Mutation Analysis (MutSigCV v0.9)
Skin Cutaneous Melanoma (Metastatic)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1DB814G
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/C1DB814G
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: SKCM-TM

  • Number of patients in set: 290

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:SKCM-TM.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 450

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: SKCM-TM.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: 450. 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
BRAF 500835 142972 100724 169 145 19 5 0 19 0.84 2.2e-16 410 0.19 4.1e-12
NRAS 133692 35380 24355 90 87 10 1 0 20 0.74 2.1e-15 300 0.18 1.9e-11
CDKN2A 174354 49530 18001 42 41 21 6 0 6 0.96 3.3e-15 180 0.21 2e-11
PTEN 282423 67851 51320 25 25 23 0 0 20 0.42 6.1e-15 120 0.28 2.5e-11
TP53 274050 80040 60265 54 48 43 2 0 4 1.2 6.8e-15 160 0.19 2.5e-11
ARMC4 688730 192264 102826 83 56 65 23 0 15 1.4 3.5e-13 130 0.23 1.1e-09
PPP6C 214054 58594 39923 21 20 15 2 1 20 0.71 1.9e-11 68 0.19 4.8e-08
DCDC1 252887 68731 44759 48 36 45 14 0 20 1.7 2.5e-11 85 0.29 5.8e-08
TMPRSS11B 291749 75983 58326 29 24 25 6 0 11 1.1 1.5e-10 79 0.24 3.1e-07
DSG1 718630 207062 88849 64 46 56 12 0 20 0.81 2.8e-10 97 0.17 5.1e-07
MS4A2 171707 49888 38680 13 13 12 2 0 11 0.27 7.2e-10 49 0.17 1.1e-06
COL5A1 1056872 340788 426938 84 57 80 32 0 10 0.75 7.4e-10 110 0.19 1.1e-06
AOAH 423810 101537 120754 27 25 25 11 3 20 0.55 8e-10 70 0.17 1.1e-06
PREX2 1172758 304209 363170 98 71 84 35 0 12 1.3 1e-09 130 0.17 1.4e-06
MPP7 406288 106720 93080 40 32 33 6 0 8 0.87 1.1e-09 79 0.19 1.4e-06
VEGFC 278106 71628 38034 23 20 20 2 0 6 0.48 1.3e-09 65 0.17 1.4e-06
PPIAL4G 113101 30160 7185 14 14 11 4 0 20 0.46 1.3e-09 46 0.22 1.4e-06
OGDHL 675948 194579 119665 44 35 41 15 0 14 0.53 1.6e-09 83 0.2 1.6e-06
CAPZA3 210541 54230 13135 32 29 23 14 5 15 2.8 2.6e-09 85 0.22 2.4e-06
PROL1 161245 55391 12306 27 24 21 7 0 20 0.83 2.6e-09 57 0.21 2.4e-06
SYCP1 627321 146751 146212 38 30 31 8 0 6 0.54 3.7e-09 79 0.33 3.2e-06
CYP4Z1 343363 89031 62939 29 27 22 10 0 20 0.75 4.3e-09 66 0.2 3.6e-06
STXBP5L 825068 227364 154594 76 58 69 13 0 16 1.2 4.6e-09 110 0.21 3.6e-06
ITGA4 729363 194012 176940 45 38 40 12 0 15 0.63 5e-09 82 0.24 3.8e-06
KIAA2022 1027730 274621 15313 77 56 67 29 0 11 1 5.3e-09 110 0.23 3.9e-06
STK31 727905 181541 142171 51 38 45 13 0 18 0.68 5.6e-09 85 0.25 3.9e-06
PCSK1 522295 142972 81545 31 27 27 13 0 20 0.65 5.7e-09 76 0.17 3.9e-06
C8orf34 303046 80330 76883 34 31 31 6 2 20 1.8 7.2e-09 82 0.17 4.7e-06
LRRC4C 428043 128181 7063 50 43 41 18 0 13 1.8 1.4e-08 97 0.19 8.6e-06
TPTE2 371511 96574 152595 38 31 34 6 0 14 1.4 1.5e-08 83 0.23 9.1e-06
NF1 2729532 765031 350825 53 38 48 9 1 0 0.34 2.8e-08 140 0.17 0.000017
COL3A1 891489 302770 267224 88 59 80 14 0 0 0.93 2.9e-08 130 0.3 0.000017
GK2 371491 109330 6676 41 34 34 6 0 20 1.3 3.5e-08 77 0.17 0.000019
LCE1B 80330 23199 7128 15 15 15 0 0 20 1.2 3.6e-08 45 0.21 0.000019
LUZP2 244768 62352 68927 28 27 25 4 0 8 1.3 4.9e-08 70 0.18 0.000025
BRAF

