Kidney Renal Clear Cell 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: KIRC

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:KIRC.final_analysis_set.maf

- Significantly mutated genes (q ≤ 0.1): 24

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: 24. 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
--- | --- | --- | --- | --- | --- | --- | ---
KIAA1731 | 2529 | 2 | 1 | 6 | 0 | 0 | 0
PBRM1 | 254039 | 7 | 3 | 154 | 123 | 1.6e-289 | 1.6e-285
FAM200A | 9688 | 0 | 0 | 10 | 0 | 1.8e-237 | 1.1e-233
VHL | 17868 | 2 | 7 | 198 | 118 | 5.3e-130 | 2.5e-126
ZNF717 | 281 | 2 | 2 | 4 | 3 | 6.9e-113 | 2.6e-109
BAP1 | 94426 | 2 | 1 | 43 | 25 | 5.9e-112 | 1.9e-108
SETD2 | 304933 | 4 | 2 | 60 | 38 | 8.2e-62 | 2.2e-58
POTED | 20731 | 0 | 0 | 6 | 0 | 1.5e-61 | 3.5e-58
KDM5C | 169432 | 1 | 1 | 27 | 16 | 4.1e-27 | 8.5e-24
PTEN | 64163 | 3 | 4 | 25 | 13 | 3.9e-24 | 7.4e-21
CT47B1 | 19951 | 0 | 1 | 5 | 0 | 2.2e-19 | 3.7e-16
ZNF844 | 45241 | 2 | 0 | 6 | 0 | 1e-16 | 1.6e-13
ANKRD30B | 67721 | 4 | 1 | 7 | 0 | 4.8e-10 | 7e-07
TP53 | 51534 | 1 | 5 | 23 | 3 | 2.8e-08 | 0.000038
ETF1 | 69274 | 1 | 0 | 6 | 5 | 2.8e-07 | 0.00035
PIK3CA | 172751 | 3 | 2 | 19 | 1 | 3.2e-06 | 0.0037
STAU2 | 71447 | 5 | 0 | 7 | 0 | 3.5e-06 | 0.0039
VCX2 | 6416 | 0 | 0 | 5 | 2 | 0.000013 | 0.014
EBPL | 24540 | 0 | 0 | 8 | 0 | 0.000018 | 0.018
WDR52 | 164571 | 6 | 1 | 11 | 0 | 0.000021 | 0.02
GPR112 | 462604 | 2 | 2 | 11 | 1 | 0.000031 | 0.027
MUC17 | 555166 | 0 | 3 | 22 | 0 | 0.000064 | 0.052
MTOR | 418151 | 6 | 3 | 32 | 0 | 0.000064 | 0.052
CR1 | 213857 | 2 | 0 | 13 | 6 | 0.00012 | 0.095
NF2 | 83308 | 7 | 3 | 7 | 5 | 0.0002 | 0.15
KBTBD8 | 91794 | 0 | 0 | 9 | 0 | 0.00028 | 0.2
UPF2 | 199464 | 6 | 3 | 9 | 0 | 0.00037 | 0.26
MAN2C1 | 116921 | 0 | 1 | 9 | 0 | 0.00043 | 0.29
UNC80 | 39545 | 4 | 2 | 6 | 0 | 0.00063 | 0.4
C22orf42 | 33538 | 3 | 0 | 5 | 0 | 0.00064 | 0.4
ACSBG2 | 104857 | 2 | 1 | 6 | 0 | 0.00072 | 0.44
DNAH6 | 68378 | 4 | 2 | 6 | 0 | 0.00075 | 0.44
SMCR8 | 144377 | 0 | 0 | 6 | 0 | 0.00079 | 0.45
MUC16 | 1887204 | 8 | 21 | 49 | 1 | 0.00098 | 0.55
COL11A1 | 199642 | 6 | 1 | 11 | 7 | 0.001 | 0.55

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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