Mutation Assessor
Uveal Melanoma (Primary solid tumor)
13 July 2018 | None
Maintainer Information
Maintained by David Heiman (Broad Institute)
Overview
Introduction

This report serves to summarize the functional impact of missense mutations in each gene as determined by Mutation Assessor[1].

Summary

- High Functional Impact Missense Mutations: 160

- Medium Functional Impact Missense Mutations: 405

- Low Functional Impact Missense Mutations: 291

- Neutral Functional Impact Mutations: 173

Results
Functional Impact by Gene

Table 1. Get Full Table A gene-level breakdown of missense mutation functional impact, ordered by MutSig rank. Includes missense mutation counts broken down by level of functional impact (high, medium, low, neutral), median functional impact score and level, and most common level(s) of functional impact (mode) per gene.

MutSig Rank | Gene | High Functional Impact Count | Medium Functional Impact Count | Low Functional Impact Count | Neutral Functional Impact Count | Median Functional Impact Score | Median Functional Impact Level | Mode Functional Impact Level
--- | --- | --- | --- | --- | --- | --- | --- | ---
2 | BAP1 | 1 | 2 | 1 | 1 | 2.5050 | medium | medium
3 | GNAQ | 40 | 0 | 0 | 0 | 4.3400 | high | high
4 | GNA11 | 35 | 0 | 0 | 0 | 3.7700 | high | high
5 | SF3B1 | 4 | 14 | 0 | 0 | 3.4600 | medium | medium
7 | CYSLTR2 | 3 | 0 | 0 | 0 | 4.1450 | high | high
8 | EIF1B | 0 | 1 | 0 | 0 | 2.3800 | medium | medium
10 | TMEM39A | 0 | 1 | 1 | 0 | 1.6375 | low | medium/low
14 | WISP1 | 1 | 0 | 0 | 0 | 3.5850 | high | high
16 | SERTM1 | 0 | 0 | 1 | 0 | 0.8950 | low | low
18 | RNF43 | 0 | 0 | 1 | 1 | -0.0750 | neutral | low/neutral

Methods & Data
Input

- TCGA-UVM-TP.ma.maf

- sig_genes.txt

- Mutation Assessor Scores Release 3:

- hg38

A lookup is done against the relevant Mutation Assessor Scores table for each missense mutation in a given MAF file, and available functional impact score and level are appended as two new columns to generate TCGA-UVM-TP.ma.maf. These are summarized in Table 1, sorted by MutSig rank.

References
[1] Boris Reva, Yevgeniy Antipin, and Chris Sander, Predicting the functional impact of protein mutations: application to cancer genomics, Nucl. Acids Res. 39(17):e118 (2011)
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