Mutation Analysis (MutSig v2.0)
Pheochromocytoma and Paraganglioma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C17M07C8
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Analysis (MutSig v2.0). Broad Institute of MIT and Harvard. doi:10.7908/C17M07C8
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 v2.0 was used to generate the results found in this report.

  • Working with individual set: PCPG-TP

  • Number of patients in set: 179

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:PCPG-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 8

  • Mutations seen in COSMIC: 36

  • Significantly mutated genes in COSMIC territory: 4

  • Significantly mutated genesets: 65

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

Mutation Preprocessing

  • Read 179 MAFs of type "maf1"

  • Total number of mutations in input MAFs: 4530

  • After removing 865 blacklisted mutations: 3665

  • After removing 485 noncoding mutations: 3180

  • After collapsing adjacent/redundant mutations: 3145

Mutation Filtering

  • Number of mutations before filtering: 3145

  • After removing 161 mutations outside gene set: 2984

  • After removing 4 mutations outside category set: 2980

  • After removing 1 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 2814

Results
Breakdown of Mutations by Type

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

type count
De_novo_Start_OutOfFrame 10
Frame_Shift_Del 213
Frame_Shift_Ins 28
In_Frame_Del 42
In_Frame_Ins 9
Missense_Mutation 1788
Nonsense_Mutation 73
Nonstop_Mutation 3
Silent 704
Splice_Site 107
Start_Codon_SNP 3
Total 2980
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 422 275155092 1.5e-06 1.5 3.4 2.1
*Cp(A/C/T)->T 412 2311423697 1.8e-07 0.18 0.4 1.7
A->G 339 2521778647 1.3e-07 0.13 0.3 2.3
transver 618 5108357436 1.2e-07 0.12 0.27 5.1
indel+null 482 5108357436 9.4e-08 0.094 0.21 NaN
double_null 3 5108357436 5.9e-10 0.00059 0.0013 NaN
Total 2276 5108357436 4.5e-07 0.45 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: PCPG-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: *Cp(A/C/T)->T

  • n3 = number of nonsilent mutations of type: A->G

  • n4 = number of nonsilent mutations of type: transver

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

  • n6 = number of nonsilent mutations of type: double_null

  • p_classic = p-value for the observed amount of nonsilent mutations being elevated in this gene

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

  • p_cons = p-value for enrichment of mutations at evolutionarily most-conserved sites in gene

  • p_joint = p-value for clustering + conservation

  • 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: 8. 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_classic p_ns_s p_clust p_cons p_joint p q
1 HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog 111944 18 18 3 0 0 0 13 5 0 0 3.7e-15 0.047 0 6e-05 0 <1.00e-15 <6.00e-12
2 EPAS1 endothelial PAS domain protein 1 442395 8 8 4 0 0 5 2 1 0 0 1.4e-11 0.027 0 0.00012 0 <1.00e-15 <6.00e-12
3 OSBPL6 oxysterol binding protein-like 6 537345 2 2 2 0 0 0 0 1 1 0 0.012 0.64 0.93 0 0 <1.00e-15 <6.00e-12
4 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 1543349 15 15 15 0 0 0 0 1 13 1 5.7e-15 0.29 0.24 0.46 0.41 7.99e-14 3.60e-10
5 RET ret proto-oncogene 529007 7 6 4 0 0 0 5 2 0 0 3.4e-08 0.22 0.022 0.019 0.02 1.49e-08 5.38e-05
6 VHL von Hippel-Lindau tumor suppressor 69184 3 3 3 0 1 0 0 1 1 0 1.6e-06 0.56 0.15 0.81 0.32 8.09e-06 0.0230
7 CSDE1 cold shock domain containing E1, RNA-binding 441152 4 4 4 0 0 0 0 0 4 0 0.000037 0.58 0.0096 0.23 0.016 8.93e-06 0.0230
8 GYPE glycophorin E 37415 2 2 1 0 0 0 0 0 2 0 0.000032 0.79 NaN NaN NaN 3.16e-05 0.0712
9 GPR128 G protein-coupled receptor 128 439620 4 4 4 0 1 1 0 2 0 0 7.7e-06 0.32 1 0.94 1 9.82e-05 0.196
10 NDUFAF2 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, assembly factor 2 81331 2 2 1 0 0 0 0 0 2 0 0.00017 0.44 NaN NaN NaN 0.000171 0.309
11 SOX4 SRY (sex determining region Y)-box 4 61797 2 2 2 0 0 0 0 0 2 0 0.000023 1 0.39 0.56 0.74 0.000204 0.334
12 ABCA13 ATP-binding cassette, sub-family A (ABC1), member 13 2343015 6 6 6 0 1 3 0 2 0 0 0.000059 0.12 0.57 0.87 0.75 0.000489 0.733
13 C20orf85 chromosome 20 open reading frame 85 64198 1 1 1 0 0 0 1 0 0 0 0.00057 0.74 NaN NaN NaN 0.000571 0.786
14 FAM83D family with sequence similarity 83, member D 259949 3 3 3 0 1 0 0 1 1 0 0.000059 0.53 0.62 0.63 1 0.000636 0.786
15 AMMECR1 Alport syndrome, mental retardation, midface hypoplasia and elliptocytosis chromosomal region, gene 1 113997 2 2 1 0 0 0 0 0 2 0 0.0004 1 0.015 1 0.16 0.000676 0.786
16 NRL neural retina leucine zipper 68489 1 1 1 0 0 0 1 0 0 0 0.0007 0.7 NaN NaN NaN 0.000699 0.786
17 MGST1 microsomal glutathione S-transferase 1 85914 1 1 1 0 1 0 0 0 0 0 0.00076 0.83 NaN NaN NaN 0.000760 0.804
18 MDK midkine (neurite growth-promoting factor 2) 52168 1 1 1 0 0 0 0 1 0 0 0.00084 0.84 NaN NaN NaN 0.000843 0.843
19 IFNA7 interferon, alpha 7 102746 1 1 1 0 1 0 0 0 0 0 0.001 0.83 NaN NaN NaN 0.00101 0.932
20 HNRNPM heterogeneous nuclear ribonucleoprotein M 381469 3 3 3 0 1 1 0 0 1 0 0.00054 0.34 0.24 0.049 0.19 0.00104 0.932
21 KCNH5 potassium voltage-gated channel, subfamily H (eag-related), member 5 541195 3 3 3 1 1 1 0 0 1 0 0.00011 0.56 0.79 0.94 1 0.00114 0.936
22 ATP6V1G3 ATPase, H+ transporting, lysosomal 13kDa, V1 subunit G3 74984 2 2 2 0 0 0 1 1 0 0 0.0004 0.69 0.28 0.22 0.3 0.00121 0.936
23 ATRX alpha thalassemia/mental retardation syndrome X-linked (RAD54 homolog, S. cerevisiae) 1358278 5 5 5 0 0 1 0 1 3 0 0.00019 0.64 0.69 0.34 0.69 0.00131 0.936
24 AQP7 aquaporin 7 167743 2 2 2 0 0 1 1 0 0 0 0.0013 0.46 NaN NaN NaN 0.00131 0.936
25 C2orf73 chromosome 2 open reading frame 73 54918 1 1 1 0 0 1 0 0 0 0 0.0013 0.72 NaN NaN NaN 0.00134 0.936
26 MAP3K4 mitogen-activated protein kinase kinase kinase 4 851777 3 3 2 0 0 2 1 0 0 0 0.002 0.3 0.048 0.88 0.068 0.00135 0.936
27 SHC1 SHC (Src homology 2 domain containing) transforming protein 1 318140 2 2 2 0 1 0 0 1 0 0 0.0054 0.57 0.66 0.018 0.029 0.00151 0.965
28 NKX6-3 NK6 homeobox 3 22503 1 1 1 0 0 0 0 0 1 0 0.0016 1 NaN NaN NaN 0.00157 0.965
29 TSPAN13 tetraspanin 13 113333 1 1 1 0 0 1 0 0 0 0 0.0017 0.62 NaN NaN NaN 0.00174 0.965
30 MARCH5 membrane-associated ring finger (C3HC4) 5 141522 1 1 1 0 1 0 0 0 0 0 0.0018 0.82 NaN NaN NaN 0.00175 0.965
31 PTCRA pre T-cell antigen receptor alpha 97364 1 1 1 0 1 0 0 0 0 0 0.0018 0.53 NaN NaN NaN 0.00176 0.965
32 MACROD1 MACRO domain containing 1 55992 1 1 1 0 0 0 1 0 0 0 0.0018 0.89 NaN NaN NaN 0.00177 0.965
33 CARD18 caspase recruitment domain family, member 18 49576 1 1 1 0 1 0 0 0 0 0 0.0018 1 NaN NaN NaN 0.00180 0.965
34 AQP12A aquaporin 12A 71626 1 1 1 0 0 0 0 1 0 0 0.0018 0.79 NaN NaN NaN 0.00184 0.965
35 PCDHB4 protocadherin beta 4 426922 3 3 3 0 2 0 0 1 0 0 0.00054 0.28 0.22 0.98 0.37 0.00191 0.965
HRAS

