Mutation Analysis (MutSigCV v0.9)
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C15M654Z
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/C15M654Z
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: 282

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): 4

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: 4. 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 361242 91086 1400 10 10 10 0 0 20 0.84 3.3e-09 56 0.23 6e-05
PTEN 274668 65988 870 8 7 8 0 0 20 0.57 2.8e-06 37 0.28 0.025
CUL3 515778 132258 1495 12 10 11 0 0 20 1 5.5e-06 46 0.25 0.033
PBRM1 1127718 293280 3030 12 11 12 0 0 20 0.75 0.000015 52 0.25 0.07
BAP1 444996 133668 1475 12 10 12 1 0 20 2.2 0.000047 47 0.25 0.17
SAV1 253236 70500 510 7 6 7 0 0 20 1.6 0.000056 33 0.28 0.17
PARD6B 232086 65988 210 7 7 7 0 0 20 0.64 0.00016 28 0.23 0.39
SMARCB1 275796 76140 890 6 6 5 0 0 16 0.37 0.00017 30 0.25 0.39
CFC1 35814 9870 105 2 2 2 0 0 20 0.85 0.00046 14 0.2 0.89
NEFH 470376 130002 300 7 6 6 0 0 20 0.84 0.00049 32 0.22 0.89
CFC1B 35814 9870 105 2 2 2 0 0 20 0.74 0.00053 14 0.2 0.89
ING4 178224 40326 800 3 3 3 0 0 20 0.88 0.00099 19 0.86 1
RIMBP3B 352500 106596 105 4 4 1 0 0 20 0.82 0.0019 24 0.24 1
ASIP 54144 17484 220 2 2 1 0 0 20 1.5 0.0023 13 0.22 1
NUDT16L1 85164 28482 135 3 3 3 0 0 20 0.51 0.0024 14 0.24 1
WDR81 474324 154818 730 8 8 8 1 0 20 1.1 0.0026 31 0.23 1
ERRFI1 301740 91086 320 3 3 3 0 0 20 0.36 0.0029 19 0.23 1
SLC10A4 181608 57246 260 4 4 4 0 0 20 1.2 0.0033 19 0.28 1
KRTAP4-5 115620 29328 100 5 5 4 0 0 20 1.7 0.0033 19 0.22 1
F12 283974 87138 875 6 6 6 2 0 20 2.1 0.0044 24 0.25 1
NPS 60630 17484 320 2 2 2 0 0 13 0.86 0.0045 13 0.2 1
PAWR 119568 31020 500 3 3 3 0 0 20 0.39 0.0052 11 0.19 1
SETD2 1391106 372522 1845 18 16 18 1 0 7 1.3 0.0057 65 0.28 1
NIPBL 1881504 498858 4385 8 8 8 0 0 20 0 0.0066 38 0.22 1
UQCR10 46812 13818 150 2 2 2 0 0 20 0.29 0.0071 11 0.18 1
DNAJC5G 130848 33276 410 3 3 3 0 0 20 0.48 0.0072 13 0.21 1
SULT1C4 211782 50760 720 4 4 4 1 0 18 1.1 0.0077 16 0.2 1
UNKL 198810 60630 640 3 3 3 0 0 20 0.42 0.0078 16 0.21 1
LRRC1 349680 102366 1355 4 4 4 0 0 20 0.55 0.0083 18 0.22 1
AMY1A 114774 30174 420 2 2 2 0 0 20 0.85 0.0084 13 0.2 1
DNAJB12 249288 71628 715 4 4 4 0 0 20 0.48 0.0085 16 0.22 1
AR 426666 122670 815 16 13 9 1 0 5 1.2 0.0086 34 0.24 1
FNIP2 699924 197964 1205 6 6 6 1 0 20 0.26 0.0089 26 0.25 1
LYAR 266208 61758 815 3 3 3 0 0 20 0.49 0.0089 16 0.28 1
KRT2 401568 122670 855 5 5 4 0 0 20 0.83 0.01 23 0.27 1
NF2

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

PTEN

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

CUL3

Figure S3.  This figure depicts the distribution of mutations and mutation types across the CUL3 significant gene.

PBRM1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the PBRM1 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 © 2016 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle