Mutation Analysis (MutSigCV v0.9)
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1SB44ZC
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1SB44ZC
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: 161

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 206241 52003 3080 10 10 10 1 0 20 1.6 6.8e-09 53 0.13 0.00012
HNRNPM 267743 75670 3300 10 10 2 0 1 20 1.9 6.5e-07 52 0.17 0.006
TDG 182735 46046 2299 5 5 3 0 0 20 0 2e-05 27 0.14 0.12
NEFH 268548 74221 660 14 10 6 1 0 20 0.96 0.00017 32 0.12 0.78
B3GNT6 68425 22701 165 3 3 2 0 0 20 0.2 0.00026 17 0.12 0.95
NDUFA11 21252 7245 341 2 2 1 0 0 20 0.65 0.00042 14 0.11 1
SAV1 144578 40250 1122 5 5 5 0 0 20 1.1 0.00078 23 0.13 1
PTEN 156814 37674 1914 5 5 5 0 0 20 1.3 0.0015 23 0.12 1
ACTB 139587 41055 1078 6 6 6 0 0 20 0.72 0.0017 18 0.12 1
SEPT7 116403 29302 1716 4 3 4 0 0 20 0.49 0.0018 16 0.13 1
ZNF598 199157 61502 1023 10 10 1 1 0 20 1.3 0.002 28 0.13 1
BHMT 154238 42987 1661 5 5 5 0 0 20 0.66 0.0023 21 0.12 1
IGFBP6 48944 15134 671 2 2 2 0 0 20 0.47 0.0027 13 0.12 1
C6orf195 47334 14329 264 2 2 2 0 0 20 0.94 0.0028 13 0.12 1
VPS37D 22701 8050 132 2 2 2 0 0 20 0.68 0.0028 10 0.1 1
HOXD8 78568 22057 341 4 4 3 0 0 20 0.87 0.0029 18 0.15 1
SLC10A4 103684 32683 572 3 3 3 0 0 20 0.47 0.0032 16 0.13 1
LRFN4 116886 43792 297 4 4 4 0 0 20 1.2 0.0037 18 0.13 1
PARD6B 132503 37674 462 4 4 4 0 0 20 0.37 0.0037 18 0.13 1
NPS 34615 9982 704 2 2 2 0 0 13 1 0.0044 13 0.12 1
SMARCB1 157458 43470 1958 4 4 3 0 0 16 0.85 0.0046 20 0.13 1
LGI4 78246 25116 594 4 4 4 0 0 20 0.61 0.005 13 0.12 1
KCNK5 174363 53452 1012 3 3 3 0 0 20 0.56 0.005 18 0.12 1
STARD3 171465 46046 2816 3 3 3 0 0 20 0.35 0.0063 18 0.14 1
SKI 113827 36386 594 6 6 1 1 0 20 0.85 0.0069 17 0.13 1
F12 162127 49749 1925 4 4 4 2 0 20 1.3 0.0072 18 0.12 1
GNG10 16261 4830 220 1 1 1 0 0 20 0.83 0.0073 7 0.083 1
KLRK1 86457 19642 1496 3 3 3 0 0 20 0.82 0.0082 13 0.12 1
ING4 101752 23023 1760 2 2 2 0 0 20 0.68 0.0086 13 0.14 1
PBRM1 643839 167440 6666 6 6 6 1 0 20 0.65 0.013 26 0.14 1
MRPL9 86618 25438 1276 2 2 2 0 0 20 0 0.013 9.9 0.11 1
AHCY 161483 47012 1969 4 4 4 1 0 20 1.2 0.013 17 0.23 1
CHRFAM7A 72128 20286 935 2 2 2 0 0 20 1.1 0.014 12 0.11 1
UQCR10 26726 7889 330 2 2 2 0 0 20 1 0.014 9.8 0.1 1
ELF3 143129 39284 1738 3 3 3 0 0 20 0.78 0.014 15 0.14 1
NF2

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

HNRNPM

Figure S2.  This figure depicts the distribution of mutations and mutation types across the HNRNPM 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)
Copyright © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle