Mutation Analysis (MutSig 2CV v3.1)
Thyroid Adenocarcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C11Z43B8
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C11Z43B8
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. MutSig 2CV v3.1 was used to generate the results found in this report.

  • Working with individual set: THCA-TP

  • Number of patients in set: 401

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:THCA-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 11

Results
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 1.  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 2.  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 3.  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

  • 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: 11. 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).

rank gene longname codelen nnei nncd nsil nmis nstp nspl nind nnon npat nsite pCV pCL pFN p q
1 BRAF v-raf murine sarcoma viral oncogene homolog B1 2371 59 0 1 236 0 0 4 240 240 6 9.6e-15 1e-05 1e-05 1e-16 6.1e-13
2 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 590 34 0 0 34 0 0 0 34 34 2 1.3e-16 1e-05 0.0072 1e-16 6.1e-13
3 HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog 659 104 0 0 14 0 0 0 14 14 2 4.7e-16 1e-05 0.0038 1e-16 6.1e-13
4 EIF1AX eukaryotic translation initiation factor 1A, X-linked 459 175 0 0 4 0 2 0 6 6 5 6.6e-11 0.0002 0.21 5.4e-13 2.5e-09
5 NUP93 nucleoporin 93kDa 2544 232 0 0 1 3 0 0 4 4 2 4.4e-06 0.0002 0.23 1.9e-08 7e-05
6 PPM1D protein phosphatase 1D magnesium-dependent, delta isoform 1838 267 0 0 1 0 0 4 5 5 5 1.1e-09 1 0.89 2.3e-08 0.000071
7 DNMT3A DNA (cytosine-5-)-methyltransferase 3 alpha 2952 49 0 0 1 2 0 0 3 3 3 2.3e-06 1 0.41 0.000032 0.071
8 MSLNL mesothelin-like 3222 67 0 0 1 0 1 0 2 2 2 0.00012 1 0.015 0.000035 0.071
9 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 709 122 0 0 4 0 0 0 4 4 3 0.000074 0.034 0.11 0.000035 0.071
10 DLC1 deleted in liver cancer 1 4732 152 0 1 4 0 0 0 4 4 3 0.025 0.0045 0.0094 0.000045 0.082
11 ITGAL integrin, alpha L (antigen CD11A (p180), lymphocyte function-associated antigen 1; alpha polypeptide) 3633 37 0 0 3 0 0 1 4 4 4 0.000064 1 0.059 6e-05 0.099
12 NLRP6 NLR family, pyrin domain containing 6 2706 132 0 0 3 0 0 0 3 3 1 0.014 0.001 0.001 0.00017 0.26
13 ABCD1 ATP-binding cassette, sub-family D (ALD), member 1 2274 376 0 0 2 1 0 0 3 3 3 0.00024 NaN NaN 0.00024 0.32
14 LMX1B LIM homeobox transcription factor 1, beta 1272 129 0 0 0 0 0 2 2 2 1 0.0021 0.01 0.058 0.00025 0.32
15 HEATR1 HEAT repeat containing 1 6611 12 0 0 1 1 1 0 3 3 3 0.00013 1 0.13 0.00035 0.42
16 ARFGEF2 ADP-ribosylation factor guanine nucleotide-exchange factor 2 (brefeldin A-inhibited) 5510 57 0 0 3 1 0 0 4 4 4 0.000033 1 0.94 0.00037 0.42
17 TSG101 tumor susceptibility gene 101 1209 250 0 0 2 0 0 0 2 2 1 0.0038 0.01 0.47 0.00043 0.46
18 TRIM46 tripartite motif-containing 46 2318 60 0 0 2 0 1 0 3 3 3 0.000044 1 0.67 0.00048 0.49
19 JMJD1C jumonji domain containing 1C 7723 12 0 1 0 3 0 1 4 4 4 5e-05 1 0.57 0.00055 0.53
20 TG thyroglobulin 8498 137 0 4 6 0 0 5 11 11 11 0.000055 1 0.57 0.00059 0.54
21 OR56A1 olfactory receptor, family 56, subfamily A, member 1 961 75 0 0 2 0 0 0 2 2 2 0.0061 1 0.01 0.00065 0.56
22 ANKRD54 ankyrin repeat domain 54 933 13 0 0 0 0 1 1 2 2 2 0.00069 NaN NaN 0.00069 0.57
23 CREB3L3 cAMP responsive element binding protein 3-like 3 1422 50 0 0 3 0 0 0 3 3 3 0.0003 1 0.21 0.00081 0.64
24 MECP2 methyl CpG binding protein 2 (Rett syndrome) 1539 2 0 0 0 0 1 1 2 2 2 0.000081 1 0.79 0.00085 0.64
25 RRBP1 ribosome binding protein 1 homolog 180kDa (dog) 3030 18 0 0 0 1 0 1 2 2 2 0.00091 NaN NaN 0.00091 0.64
26 GBF1 golgi-specific brefeldin A resistance factor 1 5736 19 0 0 1 0 2 1 4 4 4 0.000088 1 0.76 0.00091 0.64
27 ATP6V1A ATPase, H+ transporting, lysosomal 70kDa, V1 subunit A 1910 156 0 0 2 0 0 0 2 2 1 0.011 0.01 0.91 0.0011 0.73
28 MAS1L MAS1 oncogene-like 1135 22 0 0 2 0 0 0 2 2 1 0.011 0.01 0.47 0.0012 0.75
29 OR6K6 olfactory receptor, family 6, subfamily K, member 6 1032 40 0 0 2 0 0 0 2 2 1 0.012 0.01 0.87 0.0012 0.78
30 TRPM4 transient receptor potential cation channel, subfamily M, member 4 3743 21 0 0 3 1 0 0 4 4 4 0.00013 1 0.96 0.0013 0.78
31 SLC25A45 solute carrier family 25, member 45 891 35 0 1 3 0 0 0 3 3 3 0.00042 1 0.36 0.0015 0.84
32 CACNA2D4 calcium channel, voltage-dependent, alpha 2/delta subunit 4 3562 30 0 0 1 0 0 1 2 2 2 0.00057 1 0.24 0.0015 0.84
33 POTEE POTE ankyrin domain family, member E 3286 117 0 1 2 0 0 0 2 2 1 0.016 0.01 0.84 0.0016 0.84
34 SLC12A5 solute carrier family 12, (potassium-chloride transporter) member 5 3576 24 0 0 1 0 1 0 2 2 2 0.0019 1 0.05 0.0016 0.84
35 PLP1 proteolipid protein 1 (Pelizaeus-Merzbacher disease, spastic paraplegia 2, uncomplicated) 858 28 0 1 2 0 0 0 2 2 2 0.00017 1 0.97 0.0016 0.84
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.

HRAS

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

EIF1AX

Figure S4.  This figure depicts the distribution of mutations and mutation types across the EIF1AX significant gene.

NUP93

Figure S5.  This figure depicts the distribution of mutations and mutation types across the NUP93 significant gene.

PPM1D

Figure S6.  This figure depicts the distribution of mutations and mutation types across the PPM1D significant gene.

DNMT3A

Figure S7.  This figure depicts the distribution of mutations and mutation types across the DNMT3A significant gene.

MSLNL

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

KRAS

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

DLC1

Figure S10.  This figure depicts the distribution of mutations and mutation types across the DLC1 significant gene.

ITGAL

Figure S11.  This figure depicts the distribution of mutations and mutation types across the ITGAL 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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