Mutation Analysis (MutSig 2CV v3.1)
Bladder Urothelial Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
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/C1XP73NR
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: BLCA-TP

  • Number of patients in set: 130

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:BLCA-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 31

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: 31. 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 TP53 tumor protein p53 1314 51 0 1 57 13 0 5 75 64 50 2.1e-16 1e-05 1e-05 1e-16 9.1e-13
2 KDM6A lysine (K)-specific demethylase 6A 4318 120 0 2 4 16 1 11 32 31 26 1e-16 0.0074 0.068 1e-16 9.1e-13
3 RB1 retinoblastoma 1 (including osteosarcoma) 2891 10 0 0 4 8 4 3 19 17 17 1e-16 0.12 0.55 5.6e-16 3.4e-12
4 ARID1A AT rich interactive domain 1A (SWI-like) 6934 2 0 2 12 16 0 10 38 32 36 3.2e-16 0.27 0.84 4.7e-15 2.1e-11
5 CDKN1A cyclin-dependent kinase inhibitor 1A (p21, Cip1) 503 334 0 0 3 3 0 12 18 18 17 3.3e-15 0.4 0.77 6e-14 2.2e-10
6 MLL2 myeloid/lymphoid or mixed-lineage leukemia 2 16826 1 0 5 15 14 2 9 40 36 40 1.6e-14 1 0.5 5.3e-13 1.6e-09
7 ELF3 E74-like factor 3 (ets domain transcription factor, epithelial-specific ) 1148 44 0 0 6 1 0 8 15 11 14 7.1e-11 0.35 0.26 3.4e-10 8.8e-07
8 FGFR3 fibroblast growth factor receptor 3 (achondroplasia, thanatophoric dwarfism) 2642 12 0 2 20 0 0 1 21 16 11 0.000017 2e-05 0.063 4e-09 9.2e-06
9 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 1002 22 0 0 4 2 1 1 8 7 8 0.000036 1 1e-05 8.1e-09 0.000016
10 STAG2 stromal antigen 2 3939 8 0 3 2 5 2 5 14 14 13 3.3e-09 0.2 0.34 8.9e-09 0.000016
11 NFE2L2 nuclear factor (erythroid-derived 2)-like 2 1834 43 0 0 12 0 0 0 12 11 9 2.3e-06 0.00042 0.012 1.6e-08 0.000027
12 ERCC2 excision repair cross-complementing rodent repair deficiency, complementation group 2 (xeroderma pigmentosum D) 2430 43 0 2 16 0 0 0 16 16 13 4.5e-07 0.004 0.12 1.8e-08 0.000027
13 FBXW7 F-box and WD repeat domain containing 7 2580 1 0 0 10 4 0 2 16 13 12 1e-05 0.0019 0.027 1.4e-07 0.0002
14 RHOB ras homolog gene family, member B 591 20 0 1 7 0 0 0 7 7 6 1.8e-06 0.0097 0.41 1.5e-07 0.0002
15 TSC1 tuberous sclerosis 1 3579 6 0 0 3 5 2 1 11 11 11 1.9e-08 1 0.26 3.5e-07 0.00043
16 FOXQ1 forkhead box Q1 1212 6 0 1 3 1 0 3 7 7 4 1.7e-06 0.027 0.087 7.2e-07 0.00082
17 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 7 0 1 26 0 0 0 26 26 11 0.043 1e-05 0.00066 6.7e-06 0.0072
18 RXRA retinoid X receptor, alpha 1425 3 0 2 12 0 0 0 12 12 6 0.0061 0.00012 0.052 7.6e-06 0.0077
19 CREBBP CREB binding protein (Rubinstein-Taybi syndrome) 7449 30 0 3 12 5 0 1 18 17 17 4e-06 0.13 0.87 9.6e-06 0.0092
20 ERBB3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) 4266 3 0 1 14 0 0 0 14 14 10 0.11 1e-05 0.75 0.000016 0.014
21 FAT1 FAT tumor suppressor homolog 1 (Drosophila) 13871 9 0 2 6 7 0 4 17 15 17 3.8e-06 1 0.31 0.000022 0.019
22 MLL myeloid/lymphoid or mixed-lineage leukemia (trithorax homolog, Drosophila) 12052 3 0 3 11 7 0 3 21 18 20 0.000014 0.15 0.38 0.000031 0.026
23 HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog 659 93 0 0 6 0 0 0 6 6 5 0.00018 0.12 0.047 0.000038 0.03
24 ACRC acidic repeat containing 2124 10 0 0 4 1 0 0 5 4 4 0.022 0.0001 0.71 0.000042 0.032
25 TXNIP thioredoxin interacting protein 1204 1 0 2 7 0 0 5 12 9 12 0.000034 1 0.057 0.000053 0.038
26 ECM1 extracellular matrix protein 1 1659 17 0 0 4 1 0 0 5 5 4 0.023 9e-05 0.85 0.000058 0.041
27 KLF5 Kruppel-like factor 5 (intestinal) 1386 4 0 2 8 0 0 3 11 10 10 0.00016 0.22 0.046 0.000074 0.05
28 RBM26 RNA binding motif protein 26 3025 18 0 0 9 1 0 0 10 9 9 0.00015 0.081 0.45 0.000098 0.064
29 RAD51C RAD51 homolog C (S. cerevisiae) 1167 103 0 1 5 0 0 0 5 4 3 0.096 0.00012 0.69 0.00011 0.067
30 CEP192 centrosomal protein 192kDa 7790 15 0 2 2 2 1 0 5 5 4 0.016 0.0095 0.015 0.00011 0.067
31 ASXL2 additional sex combs like 2 (Drosophila) 4352 8 0 0 7 4 0 0 11 9 11 0.00019 1 0.017 0.00012 0.071
32 PSIP1 PC4 and SFRS1 interacting protein 1 1678 13 0 0 3 1 2 1 7 7 7 0.000058 1 0.36 0.0003 0.17
33 ZNF513 zinc finger protein 513 1638 12 0 0 3 1 0 3 7 7 7 0.000027 1 0.54 0.00031 0.17
34 FOXA1 forkhead box A1 1423 114 0 1 2 0 0 5 7 7 7 0.000028 1 0.36 0.00032 0.17
35 TRIP11 thyroid hormone receptor interactor 11 6020 14 0 1 4 1 0 0 5 5 4 0.13 0.0066 0.028 0.00045 0.23
TP53