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

NRAS

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

CDKN2A

Figure S3.  This figure depicts the distribution of mutations and mutation types across the CDKN2A significant gene.

PTEN

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

TP53

Figure S5.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

ARMC4

Figure S6.  This figure depicts the distribution of mutations and mutation types across the ARMC4 significant gene.

PPP6C

Figure S7.  This figure depicts the distribution of mutations and mutation types across the PPP6C significant gene.

DCDC1

Figure S8.  This figure depicts the distribution of mutations and mutation types across the DCDC1 significant gene.

TMPRSS11B

Figure S9.  This figure depicts the distribution of mutations and mutation types across the TMPRSS11B significant gene.

DSG1

Figure S10.  This figure depicts the distribution of mutations and mutation types across the DSG1 significant gene.

MS4A2

Figure S11.  This figure depicts the distribution of mutations and mutation types across the MS4A2 significant gene.

COL5A1

Figure S12.  This figure depicts the distribution of mutations and mutation types across the COL5A1 significant gene.

PREX2

Figure S13.  This figure depicts the distribution of mutations and mutation types across the PREX2 significant gene.

MPP7

Figure S14.  This figure depicts the distribution of mutations and mutation types across the MPP7 significant gene.

PPIAL4G

Figure S15.  This figure depicts the distribution of mutations and mutation types across the PPIAL4G significant gene.

OGDHL

Figure S16.  This figure depicts the distribution of mutations and mutation types across the OGDHL significant gene.

CAPZA3

Figure S17.  This figure depicts the distribution of mutations and mutation types across the CAPZA3 significant gene.

PROL1

Figure S18.  This figure depicts the distribution of mutations and mutation types across the PROL1 significant gene.

SYCP1

Figure S19.  This figure depicts the distribution of mutations and mutation types across the SYCP1 significant gene.

CYP4Z1

Figure S20.  This figure depicts the distribution of mutations and mutation types across the CYP4Z1 significant gene.

STXBP5L

Figure S21.  This figure depicts the distribution of mutations and mutation types across the STXBP5L significant gene.

ITGA4

Figure S22.  This figure depicts the distribution of mutations and mutation types across the ITGA4 significant gene.

KIAA2022

Figure S23.  This figure depicts the distribution of mutations and mutation types across the KIAA2022 significant gene.

STK31

Figure S24.  This figure depicts the distribution of mutations and mutation types across the STK31 significant gene.

PCSK1

Figure S25.  This figure depicts the distribution of mutations and mutation types across the PCSK1 significant gene.

C8orf34

Figure S26.  This figure depicts the distribution of mutations and mutation types across the C8orf34 significant gene.

LRRC4C

Figure S27.  This figure depicts the distribution of mutations and mutation types across the LRRC4C significant gene.

TPTE2

Figure S28.  This figure depicts the distribution of mutations and mutation types across the TPTE2 significant gene.

NF1

Figure S29.  This figure depicts the distribution of mutations and mutation types across the NF1 significant gene.

COL3A1

Figure S30.  This figure depicts the distribution of mutations and mutation types across the COL3A1 significant gene.

GK2

Figure S31.  This figure depicts the distribution of mutations and mutation types across the GK2 significant gene.

LCE1B

Figure S32.  This figure depicts the distribution of mutations and mutation types across the LCE1B 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