Mutation Analysis (MutSigCV v0.9)
Stomach Adenocarcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1T72FFW
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Stomach Adenocarcinoma (Primary solid tumor cohort) - 21 April 2013: Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1T72FFW
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: 116

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

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: 5. 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 109620 32016 87904 57 52 42 1 1 4 0.82 4.3e-15 170 0.11 7.9e-11
CBWD1 86584 22748 133628 15 14 2 0 3 5 0.62 3.8e-12 65 0.12 3.4e-08
RPL22 35088 8468 33712 9 9 1 0 1 20 1.2 1.9e-08 44 0.11 0.00012
ACVR2A 144752 38160 133944 14 13 5 0 7 10 1.1 3.6e-06 52 0.11 0.015
ARID1A 518396 152712 223208 26 22 25 1 5 2 0.53 4e-06 75 0.12 0.015
RNF43 192524 60852 112588 14 13 9 2 4 8 0.87 4e-05 48 0.11 0.12
KRAS 70516 17224 61632 14 14 6 0 0 1 0 0.00034 34 0.11 0.89
PGM5 123172 36436 106168 19 16 5 0 4 20 0.98 0.00046 36 0.11 1
PHF2 225192 63444 193000 13 12 5 4 6 6 0.85 0.0022 42 0.11 1
PIK3CA 302212 77252 222112 32 24 19 2 10 20 1.4 0.0028 53 0.11 1
FGF22 14324 4812 3192 3 3 1 1 0 20 1.2 0.0029 16 0.1 1
RHOA 77360 22500 84052 8 7 4 0 5 20 0.83 0.0048 23 0.11 1
PPPDE1 54576 14964 85192 3 3 3 0 5 20 0.16 0.006 11 0.089 1
PRRG3 61944 19084 39508 6 6 6 2 0 20 0.81 0.0065 20 0.11 1
WSB2 110432 32248 102788 5 5 5 0 0 17 0.28 0.0072 16 0.1 1
GPR137 130508 41564 58080 7 7 7 1 2 20 0.51 0.0096 22 0.11 1
OCEL1 60148 17924 59204 4 4 4 0 1 20 0.63 0.0097 17 0.1 1
SLC3A2 129652 40084 149288 5 5 3 0 1 20 0.5 0.011 21 0.11 1
MXRA8 61628 19692 35452 5 4 3 1 4 20 0.51 0.011 16 0.1 1
TNFAIP2 94452 28544 75360 4 4 4 1 1 20 0.41 0.011 17 0.1 1
GHRH 30324 8292 52392 3 3 3 0 2 20 0.73 0.012 12 0.097 1
PTH2 16776 6852 13652 3 3 1 0 0 20 0.92 0.013 12 0.099 1
UPF3A 98812 25456 72436 6 6 3 1 4 20 1.1 0.013 24 0.11 1
TMSB15B 13452 3132 55472 2 2 2 0 2 20 0.68 0.014 8.3 0.074 1
IL11 12748 5040 6080 2 2 2 0 0 20 0.73 0.018 8.9 0.087 1
IAPP 24412 7424 25376 4 4 3 0 0 20 1.3 0.018 13 0.099 1
HIST1H1A 56372 18676 15104 5 5 5 0 0 20 0.67 0.021 15 0.1 1
AP1S1 28980 7608 23740 3 3 3 0 0 20 0.77 0.021 11 0.1 1
PTEN 110912 26640 97576 8 7 8 3 2 20 1.2 0.021 25 0.11 1
KCNJ10 100284 31728 10448 7 7 7 0 0 20 0.64 0.022 19 0.11 1
ASCL4 20976 7648 9056 3 3 2 3 0 20 1.1 0.022 12 0.1 1
AVP 15300 4636 19968 3 3 3 0 0 20 0.98 0.023 10 0.095 1
PRRT1 43364 15536 30176 4 4 4 0 1 20 0.39 0.024 11 0.094 1
HIST1H1B 59388 19428 11964 6 5 6 2 0 20 0.95 0.024 18 0.1 1
C16orf74 3300 696 8580 1 1 1 0 0 20 0.95 0.026 5.9 0.062 1
TP53

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

RPL22

Figure S2.  This figure depicts the distribution of mutations and mutation types across the RPL22 significant gene.

ACVR2A

Figure S3.  This figure depicts the distribution of mutations and mutation types across the ACVR2A significant gene.

ARID1A

Figure S4.  This figure depicts the distribution of mutations and mutation types across the ARID1A 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

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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