Mutation Analysis (MutSig v2.0)
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1571B67
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSig v2.0). Broad Institute of MIT and Harvard. doi:10.7908/C1571B67
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: CESC-TP

  • Number of patients in set: 194

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:CESC-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 16

  • Mutations seen in COSMIC: 192

  • Significantly mutated genes in COSMIC territory: 10

  • Significantly mutated genesets: 34

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

Mutation Preprocessing

  • Read 194 MAFs of type "maf1"

  • Total number of mutations in input MAFs: 46462

  • After removing 120 mutations outside chr1-24: 46342

  • After removing 300 blacklisted mutations: 46042

  • After removing 7441 noncoding mutations: 38601

  • After collapsing adjacent/redundant mutations: 38003

Mutation Filtering

  • Number of mutations before filtering: 38003

  • After removing 2721 mutations outside gene set: 35282

  • After removing 59 mutations outside category set: 35223

  • After removing 5 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 32438

Results
Breakdown of Mutations by Type

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

type count
De_novo_Start_InFrame 10
De_novo_Start_OutOfFrame 19
Frame_Shift_Del 144
Frame_Shift_Ins 180
In_Frame_Del 36
In_Frame_Ins 22
Missense_Mutation 23015
Nonsense_Mutation 2256
Nonstop_Mutation 57
Silent 8609
Splice_Site 836
Start_Codon_Ins 3
Start_Codon_SNP 35
Stop_Codon_Ins 1
Total 35223
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
Tp*C->(T/G) 14719 783553866 0.000019 19 4.2 3
Tp*C->A 1186 783553866 1.5e-06 1.5 0.34 4
(A/C/G)p*C->mut 5311 2209833766 2.4e-06 2.4 0.53 3.2
A->mut 1831 2900241563 6.3e-07 0.63 0.14 3.9
indel+null 3512 5893629195 6e-07 0.6 0.13 NaN
double_null 52 5893629195 8.8e-09 0.0088 0.002 NaN
Total 26611 5893629195 4.5e-06 4.5 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: CESC-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.

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: Tp*C->(T/G)

