Mutation Analysis (MutSigCV v0.9)
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
Due to issues with GAF 3.0, we strongly advise caution in the use of these results.
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1G44NM7
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1G44NM7
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 v0.9 was used to generate the results found in this report.

  • Working with individual set: KIRP-TP

  • Number of patients in set: 115

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

Lego Plots

The mutation spectrum is depicted in the lego plots below in which the 96 possible mutation types are subdivided into six large blocks, color-coded to reflect the base substitution type. Each large block is further subdivided into the 16 possible pairs of 5' and 3' neighbors, as listed in the 4x4 trinucleotide context legend. The height of each block corresponds to the mutation frequency for that kind of mutation (counts of mutations normalized by the base coverage in a given bin). The shape of the spectrum is a signature for dominant mutational mechanisms in different tumor types.

Figure 3.  Get High-res Image SNV Mutation rate lego plot for entire set. Each bin is normalized by base coverage for that bin. Colors represent the six SNV types on the upper right. The three-base context for each mutation is labeled in the 4x4 legend on the lower right. The fractional breakdown of SNV counts is shown in the pie chart on the upper left. If this figure is blank, not enough information was provided in the MAF to generate it.

Figure 4.  Get High-res Image SNV Mutation rate lego plots for 4 slices of mutation allele fraction (0<=AF<0.1, 0.1<=AF<0.25, 0.25<=AF<0.5, & 0.5<=AF) . The color code and three-base context legends are the same as the previous figure. If this figure is blank, not enough information was provided in the MAF to generate it.

CoMut Plot

Figure 5.  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: 2. 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 147315 37145 560 7 7 7 0 0 20 0.64 1e-07 40 0.08 0.0019
IL32 46920 12305 218 4 4 2 0 0 20 0.62 6.1e-06 25 0.087 0.055
PCDHGC5 246100 81305 146 18 14 16 6 0 5 5.4 0.0003 44 0.089 1
ELF3 102235 28060 316 4 4 3 0 0 20 0 0.00031 21 0.083 1
PRB2 101430 36915 106 3 3 3 0 0 12 0.51 0.001 18 0.081 1
PIPOX 106605 31165 326 3 3 2 0 0 20 0 0.001 18 0.081 1
C6orf195 33810 10235 48 2 2 2 0 0 20 0.93 0.0017 13 0.083 1
CDC27 224020 60605 702 4 4 1 0 0 20 0.56 0.002 22 0.087 1
PCDHAC2 248285 79005 124 5 5 5 4 0 20 0.7 0.003 15 0.085 1
MLX 100510 24380 424 3 3 2 0 0 20 1.2 0.0032 17 0.073 1
BHMT 110170 30705 302 4 4 4 0 0 20 0.66 0.0037 17 0.091 1
RAB27B 60950 16100 206 2 2 1 0 0 20 0.4 0.0039 13 0.074 1
STAG2 359375 89240 1304 6 6 5 1 0 20 1.4 0.0039 31 0.087 1
PARD6B 94645 26910 84 4 4 4 0 0 20 0.73 0.004 17 0.083 1
LRFN4 83490 31280 54 3 3 3 0 0 20 0.48 0.0049 15 0.078 1
POMC 43240 12765 70 3 3 3 0 0 20 0 0.006 12 0.069 1
LGI4 55890 17940 108 4 4 4 0 0 20 1.2 0.0062 12 0.079 1
SEPT7 83145 20930 312 2 2 2 0 0 20 0.65 0.0065 12 0.074 1
SAV1 103270 28750 204 3 3 3 0 0 20 0.72 0.011 14 0.079 1
ZNF367 59570 16790 162 2 2 2 0 0 20 1.1 0.011 12 0.068 1
DARS 140070 36685 624 3 3 3 0 0 20 0 0.011 15 0.078 1
POU4F2 89240 26680 86 2 2 1 0 0 20 0.45 0.012 12 0.075 1
RAET1L 58190 16100 120 2 2 1 0 0 20 0.67 0.012 12 0.069 1
LHFPL4 63365 19780 116 2 2 1 0 0 20 0.54 0.012 12 0.074 1
H2AFJ 33120 11615 48 2 2 2 0 0 20 0 0.014 9.7 0.068 1
SMARCB1 112470 31050 356 3 3 3 0 0 16 0 0.015 14 0.079 1
WFDC10A 21735 6095 86 1 1 1 0 0 20 0.49 0.017 6.6 0.053 1
CYHR1 45425 16560 82 2 2 1 0 0 6 0 0.017 12 0.076 1
VPS37D 16215 5750 24 1 1 1 0 0 20 0.45 0.018 6.8 0.055 1
CALML4 54625 14605 204 3 3 2 0 0 8 0 0.019 9.4 0.068 1
PPARGC1B 244490 73140 448 3 3 1 0 0 20 1.2 0.021 17 0.081 1
SIGIRR 71415 23345 198 3 3 3 0 0 20 0.89 0.021 9.3 0.064 1
LYPD3 88205 29670 186 2 2 2 0 0 15 1.1 0.022 12 0.075 1
CCDC28B 69460 21620 254 2 2 1 0 0 20 3 0.024 12 0.085 1
KDELR3 59110 16905 182 2 2 2 0 0 20 0.59 0.024 9.3 0.062 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.

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

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