Mutation Analysis (MutSigCV v0.9)
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1Q52NCV
Maintained by David Heiman (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/C1Q52NCV
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: 168

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:KIRP-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: 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 215208 54264 33600 12 12 12 1 0 20 1.2 1.3e-10 58 0.16 2.3e-06
HNRNPM 279384 78960 36000 11 11 2 0 0 20 1.4 8.1e-09 59 0.2 0.000074
ZNF598 207816 64176 11160 14 13 5 2 0 20 1.2 0.000067 35 0.16 0.3
NEFH 280224 77448 7200 14 10 6 1 0 20 1.2 0.000083 34 0.15 0.3
TDG 190680 48048 25080 5 5 3 0 0 20 0.8 0.000093 27 0.15 0.3
C6orf195 49392 14952 2880 5 4 5 0 0 20 1.9 0.0001 20 0.15 0.3
SAV1 150864 42000 12240 5 5 5 0 0 20 0.58 0.00012 25 0.15 0.3
BAGE 17136 4536 4440 2 2 1 0 0 20 1.3 0.0002 14 0.14 0.45
NDUFA11 22176 7560 3720 2 2 1 0 0 20 0.69 0.0003 14 0.13 0.61
FBF1 279216 85008 30240 7 7 6 1 0 20 0.7 0.0004 27 0.15 0.73
PTEN 163632 39312 20880 5 5 5 0 0 20 1.2 0.00053 24 0.15 0.89
MID1IP1 67032 19152 2640 3 3 3 0 0 20 0.11 0.00059 14 0.14 0.9
SCIN 156912 41832 17040 6 6 6 0 0 7 0.16 0.00079 19 0.15 1
B3GNT6 71400 23688 1800 3 3 2 0 0 20 0.75 0.00081 17 0.15 1
MUC2 632688 183288 69840 33 26 31 21 0 20 2.6 0.00095 52 0.16 1
PEF1 110208 33432 11520 5 4 5 1 0 20 0.47 0.0011 17 0.14 1
MLL3 1927800 547680 148800 18 17 18 1 0 13 0.78 0.0013 59 0.16 1
PLA2G4F 307104 93576 41640 7 7 7 0 0 19 0.97 0.0014 28 0.15 1
SETD2 828744 221928 44280 17 16 17 2 0 7 1.3 0.0016 59 0.16 1
SEPT7 121464 30576 18720 4 3 4 0 0 20 0.85 0.0016 16 0.14 1
AGPS 235704 61992 44520 7 7 7 0 0 20 0.61 0.0018 20 0.15 1
ACTB 145656 42840 11760 7 7 7 0 0 20 1 0.0019 21 0.15 1
HOXD8 81984 23016 3720 4 4 3 0 0 20 0.96 0.0021 18 0.15 1
IGFBP6 51072 15792 7320 2 2 2 0 0 20 0.88 0.0024 13 0.14 1
RPUSD4 148680 44688 16920 5 4 5 0 0 20 0.77 0.0026 20 0.15 1
SMARCB1 164304 45360 21360 5 5 4 1 0 16 1 0.0026 23 0.15 1
VPS37D 23688 8400 1440 2 2 2 0 0 20 0.94 0.0028 11 0.12 1
FCGR2A 127848 36120 16920 5 5 4 0 0 20 1.1 0.0033 17 0.15 1
BHMT 160944 44856 18120 6 6 6 1 0 20 0.92 0.0036 19 0.15 1
LRFN4 121968 45696 3240 4 4 4 0 0 20 1.3 0.0041 19 0.15 1
STAG2 525000 130368 78240 9 9 9 3 0 20 1.7 0.0042 36 0.16 1
NKD2 89880 27048 9360 4 4 4 1 0 20 0.63 0.0043 14 0.14 1
F12 169176 51912 21000 5 5 5 2 0 20 1.5 0.0043 22 0.15 1
ATP6V0E2 41160 14280 4800 3 3 3 0 0 20 0.77 0.0044 11 0.13 1
ACADL 164136 43512 23280 5 4 5 0 0 20 0.69 0.0045 16 0.15 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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