  • n2 = number of nonsilent mutations of type: Tp*C->A

  • n3 = number of nonsilent mutations of type: (A/C/G)p*C->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_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: 16. 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 FBXW7 F-box and WD repeat domain containing 7 495633 20 19 14 1 8 0 7 0 5 0 6e-15 0.067 4.4e-06 0.17 0.000014 0.000 0.000
2 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 636279 58 53 19 0 45 1 4 7 1 0 5.8e-15 0.000014 0 5.8e-06 0 <1.00e-15 <4.58e-12
3 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 136796 11 11 5 0 0 0 10 1 0 0 7e-15 0.15 0 0.01 0 <1.00e-15 <4.58e-12
4 ANKRD12 ankyrin repeat domain 12 1197621 20 6 17 3 14 0 1 2 3 0 0.54 0.74 0 0.12 0 <1.00e-15 <4.58e-12
5 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 239894 17 15 15 0 6 0 0 0 11 0 7.2e-15 0.09 0.18 0.88 0.28 7.09e-14 2.60e-10
6 HLA-A major histocompatibility complex, class I, A 155775 17 16 14 1 2 0 1 1 13 0 1.2e-14 0.068 0.4 0.68 0.57 2.24e-13 6.85e-10
7 MAPK1 mitogen-activated protein kinase 1 192420 9 9 3 0 9 0 0 0 0 0 4.4e-09 0.067 4e-06 0.0082 6.8e-06 9.64e-13 2.53e-09
8 EP300 E1A binding protein p300 1421010 23 21 19 1 9 2 1 0 8 3 1.6e-11 0.042 0.015 0.32 0.025 1.20e-11 2.75e-08
9 HLA-B major histocompatibility complex, class I, B 171702 11 11 8 0 0 0 2 1 7 1 6.8e-12 0.053 0.53 0.1 0.25 4.83e-11 9.85e-08
10 NFE2L2 nuclear factor (erythroid-derived 2)-like 2 350802 16 12 14 1 11 2 0 1 2 0 8.7e-09 0.27 0.041 0.019 0.0062 1.32e-09 2.42e-06
11 MUC4 mucin 4, cell surface associated 656842 85 37 83 12 56 2 11 4 10 2 4.8e-10 0.000017 0.61 0.67 1 1.08e-08 1.79e-05
12 CASP8 caspase 8, apoptosis-related cysteine peptidase 339514 9 9 8 1 1 0 0 2 6 0 3.6e-07 0.34 0.23 0.15 0.14 9.10e-07 0.00139
13 C3orf70 chromosome 3 open reading frame 70 144474 4 4 1 1 3 0 0 0 1 0 0.0026 0.76 8.2e-06 0.97 0.000034 1.52e-06 0.00215
14 ARID1A AT rich interactive domain 1A (SWI-like) 1119004 14 14 14 1 4 0 1 0 6 3 2.6e-06 0.24 0.22 0.33 0.35 1.39e-05 0.0171
15 RB1 retinoblastoma 1 (including osteosarcoma) 528233 9 9 9 1 1 2 1 0 4 1 4.2e-06 0.6 0.17 0.18 0.22 1.40e-05 0.0171
16 ZNF578 zinc finger protein 578 346290 7 6 7 0 4 1 1 0 1 0 0.0018 0.36 0.0012 0.37 0.0028 6.77e-05 0.0776
17 SMAD4 SMAD family member 4 330304 7 7 7 0 2 0 3 0 2 0 8e-05 0.29 0.19 0.28 0.16 0.000155 0.164
18 PTPRC protein tyrosine phosphatase, receptor type, C 763686 9 9 9 0 3 1 0 1 4 0 0.00061 0.18 0.012 0.32 0.022 0.000163 0.164
19 MSH4 mutS homolog 4 (E. coli) 527272 5 5 5 0 4 0 0 1 0 0 0.033 0.39 0.00098 0.01 0.00042 0.000170 0.164
20 HIST1H4E histone cluster 1, H4e 61304 4 4 4 0 3 0 1 0 0 0 0.000039 0.21 0.56 0.26 0.5 0.000235 0.213
21 DHRSX dehydrogenase/reductase (SDR family) X-linked 176122 4 4 4 0 2 0 1 1 0 0 0.0011 0.24 0.096 0.023 0.019 0.000244 0.213
22 LIN9 lin-9 homolog (C. elegans) 336749 7 7 6 0 4 0 1 0 2 0 0.000099 0.27 0.16 0.35 0.24 0.000274 0.229
23 MED1 mediator complex subunit 1 933799 14 11 14 0 7 1 2 0 4 0 0.00029 0.079 0.093 0.53 0.14 0.000440 0.351
24 COL19A1 collagen, type XIX, alpha 1 695739 9 9 9 1 3 1 1 1 3 0 0.000084 0.54 0.42 0.8 0.58 0.000540 0.400
25 STK11 serine/threonine kinase 11 210281 8 5 8 1 3 1 2 1 1 0 0.0001 0.26 0.77 0.19 0.48 0.000545 0.400
26 ERBB3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) 825967 12 11 9 1 5 1 4 2 0 0 0.00012 0.14 0.35 0.42 0.47 0.000582 0.406
27 HLA-C major histocompatibility complex, class I, C 180752 4 4 4 1 1 0 1 0 1 1 0.00095 0.71 0.034 0.71 0.062 0.000637 0.406
28 NHS Nance-Horan syndrome (congenital cataracts and dental anomalies) 862695 12 12 12 1 7 0 2 0 3 0 0.00038 0.3 0.14 0.24 0.16 0.000638 0.406
29 UNC93B1 unc-93 homolog B1 (C. elegans) 200792 3 3 2 0 0 0 3 0 0 0 0.014 0.37 0.0023 0.98 0.0045 0.000658 0.406
30 ZNF750 zinc finger protein 750 422763 11 10 11 2 3 0 1 1 6 0 0.00078 0.63 0.11 0.16 0.083 0.000693 0.406
31 UROC1 urocanase domain containing 1 440110 7 7 7 0 0 0 5 0 2 0 0.00064 0.1 0.055 0.9 0.1 0.000709 0.406
32 PDE1C phosphodiesterase 1C, calmodulin-dependent 70kDa 381724 6 6 6 1 1 0 3 1 1 0 0.00046 0.51 0.3 0.059 0.15 0.000716 0.406
33 CLEC11A C-type lectin domain family 11, member A 76213 2 2 2 1 2 0 0 0 0 0 0.03 0.88 0.3 0.011 0.0023 0.000734 0.406
34 SOX2 SRY (sex determining region Y)-box 2 97393 4 4 4 0 1 0 2 0 1 0 0.00066 0.26 0.9 0.016 0.11 0.000753 0.406
35 PCIF1 PDX1 C-terminal inhibiting factor 1 409924 4 4 4 0 3 0 1 0 0 0 0.072 0.28 0.00064 0.4 0.0012 0.000901 0.447
FBXW7

Figure S1.  This figure depicts the distribution of mutations and mutation types across the FBXW7 significant gene.

