Mutation Analysis (MutSigCV v0.9)
Stomach Adenocarcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1CC0Z2R
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1CC0Z2R
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: STAD-TP

  • Number of patients in set: 221

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: STAD-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: 22. 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
CBWD1 167518 43979 9585 30 28 3 0 0 5 0.85 0 150 0.13 0
TP53 208845 60996 9270 104 99 67 1 0 4 1.1 2.7e-15 340 0.13 2.4e-11
ARID1A 987207 290836 16920 47 41 47 2 0 2 0.49 9.7e-15 160 0.13 5.9e-11
PIK3CA 574379 146965 17820 62 48 31 2 0 20 0.82 1.5e-14 110 0.13 6.7e-11
PTEN 215254 51714 7830 18 14 16 4 0 20 0.76 1.1e-09 58 0.14 3.9e-06
MXRA8 112931 36023 4455 13 11 6 2 0 20 0.84 1.7e-09 54 0.13 5.2e-06
PGM5 236691 70057 8100 25 22 7 1 0 20 0.63 4.2e-09 55 0.12 0.000011
SMAD4 292604 81549 9990 22 19 19 1 0 20 0.98 1.6e-08 63 0.13 0.000035
RHOA 146744 42653 4995 14 13 10 0 0 20 0.37 7.5e-08 42 0.12 0.00015
B2M 63427 17680 2880 9 8 9 0 0 20 0.71 8.1e-08 35 0.12 0.00015
IRF2 187850 50167 7245 18 15 16 2 0 20 0.96 1.2e-07 50 0.14 0.0002
APC 1474512 413270 13095 39 33 34 5 0 4 0.63 4.8e-06 100 0.13 0.0074
MAP2K7 146744 40664 8910 20 14 20 0 0 20 1.2 6.6e-06 43 0.14 0.0093
FBXW7 429182 117793 10755 20 19 13 1 0 20 0.84 7.9e-06 53 0.13 0.01
CDH1 431834 128401 15570 19 18 18 5 0 20 0.93 0.000013 57 0.12 0.016
TRPS1 674934 186082 5535 34 30 34 12 0 20 1.5 0.000016 70 0.13 0.018
BCOR 787644 235586 9225 18 16 18 3 0 20 0.71 0.000017 63 0.13 0.019
KRAS 133705 32708 4770 25 25 6 0 0 1 0 0.000027 59 0.13 0.028
WSB2 210392 61438 7245 7 7 7 1 0 17 0.18 0.000034 23 0.11 0.033
BCL7C 111163 36686 5445 7 7 7 0 0 20 0.5 0.000066 28 0.12 0.06
C16orf74 5967 1326 765 2 2 1 0 0 20 1.1 0.000098 14 0.082 0.085
RNF43 367965 116467 7875 10 9 10 2 0 8 0.58 0.00011 40 0.12 0.09
CBLN3 73151 25857 2295 4 4 3 2 0 20 0.71 0.00039 21 0.12 0.31
EDNRB 234702 67405 16470 20 18 17 4 0 4 1.1 0.00047 50 0.12 0.36
NRIP3 98124 28288 5445 6 6 6 0 0 20 0.59 0.0012 19 0.11 0.87
RXFP3 204204 66521 990 19 18 19 6 0 14 1.4 0.0015 40 0.13 0.99
C13orf33 125528 33371 4005 6 6 2 1 0 4 0.82 0.0015 33 0.12 0.99
CXorf56 121329 31603 6435 7 7 7 0 0 20 1.2 0.0016 26 0.12 0.99
LRIT1 256802 83980 2880 9 9 8 1 0 15 0.45 0.0016 31 0.14 0.99
HIST1H1B 112931 36907 1035 8 7 7 2 0 20 1.1 0.0018 26 0.14 1
MREG 97682 26078 2880 7 6 7 2 0 20 1.3 0.0019 24 0.12 1
MLL2 2340611 773721 34695 47 37 45 11 0 20 0.79 0.0021 90 0.13 1
CCDC39 370175 88842 9360 15 13 15 2 0 12 1 0.0021 39 0.13 1
FBXO28 150059 40885 3420 5 5 5 0 0 20 0.41 0.0022 21 0.12 1
SAMSN1 201110 50167 7200 9 9 9 2 0 15 0.93 0.0024 29 0.12 1
CBWD1

Figure S1.  This figure depicts the distribution of mutations and mutation types across the CBWD1 significant gene.

TP53

Figure S2.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

ARID1A

Figure S3.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

PIK3CA

Figure S4.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

PTEN

Figure S5.  This figure depicts the distribution of mutations and mutation types across the PTEN significant gene.

MXRA8

Figure S6.  This figure depicts the distribution of mutations and mutation types across the MXRA8 significant gene.

PGM5

Figure S7.  This figure depicts the distribution of mutations and mutation types across the PGM5 significant gene.

SMAD4

Figure S8.  This figure depicts the distribution of mutations and mutation types across the SMAD4 significant gene.

RHOA

Figure S9.  This figure depicts the distribution of mutations and mutation types across the RHOA significant gene.

B2M

Figure S10.  This figure depicts the distribution of mutations and mutation types across the B2M significant gene.

IRF2

Figure S11.  This figure depicts the distribution of mutations and mutation types across the IRF2 significant gene.

APC

Figure S12.  This figure depicts the distribution of mutations and mutation types across the APC significant gene.

MAP2K7

Figure S13.  This figure depicts the distribution of mutations and mutation types across the MAP2K7 significant gene.

FBXW7

Figure S14.  This figure depicts the distribution of mutations and mutation types across the FBXW7 significant gene.

CDH1

Figure S15.  This figure depicts the distribution of mutations and mutation types across the CDH1 significant gene.

TRPS1

Figure S16.  This figure depicts the distribution of mutations and mutation types across the TRPS1 significant gene.

BCOR

Figure S17.  This figure depicts the distribution of mutations and mutation types across the BCOR significant gene.

KRAS

Figure S18.  This figure depicts the distribution of mutations and mutation types across the KRAS significant gene.

WSB2

Figure S19.  This figure depicts the distribution of mutations and mutation types across the WSB2 significant gene.

BCL7C

Figure S20.  This figure depicts the distribution of mutations and mutation types across the BCL7C significant gene.

C16orf74

Figure S21.  This figure depicts the distribution of mutations and mutation types across the C16orf74 significant gene.

RNF43

Figure S22.  This figure depicts the distribution of mutations and mutation types across the RNF43 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)
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