Mutation Analysis (MutSigCV v0.6)
Lung Adenocarcinoma (MAGNOID)
07 February 2013  |  awg_luad__2013_02_07
Maintainer Information
Citation Information
doi:10.7908/C1NS0RZJ
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/C1NS0RZJ
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: MAGNOID

  • Number of patients in set: 36

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: MAGNOID.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: 14. 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 34020 9936 47421 16 14 13 1 0 4 0.87 1e-14 53 0.023 1.8e-10
KEAP1 47430 13890 24412 13 13 11 0 1 20 0.62 8.9e-14 43 0.02 8.1e-10
STK11 19608 5639 17127 9 8 9 0 0 20 0.87 4.8e-13 38 0.022 2.9e-09
KRAS 21990 5363 34651 14 14 2 0 0 1 0 1.7e-10 37 0.02 7.6e-07
SMARCA4 120594 34131 184336 10 9 8 1 1 20 0.84 6.6e-08 35 0.022 0.00024
RBM10 53169 15032 60994 5 5 4 0 2 16 1 1.9e-07 29 0.022 0.00057
ARID2 152898 45865 122522 7 6 6 0 1 5 0.29 1.4e-06 28 0.023 0.0038
RIT1 19078 5292 34509 6 5 5 1 1 20 0.71 7.3e-06 17 0.021 0.017
IFNG 14319 3382 15892 3 3 2 0 0 20 0.83 0.000014 16 0.021 0.028
OR10G9 24990 8030 6117 4 4 2 0 0 11 1.1 0.000023 19 0.021 0.042
CNIH 7491 1837 22602 2 2 1 0 0 20 1.2 0.000029 13 0.015 0.048
FSCN3 40347 11877 46833 5 5 4 0 0 20 1.4 0.000054 21 0.022 0.079
RHEB 15583 3995 98949 3 3 3 0 1 20 0.57 0.000056 13 0.019 0.079
CSNK1G1 36718 9936 76694 4 4 2 1 0 20 0.92 0.000075 17 0.018 0.098
ODZ4 160632 46757 116375 10 7 9 0 1 1 0.29 0.00011 26 0.022 0.13
SH2D2A 27261 8211 49491 7 6 5 2 1 20 1.1 0.00012 16 0.021 0.13
HRH4 32975 9468 21344 3 3 2 0 0 20 0.98 0.00017 16 0.021 0.18
TDGF1 16486 4680 44154 3 3 2 0 0 20 0.89 0.00023 13 0.02 0.22
OR5B3 26243 7632 7377 3 3 2 1 0 20 1.3 0.00023 15 0.02 0.22
C8orf31 11338 3385 19856 2 2 1 0 0 20 0.96 0.00028 12 0.017 0.26
LCK 40212 11449 58765 5 4 5 0 2 20 0.74 0.00035 15 0.021 0.31
DACT1 56191 17111 25374 5 5 3 1 0 20 1.3 0.00041 19 0.019 0.33
SHISA2 15191 4787 4606 4 4 4 0 0 13 0.81 0.00042 12 0.019 0.33
LGR5 77145 23111 133640 6 6 5 1 0 20 0.99 0.00046 18 0.022 0.35
ATM 267622 68804 349273 8 6 7 1 1 2 0.4 0.00049 23 0.022 0.35
SLC32A1 42293 13353 9623 5 5 5 0 0 20 0.7 0.00049 15 0.021 0.35
DHFR 18703 5190 27785 2 2 1 0 0 20 0.74 0.00056 12 0.017 0.37
S100PBP 36612 10223 39392 3 3 2 0 1 20 0.58 0.00056 13 0.02 0.37
VWC2 8819 2304 13692 2 2 1 0 0 20 1.8 0.00062 12 0.019 0.39
CLK1 43846 10187 79131 3 3 2 1 1 20 0.61 0.00064 13 0.02 0.39
PCDH15 201943 56439 252952 14 9 10 7 18 20 2.8 0.00071 32 0.023 0.42
ELAC1 30493 9036 16967 2 2 1 0 0 20 0.84 0.00073 13 0.017 0.42
ACADM 37219 10007 122075 3 3 2 0 0 11 1 0.00078 14 0.021 0.43
TLR9 82825 26996 14245 5 5 4 0 0 20 0.71 0.0009 17 0.021 0.47
FBN2 252954 64364 474128 12 8 11 2 7 2 1.1 0.00093 27 0.023 0.47
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. ##REF##46

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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle