Mutation Analysis (MutSigCV v0.9)
Lung Adenocarcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C17M06CV
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/C17M06CV
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: LUAD-TP

  • Number of patients in set: 248

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
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: LUAD-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: 159. 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 234360 68448 328837 137 128 106 2 3 4 0.86 5.6e-16 510 0.15 1e-11
KEAP1 326774 95698 169320 42 42 37 0 2 20 0.58 2.8e-15 140 0.15 2.2e-11
EGFR 762139 208330 1647564 34 28 19 7 26 20 0.91 3.6e-15 100 0.15 2.2e-11
STK11 135168 38871 118819 21 20 20 0 2 20 0.66 7.3e-15 100 0.15 3.2e-11
KRAS 151498 36947 240371 64 64 6 0 1 1 0.64 8.8e-15 170 0.15 3.2e-11
RBM10 366145 103516 422882 14 14 12 1 4 16 0.77 3.8e-12 73 0.15 1.2e-08
CDKN2A 94378 27828 65677 15 15 14 1 4 6 1.4 1.3e-11 61 0.15 3e-08
NF1 2346812 656897 2970763 35 29 32 4 14 0 0.44 1.4e-11 110 0.15 3e-08
SMARCA4 830974 235187 1278748 20 19 18 2 7 20 0.67 1.5e-11 86 0.15 3e-08
GPR112 1753032 556983 1165020 60 50 56 20 21 20 0.86 4.5e-11 110 0.15 8.3e-08
FLG 2289073 679455 115159 137 63 129 25 4 15 1 1e-10 130 0.15 1.7e-07
COL11A1 1077726 338658 2665261 71 53 65 15 94 18 3 1.6e-08 120 0.15 0.000024
MUC7 206073 76627 103495 19 17 18 1 1 20 1.3 2.2e-08 50 0.15 0.000031
CSMD3 2219330 619687 3353706 177 107 160 40 186 5 5.1 5.9e-08 190 0.15 0.000077
BRAF 429511 122750 917805 19 19 12 1 11 19 0.89 8.9e-08 54 0.15 0.00011
RB1 713108 187949 1498581 13 13 13 1 14 20 0.76 1.5e-07 59 0.15 0.00017
HRNR 1194657 395410 112967 54 32 54 11 3 20 1.1 2.8e-07 86 0.15 0.0003
RIMS2 834187 234583 2133181 47 41 45 4 66 6 2 4e-07 94 0.16 0.00041
RIT1 131430 36456 239389 11 10 9 1 2 20 0.94 8.1e-07 36 0.16 0.00078
ZCCHC5 241236 68746 12483 16 15 15 4 0 11 0.9 1.1e-06 41 0.15 0.001
LRP1B 2802163 699979 4354925 158 92 146 28 168 6 3.4 1.2e-06 180 0.16 0.001
LTBP1 956591 254468 1763850 31 31 28 7 37 20 1.2 1.2e-06 75 0.15 0.001
MYL10 103664 25792 237044 7 7 7 1 6 20 0.81 2e-06 31 0.14 0.0016
FTSJD1 454554 118787 39000 12 12 11 0 1 20 1.1 2.2e-06 51 0.15 0.0017
ARID1A 1108788 326611 872549 17 15 16 1 4 2 0.64 2.5e-06 70 0.15 0.0018
SMAD4 329324 91755 469614 9 9 8 1 3 20 0.77 3.5e-06 41 0.15 0.0025
OR4Q3 178808 53320 35127 14 14 14 5 3 20 1.6 3.7e-06 40 0.16 0.0025
SVOP 121567 37290 216214 7 7 7 0 6 8 0.74 5.5e-06 30 0.14 0.0036
MGA 1641699 481751 779596 25 18 25 3 5 3 0.69 5.9e-06 77 0.15 0.0037
OVCH1 595102 165704 903961 30 21 26 7 30 20 1.7 8.4e-06 63 0.15 0.0051
SETD2 1240637 332711 988668 23 19 22 1 2 7 0.88 0.000012 71 0.15 0.0069
COL19A1 668801 205582 2178715 28 25 28 6 53 9 1.5 0.000013 65 0.15 0.0074
PCK1 368513 102176 429324 14 14 12 2 9 20 1.1 0.000014 47 0.15 0.0077
ELTD1 408822 109090 490599 21 21 20 0 12 15 1.9 0.000015 55 0.15 0.0082
OR10G9 172142 55314 42417 12 12 10 3 1 11 1.7 0.000016 40 0.15 0.0083
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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