Mutation Assessor - Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)

Mutation Assessor

Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

Maintainer Information

Citation Information

Maintained by David Heiman (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C1XS5TKT

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: 394
Medium Functional Impact Missense Mutations: 2531
Low Functional Impact Missense Mutations: 3553
Neutral Functional Impact Mutations: 5078

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.

Gene	MutSig Rank	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
MYD88	1	0	0	1	0	1.3750	low	low
FRG1	2	0	1	6	2	1.6250	low	low
MLL2	3	3	0	1	2	2.4725	medium	high
TPPP	4	0	0	5	0	0.8950	low	low
CHIT1	5	0	5	0	0	2.7650	medium	medium
B2M	6	1	3	1	2	2.2400	medium	medium
TPTE2	7	0	0	0	11	0.6650	neutral	neutral
RHPN2	8	0	6	0	0	2.4550	medium	medium
FOXD4L1	9	0	1	0	6	-1.4550	neutral	neutral
DND1	10	0	2	0	3	0.5500	neutral	neutral

Methods & Data

Input

DLBC-TP.maf.annotated
sig_genes.txt
Mutation Assessor Scores Release 2:

hg18
hg19

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 DLBC-TP.maf.annotated. These are summarized in Table 1, sorted by MutSig rank.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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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