be reflective of somatic burden. Looks like there is a harmonization effort here, but not sure what they have decided. One other note is that you use coding only, then you should calculate the coding region in Mb from WGS/WES used for this. Currently, I think much of the data you have was derived from WGS, but some incoming data will be WES and we can provide the WES capture region for this.

I think #s1-4 look good!

[cansavvy]

Looks like there is a harmonization effort here, but not sure what they have decided.

This is interesting. Glad someone is working on making things uniform, but yeah, hard to tell what the conclusion is from this.

[cansavvy]

I think the first part of this analysis will be determining if there are differences between different methods of calculations of TMB.

My thought is I should calculate TMBs and compare for the following 2 sets of variables:

1. Coding mutations only vs all mutations.
2. SNVs + CNVs vs only using SNVs - this however requires that we have an integration of CNV data - Planned Analysis: Integrated CNV and SV analyses and chromothripsis #27 (Let me know if there is something I can do to help with this).

So this would result in four different methods of calculating TMBs for each sample. All being Mutations per Mb.

	All mutations	Coding Variants Only
SNVs + CNVs
SNVs only

After calculating these TMBs I will test the differences/similarities of the calculations by:

1. Correlating sample's TMB calculations (probably both Spearman's and Pearson's).
2. Comparing TMB distributions (as well as comparing mean and medians)

How does this sound?

[jharenza]

TMB typically does not include CNV data, so I would recommend against using it. Otherwise, I would add to compare to adult tumors, as mentioned above in the one reference.

[afarrel]

Just to reiterate what @jharenza said, typically we include SNVs and small InDels in TMB calls. In regards to TMBs you need to define if you are calling all SNVs or only non-synonymous mutations. In some of our applications, we perform TMB calls using Tumor-Only methods because we don't have matched normal data and exclude synonymous mutation calls to reduce false positives and better reflect somatic mutation burden.

[cansavvy]

Is there a more exact way to determine the number of Megabases sequences per each sample? I can’t find anyone’s exact code for determining TMB so I am unsure what number(s) to use for the “per coding Megabases” part

[cansavvy]

Also were these data filtered to take out intergenic mutations? I’m assuming yes based on what I’m seeing in the annotation but how was this done? And what other filters may have been applied to this data? Can we see the command that was given to run Strelka2? I did notice the methods so far say that Strelka2 was run using best practices so I will investigate that a bit more to see if anything there makes more sense of the data.

[jharenza]

Is there a more exact way to determine the number of Megabases sequences per each sample? I can’t find anyone’s exact code for determining TMB so I am unsure what number(s) to use for the “per coding Megabases” part

For WES data, it is calculated using the bed regions file and for WGS, you can use the size of the whole genome - enlisting @migbro for this reference file and a size, as I just noticed that the links to the code are not in the manuscript...