Mutation Analysis (MutSigCV v0.9)
Stomach Adenocarcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C19G5KK7
Maintained by David Heiman (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/C19G5KK7
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

  • MAF used for this analysis:STAD-TP.final_analysis_set.maf

  • Blacklist used for this analysis: pancan_mutation_blacklist.v14.hg19.txt

  • Significantly mutated genes (q ≤ 0.1): 18

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: 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: 18. 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 208845 60996 4738 104 99 67 1 0 4 1.1 3e-15 340 0.15 2.6e-11
CBWD1 167518 43979 4899 30 28 3 0 0 5 0.86 4e-15 150 0.13 2.6e-11
ARID1A 987207 290836 8648 47 41 47 2 0 2 0.49 4.4e-15 160 0.13 2.6e-11
PIK3CA 574379 146965 9108 62 48 31 2 0 20 0.83 5.8e-15 110 0.15 2.6e-11
PTEN 215254 51714 4002 18 14 16 4 0 20 0.77 1.8e-09 58 0.15 6.7e-06
PGM5 236691 70057 4140 25 22 7 1 0 20 0.63 7e-09 55 0.15 0.000021
SMAD4 292604 81549 5106 22 19 19 1 0 20 0.99 2.8e-08 62 0.13 0.000072
B2M 63427 17680 1472 9 8 9 0 0 20 0.71 1.1e-07 35 0.12 0.00024
RHOA 146744 42653 2553 14 13 10 0 0 20 0.38 1.3e-07 41 0.12 0.00026
IRF2 187850 50167 3703 17 14 15 1 0 20 0.93 4.3e-07 48 0.13 0.00079
APC 1474512 413270 6693 38 33 33 5 0 4 0.62 8.9e-06 100 0.13 0.014
FBXW7 429182 117793 5497 20 19 13 1 0 20 0.81 8.9e-06 53 0.14 0.014
MAP2K7 146744 40664 4554 20 14 20 0 0 20 1.2 1e-05 42 0.15 0.014
TRPS1 674934 186082 2829 34 30 34 12 0 20 1.5 0.000016 70 0.13 0.021
CDH1 431834 128401 7958 19 18 18 4 0 20 0.9 0.000018 57 0.14 0.022
WSB2 210392 61438 3703 7 7 7 1 0 17 0.17 0.000033 23 0.12 0.038
KRAS 133705 32708 2438 25 25 6 0 0 1 0 0.000036 59 0.13 0.039
BCOR 787644 235586 4715 17 15 17 3 0 20 0.72 0.000056 61 0.13 0.057
C16orf74 5967 1326 391 2 2 1 0 0 20 1.1 0.00011 14 0.084 0.1
RNF43 367965 116467 4025 10 9 10 2 0 8 0.58 0.00013 40 0.12 0.12
CBLN3 73151 25857 1173 4 4 3 2 0 20 0.7 0.00044 21 0.12 0.39
EDNRB 234702 67405 8418 20 18 17 4 0 4 1.1 0.00083 49 0.14 0.69
BCL7C 111163 36686 2783 6 6 6 0 0 20 0.51 0.0012 22 0.12 0.98
NRIP3 98124 28288 2783 6 6 6 0 0 20 0.6 0.0015 19 0.11 1
RXFP3 204204 66521 506 19 18 19 6 0 14 1.4 0.0018 40 0.13 1
LRIT1 256802 83980 1472 9 9 8 1 0 15 0.44 0.0019 31 0.12 1
HIST1H1B 112931 36907 529 8 7 7 2 0 20 1 0.0019 26 0.12 1
CXorf56 121329 31603 3289 7 7 7 0 0 20 1.3 0.0021 26 0.14 1
C13orf33 125528 33371 2047 6 6 2 1 0 4 0.84 0.0021 33 0.12 1
MREG 97682 26078 1472 7 6 7 2 0 20 1.3 0.0023 24 0.14 1
FBXO28 150059 40885 1748 5 5 5 0 0 20 0.42 0.0027 21 0.12 1
CCDC39 370175 88842 4784 15 13 15 2 0 12 1 0.0027 39 0.13 1
CYP7B1 245752 64090 2231 12 11 11 1 0 19 0.97 0.003 31 0.14 1
SAMSN1 201110 50167 3680 9 9 9 2 0 15 0.93 0.003 29 0.12 1
OR5M3 156689 44421 575 9 8 9 1 0 20 1 0.0032 25 0.13 1
TP53

Figure S1.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

CBWD1

Figure S2.  This figure depicts the distribution of mutations and mutation types across the CBWD1 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.

PGM5

Figure S6.  This figure depicts the distribution of mutations and mutation types across the PGM5 significant gene.

SMAD4

Figure S7.  This figure depicts the distribution of mutations and mutation types across the SMAD4 significant gene.

B2M

Figure S8.  This figure depicts the distribution of mutations and mutation types across the B2M significant gene.

RHOA

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

IRF2

Figure S10.  This figure depicts the distribution of mutations and mutation types across the IRF2 significant gene.

APC

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

FBXW7

Figure S12.  This figure depicts the distribution of mutations and mutation types across the FBXW7 significant gene.

MAP2K7

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

TRPS1

Figure S14.  This figure depicts the distribution of mutations and mutation types across the TRPS1 significant gene.

CDH1

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

WSB2

Figure S16.  This figure depicts the distribution of mutations and mutation types across the WSB2 significant gene.

KRAS

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

BCOR

Figure S18.  This figure depicts the distribution of mutations and mutation types across the BCOR 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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