Mutation Analysis (MutSig 2CV v3.1)
Colorectal Adenocarcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1PG1QF0
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/C1PG1QF0
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: COADREAD-TP

  • Number of patients in set: 223

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:COADREAD-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 35

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: 35. 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 APC adenomatous polyposis coli 8592 1 0 4 32 165 1 48 246 163 156 1.9e-15 1e-05 1 1e-16 4.6e-13
2 TP53 tumor protein p53 1889 9 0 2 81 21 0 17 119 117 67 2.3e-15 1e-05 1e-05 1e-16 4.6e-13
3 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 709 17 0 0 96 1 0 0 97 97 12 1e-16 1e-05 1e-05 1e-16 4.6e-13
4 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 941 34 0 0 20 0 0 0 20 20 8 1.1e-15 1e-05 0.019 1e-16 4.6e-13
5 SMAD4 SMAD family member 4 1699 22 0 0 25 3 0 2 30 26 23 1.9e-13 0.0038 0.0079 2.2e-15 7.8e-12
6 FBXW7 F-box and WD repeat domain containing 7 2580 4 0 2 34 7 0 2 43 38 26 1.3e-14 0.0019 0.26 2.6e-15 7.8e-12
7 SMAD2 SMAD family member 2 1444 58 0 1 11 5 0 0 16 15 12 4.7e-11 0.00039 0.024 1.3e-13 3.4e-10
8 FAM123B family with sequence similarity 123B 3412 18 0 2 7 13 0 5 25 24 22 6.4e-12 0.44 0.53 1.7e-10 3.9e-07
9 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 20 0 2 40 0 0 0 40 33 21 6.5e-06 1e-05 0.00084 1.6e-09 3.2e-06
10 TCF7L2 transcription factor 7-like 2 (T-cell specific, HMG-box) 2138 14 0 4 13 5 0 2 20 18 18 2.3e-07 0.021 0.16 6.2e-08 0.00011
11 ARID1A AT rich interactive domain 1A (SWI-like) 6934 3 0 3 9 10 0 2 21 20 19 3.6e-08 0.28 0.13 1.3e-07 0.00022
12 BRAF v-raf murine sarcoma viral oncogene homolog B1 2371 9 0 0 22 0 0 0 22 21 3 0.00083 1e-05 0.0039 1.6e-07 0.00025
13 MGC42105 1319 47 0 1 8 3 0 0 11 11 11 9.3e-09 1 0.94 1.8e-07 0.00026
14 ACVR1B activin A receptor, type IB 1679 9 0 0 13 1 0 1 15 14 15 1.3e-06 0.31 0.01 1.3e-06 0.0016
15 CDC27 cell division cycle 27 homolog (S. cerevisiae) 2565 4 0 4 11 3 0 0 14 13 7 0.019 1e-05 1 3.1e-06 0.0037
16 KIAA1804 3147 5 0 0 16 1 0 1 18 15 16 4e-05 0.018 0.012 3.3e-06 0.0037
17 PCDHGB1 protocadherin gamma subfamily B, 1 45770 13 0 0 7 2 0 0 9 8 9 2.4e-07 1 0.95 4e-06 0.0041
18 PCBP1 poly(rC) binding protein 1 1071 33 0 0 5 0 0 0 5 5 2 0.025 1e-05 0.014 4e-06 0.0041
19 ZHX2 zinc fingers and homeoboxes 2 2518 6 0 0 7 1 0 2 10 10 9 7.1e-06 0.2 0.52 0.000024 0.023
20 PCDHA3 protocadherin alpha 3 30618 13 0 0 15 1 0 0 16 13 15 4e-06 0.38 0.55 0.000027 0.025
21 PCDHA2 protocadherin alpha 2 33091 13 0 1 14 1 0 0 15 12 14 5.4e-06 0.37 1 0.000033 0.029
22 ELF3 E74-like factor 3 (ets domain transcription factor, epithelial-specific ) 1148 44 0 0 2 1 0 5 8 8 7 0.000051 0.1 0.29 0.000045 0.037
23 CRTC1 CREB regulated transcription coactivator 1 2011 17 0 1 1 0 0 3 4 4 2 0.038 0.0001 0.17 0.000051 0.039
24 CASP8 caspase 8, apoptosis-related cysteine peptidase 1749 9 0 0 7 2 0 2 11 10 10 0.000016 0.38 0.32 0.000051 0.039
25 CACNG3 calcium channel, voltage-dependent, gamma subunit 3 960 44 0 2 7 1 0 0 8 8 6 0.0079 0.00076 0.45 0.000053 0.039
26 PCDHGA7 protocadherin gamma subfamily A, 7 31478 8 0 1 4 1 0 0 5 5 4 0.00016 0.066 0.38 0.000064 0.045
27 CCBP2 chemokine binding protein 2 1159 77 0 0 10 0 0 0 10 10 10 5.2e-06 1 0.55 0.000069 0.046
28 MAP2K4 mitogen-activated protein kinase kinase 4 1242 83 0 0 9 1 1 0 11 11 10 0.000046 0.29 0.13 7e-05 0.046
29 PCDHGA9 protocadherin gamma subfamily A, 9 22888 12 0 0 5 1 0 0 6 6 6 8.2e-06 1 0.78 0.0001 0.064
30 RBM10 RNA binding motif protein 10 2882 6 0 0 2 4 0 2 8 8 7 0.000027 0.28 0.69 0.00011 0.064
31 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 15 0 1 6 4 0 1 11 7 9 0.00019 0.14 0.12 0.00013 0.075
32 DNMT1 DNA (cytosine-5-)-methyltransferase 1 5059 20 0 3 17 0 0 1 18 17 14 0.072 0.00023 0.11 0.00013 0.075
33 BCOR BCL6 co-repressor 5324 0 0 2 5 3 0 2 10 9 10 0.0002 1 0.0074 0.00014 0.075
34 PCDHGB5 protocadherin gamma subfamily B, 5 24615 13 0 0 8 0 0 0 8 7 8 0.000011 1 0.5 0.00014 0.075
35 ZFP3 zinc finger protein 3 homolog (mouse) 1513 53 0 1 9 1 0 0 10 8 9 0.000092 0.12 0.68 0.00016 0.085
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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