Mutation Analysis (MutSig 2CV v3.1)
Rectum Adenocarcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C19Z940K
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C19Z940K
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: READ-TP

  • Number of patients in set: 122

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:READ-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 33

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: 33. 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 2 0 25 51 110 1 28 190 106 136 3e-15 1e-05 1 1e-16 1.8e-12
2 CRIPAK cysteine-rich PAK1 inhibitor 1341 42 0 1 6 0 0 7 13 11 8 2.3e-11 3e-05 0.34 7.1e-14 6.3e-10
3 TP53 tumor protein p53 1314 0 0 5 81 12 4 10 107 90 70 3e-10 6e-05 1e-05 1e-13 6.3e-10
4 MUC4 mucin 4, cell surface associated 3623 0 0 4 6 0 0 28 34 25 19 4.6e-12 NaN NaN 4.6e-12 2.1e-08
5 TCF7L2 transcription factor 7-like 2 (T-cell specific, HMG-box) 2138 144 0 1 8 2 1 2 13 13 12 1.8e-11 0.33 0.16 6.8e-11 2.5e-07
6 FMN2 formin 2 5237 35 0 3 4 0 0 11 15 14 8 2.3e-06 1e-05 1 5.8e-10 1.8e-06
7 SHROOM4 shroom family member 4 4516 2 0 2 3 0 0 5 8 7 4 0.000016 1e-05 1 3.9e-09 1e-05
8 RPTN repetin 2363 182 0 1 0 0 0 5 5 5 1 0.00017 1e-05 0.32 3.6e-08 0.000081
9 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 590 4 0 1 11 0 0 0 11 11 6 0.00019 1e-05 0.061 4e-08 0.000081
10 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 2709 126 0 2 10 1 1 0 12 12 12 7.7e-08 1 0.14 3.2e-07 0.00059
11 ARID1A AT rich interactive domain 1A (SWI-like) 6934 80 0 2 6 5 0 2 13 11 13 6.8e-07 0.14 0.054 3.8e-07 0.00063
12 RBM38 RNA binding motif protein 38 493 662 0 0 1 0 0 7 8 8 2 8.1e-07 NaN NaN 8.1e-07 0.0012
13 SMAD2 SMAD family member 2 1444 98 0 0 6 1 0 0 7 7 6 3.7e-06 0.1 0.05 6e-06 0.0085
14 WASH1 WAS protein family homolog 1 1438 2 0 0 0 0 0 2 2 2 1 0.000056 0.011 0.95 0.000012 0.015
15 VCX2 variable charge, X-linked 2 426 90 0 0 1 0 0 4 5 5 2 0.00021 0.0038 0.98 0.000013 0.016
16 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 3872 9 0 0 8 0 0 0 8 8 6 0.018 0.003 0.0035 0.000016 0.018
17 RBM10 RNA binding motif protein 10 2881 178 0 0 0 5 0 0 5 5 4 4e-06 0.28 0.9 0.000021 0.022
18 PON3 paraoxonase 3 1101 11 0 1 3 0 1 0 4 4 2 0.016 0.0001 0.084 0.000023 0.022
19 HIST2H2AC histone cluster 2, H2ac 392 181 0 0 2 1 0 0 3 3 2 0.0016 0.001 0.31 0.000023 0.022
20 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 2406 7 0 6 21 0 0 0 21 19 19 0.033 0.00011 0.36 0.000032 0.029
21 KIAA1804 3147 11 0 0 12 0 0 0 12 10 10 0.00027 0.065 0.01 0.000036 0.032
22 OXSM 3-oxoacyl-ACP synthase, mitochondrial 1390 80 0 0 3 0 0 1 4 4 3 0.0019 0.011 0.057 4e-05 0.033
23 BCL9 B-cell CLL/lymphoma 9 4305 24 0 0 4 1 1 0 6 5 6 3e-06 1 0.82 0.000041 0.033
24 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 709 0 0 4 61 1 0 1 63 63 14 0.33 1e-05 0.027 0.000045 0.034
25 SLC12A6 solute carrier family 12 (potassium/chloride transporters), member 6 3669 15 0 1 3 2 1 0 6 6 5 0.0056 0.021 0.01 0.000048 0.035
26 ZNF354C zinc finger protein 354C 1681 13 0 0 5 0 0 0 5 5 3 0.04 8e-05 0.87 0.000059 0.041
27 GABRP gamma-aminobutyric acid (GABA) A receptor, pi 1359 7 0 0 2 2 0 0 4 4 3 0.00094 0.0076 0.62 0.000092 0.062
28 BRAF v-raf murine sarcoma viral oncogene homolog B1 2371 1 0 5 12 1 0 0 13 12 10 0.83 5e-05 0.003 0.00011 0.069
29 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 0 0 16 55 1 0 0 56 37 47 0.99 1e-05 0.0054 0.00012 0.078
30 CEP290 centrosomal protein 290kDa 7652 44 0 1 6 0 0 0 6 6 4 0.093 7e-05 0.39 0.00014 0.083
31 LIG1 ligase I, DNA, ATP-dependent 2868 10 0 1 4 0 0 0 4 4 1 0.11 0.0001 0.91 0.00014 0.083
32 PCBP1 poly(rC) binding protein 1 1071 173 0 0 4 0 0 0 4 4 3 0.0011 0.027 0.15 0.00015 0.083
33 ATP8B1 ATPase, class I, type 8B, member 1 3864 1 0 0 1 3 0 1 5 5 5 0.000024 1 0.29 0.00018 0.098
34 FLT1 fms-related tyrosine kinase 1 (vascular endothelial growth factor/vascular permeability factor receptor) 4390 15 0 2 9 0 0 0 9 8 7 0.055 0.0012 0.0097 0.00024 0.13
35 OBSCN obscurin, cytoskeletal calmodulin and titin-interacting RhoGEF 25533 1 0 2 11 4 0 0 15 13 14 0.00022 0.21 0.32 0.00025 0.13
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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