Mutation Analysis (MutSig v2.0)
Colorectal Adenocarcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1T43RTW
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSig v2.0). Broad Institute of MIT and Harvard. doi:10.7908/C1T43RTW
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: COADREAD-TP

  • Number of patients in set: 223

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:COADREAD-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 42

  • Mutations seen in COSMIC: 738

  • Significantly mutated genes in COSMIC territory: 29

  • Significantly mutated genesets: 35

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

Mutation Preprocessing

  • Read 140 MAFs of type "Broad"

  • Read 87 MAFs of type "Baylor-SOLiD"

  • Total number of mutations in input MAFs: 91943

  • After removing 1369 invalidated mutations: 90574

  • After removing 1176 noncoding mutations: 89398

  • After collapsing adjacent/redundant mutations: 82119

Mutation Filtering

  • Number of mutations before filtering: 82119

  • After removing 847 mutations outside gene set: 81272

  • After removing 343 mutations outside category set: 80929

  • After removing 12 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 79727

Results
Breakdown of Mutations by Type

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

type count
De_novo_Start_InFrame 20
De_novo_Start_OutOfFrame 156
Frame_Shift_Del 1289
Frame_Shift_Ins 808
In_Frame_Del 166
In_Frame_Ins 26
Missense_Mutation 54063
Nonsense_Mutation 5054
Nonstop_Mutation 36
Read-through 10
Silent 19113
Splice_Site 186
Translation_Start_Site 2
Total 80929
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 20367 334435554 0.000061 61 6 2.1
*Cp(A/C/T)->mut 21024 2765622679 7.6e-06 7.6 0.75 3.3
A->mut 12105 3006258245 4e-06 4 0.4 3.9
*CpG->(G/A) 561 334435554 1.7e-06 1.7 0.17 2.7
indel+null 7446 6106316478 1.2e-06 1.2 0.12 NaN
double_null 304 6106316478 5e-08 0.05 0.0049 NaN
Total 61807 6106316478 1e-05 10 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: COADREAD-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)->mut

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

  • n4 = number of nonsilent mutations of type: *CpG->(G/A)

