Mutation Analysis (MutSigCV v0.9)
Prostate Adenocarcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1GF0RJ5
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/C1GF0RJ5
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: PRAD-TP

  • Number of patients in set: 83

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: PRAD-TP.patients.counts_and_rates.txt

Needs description.

Figure 3.  Needs description.

Figure 4.  Needs description.

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: 0. 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
NKX3-1 32370 9794 1440 5 5 5 0 0 11 0 7e-05 18 0.096 1
CDKN1B 38595 10707 10440 2 2 2 0 0 20 0 0.004 12 0.09 1
BANF2 18758 4648 2340 2 2 2 0 0 20 0.74 0.0045 7.5 0.039 1
SLITRK4 162265 45899 1380 3 3 2 0 0 11 2.1 0.0082 17 0.078 1
FBXO4 66898 17596 7440 2 2 2 0 0 20 0 0.009 9.7 0.082 1
SPOP 75945 19920 11040 4 4 3 0 0 20 0 0.011 12 0.082 1
ZMYM3 217543 62416 20340 5 4 5 0 0 20 1.9 0.011 19 0.099 1
LCE2D 21414 6142 1440 2 2 2 0 0 20 0.67 0.011 6.8 0.039 1
TBL1XR1 88561 24153 10620 2 2 2 0 0 20 0.72 0.013 12 0.088 1
CPEB4 143341 40836 12120 3 3 3 0 0 20 0 0.017 14 0.076 1
GPATCH4 94454 24651 11460 2 2 1 0 0 15 0 0.018 12 0.05 1
SFRS2 58681 19090 6900 2 2 2 1 0 20 1.8 0.019 12 0.09 1
KRT25 88644 25730 9840 3 3 3 0 0 20 0 0.019 12 0.089 1
PTEN 80842 19422 10440 3 3 3 0 0 20 0.79 0.019 11 0.09 1
YBX1 52622 14608 6960 4 3 2 0 0 20 0 0.02 9.2 0.076 1
ESCO1 167494 42828 18720 3 3 3 0 0 20 0 0.021 14 0.066 1
FOXA1 67728 19837 2580 3 2 3 0 0 20 1.8 0.022 12 0.051 1
ATG10 45235 11454 7320 1 1 1 0 0 18 0.91 0.023 6.5 0.066 1
C19orf53 18177 5561 3240 1 1 1 0 0 20 1.7 0.024 6.6 0.037 1
NANOS1 9213 3071 0 1 1 1 0 0 20 0 0.025 6.6 0.062 1
GGTLC2 32453 9628 8880 2 2 2 1 0 17 1.1 0.025 6.5 0.068 1
NKX2-4 14442 4233 960 1 1 1 0 0 20 0.99 0.025 6.5 0.068 1
SLC10A2 67977 20252 7380 2 2 2 0 0 18 0.88 0.025 6.5 0.037 1
PRB1 38263 13197 3660 2 2 2 0 0 12 1.9 0.028 6.4 0.039 1
GNPNAT1 37018 10126 6180 1 1 1 0 0 20 0.63 0.029 6.3 0.076 1
SHFM1 15189 3071 3840 1 1 1 0 0 20 0.93 0.029 6.5 0.066 1
C20orf111 56274 17015 3720 2 2 2 0 0 20 0 0.031 9 0.053 1
NEUROD6 66151 17845 1440 2 2 2 0 0 20 0 0.032 6.4 0.07 1
TCF15 4814 1826 0 1 1 1 0 0 20 1.1 0.034 3.6 0.038 1
MLL3 952425 270580 74400 10 7 9 0 0 13 0 0.035 28 0.066 1
TM4SF18 39923 11371 6120 1 1 1 0 0 20 0 0.035 6.4 0.066 1
CRIPAK 81672 27141 1380 3 3 3 1 0 20 1.8 0.036 8.8 0.083 1
ATP6V1F 21995 6640 2220 1 1 1 0 0 20 0.9 0.037 3.8 0.024 1
ACTR8 115785 31789 14100 2 2 2 0 0 20 0 0.038 11 0.059 1
SOD3 13031 4067 480 1 1 1 0 0 20 0 0.039 3.8 0.03 1
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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