Mutation Analysis (MutSigCV v0.6)
Lung Adenocarcinoma (Primary solid tumor)
22 February 2013  |  analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C1PN93VP
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Analysis (MutSigCV v0.6). Broad Institute of MIT and Harvard. doi:10.7908/C1PN93VP
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 v0.6 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).

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

CoMut Plot

Figure 3.  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: 20. 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
KEAP1 326774 95698 169320 42 42 37 0 2 20 0.58 3.3e-16 130 0.25 6.1e-12
TP53 234360 68448 328837 137 128 106 2 3 4 0.86 1.7e-15 500 0.24 1.5e-11
KRAS 151498 36947 240371 64 64 6 0 1 1 0.64 5e-15 170 0.24 3e-11
STK11 135168 38871 118819 21 20 20 0 2 20 0.66 8.4e-15 98 0.24 3.8e-11
CRIPAK 246948 82068 57463 15 13 8 4 0 20 0.78 5e-10 66 0.24 1.8e-06
EGFR 762139 208330 1647564 34 28 19 7 26 20 0.91 1.5e-09 96 0.24 4.1e-06
CDKN2A 94378 27828 65677 15 15 14 1 4 6 1.4 1.6e-09 59 0.25 4.1e-06
NF1 2346812 656897 2970763 35 29 32 4 14 0 0.44 2.8e-09 110 0.26 6.3e-06
RBM10 366145 103516 422882 14 14 12 1 4 16 0.77 3.1e-09 71 0.26 6.3e-06
SMARCA4 830974 235187 1278748 20 19 18 2 7 20 0.67 8.9e-08 81 0.26 0.00016
SIK2 526128 152808 747267 8 7 8 0 0 1 0 3.9e-06 29 0.23 0.0065
MUC7 206073 76627 103495 20 18 19 1 1 20 1.3 9.2e-06 48 0.26 0.014
PSG7 244275 72907 495972 14 14 13 3 0 0 0.47 0.000013 41 0.24 0.018
GPR112 1753032 556983 1165020 60 50 56 20 21 20 0.86 0.000026 95 0.24 0.034
FLG 2289073 679455 115159 140 65 132 26 4 15 1 0.000051 120 0.24 0.062
RIT1 131430 36456 239389 11 10 9 1 2 20 0.94 0.000059 33 0.24 0.067
MYL10 103664 25792 237044 7 7 7 1 6 20 0.81 0.000066 29 0.23 0.071
SPRR3 96715 29512 50421 7 7 4 0 1 20 1.7 0.000085 34 0.22 0.087
BRAF 429511 122750 917805 19 19 12 1 11 19 0.9 0.000098 50 0.24 0.092
RB1 713108 187949 1498581 13 13 13 1 14 20 0.76 0.0001 56 0.25 0.092
ARID1A 1108788 326611 872549 17 15 16 1 4 2 0.64 0.00012 69 0.24 0.11
ZCCHC5 241236 68746 12483 16 15 15 4 0 11 0.9 0.00014 38 0.24 0.11
SVOP 121567 37290 216214 7 7 7 0 6 8 0.74 0.00014 28 0.24 0.11
SMAD4 329324 91755 469614 9 9 8 1 3 20 0.76 0.00023 40 0.24 0.17
GNG2 43633 11408 113944 4 4 4 0 6 20 0.99 0.00023 20 0.22 0.17
PDGFA 90157 25514 228667 7 6 6 0 1 20 0.95 0.00062 25 0.25 0.43
FTSJD1 454554 118787 39000 12 12 11 0 1 20 1.1 0.00068 48 0.24 0.46
TBX21 220497 66221 240331 9 9 8 0 1 20 0.52 0.00075 27 0.24 0.48
LAX1 234618 64981 261983 10 10 8 2 4 20 0.63 0.00079 29 0.24 0.48
MGA 1641699 481751 779596 25 18 25 3 5 3 0.69 0.00079 75 0.3 0.48
OR4Q3 178808 53320 35127 14 14 14 5 3 20 1.5 0.00081 36 0.25 0.48
PON3 212774 59024 549123 5 5 5 0 0 0 0 0.00087 17 0.21 0.5
IL12RB2 511609 143592 771582 13 12 12 3 2 0 0.48 0.00092 40 0.23 0.51
SETD2 1240637 332711 988668 23 19 22 1 2 7 0.88 0.00095 72 0.24 0.51
KRT28 269090 77877 352401 9 9 8 2 4 20 0.7 0.00099 33 0.23 0.52
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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] TCGA, Integrated genomic analyses of ovarian carcinoma, Nature 474:609 - 615 (2011)
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