Mutation Analysis (MutSig 2CV v3.1)
Prostate Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C1Z037MV
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: PRAD-TP

  • Number of patients in set: 498

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:PRAD-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 SPOP speckle-type POZ protein 1161 201 0 0 56 0 0 2 58 57 19 1e-16 1e-05 0.28 1e-16 9.1e-13
2 TP53 tumor protein p53 1314 498 0 0 42 2 4 12 60 57 45 1e-16 0.00034 1e-05 1e-16 9.1e-13
3 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 84 0 0 5 1 1 10 17 17 17 1e-16 0.36 0.83 1.8e-15 1.1e-11
4 NUDT11 nudix (nucleoside diphosphate linked moiety X)-type motif 11 500 111 0 0 0 0 0 11 11 11 1 2.1e-10 1e-05 1 7.2e-14 3.3e-10
5 MLL2 myeloid/lymphoid or mixed-lineage leukemia 2 16826 1 0 1 17 4 0 15 36 29 36 1.9e-11 1 0.019 3.5e-11 1.3e-07
6 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 2406 31 0 0 13 0 0 0 13 13 10 8.6e-07 1e-05 0.035 2.3e-10 6.9e-07
7 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 54 0 2 16 0 0 0 16 14 13 6e-06 1e-05 0.18 1.5e-09 3.9e-06
8 KDM6A lysine (K)-specific demethylase 6A 4318 76 0 2 4 2 2 5 13 13 13 1.2e-09 1 0.079 8.3e-09 0.000019
9 FOXA1 forkhead box A1 1423 3 0 3 14 1 0 14 29 28 20 0.00069 1e-05 0.11 1.4e-07 0.00028
10 MLL3 myeloid/lymphoid or mixed-lineage leukemia 3 14968 16 0 6 16 6 2 10 34 29 33 2.5e-07 0.13 0.4 3.9e-07 0.00071
11 GAGE2A G antigen 2A 1486 67 0 0 1 0 0 4 5 5 2 0.000025 0.00073 0.93 8.2e-07 0.0014
12 TNRC18 trinucleotide repeat containing 18 9019 0 0 0 5 0 0 5 10 9 8 0.000048 0.0034 0.11 1.1e-06 0.0016
13 AGAP6 ArfGAP with GTPase domain, ankyrin repeat and PH domain 6 2089 184 0 1 3 0 1 1 5 5 3 0.0017 5e-05 0.89 1.7e-06 0.0024
14 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 1277 804 0 0 6 0 0 0 6 6 3 0.001 0.00014 0.93 2.4e-06 0.0031
15 GATA6 GATA binding protein 6 1812 52 0 0 0 0 0 4 4 4 2 0.00059 0.0003 0.99 3.3e-06 0.004
16 SMG7 Smg-7 homolog, nonsense mediated mRNA decay factor (C. elegans) 3879 8 0 0 4 1 0 3 8 8 7 0.000098 0.025 0.026 3.5e-06 0.004
17 CNTNAP1 contactin associated protein 1 4247 33 0 2 7 0 1 1 9 9 7 0.00079 0.00028 0.62 5.3e-06 0.0056
18 CDKN1B cyclin-dependent kinase inhibitor 1B (p27, Kip1) 605 178 0 0 0 2 0 4 6 6 6 7.5e-07 1 0.4 5.5e-06 0.0056
19 EOMES eomesodermin homolog (Xenopus laevis) 2081 2 0 1 3 0 0 2 5 5 4 0.0054 7e-05 0.58 7.6e-06 0.0073
20 NBPF1 neuroblastoma breakpoint family, member 1 3519 59 0 2 10 0 2 2 14 9 14 7e-06 0.095 0.72 0.000012 0.011
21 LMOD2 leiomodin 2 (cardiac) 1652 48 0 0 2 0 0 4 6 6 4 0.0022 0.0014 0.078 0.000016 0.014
22 EHHADH enoyl-Coenzyme A, hydratase/3-hydroxyacyl Coenzyme A dehydrogenase 2198 30 0 0 4 0 0 1 5 5 5 0.000027 0.049 0.88 0.000025 0.021
23 MED12 mediator complex subunit 12 6710 10 0 1 8 0 0 0 8 8 5 0.25 1e-05 0.17 0.000035 0.028
24 ZNF709 zinc finger protein 709 1938 10 0 2 5 0 0 0 5 5 2 0.006 0.0002 0.95 0.000039 0.03
25 TCEB3 transcription elongation factor B (SIII), polypeptide 3 (110kDa, elongin A) 2439 28 0 0 1 1 1 1 4 4 4 0.000045 1 0.056 5e-05 0.036
26 MED15 mediator complex subunit 15 2437 22 0 1 5 0 1 2 8 7 8 0.000014 1 0.25 0.000068 0.048
27 ERF Ets2 repressor factor 1659 32 0 0 1 0 0 4 5 5 5 6.3e-06 1 0.98 0.000082 0.054
28 ERN1 endoplasmic reticulum to nucleus signaling 1 3018 48 0 2 2 0 0 3 5 4 4 0.0003 0.029 0.47 0.000083 0.054
29 MLLT10 myeloid/lymphoid or mixed-lineage leukemia (trithorax homolog, Drosophila); translocated to, 10 3174 1 0 0 4 0 0 1 5 5 3 0.092 7e-05 0.36 0.000094 0.059
30 ZFHX3 zinc finger homeobox 3 11148 8 0 2 8 4 0 7 19 16 19 0.000019 1 0.25 0.00011 0.067
31 ZMYM3 zinc finger, MYM-type 3 4227 0 0 0 6 1 1 5 13 12 13 6e-05 0.14 0.56 0.00012 0.071
32 FMN1 formin 1 3655 11 1 0 1 0 0 3 4 4 4 0.00077 1 0.0067 0.00013 0.076
33 AKT1 v-akt murine thymoma viral oncogene homolog 1 1495 51 0 0 3 0 0 0 3 3 2 0.0074 0.014 0.031 0.00014 0.076
34 ATM ataxia telangiectasia mutated 9419 4 0 2 17 0 0 5 22 22 22 0.0001 1 0.021 0.00016 0.084
35 STRC stereocilin 5442 12 0 0 2 1 1 1 5 5 5 0.000014 1 1 0.00017 0.086
SPOP

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

TP53

Figure S2.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

PTEN

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

CTNNB1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the CTNNB1 significant gene.

