Mutation Analysis (MutSig 2CV v3.1)
Rectum Adenocarcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
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/C17D2T3D
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: 69

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): 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 APC adenomatous polyposis coli 8592 1 0 0 9 63 1 10 83 56 67 1.6e-15 0.00014 1 1e-16 6.1e-13
2 TP53 tumor protein p53 1314 25 0 1 32 8 0 4 44 44 29 8.6e-16 0.0045 1e-05 1e-16 6.1e-13
3 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 709 1000 0 0 37 1 0 0 38 38 8 3.6e-16 1e-05 0.0011 1e-16 6.1e-13
4 SMAD4 SMAD family member 4 1699 262 0 0 8 0 0 0 8 8 6 6.1e-10 0.0015 0.26 1.3e-11 6e-08
5 FBXW7 F-box and WD repeat domain containing 7 2580 11 0 0 8 1 0 1 10 9 8 1.3e-07 0.044 0.082 2.7e-08 0.0001
6 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 590 34 0 0 5 0 0 0 5 5 4 4e-05 0.0006 0.2 6e-07 0.0018
7 ARID1A AT rich interactive domain 1A (SWI-like) 6934 13 0 0 0 4 0 1 5 5 5 2.3e-06 1 0.05 5.4e-06 0.012
8 RBM10 RNA binding motif protein 10 2881 178 0 0 0 4 0 0 4 4 3 1e-06 0.27 0.91 5.4e-06 0.012
9 TCF7L2 transcription factor 7-like 2 (T-cell specific, HMG-box) 2138 144 0 1 5 1 0 1 7 7 7 3.9e-06 1 0.071 0.000011 0.022
10 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 3872 30 0 0 4 0 0 0 4 4 2 0.017 0.00032 0.0096 0.000036 0.061
11 KIAA1804 3147 36 0 0 11 0 0 0 11 9 9 0.00018 0.05 0.018 0.000037 0.061
12 CCDC88C coiled-coil domain containing 88C 6205 1 0 0 1 0 0 2 3 3 2 0.0054 0.0025 0.97 0.00019 0.29
13 SMAD2 SMAD family member 2 1444 98 0 0 4 1 0 0 5 5 5 0.000019 1 0.28 0.00022 0.31
14 FAM123B family with sequence similarity 123B 3412 18 0 1 2 2 0 2 6 5 6 0.000029 1 0.52 0.00034 0.44
15 SYTL3 synaptotagmin-like 3 1681 267 0 0 1 1 0 1 3 3 3 0.00074 1 0.043 0.00045 0.54
16 ELF3 E74-like factor 3 (ets domain transcription factor, epithelial-specific ) 1148 44 0 0 0 1 0 2 3 3 3 0.00018 1 0.22 0.00061 0.66
17 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 2361 35 0 0 2 2 0 1 5 4 5 0.000096 1 0.42 0.00063 0.66
18 CCBP2 chemokine binding protein 2 1159 828 0 0 5 0 0 0 5 5 5 0.00012 1 0.31 0.00065 0.66
19 OSBPL6 oxysterol binding protein-like 6 3013 34 0 0 5 0 0 0 5 5 5 0.00051 1 0.14 0.0012 1
20 NOTCH4 Notch homolog 4 (Drosophila) 6132 52 0 1 3 1 0 0 4 4 4 0.0004 1 0.33 0.0014 1
21 ZIM3 zinc finger, imprinted 3 1435 93 0 0 5 1 0 0 6 5 6 0.0032 1 0.017 0.0014 1
22 ARHGEF6 Rac/Cdc42 guanine nucleotide exchange factor (GEF) 6 2415 11 0 0 1 2 0 0 3 2 3 0.00066 1 0.17 0.0014 1
23 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 18 0 1 7 0 0 0 7 7 7 0.0077 0.019 0.53 0.0014 1
24 KCNS2 potassium voltage-gated channel, delayed-rectifier, subfamily S, member 2 1438 205 0 0 5 0 0 0 5 5 5 0.00015 1 0.64 0.0015 1
25 POLE polymerase (DNA directed), epsilon 7055 3 0 1 3 1 0 0 4 3 3 0.043 0.0035 0.68 0.0015 1
26 OSMR oncostatin M receptor 3044 12 0 0 0 2 0 0 2 2 2 0.0038 1 0.05 0.0018 1
27 DNAJC11 DnaJ (Hsp40) homolog, subfamily C, member 11 1742 165 0 0 3 0 0 1 4 3 4 0.0023 1 0.051 0.002 1
28 LIMK2 LIM domain kinase 2 2386 12 0 0 0 1 0 0 1 1 1 0.002 NaN NaN 0.002 1
29 BCL9L B-cell CLL/lymphoma 9-like 4528 26 0 0 1 2 0 0 3 3 3 0.00022 1 0.78 0.0021 1
30 STAT2 signal transducer and activator of transcription 2, 113kDa 2648 65 0 0 1 1 0 0 2 2 2 0.014 1 0.02 0.0028 1
31 AGMAT agmatine ureohydrolase (agmatinase) 1083 87 0 0 3 1 0 0 4 4 3 0.002 0.14 0.85 0.0029 1
32 TET1 tet oncogene 1 6451 70 0 2 7 1 0 0 8 5 8 0.0013 1 0.2 0.003 1
33 ACSBG2 acyl-CoA synthetase bubblegum family member 2 2057 79 0 1 5 0 0 0 5 4 5 0.00034 1 0.76 0.003 1
34 CYP11B1 cytochrome P450, family 11, subfamily B, polypeptide 1 1546 105 0 0 3 1 0 0 4 4 4 0.00037 1 0.9 0.0033 1
35 PCDHA2 protocadherin alpha 2 2946 19 0 0 3 1 0 0 4 4 4 0.00042 1 0.82 0.0037 1
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)