PIK3CA

Figure S2.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

KRAS

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

ANKRD12

Figure S4.  This figure depicts the distribution of mutations and mutation types across the ANKRD12 significant gene.

PTEN

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

HLA-A

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

MAPK1

Figure S7.  This figure depicts the distribution of mutations and mutation types across the MAPK1 significant gene.

EP300

Figure S8.  This figure depicts the distribution of mutations and mutation types across the EP300 significant gene.

HLA-B

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

NFE2L2

Figure S10.  This figure depicts the distribution of mutations and mutation types across the NFE2L2 significant gene.

MUC4

Figure S11.  This figure depicts the distribution of mutations and mutation types across the MUC4 significant gene.

C3orf70

Figure S12.  This figure depicts the distribution of mutations and mutation types across the C3orf70 significant gene.

ARID1A

Figure S13.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

RB1

Figure S14.  This figure depicts the distribution of mutations and mutation types across the RB1 significant gene.

ZNF578

Figure S15.  This figure depicts the distribution of mutations and mutation types across the ZNF578 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: 10. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 58 220 52 42680 24079 0 0
2 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 11 52 11 10088 106540 0 0
3 FBXW7 F-box and WD repeat domain containing 7 20 91 14 17654 534 0 0
4 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 17 767 16 148798 773 0 0
5 TP53 tumor protein p53 12 356 10 69064 707 1.3e-12 1.1e-09
6 ERBB3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) 12 6 4 1164 4 3.1e-11 2.4e-08
7 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 11 42 5 8148 11 5.4e-10 3.5e-07
8 RB1 retinoblastoma 1 (including osteosarcoma) 9 267 5 51798 12 4.8e-06 0.0027
9 STK11 serine/threonine kinase 11 8 130 4 25220 7 6.4e-06 0.0032
10 MTOR mechanistic target of rapamycin (serine/threonine kinase) 12 10 2 1940 2 0.000038 0.017