PIK3CA

Figure S5.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

KDM6A

Figure S6.  This figure depicts the distribution of mutations and mutation types across the KDM6A significant gene.

FOXA1

Figure S7.  This figure depicts the distribution of mutations and mutation types across the FOXA1 significant gene.

TNRC18

Figure S8.  This figure depicts the distribution of mutations and mutation types across the TNRC18 significant gene.

AGAP6

Figure S9.  This figure depicts the distribution of mutations and mutation types across the AGAP6 significant gene.

IDH1

Figure S10.  This figure depicts the distribution of mutations and mutation types across the IDH1 significant gene.

GATA6

Figure S11.  This figure depicts the distribution of mutations and mutation types across the GATA6 significant gene.

SMG7

Figure S12.  This figure depicts the distribution of mutations and mutation types across the SMG7 significant gene.

CNTNAP1

Figure S13.  This figure depicts the distribution of mutations and mutation types across the CNTNAP1 significant gene.

CDKN1B

Figure S14.  This figure depicts the distribution of mutations and mutation types across the CDKN1B significant gene.

EOMES

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

NBPF1

Figure S16.  This figure depicts the distribution of mutations and mutation types across the NBPF1 significant gene.

LMOD2

Figure S17.  This figure depicts the distribution of mutations and mutation types across the LMOD2 significant gene.

EHHADH

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

MED12

Figure S19.  This figure depicts the distribution of mutations and mutation types across the MED12 significant gene.

ZNF709

Figure S20.  This figure depicts the distribution of mutations and mutation types across the ZNF709 significant gene.

TCEB3

Figure S21.  This figure depicts the distribution of mutations and mutation types across the TCEB3 significant gene.

MED15

Figure S22.  This figure depicts the distribution of mutations and mutation types across the MED15 significant gene.

ERF

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

MLLT10

Figure S24.  This figure depicts the distribution of mutations and mutation types across the MLLT10 significant gene.

ZFHX3

Figure S25.  This figure depicts the distribution of mutations and mutation types across the ZFHX3 significant gene.

ZMYM3

Figure S26.  This figure depicts the distribution of mutations and mutation types across the ZMYM3 significant gene.

FMN1

Figure S27.  This figure depicts the distribution of mutations and mutation types across the FMN1 significant gene.

AKT1

Figure S28.  This figure depicts the distribution of mutations and mutation types across the AKT1 significant gene.

ATM

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

Methods & Data
Methods

MutSig and its evolving algorithms have existed since the youth of clinical sequencing, with early versions used in multiple publications. [1][2][3]

"Three significance metrics [are] calculated for each gene, using the […] methods MutSigCV [4], MutSigCL, and MutSigFN [5]. These measure the significance of mutation burden, clustering, and functional impact, respectively […]. MutSigCV determines the P value for observing the given quantity of non-silent mutations in the gene, given the background model determined by silent (and noncoding) mutations in the same gene and the neighbouring genes of covariate space that form its 'bagel'. […] MutSigCL and MutSigFN measure the significance of the positional clustering of the mutations observed, as well as the significance of the tendency for mutations to occur at positions that are highly evolutionarily conserved (using conservation as a proxy for probably functional impact). MutSigCL and MutSigFN are permutation-based methods and their P values are calculated as follows: The observed nonsilent coding mutations in the gene are permuted T times (to simulate the null hypothesis, T = 108 for the most significant genes), randomly reassigning their positions, but preserving their mutational 'category', as determined by local sequence context. We [use] the following context categories: transitions at CpG dinucleotides, transitions at other C-G base pairs, transversions at C-G base pairs, mutations at A-T base pairs, and indels. Indels are unconstrained in terms of where they can move to in the permutations. For each of the random permutations, two scores are calculated: SCL and SFN, measuring the amount of clustering and function impact (measured by conservation) respectively. SCL is defined to be the fraction of mutations occurring in hotspots. A hotspot is defined as a 3-base-pair region of the gene containing many mutations: at least 2, and at least 2% of the total mutations. SFN is defined to be the mean of the base-pair-level conservation values for the position of each non-silent mutation […]. To determine a PCL, the P value for the observed degree of positional clustering, the observed value of SCL (computed for the mutations actually observed), [is] compared to the distribution of SCL obtained from the random permutations, and the P value [is] defined to be the fraction of random permutations in which SCL [is] at least as large as the observed SCL. The P value for the conservation of the mutated positions, PFN, [is] computed analogously." [6]

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] Getz G, Höfling H, Mesirov JP, Golub TR, Meyerson M, Tibshirani R, Lander ES, Comment on "The Consensus Coding Sequences of Human Breast and Colorectal Cancers", Science 317(5844):1500b (2007)
[3] TCGA, Integrated genomic analyses of ovarian carcinoma, Nature 474(7353):609-615 (2011)
[4] Lawrence MS, et al., Mutational heterogeneity in cancer and the search for new cancer-associated genes, Nature 499(7457):214-218 (2013)
[6] Lawrence MS, et al., Discovery and saturation analysis of cancer genes across 21 tumour types, Nature 505(7484):495-501 (2014)