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

EPAS1

Figure S2.  This figure depicts the distribution of mutations and mutation types across the EPAS1 significant gene.

OSBPL6

Figure S3.  This figure depicts the distribution of mutations and mutation types across the OSBPL6 significant gene.

NF1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the NF1 significant gene.

RET

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

VHL

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

CSDE1

Figure S7.  This figure depicts the distribution of mutations and mutation types across the CSDE1 significant gene.

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: 4. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog 18 19 18 3401 3990 1.6e-13 7.3e-10
2 RET ret proto-oncogene 7 49 6 8771 968 4.1e-13 9.3e-10
3 FGFR1 fibroblast growth factor receptor 1 (fms-related tyrosine kinase 2, Pfeiffer syndrome) 2 10 2 1790 2 3.2e-07 0.00048
4 VHL von Hippel-Lindau tumor suppressor 3 541 3 96839 82 0.000013 0.015
5 GNA11 guanine nucleotide binding protein (G protein), alpha 11 (Gq class) 1 2 1 358 1 0.00016 0.12
6 LAMC1 laminin, gamma 1 (formerly LAMB2) 1 2 1 358 1 0.00016 0.12
7 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 1 5 1 895 1492 0.0004 0.26
8 BRAF v-raf murine sarcoma viral oncogene homolog B1 1 89 1 15931 47 0.0071 1
9 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 15 285 1 51015 2 0.022 1
10 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 1 332 1 59428 8 0.026 1