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: 34. 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 CDC42RACPATHWAY PI3 kinase stimulates cell migration by activating cdc42, which activates ARP2/3, which in turn promotes formation of new actin fibers. ACTR2, ACTR3, ARHA, ARPC1A, ARPC1B, ARPC2, ARPC3, ARPC4, CDC42, PAK1, PDGFRA, PIK3CA, PIK3R1, RAC1, WASL 14 ACTR2(1), ARPC1A(2), ARPC2(3), CDC42(1), PAK1(1), PDGFRA(2), PIK3CA(58), PIK3R1(9), RAC1(1), WASL(4) 3919913 82 66 43 4 58 2 7 9 6 0 0.000013 1.2e-15 2.4e-13
2 AKTPATHWAY Second messenger PIP3 promotes cell survival by activating the anti-apoptotic kinase AKT. AKT1, BAD, CASP9, CHUK, FOXO1A, FOXO3A, GH1, GHR, HSPCA, MLLT7, NFKB1, NFKBIA, PDPK1, PIK3CA, PIK3R1, PPP2CA, RELA, TNFSF6, YWHAH 14 AKT1(3), BAD(2), CASP9(1), CHUK(2), GHR(6), NFKB1(2), NFKBIA(2), PDPK1(2), PIK3CA(58), PIK3R1(9), RELA(5) 4124019 92 71 52 7 63 3 9 10 7 0 0.00011 1.6e-15 2.4e-13
3 GCRPATHWAY Corticosteroids activate the glucocorticoid receptor (GR), which inhibits NF-kB and activates Annexin-1, thus inhibiting the inflammatory response. ADRB2, AKT1, ANXA1, CALM1, CALM2, CALM3, CRN, GNAS, GNB1, GNGT1, HSPCA, NFKB1, NOS3, NPPA, NR3C1, PIK3CA, PIK3R1, RELA, SYT1 17 ADRB2(2), AKT1(3), CALM1(2), CALM3(1), GNAS(6), GNB1(1), GNGT1(1), NFKB1(2), NOS3(6), NPPA(1), NR3C1(4), PIK3CA(58), PIK3R1(9), RELA(5), SYT1(2) 5184985 103 71 63 6 71 3 12 10 7 0 3.6e-06 1.7e-15 2.4e-13
4 HCMVPATHWAY Cytomegalovirus activates MAP kinase pathways in the host cell, inducing transcription of viral genes. AKT1, CREB1, MAP2K1, MAP2K2, MAP2K3, MAP2K6, MAP3K1, MAPK1, MAPK14, MAPK3, NFKB1, PIK3CA, PIK3R1, RB1, RELA, SP1 16 AKT1(3), CREB1(2), MAP2K1(2), MAP2K3(2), MAP3K1(4), MAPK1(9), MAPK3(1), NFKB1(2), PIK3CA(58), PIK3R1(9), RB1(9), RELA(5), SP1(3) 5748570 109 75 63 7 74 4 11 10 9 1 5.2e-06 2e-15 2.4e-13
5 PTENPATHWAY PTEN suppresses AKT-induced cell proliferation and antagonizes the action of PI3K. AKT1, BCAR1, CDKN1B, FOXO3A, GRB2, ILK, ITGB1, MAPK1, MAPK3, PDK2, PDPK1, PIK3CA, PIK3R1, PTEN, PTK2, SHC1, SOS1, TNFSF6 16 AKT1(3), ITGB1(2), MAPK1(9), MAPK3(1), PDK2(1), PDPK1(2), PIK3CA(58), PIK3R1(9), PTEN(17), PTK2(2), SOS1(5) 5594594 109 76 61 6 73 5 8 9 14 0 2.5e-06 2.3e-15 2.4e-13
6 ERK5PATHWAY Signaling between a tissue and its innervating axon stimulates retrograde transport via Trk receptors, which activate Erk5, which induces transcription of anti-apoptotic factors. AKT1, CREB1, GRB2, HRAS, MAPK1, MAPK3, MAPK7, MEF2A, MEF2B, MEF2C, MEF2D, NTRK1, PIK3CA, PIK3R1, PLCG1, RPS6KA1, SHC1 17 AKT1(3), CREB1(2), HRAS(1), MAPK1(9), MAPK3(1), MAPK7(2), MEF2A(1), MEF2B(1), MEF2C(1), MEF2D(2), PIK3CA(58), PIK3R1(9), PLCG1(3), RPS6KA1(1) 5735427 94 73 48 5 67 4 10 9 4 0 2.2e-07 2.8e-15 2.4e-13
7 ACHPATHWAY Nicotinic acetylcholine receptors are ligand-gated ion channels that primarily mediate neuromuscular signaling and may inhibit neuronal apoptosis via the AKT pathway. AKT1, BAD, CHRNB1, CHRNG, FOXO3A, MUSK, PIK3CA, PIK3R1, PTK2, PTK2B, RAPSN, SRC, TERT, TNFSF6, YWHAH 13 AKT1(3), BAD(2), CHRNB1(1), CHRNG(1), MUSK(2), PIK3CA(58), PIK3R1(9), PTK2(2), PTK2B(3), TERT(1) 4886895 82 67 42 5 54 3 9 10 6 0 1.3e-06 2.8e-15 2.4e-13
8 PLCPATHWAY Phospholipase C hydrolyzes the membrane lipid PIP2 to DAG, which activates protein kinase C, and IP3, which causes calcium influx. AKT1, PIK3CA, PIK3R1, PLCB1, PLCG1, PRKCA, PRKCB1, VAV1 7 AKT1(3), PIK3CA(58), PIK3R1(9), PLCB1(10), PLCG1(3), PRKCA(3), VAV1(2) 3764925 88 70 48 5 65 2 7 9 5 0 4.7e-06 3.1e-15 2.4e-13
