Mutation Analysis (MutSig 2CV v3.1)
Adrenocortical Carcinoma (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/C1610ZNC
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: ACC-TP

  • Number of patients in set: 62

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:ACC-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 109

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: 109. 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 ZFPM1 zinc finger protein, multitype 1 3059 28 0 0 3 0 1 35 39 24 6 1.3e-16 1e-05 1 1e-16 3.7e-13
2 LACTB lactamase, beta 1668 100 0 0 18 0 0 1 19 19 2 9.2e-16 1e-05 1 1e-16 3.7e-13
3 CCDC102A coiled-coil domain containing 102A 1681 177 0 0 17 0 0 0 17 17 1 3.1e-16 1e-05 0.8 1e-16 3.7e-13
4 ZNF517 zinc finger protein 517 1495 326 0 0 14 0 0 0 14 13 2 2.3e-14 1e-05 0.99 1e-16 3.7e-13
5 MAL2 mal, T-cell differentiation protein 2 546 2 0 1 0 0 1 11 12 11 2 1.4e-15 1e-05 0.24 1e-16 3.7e-13
6 TOR3A torsin family 3, member A 1214 169 0 0 12 0 0 0 12 12 1 1.8e-12 1e-05 1 6.7e-16 2e-12
7 TP53 tumor protein p53 1889 7 0 0 5 3 3 4 15 13 15 1e-16 0.78 0.13 1e-15 2.6e-12
8 CLDN23 claudin 23 879 36 0 0 10 0 0 0 10 10 1 1e-08 1e-05 0.99 3.2e-12 7.4e-09
9 GDF1 growth differentiation factor 1 1146 63 0 0 5 0 0 0 5 5 1 2.1e-08 1e-05 0.85 6.2e-12 1.3e-08
10 LZTR1 leucine-zipper-like transcription regulator 1 2621 12 0 0 0 0 0 6 6 6 1 4.2e-08 1e-05 2e-05 1.2e-11 2.2e-08
11 ANKRD43 ankyrin repeat domain 43 1650 2 0 0 19 0 0 0 19 19 1 7.6e-09 1e-05 1 2.6e-11 4.3e-08
12 KCNK17 potassium channel, subfamily K, member 17 1296 191 0 0 9 0 0 0 9 9 2 1.4e-07 1e-05 0.97 4e-11 6e-08
13 RINL Ras and Rab interactor-like 1569 72 0 0 8 0 0 0 8 8 1 3.1e-07 1e-05 0.45 8.5e-11 1.2e-07
14 ZAR1 zygote arrest 1 1378 5 0 0 11 0 0 0 11 11 2 5.4e-07 1e-05 1 1.5e-10 1.9e-07
15 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 2406 53 0 0 6 0 0 2 8 8 5 2.9e-07 3e-05 0.54 2.3e-10 2.8e-07
16 APOE apolipoprotein E 969 130 0 0 8 0 0 0 8 7 2 3.1e-07 1e-05 0.85 4e-10 4.6e-07
17 GPRIN2 G protein regulated inducer of neurite outgrowth 2 1381 174 0 1 8 0 0 0 8 8 1 1.9e-08 0.00053 0.61 6.6e-10 6.5e-07
18 ASPDH aspartate dehydrogenase domain containing 913 40 0 0 8 0 0 0 8 8 2 3.2e-08 0.00034 1 6.6e-10 6.5e-07
19 ERCC2 excision repair cross-complementing rodent repair deficiency, complementation group 2 (xeroderma pigmentosum D) 2430 33 0 0 10 0 0 0 10 10 1 2.7e-06 1e-05 0.0022 6.7e-10 6.5e-07
20 IDUA iduronidase, alpha-L- 2072 88 0 1 8 0 0 0 8 8 2 3.6e-06 1e-05 0.00016 9e-10 8.2e-07
21 C1orf106 chromosome 1 open reading frame 106 2030 27 0 0 9 0 0 0 9 9 2 2.3e-07 0.00025 1 2.4e-09 2e-06
22 C10orf95 chromosome 10 open reading frame 95 780 48 0 0 6 0 0 0 6 6 1 2e-07 0.00019 0.8 2.4e-09 2e-06
23 RGS9BP regulator of G protein signaling 9 binding protein 708 37 0 0 8 0 0 0 8 8 1 2.2e-06 5e-05 0.79 6.6e-09 5.3e-06
24 THEM4 thioesterase superfamily member 4 743 89 0 0 5 0 0 0 5 5 1 0.000039 1e-05 0.94 8.9e-09 6.8e-06
25 TSC22D2 TSC22 domain family, member 2 2355 16 0 0 7 1 0 0 8 8 3 0.000044 1e-05 1 9.9e-09 7.3e-06
26 SYT8 synaptotagmin VIII 1240 46 0 0 8 0 0 0 8 8 3 8.7e-08 0.011 0.087 1.2e-08 8.6e-06
27 PLIN5 perilipin 5 1424 51 0 0 5 0 0 0 5 5 1 0.000069 0.00031 1e-05 1.5e-08 1e-05
28 LRIG1 leucine-rich repeats and immunoglobulin-like domains 1 3356 28 0 0 26 0 0 0 26 16 2 0.000086 1e-05 0.94 1.9e-08 0.000012
29 HHIPL1 HHIP-like 1 2482 84 0 0 6 0 0 0 6 6 1 0.00012 1e-05 1 2.5e-08 0.000016
30 CCDC105 coiled-coil domain containing 105 1526 111 0 0 6 0 0 0 6 6 1 0.000035 3e-05 1 3.7e-08 0.000023
31 C19orf10 chromosome 19 open reading frame 10 544 53 0 0 7 0 0 0 7 7 1 4e-07 0.0027 1 5.6e-08 0.000033
32 OPRD1 opioid receptor, delta 1 1127 2 0 1 12 0 0 0 12 12 1 0.0003 1e-05 1 6.2e-08 0.000036
33 ATXN1 ataxin 1 2452 14 0 1 5 0 0 6 11 10 8 0.000031 4e-05 1 9.5e-08 0.000052
34 AATK apoptosis-associated tyrosine kinase 4179 111 0 1 7 0 0 0 7 6 2 0.00012 1e-05 0.76 9.6e-08 0.000052
35 ZNF628 zinc finger protein 628 3172 24 0 1 8 0 0 0 8 7 3 0.00013 1e-05 1 1.3e-07 0.000066
ZFPM1