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: 65. 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 IL3PATHWAY IL-3 promotes proliferation and differentiation of hematopoietic cells via a heterodimeric receptor that activates the Stat5 and MAP kinase pathways. CSF2RB, FOS, GRB2, HRAS, IL3, IL3RA, JAK2, MAP2K1, MAPK3, PTPN6, RAF1, SHC1, SOS1, STAT5A, STAT5B 15 HRAS(18), JAK2(1), MAP2K1(1), SHC1(2) 4629404 22 22 7 1 1 0 13 7 1 0 0.015 3.9e-15 5.1e-13
2 RECKPATHWAY RECK is a membrane-anchored inhibitor of matrix metalloproteinases, which are expressed by tumor cells and promote metastasis. HRAS, MMP14, MMP2, MMP9, RECK, TIMP1, TIMP2, TIMP3, TIMP4 9 HRAS(18), MMP14(1), MMP2(1) 1969279 20 20 5 0 1 0 13 6 0 0 0.0055 4.4e-15 5.1e-13
3 LONGEVITYPATHWAY Caloric restriction in animals often increases lifespan, which may occur via decreased IGF receptor expression and consequent expression of stress-resistance proteins. AKT1, CAT, FOXO3A, GH1, GHR, HRAS, IGF1, IGF1R, PIK3CA, PIK3R1, SHC1, SOD1, SOD2, SOD3 12 HRAS(18), IGF1R(1), SHC1(2) 3477984 21 21 6 1 2 0 13 6 0 0 0.014 5.1e-15 5.1e-13
4 PYK2PATHWAY Pyk2 and Rac1 stimulate the JNK cascade and activate MKK3, which activates p38. BCAR1, CALM1, CALM2, CALM3, CRKL, GNAQ, GRB2, HRAS, JUN, MAP2K1, MAP2K2, MAP2K3, MAP2K4, MAP3K1, MAPK1, MAPK14, MAPK3, MAPK8, PAK1, PLCG1, PRKCA, PRKCB1, PTK2B, RAC1, RAF1, SHC1, SOS1, SRC, SYT1 28 BCAR1(2), HRAS(18), JUN(1), MAP2K1(1), SHC1(2) 7445742 24 24 8 0 1 1 13 6 3 0 0.001 5.7e-15 5.1e-13
5 HBXPATHWAY Hbx is a hepatitis B protein that activates a number of transcription factors, possibly by inducing calcium release from the mitochondrion to the cytoplasm. CREB1, GRB2, HBXIP, HRAS, PTK2B, SHC1, SOS1, SRC 8 HRAS(18), SHC1(2) 2311667 20 20 5 0 1 0 13 6 0 0 0.0024 5.9e-15 5.1e-13
6 CDK5PATHWAY Cdk5, a regulatory kinase implicated in neuronal development, represses Mek1, which downregulates the MAP kinase pathway. CDK5, CDK5R1, DPM2, EGR1, HRAS, KLK2, MAP2K1, MAP2K2, MAPK1, MAPK3, NGFB, NGFR, RAF1 12 HRAS(18), MAP2K1(1) 2131777 19 19 4 0 0 0 13 5 1 0 0.014 7.7e-15 5.1e-13
7 SA_TRKA_RECEPTOR The TrkA receptor binds nerve growth factor to activate MAP kinase pathways and promote cell growth. AKT1, AKT2, AKT3, ARHA, CDKN1A, ELK1, GRB2, HRAS, MAP2K1, MAP2K2, NGFB, NGFR, NTRK1, PIK3CA, PIK3CD, SHC1, SOS1 15 HRAS(18), MAP2K1(1), SHC1(2) 4171927 21 21 6 1 1 0 13 6 1 0 0.023 8.8e-15 5.1e-13
8 TRKAPATHWAY Nerve growth factor (NGF) promotes neuronal survival and proliferation by binding its receptor TrkA, which activates PI3K/AKT, Ras, and the MAP kinase pathway. AKT1, DPM2, GRB2, HRAS, KLK2, NGFB, NTRK1, PIK3CA, PIK3R1, PLCG1, PRKCA, PRKCB1, SHC1, SOS1 12 HRAS(18), SHC1(2) 4112439 20 20 5 1 1 0 13 6 0 0 0.019 8.8e-15 5.1e-13
9 IGF1RPATHWAY Insulin-like growth factor receptor IGF-1R promotes cell growth and inhibits apoptosis on binding of ligands IGF-1 and 2 via Ras activation and the AKT pathway. AKT1, BAD, GRB2, HRAS, IGF1R, IRS1, MAP2K1, MAPK1, MAPK3, PIK3CA, PIK3R1, RAF1, SHC1, SOS1, YWHAH 15 HRAS(18), IGF1R(1), MAP2K1(1), SHC1(2) 5024360 22 22 7 1 2 0 13 6 1 0 0.014 9.1e-15 5.1e-13
10 IL6PATHWAY IL-6 binding to its receptor activates JAK kinases and a variety of transcription factors, with effects in neuronal differentiation, bone loss, and inflammation. CEBPB, CSNK2A1, ELK1, FOS, GRB2, HRAS, IL6, IL6R, IL6ST, JAK1, JAK2, JAK3, JUN, MAP2K1, MAPK3, PTPN11, RAF1, SHC1, SOS1, SRF, STAT3 21 HRAS(18), IL6ST(1), JAK2(1), JUN(1), MAP2K1(1), SHC1(2), STAT3(1) 6213199 25 25 10 1 1 1 14 7 2 0 0.0041 9.2e-15 5.1e-13

