Mutation Analysis (MutSigCV v0.9)
Kidney Chromophobe (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1RN36GB
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1RN36GB
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): 3

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: 3. 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 7210 30 22 28 1 0 4 3.2 9.1e-12 68 0.049 1.7e-07
PRSS3 38676 11418 3640 8 7 6 4 0 20 4 2.9e-06 28 0.048 0.02
PTEN 64284 15444 6090 9 6 9 0 0 20 1.3 3.3e-06 28 0.045 0.02
TAS2R30 49038 14190 700 6 5 5 2 0 20 1.4 0.000045 20 0.049 0.21
TAS2R43 42768 11748 525 5 5 5 0 0 20 1.4 0.00028 15 0.043 1
HLA-DRB5 28512 8712 2135 8 5 8 4 0 20 4.6 0.00033 18 0.046 1
FAM86B1 16764 5346 700 3 3 3 0 0 20 1.5 0.0006 13 0.04 1
CDKN1A 23958 8052 1435 3 2 3 0 0 20 0.95 0.001 12 0.049 1
RHBDD3 26796 9768 2660 2 2 1 0 0 20 0 0.0014 12 0.043 1
CBWD6 26334 6732 3990 2 2 1 0 0 20 1.4 0.0015 12 0.04 1
C16orf3 8910 3036 420 2 2 2 0 0 20 1.4 0.002 9.5 0.037 1
CCDC144NL 26928 7194 2135 3 3 3 2 0 20 1.5 0.0025 12 0.04 1
FAM174B 12012 3762 1330 2 2 2 0 0 20 0 0.003 7.3 0.032 1
RIMBP3 57156 18480 0 2 2 2 0 0 20 0 0.0038 12 0.043 1
RAB40A 41976 12804 840 2 2 2 0 0 20 0 0.0039 12 0.043 1
PRH2 25080 8184 2240 2 2 2 0 0 20 1.4 0.004 9.6 0.037 1
HLA-C 54714 16830 5285 7 7 7 5 0 20 3.5 0.0047 17 0.044 1
HLA-A 54120 16368 5390 5 5 5 0 0 20 2.1 0.0048 16 0.044 1
MRPS12 33528 11484 2275 2 2 2 0 0 20 1.1 0.0054 12 0.04 1
OR5M9 46860 13860 910 4 4 4 0 0 20 1.4 0.0058 12 0.04 1
MUC6 265122 93192 9275 37 21 35 15 0 20 12 0.0059 39 0.05 1
OR13C2 48708 13992 945 3 3 3 1 0 20 1.3 0.0059 12 0.04 1
SPCS1 16170 4224 2380 2 2 2 0 0 20 0 0.0064 7 0.034 1
FRMD8 50226 15840 4865 2 2 2 0 0 20 0.54 0.0067 12 0.04 1
PRB2 58212 21186 1855 6 6 6 6 0 12 5.1 0.0078 15 0.045 1
MUC5B 552288 198462 20195 11 11 11 3 0 17 2.3 0.0097 34 0.047 1
HNF1A 85932 27720 6160 3 3 3 0 0 20 0.62 0.011 14 0.045 1
DTWD1 48312 11946 2730 2 2 2 0 0 13 1.5 0.012 11 0.041 1
GAL3ST2 25212 7986 1855 2 2 2 0 0 18 0 0.012 9 0.036 1
KLF15 40854 12474 1330 2 2 2 0 0 8 0.87 0.012 11 0.04 1
ADAM21 112398 30426 840 3 3 3 0 0 20 0.41 0.015 11 0.04 1
STAM 86922 23364 9870 2 2 1 0 0 20 0.52 0.016 11 0.041 1
FAM98B 47454 12606 3990 2 2 2 0 0 20 0.91 0.018 8.9 0.039 1
C16orf54 9636 3168 630 1 1 1 0 0 20 1.4 0.02 6.5 0.03 1
MAP1LC3A 22770 6204 2765 2 2 2 0 0 20 0.67 0.022 6.5 0.034 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

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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