Mutation Analysis (MutSigCV v0.9)
Uterine Carcinosarcoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1V40TBM
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1V40TBM
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: UCS-TP

  • Number of patients in set: 57

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:UCS-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 8

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: UCS-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: 8. 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 53865 15732 12679 56 51 44 0 0 4 0 0 150 0.12 0
FBXW7 110700 30382 15311 24 22 15 0 0 20 0.83 4.6e-15 62 0.045 4.2e-11
PIK3CA 148156 37907 25322 22 20 13 0 0 20 1.1 4.4e-11 49 0.045 2.7e-07
PTEN 55481 13329 11126 16 11 15 0 0 20 1.6 1.3e-10 41 0.043 6.1e-07
PPP2R1A 75408 23597 17760 17 16 10 0 0 17 1.3 2.4e-08 38 0.044 0.000086
RB1 162364 42814 32887 7 6 7 0 0 20 0.36 7.2e-06 29 0.043 0.018
ZBTB7B 78078 25532 3547 7 6 7 0 0 20 0 7.8e-06 25 0.13 0.018
PIK3R1 106259 27305 22427 8 6 8 0 0 20 1.6 7.9e-06 30 0.043 0.018
HCFC1R1 19740 6447 6529 2 2 2 0 0 20 0.56 0.0009 12 0.041 1
LYPLA2 40353 12139 14217 3 3 3 0 0 20 0.92 0.001 15 0.044 1
BCL2L11 26736 7639 4013 2 2 1 0 0 20 0.39 0.0011 12 0.038 1
CHD4 264430 70568 59132 12 10 12 0 0 20 1.3 0.002 24 0.043 1
UBE2G1 22690 5986 7101 2 2 2 0 0 20 0 0.0031 9.6 0.035 1
ZFP36L1 43550 13966 2938 2 2 2 0 0 20 0.48 0.004 12 0.12 1
MYOZ1 40072 12027 6728 2 2 2 0 0 20 0.71 0.004 11 0.04 1
NDUFAF2 22397 5927 6022 3 3 1 0 0 20 1.1 0.0042 9.5 0.036 1
OSTN 17500 5530 4101 2 2 2 0 0 20 1.3 0.0052 8.6 0.035 1
DYNLRB1 13510 3648 5517 2 2 2 0 0 20 0 0.0059 6.8 0.064 1
LZTFL1 41677 10490 12123 2 2 2 0 0 19 0.56 0.0062 11 0.038 1
FAM92B 40535 11288 11005 3 3 3 0 0 18 0.52 0.0079 11 0.061 1
SPOP 52159 13680 12378 5 4 5 0 0 20 0.86 0.0088 11 0.039 1
CBX5 27933 6498 7041 2 2 2 0 0 20 0 0.0097 9.1 0.035 1
RAB11FIP2 69946 18071 6312 2 2 2 0 0 20 0 0.01 11 0.037 1
DCTN5 27471 7409 9007 2 2 2 0 0 20 0.41 0.01 8.6 0.034 1
NUDT14 22115 7201 4366 2 2 2 0 0 20 0.49 0.01 9 0.034 1
TBXAS1 72964 20863 17589 2 2 2 0 0 20 0.44 0.011 11 0.039 1
GOLGA7 16467 4044 4606 2 2 2 0 0 20 3.2 0.011 8.1 0.13 1
NSL1 41726 10488 12957 2 2 2 0 0 20 0.62 0.014 9 0.035 1
KCNA3 64244 20066 807 5 4 5 3 0 20 2 0.014 12 0.083 1
ZFP90 86643 21490 4518 3 3 3 0 0 20 0.86 0.016 10 0.12 1
FUCA1 51427 13855 9213 2 2 2 0 0 20 0.92 0.016 8.8 0.13 1
HMGN2 11174 2964 6037 1 1 1 0 0 20 0.89 0.017 6.5 0.16 1
C1orf43 36199 10431 11228 2 2 2 0 0 18 0.52 0.017 8.9 0.035 1
LYRM2 12082 3248 4024 1 1 1 0 0 20 0.37 0.017 6.6 0.031 1
C22orf24 11393 3132 1919 1 1 1 0 0 20 0 0.017 6.6 0.039 1
TP53

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

FBXW7

Figure S2.  This figure depicts the distribution of mutations and mutation types across the FBXW7 significant gene.

PIK3CA

Figure S3.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

PTEN

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

PPP2R1A

Figure S5.  This figure depicts the distribution of mutations and mutation types across the PPP2R1A significant gene.

RB1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the RB1 significant gene.

ZBTB7B

Figure S7.  This figure depicts the distribution of mutations and mutation types across the ZBTB7B significant gene.

PIK3R1

Figure S8.  This figure depicts the distribution of mutations and mutation types across the PIK3R1 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)
Copyright © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle