Mutation Analysis (MutSig v1.5)
Adrenocortical Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C11C1V8G
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSig v1.5). Broad Institute of MIT and Harvard. doi:10.7908/C11C1V8G
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 v1.5 was used to generate the results found in this report.

  • Working with individual set: ACC-TP

  • Number of patients in set: 90

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

  • Significantly mutated genes (q ≤ 0.1): 15

  • Mutations seen in COSMIC: 48

  • Significantly mutated genes in COSMIC territory: 3

  • Significantly mutated genesets: 36

  • Significantly mutated genesets: (excluding sig. mutated genes):0

Mutation Preprocessing

  • Read 90 MAFs of type "Baylor-Illumina"

  • Total number of mutations in input MAFs: 8225

  • After removing 194 blacklisted mutations: 8031

  • After removing 7 noncoding mutations: 8024

  • After collapsing adjacent/redundant mutations: 8020

Mutation Filtering

  • Number of mutations before filtering: 8020

  • After removing 419 mutations outside gene set: 7601

  • After removing 18 mutations outside category set: 7583

Results
Breakdown of Mutations by Type

Table 1.  Get Full Table Table representing breakdown of mutations by type.

type count
Frame_Shift_Del 334
Frame_Shift_Ins 106
In_Frame_Del 108
In_Frame_Ins 36
Missense_Mutation 4676
Nonsense_Mutation 348
Silent 1824
Splice_Site 151
Total 7583
Breakdown of Mutation Rates by Category Type

Table 2.  Get Full Table A breakdown of mutation rates per category discovered for this individual set.

category n N rate rate_per_mb relative_rate exp_ns_s_ratio
*CpG->T 862 135480420 6.4e-06 6.4 2.9 2.2
*CpG->(G/A) 270 135480420 2e-06 2 0.92 2.8
*Cp(A/C/T)->mut 2546 1207388700 2.1e-06 2.1 0.98 3.4
A->mut 998 1327294890 7.5e-07 0.75 0.35 3.8
indel+null 1065 2670164010 4e-07 0.4 0.18 NaN
double_null 18 2670164010 6.7e-09 0.0067 0.0031 NaN
Total 5759 2670164010 2.2e-06 2.2 1 3.5
Target Coverage for Each Individual

The x axis represents the samples. The y axis represents the exons, one row per exon, and they are sorted by average coverage across samples. For exons with exactly the same average coverage, they are sorted next by the %GC of the exon. (The secondary sort is especially useful for the zero-coverage exons at the bottom). If the figure is unpopulated, then full coverage is assumed (e.g. MutSig CV doesn't use WIGs and assumes full coverage).

Figure 1. 

Distribution of Mutation Counts, Coverage, and Mutation Rates Across Samples

Figure 2.  Patients counts and rates file used to generate this plot: ACC-TP.patients.counts_and_rates.txt

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 3.  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 4.  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 5.  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:

  • N = number of sequenced bases in this gene across the individual set

  • n = 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

  • n1 = number of nonsilent mutations of type: *CpG->T

  • n2 = number of nonsilent mutations of type: *CpG->(G/A)

  • n3 = number of nonsilent mutations of type: *Cp(A/C/T)->mut

  • n4 = number of nonsilent mutations of type: A->mut

  • n5 = number of nonsilent mutations of type: indel+null

  • n6 = number of nonsilent mutations of type: double_null

  • p_ns_s = p-value for the observed nonsilent/silent ratio being elevated in this gene

  • p = p-value (overall)

  • q = q-value, False Discovery Rate (Benjamini-Hochberg procedure)

