Mutation Analysis (MutSigCV v0.9)
Cholangiocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1FJ2G4Z
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1FJ2G4Z
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: CHOL-TP

  • Number of patients in set: 35

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:CHOL-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 2

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: CHOL-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: 2. 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
PBRM1 139965 36400 21210 8 8 8 0 0 20 1.6 7.4e-08 39 0.034 0.0014
BAP1 55230 16590 10325 7 6 7 1 0 20 2.5 9.2e-06 28 0.028 0.084
PTTG1IP 12390 3185 3535 2 2 1 0 0 20 0.83 0.0008 12 0.024 1
PSMA5 20615 5600 6230 2 2 2 0 0 20 0.72 0.002 11 0.024 1
NF2 44835 11305 9800 3 3 3 0 0 20 0 0.0027 13 0.026 1
CLIP4 58870 16940 10570 4 4 4 0 0 20 0.76 0.0036 15 0.026 1
LCE4A 8225 2240 840 2 2 2 0 0 20 1.3 0.005 6.2 0.017 1
B3GNT6 14875 4935 525 2 2 2 0 0 20 0 0.0056 8.7 0.023 1
C11orf52 10535 2835 2905 2 2 2 0 0 20 0.78 0.009 8.6 0.025 1
SATL1 36925 9310 2660 2 2 2 0 0 13 0 0.011 11 0.024 1
PXMP4 16940 5040 2555 2 2 2 0 0 20 0.73 0.011 8.5 0.029 1
IAPP 7315 2240 1540 1 1 1 0 0 20 1.1 0.012 6.3 0.018 1
TRUB1 26390 8015 5565 2 2 2 0 0 20 0 0.012 8.5 0.024 1
PPP6C 25830 7070 4585 2 2 2 0 0 20 0 0.013 8.5 0.024 1
CDC27 68180 18445 12285 5 5 5 0 0 20 0.65 0.015 12 0.026 1
FTH1 15190 3710 3185 3 3 1 0 0 20 0.76 0.015 8.5 0.024 1
PABPN1L 6160 1820 1225 1 1 1 0 0 20 0.79 0.015 6.2 0.018 1
GAGE12J 2310 595 665 1 1 1 0 0 14 0 0.015 6.5 0.021 1
SNX29 42875 11515 6650 2 2 2 1 0 19 0.74 0.016 11 0.033 1
BCL3 19110 6790 3465 2 2 2 0 0 20 0.62 0.017 8.4 0.024 1
EPHA2 74690 22155 9975 4 4 4 0 0 16 0 0.019 15 0.026 1
KRTAP5-5 18620 5460 840 2 2 2 1 0 20 1.7 0.019 6.2 0.02 1
RUFY1 51660 12915 11515 2 2 2 0 0 20 2.8 0.019 11 0.025 1
RAMP3 10710 3080 1470 1 1 1 0 0 20 1.4 0.021 6 0.031 1
SPHAR 5320 1260 805 1 1 1 0 0 20 0.99 0.022 6.2 0.016 1
ELF3 31115 8540 5530 2 2 2 0 0 20 1.2 0.022 8.3 0.023 1
STX3 37275 9940 7350 2 2 2 0 0 20 0 0.023 8.2 0.026 1
HOPX 5600 1610 1050 1 1 1 0 0 20 0 0.024 6.2 0.018 1
SLURP1 7245 2100 1750 1 1 1 0 0 20 0 0.026 6.2 0.019 1
SUGT1 28525 7000 8715 2 2 2 0 0 20 0.55 0.027 5.9 0.02 1
KREMEN2 13160 4025 2800 1 1 1 0 0 20 0.8 0.028 5.9 0.028 1
ALB 52115 13440 9765 3 3 3 0 0 9 0 0.028 12 0.026 1
SHF 26705 8050 2800 1 1 1 0 0 20 0.48 0.029 5.8 0.019 1
DMRTA1 27580 8820 1260 1 1 1 0 0 20 0.93 0.03 5.8 0.019 1
CSH2 15190 4620 2730 1 1 1 0 0 20 0.58 0.03 6 0.019 1
PBRM1

Figure S1.  This figure depicts the distribution of mutations and mutation types across the PBRM1 significant gene.

BAP1

Figure S2.  This figure depicts the distribution of mutations and mutation types across the BAP1 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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