Mutation Analysis (MutSigCV v0.9)
Kidney Chromophobe (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C13F4P1Q
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C13F4P1Q
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: KICH-TP

  • Number of patients in set: 66

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:KICH-TP.final_analysis_set.maf

  • Blacklist used for this analysis: pancan_mutation_blacklist.v14.hg19.txt

  • Significantly mutated genes (q ≤ 0.1): 2

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). If the figure is unpopulated, then full coverage is assumed (e.g. MutSig CV doesn't use WIGs and assumes full coverage).

Figure 1. 

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

Figure 2.  Patients counts and rates file used to generate this plot: KICH-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
TP53 62370 18216 9682 31 22 29 0 0 4 0 2.8e-13 70 0.057 5e-09
PTEN 64284 15444 8178 9 6 9 0 0 20 1.2 2.6e-06 28 0.05 0.024
PRSS3 38676 11418 4888 6 6 4 3 3 20 6.3 0.000046 24 0.069 0.21
TAS2R30 49038 14190 940 6 5 5 2 0 20 1.4 0.000046 20 0.058 0.21
TAS2R43 42768 11748 705 5 5 5 0 0 20 1.4 0.00025 15 0.053 0.81
HLA-DRB5 28512 8712 2867 8 5 8 4 0 20 4.5 0.00026 18 0.054 0.81
CCDC144NL 26928 7194 2867 3 3 3 2 0 20 1.5 0.00042 15 0.052 0.97
FAM86B1 16764 5346 940 3 3 3 0 0 20 1.5 0.00042 13 0.053 0.97
CDKN1A 23958 8052 1927 3 2 3 0 0 20 0.92 0.001 12 0.048 1
CBWD6 26334 6732 5358 2 2 1 0 0 20 1.4 0.0011 12 0.044 1
RHBDD3 26796 9768 3572 2 2 1 0 0 20 0 0.0011 13 0.095 1
MUC6 265122 93192 12455 37 21 35 15 0 20 10 0.0022 40 0.052 1
HLA-C 54714 16830 7097 8 7 8 4 0 20 2.7 0.0026 18 0.049 1
FAM174B 12012 3762 1786 2 2 2 0 0 20 0 0.0027 7.3 0.069 1
OR5M9 46860 13860 1222 4 4 4 0 0 20 1.4 0.0035 12 0.046 1
HLA-A 54120 16368 7238 5 5 5 0 0 20 2.1 0.0039 16 0.048 1
RAB40A 41976 12804 1128 2 2 2 0 0 20 0 0.004 12 0.064 1
PRH2 25080 8184 3008 2 2 2 0 0 20 1.4 0.0043 9.6 0.042 1
MRPS12 33528 11484 3055 2 2 2 0 0 20 1.1 0.0044 12 0.048 1
OR13C2 48708 13992 1269 3 3 3 1 0 20 1.3 0.0051 12 0.047 1
RIMBP3 57156 18480 0 2 2 2 0 0 20 0 0.0052 12 0.047 1
PRB2 58212 21186 2491 6 6 6 6 0 12 4.9 0.0062 15 0.047 1
FRMD8 50226 15840 6533 2 2 2 0 0 20 0.53 0.007 12 0.049 1
HNF1A 85932 27720 8272 3 3 3 0 0 20 0.59 0.008 14 0.048 1
GNG7 6270 1782 987 1 1 1 0 0 20 0.47 0.0081 6.8 0.039 1
DTWD1 48312 11946 3666 2 2 2 0 0 13 1.4 0.011 11 0.049 1
GAL3ST2 25212 7986 2491 2 2 2 0 0 18 0 0.011 9.1 0.05 1
ADAM21 112398 30426 1128 3 3 3 0 0 20 0.39 0.012 11 0.046 1
C16orf54 9636 3168 846 1 1 1 0 0 20 1.4 0.013 6.6 0.038 1
STAM 86922 23364 13254 2 2 1 0 0 20 0.49 0.016 11 0.054 1
PABPC3 96360 28512 1128 9 7 9 1 0 20 2.7 0.018 16 0.05 1
CNTD2 13596 4884 4653 1 1 1 1 0 20 1.7 0.019 6.4 0.037 1
C1orf177 57486 16104 8037 2 2 2 0 0 20 0 0.019 11 0.049 1
NOB1 61578 17886 7567 2 2 2 0 0 13 1.3 0.019 11 0.064 1
MUC5B 552288 198462 27119 12 12 12 2 0 17 3.8 0.019 35 0.052 1
TP53

Figure S1.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

PTEN

Figure S2.  This figure depicts the distribution of mutations and mutation types across the PTEN 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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