Table 3.  Get Full Table A Ranked List of Significantly Mutated Genes. Number of significant genes found: 15. 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 description N n npat nsite nsil n1 n2 n3 n4 n5 n6 p_ns_s p q
1 TP53 tumor protein p53 117990 16 15 16 1 1 0 3 1 10 1 0.22 6.4e-15 1.1e-10
2 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 216180 13 13 9 0 0 0 4 5 4 0 0.088 1.2e-14 1.1e-10
3 MUC5B mucin 5B, oligomeric mucus/gel-forming 1362600 16 16 3 2 0 0 1 15 0 0 0.28 2.8e-12 1.7e-08
4 PRKAR1A protein kinase, cAMP-dependent, regulatory, type I, alpha (tissue specific extinguisher 1) 106740 6 6 5 0 0 0 0 0 6 0 0.66 7.5e-11 3.4e-07
5 MEN1 multiple endocrine neoplasia I 147420 7 7 7 0 0 0 0 0 7 0 0.23 7.1e-10 2.6e-06
6 KRTAP4-5 keratin associated protein 4-5 19170 5 4 3 0 0 0 3 1 1 0 0.31 9.2e-10 2.8e-06
7 FAM194B family with sequence similarity 194, member B 192870 9 5 5 0 0 0 4 5 0 0 0.13 6.5e-07 0.0017
8 ATXN1 ataxin 1 188730 5 5 5 1 0 1 3 0 1 0 0.49 0.000015 0.031
9 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 787410 8 7 8 1 0 0 0 1 7 0 0.73 0.000016 0.031
10 ZNRF3 zinc and ring finger 3 109620 4 4 4 0 1 0 0 0 2 1 0.52 0.000019 0.034
11 ASXL3 additional sex combs like 3 (Drosophila) 607500 8 7 8 1 1 0 6 1 0 0 0.37 0.000038 0.063
12 DAXX death-associated protein 6 207630 4 4 4 0 0 1 0 0 3 0 0.57 0.000044 0.067
13 KIF1A kinesin family member 1A 443880 7 6 3 1 0 1 5 1 0 0 0.31 0.000076 0.099
14 CLTB clathrin, light chain (Lcb) 46350 4 3 4 0 1 0 1 2 0 0 0.4 8e-05 0.099
15 SHOX2 short stature homeobox 2 73800 3 3 1 0 0 0 0 0 3 0 1 0.000082 0.099
16 PRR21 proline rich 21 82980 5 3 4 0 0 0 4 0 1 0 0.29 0.0001 0.12
17 TWISTNB TWIST neighbor 75420 3 3 3 0 0 0 1 0 1 1 0.83 0.00012 0.12
18 POM121L12 POM121 transmembrane nucleoporin-like 12 40230 2 2 2 0 0 1 1 0 0 0 0.52 0.00018 0.17
19 IRF2 interferon regulatory factor 2 97380 3 3 3 0 1 0 1 0 1 0 0.49 0.00018 0.17
20 GADD45G growth arrest and DNA-damage-inducible, gamma 30960 2 2 2 0 0 0 0 0 2 0 0.73 0.0002 0.18
21 LCE1F late cornified envelope 1F 28440 2 2 2 0 0 0 1 0 1 0 0.78 0.00021 0.18
22 ARMC5 armadillo repeat containing 5 193230 4 4 4 0 1 2 0 0 1 0 0.39 0.00026 0.2
23 C19orf24 chromosome 19 open reading frame 24 11340 2 2 2 0 1 1 0 0 0 0 0.47 0.00026 0.2
24 GNAS GNAS complex locus 250380 5 5 5 1 1 0 2 0 2 0 0.82 0.00027 0.2
25 SLCO6A1 solute carrier organic anion transporter family, member 6A1 197280 4 4 4 1 0 1 1 1 1 0 0.74 0.00029 0.21
26 FAM9A family with sequence similarity 9, member A 92790 3 3 3 0 0 0 1 1 1 0 0.57 0.00031 0.21
27 ATN1 atrophin 1 252090 4 4 1 1 0 0 4 0 0 0 0.66 0.00032 0.21
28 HIST1H1E histone cluster 1, H1e 48960 2 2 2 0 0 0 1 1 0 0 0.63 0.00052 0.34
29 CHMP4C chromatin modifying protein 4C 64980 2 2 2 0 0 0 0 1 1 0 0.79 0.00056 0.35
30 KIAA1211 KIAA1211 253980 3 3 2 0 0 0 0 0 3 0 0.64 0.00061 0.37
31 FBXO8 F-box protein 8 88200 2 2 2 0 1 0 1 0 0 0 0.64 0.00063 0.37
32 TEKT4 tektin 4 70830 2 2 2 0 0 0 0 1 1 0 0.82 0.00066 0.38
33 MYEOV2 myeloma overexpressed 2 55980 2 2 2 0 0 0 1 0 1 0 0.79 0.0007 0.38
34 SVEP1 sushi, von Willebrand factor type A, EGF and pentraxin domain containing 1 933570 7 6 7 1 1 1 4 1 0 0 0.42 0.00071 0.38
35 PCDHB5 protocadherin beta 5 172800 3 3 3 1 1 1 0 1 0 0 0.59 0.00077 0.4
COSMIC analyses