Figure S1.  This figure depicts the distribution of mutations and mutation types across the ZFPM1 significant gene.

LACTB

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

CCDC102A

Figure S3.  This figure depicts the distribution of mutations and mutation types across the CCDC102A significant gene.

ZNF517

Figure S4.  This figure depicts the distribution of mutations and mutation types across the ZNF517 significant gene.

MAL2

Figure S5.  This figure depicts the distribution of mutations and mutation types across the MAL2 significant gene.

TOR3A

Figure S6.  This figure depicts the distribution of mutations and mutation types across the TOR3A significant gene.

TP53

Figure S7.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

CLDN23

Figure S8.  This figure depicts the distribution of mutations and mutation types across the CLDN23 significant gene.

GDF1

Figure S9.  This figure depicts the distribution of mutations and mutation types across the GDF1 significant gene.

LZTR1

Figure S10.  This figure depicts the distribution of mutations and mutation types across the LZTR1 significant gene.

KCNK17

Figure S11.  This figure depicts the distribution of mutations and mutation types across the KCNK17 significant gene.

RINL

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

ZAR1

Figure S13.  This figure depicts the distribution of mutations and mutation types across the ZAR1 significant gene.

CTNNB1

Figure S14.  This figure depicts the distribution of mutations and mutation types across the CTNNB1 significant gene.

APOE

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

GPRIN2

Figure S16.  This figure depicts the distribution of mutations and mutation types across the GPRIN2 significant gene.

ASPDH

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

ERCC2

Figure S18.  This figure depicts the distribution of mutations and mutation types across the ERCC2 significant gene.

IDUA

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

C1orf106

Figure S20.  This figure depicts the distribution of mutations and mutation types across the C1orf106 significant gene.

C10orf95

Figure S21.  This figure depicts the distribution of mutations and mutation types across the C10orf95 significant gene.

RGS9BP

Figure S22.  This figure depicts the distribution of mutations and mutation types across the RGS9BP significant gene.

THEM4

Figure S23.  This figure depicts the distribution of mutations and mutation types across the THEM4 significant gene.

TSC22D2

Figure S24.  This figure depicts the distribution of mutations and mutation types across the TSC22D2 significant gene.

SYT8

Figure S25.  This figure depicts the distribution of mutations and mutation types across the SYT8 significant gene.

PLIN5

Figure S26.  This figure depicts the distribution of mutations and mutation types across the PLIN5 significant gene.

LRIG1

Figure S27.  This figure depicts the distribution of mutations and mutation types across the LRIG1 significant gene.

HHIPL1

Figure S28.  This figure depicts the distribution of mutations and mutation types across the HHIPL1 significant gene.

CCDC105

Figure S29.  This figure depicts the distribution of mutations and mutation types across the CCDC105 significant gene.

C19orf10

Figure S30.  This figure depicts the distribution of mutations and mutation types across the C19orf10 significant gene.

OPRD1

Figure S31.  This figure depicts the distribution of mutations and mutation types across the OPRD1 significant gene.

ATXN1

Figure S32.  This figure depicts the distribution of mutations and mutation types across the ATXN1 significant gene.

AATK

Figure S33.  This figure depicts the distribution of mutations and mutation types across the AATK 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)