Mutation Analysis (MutSig 2CV v3.1)
Uterine Carcinosarcoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1CR5SBZ
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/C1CR5SBZ
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: UCS-TP

  • Number of patients in set: 57

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:UCS-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 TP53 tumor protein p53 1901 117 0 0 42 4 5 5 56 51 44 3.4e-16 0.00042 1e-05 1e-16 9.1e-13
2 FBXW7 F-box and WD repeat domain containing 7 2580 54 0 0 22 2 0 0 24 22 15 3.4e-16 3e-05 0.0071 1e-16 9.1e-13
3 PPP2R1A protein phosphatase 2 (formerly 2A), regulatory subunit A , alpha isoform 1826 267 0 0 17 0 0 0 17 16 10 2.5e-12 1e-05 0.034 1e-15 6.1e-12
4 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 1000 0 0 9 4 1 2 16 11 15 5.7e-15 0.55 0.84 1.4e-13 6.4e-10
5 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 709 180 0 0 7 0 0 0 7 7 2 6.3e-10 1e-05 0.078 2.1e-13 7.8e-10
6 ZBTB7B zinc finger and BTB domain containing 7B 1630 340 0 0 4 1 0 2 7 6 7 3.4e-09 1 0.08 1.1e-08 0.000034
7 CHD4 chromodomain helicase DNA binding protein 4 5895 27 0 0 9 2 1 0 12 10 12 9.7e-08 1 0.06 2.1e-07 0.00054
8 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 2361 150 0 0 1 0 0 7 8 6 8 2.6e-08 1 0.51 4.7e-07 0.0011
9 ARHGAP35 glucocorticoid receptor DNA binding factor 1 4520 23 0 0 1 1 0 4 6 6 6 1.5e-07 1 0.72 2.4e-06 0.0049
10 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 2 0 0 22 0 0 0 22 20 13 0.052 1e-05 0.0068 8.1e-06 0.015
11 MAMLD1 mastermind-like domain containing 1 3467 853 0 0 3 0 0 1 4 4 3 0.00044 0.011 0.66 0.000052 0.086
12 BCL2L11 BCL2-like 11 (apoptosis facilitator) 738 353 0 0 0 0 0 2 2 2 1 0.00079 0.01 0.11 0.0001 0.15
13 RB1 retinoblastoma 1 (including osteosarcoma) 3705 6 0 0 1 4 1 1 7 6 7 0.000011 1 0.73 0.00014 0.18
14 U2AF1 U2 small nuclear RNA auxiliary factor 1 824 240 0 0 2 0 0 0 2 2 1 0.0011 0.014 0.018 0.00014 0.18
15 LYPLA2 lysophospholipase II 732 68 0 0 1 0 0 2 3 3 3 0.000023 1 0.82 0.00027 0.32
16 NET1 neuroepithelial cell transforming gene 1 1930 172 0 0 2 0 0 1 3 3 3 0.00026 1 0.092 0.00029 0.32
17 ARHGEF10L Rho guanine nucleotide exchange factor (GEF) 10-like 3952 37 0 0 1 0 1 1 3 3 3 0.00019 1 0.12 0.00029 0.32
18 ARID1A AT rich interactive domain 1A (SWI-like) 6934 5 0 0 4 3 0 2 9 7 9 0.000035 1 0.92 0.00039 0.4
19 FER fer (fps/fes related) tyrosine kinase 2543 114 0 0 0 2 0 0 2 2 1 0.0026 0.014 0.29 0.00041 0.4
20 MYOZ1 myozenin 1 920 140 0 0 0 0 0 2 2 2 2 0.0039 0.01 0.9 0.00044 0.4
21 ZC3H18 zinc finger CCCH-type containing 18 2930 132 0 0 1 2 0 0 3 3 3 0.000059 1 0.98 0.00064 0.55
22 SPOP speckle-type POZ protein 1161 1000 0 0 5 0 0 0 5 4 5 0.000062 1 0.13 0.00067 0.55
23 CEL carboxyl ester lipase (bile salt-stimulated lipase) 2315 21 0 0 0 0 0 2 2 2 2 0.000066 1 0.95 0.00071 0.56
24 FAM92B family with sequence similarity 92, member B 943 1000 0 0 2 0 1 0 3 3 3 0.0029 1 0.015 0.00098 0.75
25 MUC17 mucin 17, cell surface associated 13532 38 0 2 8 0 0 0 8 7 8 0.015 0.0081 0.55 0.0014 1
26 LZTR1 leucine-zipper-like transcription regulator 1 2623 184 0 0 2 0 0 0 2 2 1 0.013 0.011 0.91 0.0014 1
27 KIR3DL1 killer cell immunoglobulin-like receptor, three domains, long cytoplasmic tail, 1 1371 87 0 0 2 0 0 0 2 2 2 0.0082 1 0.013 0.0017 1
28 TBXAS1 thromboxane A synthase 1 (platelet, cytochrome P450, family 5, subfamily A) 1799 88 0 0 0 1 0 1 2 2 2 0.0022 1 0.07 0.0017 1
29 TMF1 TATA element modulatory factor 1 3346 12 0 0 0 0 1 1 2 2 2 0.0025 1 0.059 0.0018 1
30 PDGFRB platelet-derived growth factor receptor, beta polypeptide 3409 174 0 1 4 0 0 0 4 4 4 0.0019 1 0.062 0.0023 1
31 SLC10A6 solute carrier family 10 (sodium/bile acid cotransporter family), member 6 1155 163 0 0 2 0 0 1 3 2 3 0.0055 1 0.018 0.0023 1
32 TRPM2 transient receptor potential cation channel, subfamily M, member 2 4638 31 0 0 4 0 0 0 4 4 4 0.00025 1 0.83 0.0023 1
33 ANLN anillin, actin binding protein 3559 6 0 0 1 0 0 1 2 2 2 0.018 1 0.01 0.0025 1
34 ZNF780A zinc finger protein 780A 2124 65 0 0 2 0 0 0 2 2 2 0.0079 1 0.029 0.0026 1
35 CDK11B cyclin-dependent kinase 11B 2551 30 0 0 2 0 0 0 2 2 2 0.0029 NaN NaN 0.0029 1
TP53

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

FBXW7

Figure S2.  This figure depicts the distribution of mutations and mutation types across the FBXW7 significant gene.

PPP2R1A

Figure S3.  This figure depicts the distribution of mutations and mutation types across the PPP2R1A significant gene.

PTEN

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

KRAS

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

ZBTB7B

Figure S6.  This figure depicts the distribution of mutations and mutation types across the ZBTB7B significant gene.

CHD4

Figure S7.  This figure depicts the distribution of mutations and mutation types across the CHD4 significant gene.

PIK3R1

Figure S8.  This figure depicts the distribution of mutations and mutation types across the PIK3R1 significant gene.

PIK3CA

Figure S9.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

MAMLD1

Figure S10.  This figure depicts the distribution of mutations and mutation types across the MAMLD1 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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