In this analysis, COSMIC is used as a filter to increase power by restricting the territory of each gene. Cosmic version: v48.

Table 4.  Get Full Table Significantly mutated genes (COSMIC territory only). To access the database please go to: COSMIC. Number of significant genes found: 3. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 13 138 13 12420 5737 2.4e-13 1.1e-09
2 TP53 tumor protein p53 16 356 15 32040 1496 5.9e-13 1.3e-09
3 MEN1 multiple endocrine neoplasia I 7 208 4 18720 23 1.1e-07 0.00016
4 ABCA12 ATP-binding cassette, sub-family A (ABC1), member 12 3 1 1 90 1 0.00019 0.17
5 ANKRD30A ankyrin repeat domain 30A 3 1 1 90 1 0.00019 0.17
6 STK11 serine/threonine kinase 11 2 130 2 11700 5 0.00031 0.22
7 GRM3 glutamate receptor, metabotropic 3 4 2 1 180 1 0.00039 0.22
8 IGFBP3 insulin-like growth factor binding protein 3 1 2 1 180 1 0.00039 0.22
9 NLRP8 NLR family, pyrin domain containing 8 2 5 1 450 1 0.00097 0.48
10 GNAS GNAS complex locus 5 7 1 630 210 0.0014 0.61

Note:

n - number of (nonsilent) mutations in this gene across the individual set.

cos = number of unique mutated sites in this gene in COSMIC

n_cos = overlap between n and cos.

N_cos = number of individuals times cos.

cos_ev = total evidence: number of reports in COSMIC for mutations seen in this gene.

p = p-value for seeing the observed amount of overlap in this gene)

q = q-value, False Discovery Rate (Benjamini-Hochberg procedure)

Geneset Analyses

Table 5.  Get Full Table A Ranked List of Significantly Mutated Genesets. (Source: MSigDB GSEA Cannonical Pathway Set).Number of significant genesets found: 36. Number of genesets displayed: 10