  • 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: 42. 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 575336 45 38 28 2 23 6 5 2 9 0 <1.00e-15 0.0019 0.000028 0.031 0.000021 <0.000 <0.000
2 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 618889 40 33 21 2 6 20 14 0 0 0 9.21e-15 0.013 0.00051 0.000022 2e-06 0.000 0.000
3 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 130677 20 20 8 0 2 14 4 0 0 0 7.33e-15 0.0075 0.000014 0.12 0.000032 0.000 0.000
4 SMAD4 SMAD family member 4 375807 29 26 22 0 10 6 9 0 3 1 6.33e-15 0.00043 0.027 0.0011 0.00043 1.11e-16 4.99e-13
5 APC adenomatous polyposis coli 1882819 187 160 128 4 6 15 11 0 103 52 1.89e-15 1.3e-09 0 0.92 0 <1.00e-15 <2.25e-12
6 TP53 tumor protein p53 266735 121 119 69 2 47 22 12 2 38 0 <1.00e-15 1.1e-10 0 0 0 <1.00e-15 <2.25e-12
7 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 157303 96 96 11 0 0 91 4 0 1 0 <1.00e-15 4.8e-07 0 0.00073 0 <1.00e-15 <2.25e-12
8 BRAF v-raf murine sarcoma viral oncogene homolog B1 491043 23 22 4 0 0 0 23 0 0 0 8.35e-12 0.0011 0 0 0 <1.00e-15 <2.25e-12
9 FAM123B family with sequence similarity 123B 639797 27 25 24 2 1 3 4 0 19 0 1.47e-12 0.13 0.0046 0.76 0.012 5.90e-13 1.18e-09
10 SMAD2 SMAD family member 2 320993 16 15 12 1 3 5 3 0 5 0 4.55e-11 0.12 0.0095 0.14 0.0082 1.11e-11 2.00e-08
11 TCF7L2 transcription factor 7-like 2 (T-cell specific, HMG-box) 399291 20 18 18 4 5 5 3 0 7 0 5.41e-09 0.15 0.0099 0.17 0.015 1.99e-09 3.26e-06
12 KRTAP5-5 keratin associated protein 5-5 119310 4 4 1 1 0 0 0 4 0 0 2.67e-06 0.31 0.0015 0.000048 0.000048 3.06e-09 4.58e-06
13 ACVR1B activin A receptor, type IB 338041 15 14 15 0 4 7 2 0 2 0 1.55e-07 0.014 0.066 0.00044 0.0014 5.23e-09 7.23e-06
14 TNFRSF10C tumor necrosis factor receptor superfamily, member 10c, decoy without an intracellular domain 166486 6 6 2 0 0 6 0 0 0 0 8.46e-05 0.24 1e-05 1 0.000053 9.14e-08 0.000117
15 KIAA1804 530272 18 15 16 0 8 7 1 0 2 0 1.87e-06 0.007 0.0054 0.011 0.0047 1.70e-07 0.000204
16 MAP2K4 mitogen-activated protein kinase kinase 4 245036 11 11 10 1 3 4 2 0 2 0 4.05e-07 0.24 0.047 0.046 0.031 2.43e-07 0.000273
17 PCBP1 poly(rC) binding protein 1 175969 6 6 3 0 0 0 6 0 0 0 0.000415 0.33 0.0022 0.0025 0.000077 5.85e-07 0.000618
18 SOX9 SRY (sex determining region Y)-box 9 (campomelic dysplasia, autosomal sex-reversal) 256394 10 10 10 0 0 0 1 0 8 1 1.21e-06 0.48 0.038 0.44 0.086 1.77e-06 0.00176
19 CCDC160 coiled-coil domain containing 160 85540 9 7 9 0 0 4 0 1 3 1 6.04e-07 0.21 0.81 0.32 0.64 6.12e-06 0.00579
20 CPXCR1 CPX chromosome region, candidate 1 185655 10 9 10 1 2 5 2 0 1 0 1.21e-06 0.39 0.95 0.16 0.35 6.59e-06 0.00593
21 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 526300 12 11 12 0 3 4 3 1 1 0 3.93e-06 0.075 0.24 0.96 0.34 1.92e-05 0.0159
22 CDH2 cadherin 2, type 1, N-cadherin (neuronal) 602675 22 16 21 0 3 15 3 1 0 0 1.10e-05 0.0041 0.063 0.53 0.12 1.95e-05 0.0159
23 DKK2 dickkopf homolog 2 (Xenopus laevis) 175823 6 6 5 1 6 0 0 0 0 0 0.00416 0.35 0.0012 0.11 0.00058 3.34e-05 0.0261
24 ACVR2A activin A receptor, type IIA 352856 10 9 9 1 0 0 4 0 5 1 0.000261 0.49 0.081 0.0093 0.011 4.02e-05 0.0293
25 WBSCR17 Williams-Beuren syndrome chromosome region 17 398799 19 19 18 3 10 5 1 0 3 0 1.73e-05 0.05 0.09 0.68 0.17 4.09e-05 0.0293
26 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 259135 10 7 8 1 3 2 1 0 3 1 0.000495 0.36 0.067 0.0048 0.0062 4.23e-05 0.0293
27 PRIM2 primase, DNA, polypeptide 2 (58kDa) 210700 9 8 8 1 0 1 3 0 5 0 2.81e-05 0.63 0.059 0.52 0.12 4.54e-05 0.0302
28 UBE2NL ubiquitin-conjugating enzyme E2N-like 102889 7 7 6 1 2 4 0 1 0 0 1.65e-05 0.51 0.12 0.92 0.24 5.33e-05 0.0342
29 GPC6 glypican 6 370380 10 10 9 0 5 4 1 0 0 0 5.15e-05 0.046 0.5 0.027 0.095 6.46e-05 0.0400
30 DKK4 dickkopf homolog 4 (Xenopus laevis) 152450 7 7 6 0 3 2 2 0 0 0 0.000150 0.16 0.058 0.17 0.042 8.17e-05 0.0459
31 OR2M4 olfactory receptor, family 2, subfamily M, member 4 209326 8 8 8 0 3 1 2 0 2 0 5.86e-05 0.13 0.47 0.028 0.11 8.29e-05 0.0459
32 OTOL1 otolin 1 178220 8 8 7 0 3 3 1 0 1 0 9.36e-06 0.1 0.72 0.34 0.69 8.37e-05 0.0459
33 MGC26647 chromosome 7 open reading frame 62 169132 7 7 7 0 1 2 1 0 3 0 3.41e-05 0.26 0.1 0.76 0.19 8.42e-05 0.0459
34 ELF3 E74-like factor 3 (ets domain transcription factor, epithelial-specific ) 238076 6 6 5 0 0 0 1 0 5 0 0.000677 0.65 0.0059 0.31 0.011 9.55e-05 0.0505
35 GGT1 gamma-glutamyltransferase 1 245448 3 3 1 1 0 3 0 0 0 0 0.438 0.59 0.000091 0.000019 0.000018 0.000101 0.0516
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: 29. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 APC adenomatous polyposis coli 187 839 137 187097 2584 0 0
2 TP53 tumor protein p53 121 824 121 183752 44631 0 0
3 ERBB3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) 14 6 6 1338 6 5.8e-14 8.8e-11
4 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 20 33 18 7359 17998 2.6e-13 2.9e-10
5 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 96 52 95 11596 1013913 3.9e-13 3.5e-10
6 BRAF v-raf murine sarcoma viral oncogene homolog B1 23 89 20 19847 287480 6.1e-13 4e-10
7 FBXW7 F-box and WD repeat domain containing 7 45 91 31 20293 1228 6.2e-13 4e-10
8 SMAD4 SMAD family member 4 29 159 19 35457 90 9.3e-13 5.1e-10
9 KRTAP5-5 keratin associated protein 5-5 4 1 4 223 4 1.1e-12 5.1e-10
10 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 40 220 34 49060 13256 1.1e-12 5.1e-10

