Breast Invasive Carcinoma: Mutation Analysis (MutSig)
Maintained by Dan DiCara (Broad Institute)
Overview
Introduction

This report serves to describe the mutational landscape and properties of a given individual set, as well as rank genes and genesets according to mutational significance. MutSig vS2N was used to generate the results found in this report.

- Working with individual set: BRCA

Input

The input for this pipeline is a set of individuals with the following files associated for each:

1. An annotated .maf file describing the mutations called for the respective individual, and their properties.

2. A .wig file that contains information about the coverage of the sample.

Summary

- MAF used for this analysis:BRCA.final_analysis_set.maf

- Significantly mutated genes (q ≤ 0.1): 83

Results
Significantly Mutated Genes

Column Descriptions:

- N = number of sequenced bases in this gene across the individual set

- nnon = number of (nonsilent) mutations in this gene across the individual set

- nnull = number of (nonsilent) null mutations in this gene across the individual set

- nflank = number of noncoding mutations from this gene's flanking region, across the individual set

- nsil = number of silent mutations in this gene across the individual set

- p = p-value (overall)

- q = q-value, False Discovery Rate (Benjamini-Hochberg procedure)

Table 1. Get Full Table A Ranked List of Significantly Mutated Genes. Number of significant genes found: 83. Number of genes displayed: 35. Click on a gene name to display its stick figure depicting the distribution of mutations and mutation types across the chosen gene (this feature may not be available for all significant genes).

gene | N | nflank | nsil | nnon | nnull | p | q
--- | --- | --- | --- | --- | --- | --- | ---
PIK3CA | 214718 | 0 | 3 | 205 | 8 | 0 | 0
ZNF841 | 1064 | 0 | 0 | 7 | 2 | 0 | 0
ANKRD30B | 964 | 0 | 3 | 6 | 1 | 0 | 0
GATA3 | 58646 | 0 | 1 | 58 | 53 | 0 | 0
TP53 | 64260 | 0 | 3 | 192 | 76 | 0 | 0
PTEN | 82086 | 0 | 0 | 18 | 13 | 0 | 0
CDH1 | 148920 | 0 | 1 | 33 | 29 | 6.8e-159 | 1.8e-155
RUNX1 | 52534 | 0 | 1 | 18 | 12 | 2.9e-133 | 6.9e-130
TBL1XR1 | 79548 | 0 | 0 | 10 | 8 | 1.1e-112 | 2.3e-109
MAP3K1 | 236648 | 0 | 3 | 64 | 55 | 7e-111 | 1.3e-107
MAP2K4 | 71404 | 0 | 0 | 22 | 14 | 1.1e-97 | 1.9e-94
ZNF90 | 832 | 0 | 0 | 4 | 1 | 1.8e-66 | 2.9e-63
DNAH6 | 82146 | 0 | 3 | 11 | 1 | 1.3e-47 | 1.9e-44
FAM75C1 | 1340 | 0 | 2 | 4 | 0 | 1.3e-43 | 1.8e-40
RGPD3 | 1676 | 0 | 2 | 4 | 1 | 1.5e-35 | 1.9e-32
EYS | 114754 | 0 | 0 | 11 | 1 | 4.2e-31 | 4.9e-28
MLL3 | 824694 | 0 | 2 | 37 | 23 | 3.8e-30 | 4.2e-27
CDKN1B | 30604 | 0 | 0 | 5 | 5 | 3.3e-28 | 3.5e-25
NCOR1 | 402398 | 0 | 1 | 18 | 15 | 1.4e-26 | 1.4e-23
TBX3 | 73938 | 0 | 0 | 13 | 10 | 5.8e-26 | 5.5e-23
CBFB | 28568 | 0 | 1 | 8 | 3 | 6.1e-21 | 5.5e-18
SF3B1 | 231544 | 0 | 0 | 10 | 1 | 4.4e-19 | 3.8e-16
RTL1 | 27250 | 0 | 0 | 5 | 0 | 2.9e-18 | 2.4e-15
PTPN22 | 160144 | 0 | 0 | 8 | 0 | 5.4e-18 | 4.2e-15
DNAH12 | 88748 | 0 | 0 | 7 | 0 | 1.4e-16 | 1.1e-13
HIST1H3B | 20400 | 0 | 0 | 5 | 2 | 1.1e-15 | 8.3e-13
PIK3R1 | 150454 | 0 | 1 | 14 | 9 | 1.4e-15 | 9.9e-13
SAAL1 | 88230 | 0 | 0 | 6 | 5 | 1.2e-13 | 8.2e-11
CTCF | 129540 | 0 | 2 | 13 | 7 | 2.9e-12 | 1.9e-09
MLLT10 | 172882 | 0 | 0 | 8 | 5 | 1.7e-11 | 1.1e-08
SARM1 | 59654 | 0 | 0 | 5 | 5 | 5.3e-11 | 3.2e-08
ANK3 | 804278 | 0 | 0 | 12 | 0 | 4.4e-09 | 2.6e-06
GPS2 | 58140 | 0 | 1 | 6 | 6 | 8.5e-09 | 4.8e-06
WNK3 | 310052 | 0 | 1 | 8 | 0 | 9.7e-09 | 5.4e-06
NF1 | 761470 | 0 | 0 | 16 | 9 | 1.5e-08 | 7.9e-06

Methods & Data
Methods

In brief, we tabulate the number of mutations and the number of covered bases for each gene. The counts are broken down by mutation context category: four context categories that are discovered by MutSig, and one for indel and 'null' mutations, which include indels, nonsense mutations, splice-site mutations, and non-stop (read-through) mutations. For each gene, we calculate the probability of seeing the observed constellation of mutations, i.e. the product P1 x P2 x ... x Pm, or a more extreme one, given the background mutation rates calculated across the dataset. [1]

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] TCGA, Integrated genomic analyses of ovarian carcinoma, Nature 474:609 - 615 (2011)
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