For Tumor Mutation Burden (TMB) usually people calculates non_synonym_mutations / Mb. 

In this Ilumina's tech report (https://www.illumina.com/content/dam/illumina-marketing/documents/products/whitepapers/trusight-tumor-170-tmb-analysis-white-paper-1170-2017-001.pdf) they calculate TMB for TruSight Tumor 170 with both, all mutations and non_synonym. What strikes me is that the all_mutations have a higher correlation with WES. Is this an artifact because the low number of genes and the fact that this is an in silico selection of the WES? other papers have good correlations with 300 and 500 genes but probably 170 is too low.   

<pre>
Results of In Silico Studies
WES data from 5336 TCGA samples were filtered and analyzed in
silico using TruSight Tumor 170. TMB estimated from the TruSight
Tumor 170 targeted regions showed a high correlation to TMB
estimated from WES, with R
2
correlation values of 0.91 for total
mutations (Figure 2A) and 0.90 for nonsynonymous mutations (Figure
2B)
</pre>

Seems that if you want a proxy for neo-antigens formation for improving immunotherapies, then counting non_synonyms would give you a better proxy. 

Doesn't matter if you use all_mut or synonym? Could someone point me to a good paper measuring these correlations? Which is the limit for the number of genes where correlation starts to drop?


Correlation is a rather poor evaluation metric this case, since it can be driven by hyper-mutated outliers that are easy to get right with small panels, i.e. the correlation depends on both the mutation rate and the panel size. 

Including silent mutations will give you a more accurate total somatic mutation rate because you add a few more data points and reduce the sampling variance. This can - in theory - also provide a slightly more accurate estimate of the true mis-sense rate.
 
I don't think there is a paper, because it's straightforward to do the power calculation or the downsampling from TCGA data.  Maybe the recent one from the Van Allen group at DFCI where they compare whole-exome and small and large panels is helpful.