rank geneset description genes N_genes mut_tally N n npat nsite nsil n1 n2 n3 n4 n5 n6 p_ns_s p q
1 SA_G1_AND_S_PHASES Cdk2, 4, and 6 bind cyclin D in G1, while cdk2/cyclin E promotes the G1/S transition. ARF1, ARF3, CCND1, CDK2, CDK4, CDKN1A, CDKN1B, CDKN2A, CFL1, E2F1, E2F2, MDM2, NXT1, PRB1, TP53 14 MDM2(2), TP53(16) 1061190 18 16 18 1 1 0 4 1 11 1 0.11 2.2e-12 9.4e-10
2 TERTPATHWAY hTERC, the RNA subunit of telomerase, and hTERT, the catalytic protein subunit, are required for telomerase activity and are overexpressed in many cancers. HDAC1, MAX, MYC, SP1, SP3, TP53, WT1, ZNF42 7 HDAC1(1), TP53(16), WT1(1) 893520 18 16 18 2 1 0 5 1 10 1 0.31 3.1e-12 9.4e-10
3 PMLPATHWAY Ring-shaped PML nuclear bodies regulate transcription and are required co-activators in p53- and DAXX-mediated apoptosis. CREBBP, DAXX, HRAS, PAX3, PML, PRAM-1, RARA, RB1, SIRT1, SP100, TNF, TNFRSF1A, TNFRSF1B, TNFRSF6, TNFSF6, TP53, UBL1 13 DAXX(4), PAX3(1), PML(1), RB1(1), SIRT1(1), TNFRSF1A(1), TP53(16) 2387610 25 21 25 2 1 1 5 1 16 1 0.15 9.8e-12 2e-09
4 PLK3PATHWAY Active Plk3 phosphorylates CDC25c, blocking the G2/M transition, and phosphorylates p53 to induce apoptosis. ATM, ATR, CDC25C, CHEK1, CHEK2, CNK, TP53, YWHAH 7 ATM(4), ATR(2), CHEK2(2), TP53(16) 2146050 24 21 24 2 2 0 5 2 14 1 0.27 2.4e-11 3.7e-09
5 P53PATHWAY p53 induces cell cycle arrest or apoptosis under conditions of DNA damage. APAF1, ATM, BAX, BCL2, CCND1, CCNE1, CDK2, CDK4, CDKN1A, E2F1, GADD45A, MDM2, PCNA, RB1, TIMP3, TP53 16 ATM(4), MDM2(2), RB1(1), TIMP3(1), TP53(16) 2444040 24 20 24 2 1 0 5 1 16 1 0.22 9.3e-11 1.1e-08
6 TIDPATHWAY On ligand binding, interferon gamma receptors stimulate JAK2 kinase to phosphorylate STAT transcription factors, which promote expression of interferon responsive genes. DNAJA3, HSPA1A, IFNG, IFNGR1, IFNGR2, IKBKB, JAK2, LIN7A, NFKB1, NFKBIA, RB1, RELA, TIP-1, TNF, TNFRSF1A, TNFRSF1B, TP53, USH1C, WT1 17 JAK2(1), NFKB1(1), RB1(1), TNFRSF1A(1), TP53(16), USH1C(1), WT1(1) 2330370 22 18 22 1 1 1 6 2 11 1 0.061 1.6e-10 1.7e-08
7 RNAPATHWAY dsRNA-activated protein kinase phosphorylates elF2a, which generally inhibits translation, and activates NF-kB to provoke inflammation. CHUK, DNAJC3, EIF2S1, EIF2S2, MAP3K14, NFKB1, NFKBIA, PRKR, RELA, TP53 9 NFKB1(1), TP53(16) 1374120 17 15 17 1 1 0 4 1 10 1 0.2 3.2e-10 2.8e-08
8 ARFPATHWAY Cyclin-dependent kinase inhibitor 2A is a tumor suppressor that induces G1 arrest and can activate the p53 pathway, leading to G2/M arrest. ABL1, CDKN2A, E2F1, MDM2, MYC, PIK3CA, PIK3R1, POLR1A, POLR1B, POLR1C, POLR1D, RAC1, RB1, TBX2, TP53, TWIST1 15 MDM2(2), PIK3CA(1), POLR1C(2), RB1(1), TP53(16) 2504430 22 19 22 2 2 0 5 2 12 1 0.16 4e-09 3.1e-07
9 RBPATHWAY The ATM protein kinase recognizes DNA damage and blocks cell cycle progression by phosphorylating chk1 and p53, which normally inhibits Rb to allow G1/S transitions. ATM, CDC2, CDC25A, CDC25B, CDC25C, CDK2, CDK4, CHEK1, MYT1, RB1, TP53, WEE1, YWHAH 12 ATM(4), RB1(1), TP53(16) 2398590 21 19 21 3 1 0 4 1 14 1 0.53 9.3e-09 6.3e-07
10 CELL2CELLPATHWAY Epithelial cell adhesion proteins such as cadherins transduce signals into the cell via catenins, which alter cell shape and motility. ACTN1, ACTN2, ACTN3, BCAR1, CSK, CTNNA1, CTNNA2, CTNNB1, PECAM1, PTK2, PXN, SRC, VCL 13 ACTN1(1), ACTN2(2), BCAR1(1), CSK(1), CTNNA2(1), CTNNB1(13) 2577510 19 18 15 1 1 1 8 5 4 0 0.079 4.1e-08 2.5e-06

