Kidney Renal Clear Cell Carcinoma: Mutation Analysis (MutSig vS2N)
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): 19

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: 19. 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
--- | --- | --- | --- | --- | --- | --- | ---
PBRM1 | 254033 | 7 | 2 | 122 | 95 | 7.1e-250 | 1.3e-245
VHL | 17867 | 1 | 6 | 180 | 102 | 5.3e-179 | 5e-175
KIAA1731 | 2520 | 2 | 1 | 5 | 0 | 1.2e-160 | 7.8e-157
ZNF717 | 280 | 2 | 2 | 4 | 3 | 4.8e-123 | 2.2e-119
BAP1 | 94416 | 2 | 1 | 34 | 18 | 2.8e-82 | 1.1e-78
POTED | 20730 | 0 | 0 | 6 | 0 | 8.3e-66 | 2.6e-62
FAM200A | 9687 | 0 | 0 | 5 | 1 | 1.5e-42 | 4.1e-39
SETD2 | 304922 | 4 | 2 | 46 | 25 | 6e-42 | 1.4e-38
PTEN | 64169 | 3 | 4 | 24 | 12 | 4.4e-23 | 9.3e-20
KDM5C | 169452 | 1 | 1 | 22 | 13 | 1.8e-22 | 3.4e-19
CT47B1 | 19880 | 0 | 1 | 5 | 0 | 4e-21 | 6.9e-18
ANKRD30B | 67480 | 4 | 1 | 7 | 0 | 9.4e-11 | 1.5e-07
PIK3CA | 172749 | 3 | 2 | 19 | 1 | 3.6e-07 | 0.00052
TP53 | 51534 | 1 | 5 | 19 | 2 | 1.3e-06 | 0.0017
VCX2 | 6415 | 0 | 0 | 5 | 2 | 5.1e-06 | 0.0064
EBPL | 24540 | 0 | 0 | 8 | 0 | 6.7e-06 | 0.0079
CR1 | 213863 | 2 | 0 | 12 | 6 | 0.000015 | 0.017
MUC17 | 555164 | 0 | 3 | 22 | 0 | 0.000027 | 0.029
WDR52 | 164569 | 6 | 1 | 10 | 0 | 0.000088 | 0.087
MTOR | 418149 | 6 | 3 | 28 | 0 | 0.0002 | 0.19
UNC80 | 39544 | 4 | 2 | 6 | 0 | 0.00037 | 0.33
XPOT | 151890 | 1 | 3 | 9 | 1 | 0.00044 | 0.36
DNAH6 | 68369 | 4 | 2 | 6 | 0 | 0.00044 | 0.36
MUC16 | 1887211 | 8 | 18 | 46 | 1 | 0.00047 | 0.37
COL11A1 | 199636 | 6 | 1 | 10 | 6 | 0.003 | 1
HSPA8 | 95168 | 0 | 3 | 7 | 2 | 0.0039 | 1
NBPF10 | 105560 | 47 | 5 | 25 | 0 | 0.0098 | 1
GRIN2B | 221549 | 5 | 2 | 11 | 0 | 0.01 | 1
SMC3 | 196040 | 11 | 2 | 7 | 1 | 0.012 | 1
DNHD1 | 181401 | 2 | 3 | 9 | 1 | 0.013 | 1
ACSBG2 | 104855 | 2 | 1 | 5 | 0 | 0.018 | 1
DNAH9 | 653991 | 7 | 1 | 18 | 2 | 0.021 | 1
ZNF799 | 92972 | 2 | 1 | 7 | 0 | 0.023 | 1
PRPF8 | 358306 | 1 | 0 | 9 | 0 | 0.023 | 1
COL2A1 | 111657 | 1 | 0 | 7 | 0 | 0.024 | 1

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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