9 NKCELLSPATHWAY Natural killer (NK) lymphocytes are inhibited by MHC and activated by surface glycoproteins on tumor or virus-infected cells, which undergo perforin-mediated lysis. B2M, HLA-A, IL18, ITGB1, KLRC1, KLRC2, KLRC3, KLRC4, KLRD1, LAT, MAP2K1, MAPK3, PAK1, PIK3CA, PIK3R1, PTK2B, PTPN6, RAC1, SYK, VAV1 20 B2M(3), HLA-A(17), ITGB1(2), KLRC4(1), KLRD1(2), LAT(1), MAP2K1(2), MAPK3(1), PAK1(1), PIK3CA(58), PIK3R1(9), PTK2B(3), RAC1(1), SYK(1), VAV1(2) 5484583 104 78 62 7 59 2 10 10 23 0 5.4e-06 4.8e-15 3.3e-13
10 SA_PTEN_PATHWAY PTEN is a tumor suppressor that dephosphorylates the lipid messenger phosphatidylinositol triphosphate. AKT1, AKT2, AKT3, BPNT1, GRB2, ILK, MAPK1, MAPK3, PDK1, PIK3CA, PIK3CD, PIP3-E, PTEN, PTK2B, RBL2, SHC1, SOS1 16 AKT1(3), AKT2(1), AKT3(3), MAPK1(9), MAPK3(1), PDK1(3), PIK3CA(58), PIK3CD(1), PTEN(17), PTK2B(3), RBL2(2), SOS1(5) 5880977 106 74 58 6 74 2 7 8 15 0 4.3e-07 7.7e-15 4.7e-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 HSA00130_UBIQUINONE_BIOSYNTHESIS Genes involved in ubiquinone biosynthesis COQ2, COQ3, COQ5, COQ6, COQ7, ND1, ND2, ND3, ND4, ND4L, ND5, ND6, NDUFA12, NDUFA13, NDUFB11 8 COQ3(3), COQ5(2), COQ6(1), COQ7(1), NDUFA13(3), NDUFB11(3) 1217340 13 13 13 1 6 1 3 0 2 1 0.18 0.00027 0.17
2 GSPATHWAY Activated G-protein coupled receptors stimulate cAMP production and thus activate protein kinase A, involved in a number of signal transduction pathways. ADCY1, GNAS, GNB1, GNGT1, PRKACA, PRKAR1A 6 ADCY1(4), GNAS(6), GNB1(1), GNGT1(1), PRKACA(2) 1769339 14 14 14 1 7 1 4 1 1 0 0.076 0.039 1
3 TUBBYPATHWAY Tubby is activated by phospholipase C activity and hydrolysis of PIP2, after which it enters the nucleus and regulates transcription. CHRM1, GNAQ, GNB1, GNGT1, HTR2C, PLCB1, TUB 7 CHRM1(1), GNAQ(2), GNB1(1), GNGT1(1), HTR2C(2), PLCB1(10), TUB(3) 2066687 20 18 20 3 11 1 3 4 1 0 0.14 0.053 1
4 TERCPATHWAY hTERC, the RNA subunit of telomerase, and hTERT, the catalytic protein subunit, are required for telomerase activity and are overexpressed in many cancers. NFYA, NFYB, NFYC, RB1, SP1, SP3 5 NFYA(3), NFYB(1), NFYC(1), SP1(3), SP3(2) 1417365 10 10 10 0 8 0 2 0 0 0 0.076 0.058 1
5 HSA00643_STYRENE_DEGRADATION Genes involved in styrene degradation FAH, GSTZ1, HGD 3 FAH(3), GSTZ1(1), HGD(2) 656562 6 6 6 0 5 0 0 1 0 0 0.14 0.068 1
6 HSA00791_ATRAZINE_DEGRADATION Genes involved in atrazine degradation ADAR, APOBEC1, APOBEC2, APOBEC3A, APOBEC3B, APOBEC3C, APOBEC3F, APOBEC3G, APOBEC4 9 ADAR(5), APOBEC1(1), APOBEC3B(1), APOBEC3F(2), APOBEC3G(6), APOBEC4(3) 2134444 18 16 18 3 6 1 8 0 3 0 0.18 0.077 1
7 TCRAPATHWAY The kinases Lck and Fyn phosphorylate and activate the T cell receptor, which recognizes antigen-bound MHCII and leads to T cell activation. CD3D, CD3E, CD3G, CD3Z, CD4, FYN, HLA-DRA, HLA-DRB1, LCK, PTPRC, TRA@, TRB@, ZAP70 10 CD3D(2), CD4(1), FYN(3), HLA-DRA(1), HLA-DRB1(1), PTPRC(9), ZAP70(2) 2462474 19 18 19 3 8 3 3 1 4 0 0.21 0.079 1
8 STEROID_BIOSYNTHESIS CYP17A1, F13B, HSD17B1, HSD17B2, HSD17B3, HSD17B4, HSD17B7, HSD3B1, HSD3B2 9 CYP17A1(2), F13B(2), HSD17B4(10), HSD17B7(2), HSD3B1(2), HSD3B2(1) 2356166 19 14 19 2 10 0 3 2 4 0 0.074 0.09 1
9 IFNGPATHWAY IFN gamma signaling pathway IFNG, IFNGR1, IFNGR2, JAK1, JAK2, STAT1 6 IFNG(1), IFNGR1(6), IFNGR2(2), JAK1(1), JAK2(6), STAT1(2) 2394449 18 17 17 3 8 2 0 0 7 1 0.37 0.091 1
10 HSA00062_FATTY_ACID_ELONGATION_IN_MITOCHONDRIA Genes involved in fatty acid elongation in mitochondria ACAA2, ECHS1, HADH, HADHA, HADHB, HSD17B10, HSD17B4, MECR, PPT1, PPT2 10 ACAA2(1), HADH(1), HSD17B10(1), HSD17B4(10), MECR(1), PPT1(1), PPT2(1) 2402078 16 13 16 1 7 2 3 1 3 0 0.062 0.1 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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