Mutation Analysis (MutSigCV v0.9)
Uterine Carcinosarcoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1X928SX
Maintained by Dan DiCara (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/C1X928SX
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: 56

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). 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 52920 15456 8034 54 50 42 0 0 4 0 0 140 0.042 0
FBXW7 108752 29848 9321 22 21 13 0 0 20 0.74 1.1e-14 59 0.041 1e-10
PTEN 54544 13104 6786 13 10 12 0 0 20 0.95 9.6e-10 39 0.04 4.9e-06
PIK3CA 145544 37240 15444 20 19 12 0 0 20 1.4 1.1e-09 45 0.041 4.9e-06
PPP2R1A 74088 23184 10803 16 15 9 0 0 17 1.1 9.6e-08 36 0.039 0.00035
PCDHAC2 120904 38472 2418 11 11 11 6 0 20 2.3 7.2e-06 26 0.038 0.022
ZBTB7B 76720 25088 2223 7 6 7 0 0 20 0 0.000016 25 0.038 0.037
PIK3R1 104384 26824 13455 8 6 8 0 0 20 1.9 0.000016 29 0.039 0.037
RB1 159488 42056 18954 5 5 5 0 0 20 0.63 0.00017 24 0.038 0.34
HCFC1R1 19376 6328 4095 2 2 2 0 0 20 1 0.0011 12 0.034 1
BCL2L11 26264 7504 2496 2 2 1 0 0 20 0 0.0014 12 0.033 1
CHD4 259784 69328 36582 10 9 10 0 0 20 0.57 0.0019 23 0.043 1
ZFP36L1 42784 13720 1716 2 2 2 0 0 20 0 0.0054 12 0.034 1
LZTFL1 40936 10304 7293 2 2 2 0 0 19 0 0.0054 12 0.036 1
BAGE 5712 1512 1443 1 1 1 0 0 20 0 0.0071 6.8 0.025 1
LYPLA2 39648 11928 8931 2 2 2 1 0 20 2.5 0.0076 11 0.037 1
C1orf43 35560 10248 6435 2 2 2 0 0 18 0 0.015 8.8 0.032 1
TBXAS1 71680 20496 10179 2 2 2 0 0 20 0.8 0.015 11 0.034 1
SPOP 51240 13440 7176 5 4 5 0 0 20 1.6 0.017 11 0.036 1
HBA1 5992 2240 1092 1 1 1 0 0 20 1.3 0.018 6.5 0.025 1
LYRM2 11872 3192 2379 1 1 1 0 0 20 0.66 0.019 6.5 0.027 1
NSL1 40992 10304 8073 2 2 2 0 0 20 0.55 0.019 8.8 0.031 1
IER3 13664 5040 1287 1 1 1 0 0 20 1.6 0.02 6.3 0.028 1
YPEL4 13048 3304 2418 2 2 2 0 0 13 0 0.021 6.2 0.026 1
NUDT14 21672 7056 2613 2 2 2 0 0 20 1.8 0.021 8.3 0.03 1
FIGNL2 12376 3920 390 1 1 1 0 0 20 0.92 0.021 6.3 0.026 1
PDCD5 15008 3584 3978 1 1 1 0 0 20 0.97 0.024 6.2 0.025 1
C22orf24 11200 3080 1131 1 1 1 0 0 20 0.75 0.025 6.1 0.024 1
FER 112448 27552 13689 2 2 1 0 0 20 0 0.025 11 0.034 1
CEL 64736 19712 6474 2 2 2 0 0 20 1 0.026 11 0.035 1
HMGN2 10976 2912 3666 1 1 1 0 0 20 2.4 0.029 6.3 0.025 1
GOLGA7 16184 3976 2340 1 1 1 0 0 20 1.7 0.03 6.2 0.026 1
BMF 23576 5992 1911 1 1 1 0 0 20 1.5 0.03 6.1 0.028 1
FOXA2 45024 12432 1248 3 3 3 0 0 20 2.7 0.031 10 0.033 1
FUCA1 50512 13608 5499 2 2 2 0 0 20 2.5 0.031 8.5 0.035 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.

PTEN

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

PIK3CA

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

PPP2R1A

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

PCDHAC2

Figure S6.  This figure depicts the distribution of mutations and mutation types across the PCDHAC2 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)
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