Figure S1.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

KDM6A

Figure S2.  This figure depicts the distribution of mutations and mutation types across the KDM6A significant gene.

RB1

Figure S3.  This figure depicts the distribution of mutations and mutation types across the RB1 significant gene.

ARID1A

Figure S4.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

CDKN1A

Figure S5.  This figure depicts the distribution of mutations and mutation types across the CDKN1A significant gene.

MLL2

Figure S6.  This figure depicts the distribution of mutations and mutation types across the MLL2 significant gene.

ELF3

Figure S7.  This figure depicts the distribution of mutations and mutation types across the ELF3 significant gene.

FGFR3

Figure S8.  This figure depicts the distribution of mutations and mutation types across the FGFR3 significant gene.

CDKN2A

Figure S9.  This figure depicts the distribution of mutations and mutation types across the CDKN2A significant gene.

STAG2

Figure S10.  This figure depicts the distribution of mutations and mutation types across the STAG2 significant gene.

NFE2L2

Figure S11.  This figure depicts the distribution of mutations and mutation types across the NFE2L2 significant gene.

ERCC2

Figure S12.  This figure depicts the distribution of mutations and mutation types across the ERCC2 significant gene.

FBXW7

Figure S13.  This figure depicts the distribution of mutations and mutation types across the FBXW7 significant gene.

RHOB

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

TSC1

Figure S15.  This figure depicts the distribution of mutations and mutation types across the TSC1 significant gene.

FOXQ1

Figure S16.  This figure depicts the distribution of mutations and mutation types across the FOXQ1 significant gene.

PIK3CA

Figure S17.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

RXRA

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

CREBBP

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

ERBB3

Figure S20.  This figure depicts the distribution of mutations and mutation types across the ERBB3 significant gene.

FAT1

Figure S21.  This figure depicts the distribution of mutations and mutation types across the FAT1 significant gene.

MLL

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

HRAS

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

ACRC

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

TXNIP

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

ECM1

Figure S26.  This figure depicts the distribution of mutations and mutation types across the ECM1 significant gene.

KLF5

Figure S27.  This figure depicts the distribution of mutations and mutation types across the KLF5 significant gene.

RBM26

Figure S28.  This figure depicts the distribution of mutations and mutation types across the RBM26 significant gene.

RAD51C

Figure S29.  This figure depicts the distribution of mutations and mutation types across the RAD51C significant gene.

ASXL2

Figure S30.  This figure depicts the distribution of mutations and mutation types across the ASXL2 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)