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 SLRPPATHWAY Small leucine-rich proteoglycans (SLRPs) interact with and reorganize collagen fibers in the extracellular matrix. BGN, DCN, DSPG3, FMOD, KERA, LUM 5 DCN(1), FMOD(1), LUM(1) 954196 3 3 3 0 2 1 0 0 0 0 0.26 0.0017 0.62
2 HSA00460_CYANOAMINO_ACID_METABOLISM Genes involved in cyanoamino acid metabolism ASRGL1, GBA, GBA3, GGT1, GGTL3, GGTL4, SHMT1, SHMT2 6 ASRGL1(1), GBA(2), GGT1(1) 1378565 4 4 4 0 0 1 1 2 0 0 0.3 0.002 0.62
3 EICOSANOID_SYNTHESIS ALOX12, ALOX15, ALOX15B, ALOX5, ALOX5AP, DPEP1, GGT1, IPLA2(GAMMA), LTA4H, LTC4S, PLA2G2A, PLA2G6, PTGDS, PTGES, PTGIS, PTGS1, PTGS2, TBXAS1 17 ALOX12(1), ALOX15(1), DPEP1(1), GGT1(1), PTGS1(2) 3842060 6 6 6 1 1 2 1 1 1 0 0.33 0.004 0.7
4 SELENOAMINO_ACID_METABOLISM AHCY, CBS, CTH, GGT1, MARS, MARS2, MAT1A, MAT2B, PAPSS1, PAPSS2, SCLY, SEPHS1 12 CTH(1), GGT1(1), MARS(1), PAPSS1(1), PAPSS2(1) 3224299 5 5 5 1 1 2 0 2 0 0 0.4 0.0046 0.7
5 IL6PATHWAY IL-6 binding to its receptor activates JAK kinases and a variety of transcription factors, with effects in neuronal differentiation, bone loss, and inflammation. CEBPB, CSNK2A1, ELK1, FOS, GRB2, HRAS, IL6, IL6R, IL6ST, JAK1, JAK2, JAK3, JUN, MAP2K1, MAPK3, PTPN11, RAF1, SHC1, SOS1, SRF, STAT3 20 IL6ST(1), JAK2(1), JUN(1), MAP2K1(1), SHC1(2), STAT3(1) 6101255 7 7 7 1 1 1 1 2 2 0 0.36 0.007 0.8
6 HSA00430_TAURINE_AND_HYPOTAURINE_METABOLISM Genes involved in taurine and hypotaurine metabolism BAAT, CDO1, CSAD, GAD1, GAD2, GGT1, GGTL3, GGTL4 6 GAD1(2), GGT1(1) 1488209 3 3 3 0 1 1 0 1 0 0 0.34 0.0083 0.8
7 SULFUR_METABOLISM BPNT1, PAPSS1, PAPSS2, SULT1A2, SULT1A3, SULT1A3, SULT1A4, SULT1E1, SULT2A1, SUOX 7 PAPSS1(1), PAPSS2(1), SULT1E1(1) 1628538 3 3 3 0 1 1 0 0 1 0 0.5 0.01 0.8
8 1_AND_2_METHYLNAPHTHALENE_DEGRADATION ADH1A, ADH1A, ADH1B, ADH1C, ADH1B, ADH1C, ADH4, ADH6, ADH7, ADHFE1 7 ADH4(2), ADH6(2) 1507504 4 3 4 1 0 2 0 1 1 0 0.66 0.01 0.8
9 PROSTAGLANDIN_AND_LEUKOTRIENE_METABOLISM AKR1C3, ALOX12, ALOX15, ALOX5, CBR1, CBR3, CYP4F2, CYP4F3, CYP4F3, CYP4F2, EPX, GGT1, LPO, LTA4H, MPO, PGDS, PLA2G1B, PLA2G2A, PLA2G2E, PLA2G3, PLA2G4A, PLA2G5, PLA2G6, PRDX1, PRDX2, PRDX5, PRDX6, PTGDS, PTGES2, PTGIS, PTGS1, PTGS2, TBXAS1, TPO 31 ALOX12(1), ALOX15(1), CYP4F2(1), EPX(1), GGT1(1), PTGS1(2), TPO(1) 7165971 8 8 8 0 1 2 2 2 1 0 0.076 0.013 0.9
10 ERKPATHWAY Cell growth is promoted by Ras activation of the anti-apoptotic p44/42 MAP kinase pathway. DPM2, EGFR, ELK1, GNAS, GNB1, GNGT1, GRB2, HRAS, IGF1R, ITGB1, KLK2, MAP2K1, MAP2K2, MAPK1, MAPK3, MKNK1, MKNK2, MYC, NGFB, NGFR, PDGFRA, PPP2CA, PTPRR, RAF1, RPS6KA1, RPS6KA5, SHC1, SOS1, SRC, STAT3 28 IGF1R(1), ITGB1(1), MAP2K1(1), PDGFRA(1), RPS6KA5(1), SHC1(2), STAT3(1) 8517276 8 8 8 0 2 1 1 2 2 0 0.15 0.025 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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