Table 6.  Get Full Table A Ranked List of Significantly Mutated Genesets (Excluding Significantly Mutated Genes). Number of significant genesets found: 0. Number of genesets displayed: 10

rank geneset description genes N_genes mut_tally N n npat nsite nsil n1 n2 n3 n4 n5 n6 p_ns_s p q
1 SULFUR_METABOLISM BPNT1, PAPSS1, PAPSS2, SULT1A2, SULT1A3, SULT1A3, SULT1A4, SULT1E1, SULT2A1, SUOX 7 BPNT1(3), PAPSS2(1), SUOX(2) 818820 6 5 6 1 0 0 3 0 2 1 0.78 0.0072 1
2 N_GLYCAN_DEGRADATION AGA, FLJ21865, FUCA1, FUCA2, GLB1, HEXA, HEXB, LCT, MAN2C1, MANBA, NEU1, NEU2, NEU3, NEU4 11 AGA(1), FUCA1(1), FUCA2(1), HEXB(1), LCT(1), MANBA(2) 1963350 7 7 7 0 1 0 1 4 1 0 0.22 0.0083 1
3 CYSTEINE_METABOLISM CARS, CTH, GOT1, GOT2, LDHA, LDHB, LDHC, MPST 7 CTH(1), GOT2(2), LDHA(2) 870390 5 5 5 1 2 0 2 0 1 0 0.63 0.0084 1
4 HSA00511_N_GLYCAN_DEGRADATION Genes involved in N-glycan degradation AGA, FLJ21865, FUCA1, FUCA2, GLB1, HEXA, HEXB, LCT, MAN2B1, MAN2B2, MAN2C1, MANBA, NEU1, NEU2, NEU3, NEU4 13 AGA(1), FUCA1(1), FUCA2(1), HEXB(1), LCT(1), MAN2B1(1), MANBA(2) 2459340 8 7 8 0 1 1 1 4 1 0 0.16 0.013 1
5 PLK3PATHWAY Active Plk3 phosphorylates CDC25c, blocking the G2/M transition, and phosphorylates p53 to induce apoptosis. ATM, ATR, CDC25C, CHEK1, CHEK2, CNK, TP53, YWHAH 6 ATM(4), ATR(2), CHEK2(2) 2028060 8 7 8 1 1 0 2 1 4 0 0.58 0.018 1
6 CAPROLACTAM_DEGRADATION AKR1A1, ECHS1, EHHADH, HADHA, SDS 5 ECHS1(1), EHHADH(1), HADHA(1), SDS(2) 663390 5 4 5 0 1 0 2 0 2 0 0.3 0.022 1
7 HSA00272_CYSTEINE_METABOLISM Genes involved in cysteine metabolism CARS, CARS2, CDO1, CTH, GOT1, GOT2, LDHA, LDHAL6A, LDHAL6B, LDHB, LDHC, MPST, SDS, SULT1B1, SULT1C2, SULT1C4, SULT4A1 16 CTH(1), GOT2(2), LDHA(2), SDS(2) 1641960 7 6 7 1 3 0 3 0 1 0 0.41 0.022 1
8 P53PATHWAY p53 induces cell cycle arrest or apoptosis under conditions of DNA damage. APAF1, ATM, BAX, BCL2, CCND1, CCNE1, CDK2, CDK4, CDKN1A, E2F1, GADD45A, MDM2, PCNA, RB1, TIMP3, TP53 15 ATM(4), MDM2(2), RB1(1), TIMP3(1) 2326050 8 7 8 1 0 0 2 0 6 0 0.59 0.023 1
9 FATTY_ACID_BIOSYNTHESIS_PATH_2 ACAA1, ACAA2, ACAT1, ACAT2, ECHS1, EHHADH, HADHA, HADHB, SDS 9 ECHS1(1), EHHADH(1), HADHA(1), HADHB(1), SDS(2) 1142370 6 5 6 0 1 0 2 1 2 0 0.25 0.026 1
10 HSA00031_INOSITOL_METABOLISM Genes involved in inositol metabolism ALDH6A1, TPI1 2 ALDH6A1(2) 209880 2 2 2 0 0 0 1 0 1 0 0.78 0.03 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)
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