Mutation Analysis (MutSigCV v0.6)
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
22 February 2013  |  analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C15B00NK
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Analysis (MutSigCV v0.6). Broad Institute of MIT and Harvard. doi:10.7908/C15B00NK
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. MutSigCV v0.6 was used to generate the results found in this report.

  • Working with individual set: KIRP-TP

  • Number of patients in set: 103

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
Results
Target Coverage for Each Individual

The x axis represents the samples. The y axis represents the exons, one row per exon, and they are sorted by average coverage across samples. For exons with exactly the same average coverage, they are sorted next by the %GC of the exon. (The secondary sort is especially useful for the zero-coverage exons at the bottom).

Figure 1. 

Distribution of Mutation Counts, Coverage, and Mutation Rates Across Samples

Figure 2.  Patients counts and rates file used to generate this plot: KIRP-TP.patients.counts_and_rates.txt

CoMut Plot

Figure 3.  Get High-res Image The matrix in the center of the figure represents individual mutations in patient samples, color-coded by type of mutation, for the significantly mutated genes. The rate of synonymous and non-synonymous mutations is displayed at the top of the matrix. The barplot on the left of the matrix shows the number of mutations in each gene. The percentages represent the fraction of tumors with at least one mutation in the specified gene. The barplot to the right of the matrix displays the q-values for the most significantly mutated genes. The purple boxplots below the matrix (only displayed if required columns are present in the provided MAF) represent the distributions of allelic fractions observed in each sample. The plot at the bottom represents the base substitution distribution of individual samples, using the same categories that were used to calculate significance.

Significantly Mutated Genes

Column Descriptions:

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

  • npat = number of patients (individuals) with at least one nonsilent mutation

  • nsite = number of unique sites having a non-silent mutation

  • 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: 4. 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 Nnon Nsil Nflank nnon npat nsite nsil nflank nnei fMLE p score time q
NF2 131943 33269 17080 6 6 6 0 0 20 0.67 7.7e-09 36 0.093 0.00014
IL32 42024 11021 6649 4 4 2 0 0 20 0.68 8.4e-08 26 0.094 0.00077
ELF3 91567 25132 9638 4 4 3 0 0 20 0 5e-07 23 0.093 0.003
PIPOX 95481 27913 9943 3 3 2 0 0 20 0 5.7e-06 20 0.09 0.026
SFRS2IP 359161 95790 16775 5 5 2 1 0 20 0.5 0.000031 26 0.09 0.11
CDC27 200644 54281 21411 4 4 1 0 0 20 0.58 0.000039 24 0.091 0.12
POMC 38728 11433 2135 3 3 3 0 0 20 0 0.000049 14 0.084 0.13
STAG2 321875 79928 39772 5 5 4 1 0 20 0.74 0.000084 27 0.094 0.19
PARD6B 84769 24102 2562 4 4 4 0 0 20 0.77 0.00011 19 0.089 0.22
DARS 125454 32857 19032 3 3 3 0 0 20 0 0.00013 16 0.085 0.23
SMARCB1 100734 27810 10858 3 3 3 0 0 16 0 0.00021 16 0.086 0.35
C6orf195 30282 9167 1464 2 2 2 0 0 20 0.97 0.00031 13 0.083 0.44
LRFN4 74778 28016 1647 3 3 3 0 0 20 0.5 0.00031 16 0.084 0.44
BHMT 98674 27501 9211 3 3 3 0 0 20 0 0.00034 14 0.083 0.44
CUL3 188387 48307 18239 4 3 4 0 0 20 0 0.00036 16 0.089 0.44
RAB27B 54590 14420 6283 2 2 1 0 0 20 0.44 0.00042 13 0.084 0.48
CALML4 48925 13081 6222 3 3 2 0 0 8 0 0.00045 11 0.081 0.48
SAV1 92494 25750 6222 3 3 3 0 0 20 0.74 0.00064 16 0.084 0.62
ARRB1 92494 26986 16287 3 3 2 0 0 20 0 0.00065 11 0.083 0.62
LGI4 50058 16068 3294 4 4 4 0 0 20 1.3 0.0011 13 0.084 1
SEPT7 74469 18746 9516 2 2 2 0 0 20 0.67 0.0012 13 0.084 1
LHFPL4 56753 17716 3538 2 2 1 0 0 20 0.58 0.0014 13 0.082 1
HOXA5 60976 16995 2684 2 2 2 0 0 20 0 0.0014 10 0.08 1
OGG1 207854 55723 21716 4 4 3 0 0 12 0.85 0.0014 20 0.095 1
LYAR 97232 22557 9943 2 2 2 0 0 20 0 0.0015 10 0.078 1
CPXM1 156869 46659 15189 3 3 2 0 0 20 0 0.0015 13 0.082 1
MRPL54 30385 9064 3477 2 2 2 0 0 20 0 0.0017 7.9 0.067 1
POU4F2 79928 23896 2623 2 2 1 0 0 20 0.52 0.0018 13 0.086 1
LBR 151204 42539 15616 3 3 3 0 0 20 0 0.0019 10 0.078 1
STARD3 109695 29458 15616 2 2 2 0 0 20 0 0.002 13 0.083 1
IFNA16 45938 12566 1464 2 2 1 0 0 20 0 0.002 7.6 0.065 1
TNKS2 269757 76735 30561 3 3 2 0 0 20 0 0.0023 13 0.086 1
TMEM37 42951 13905 1342 2 2 1 0 0 20 0 0.0024 7.8 0.067 1
ZNF30 150174 37492 3111 3 3 3 0 0 13 0 0.0025 13 0.087 1
SERPINB10 98056 24720 8479 2 2 2 0 0 20 0 0.0027 10 0.076 1
NF2

Figure S1.  This figure depicts the distribution of mutations and mutation types across the NF2 significant gene.

IL32

Figure S2.  This figure depicts the distribution of mutations and mutation types across the IL32 significant gene.

ELF3

Figure S3.  This figure depicts the distribution of mutations and mutation types across the ELF3 significant gene.

PIPOX

Figure S4.  This figure depicts the distribution of mutations and mutation types across the PIPOX significant gene.

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.

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