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: 35. 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 GSK3PATHWAY Bacterial lipopolysaccharide activates AKT to promote the survival and activation of macrophages and inhibits Gsk3-beta to promote beta-catenin accumulation in the nucleus. AKT1, APC, AXIN1, CCND1, CD14, CTNNB1, DVL1, FZD1, GJA1, GNAI1, GSK3B, IRAK1, LBP, LEF1, LY96, MYD88, NFKB1, PDPK1, PIK3CA, PIK3R1, PPP2CA, PRKR, RELA, TIRAP, TLR4, TOLLIP, WNT1 26 AKT1(2), APC(187), AXIN1(2), CD14(1), CTNNB1(12), FZD1(1), GJA1(7), GNAI1(4), GSK3B(8), IRAK1(1), LBP(1), LEF1(4), LY96(2), MYD88(2), NFKB1(6), PDPK1(1), PIK3CA(40), PIK3R1(10), PPP2CA(1), RELA(2), TIRAP(1), TLR4(10), TOLLIP(1), WNT1(1) 9421260 307 183 228 28 44 50 43 1 115 54 4.99e-13 <1.00e-15 <8.55e-14
2 ST_MYOCYTE_AD_PATHWAY Cardiac myocytes have a variety of adrenergic receptors that induce subtype-specific signaling effects. ADRB1, AKT1, APC, ASAH1, BF, CAMP, CAV3, DAG1, DLG4, EPHB2, GAS, GNAI1, GNAQ, HTATIP, ITPR1, ITPR2, ITPR3, KCNJ3, KCNJ5, KCNJ9, MAPK1, PITX2, PLB, PTX1, PTX3, RAC1, RHO, RYR1 23 ADRB1(3), AKT1(2), APC(187), ASAH1(3), CAV3(3), DAG1(5), DLG4(3), EPHB2(8), GNAI1(4), GNAQ(4), ITPR1(19), ITPR2(18), ITPR3(12), KCNJ3(6), KCNJ5(4), KCNJ9(3), MAPK1(2), PITX2(2), PTX3(2), RHO(3), RYR1(25) 14036969 318 180 258 58 69 53 28 0 115 53 4.95e-07 <1.00e-15 <8.55e-14
3 TGFBPATHWAY The TGF-beta receptor responds to ligand binding by activating the SMAD family of transcriptional regulations, commonly blocking cell growth. APC, CDH1, CREBBP, EP300, MADH2, MADH3, MADH4, MADH7, MADHIP, MAP2K1, MAP3K7, MAP3K7IP1, MAPK3, SKIL, TGFB1, TGFB2, TGFB3, TGFBR1, TGFBR2 13 APC(187), CDH1(5), CREBBP(25), EP300(15), MAP2K1(4), MAP3K7(4), MAPK3(3), SKIL(1), TGFB1(1), TGFB2(9), TGFBR1(9), TGFBR2(7) 8368983 270 175 210 25 34 35 30 1 118 52 1.75e-10 <1.00e-15 <8.55e-14
4 ATRBRCAPATHWAY BRCA1 and 2 block cell cycle progression in response to DNA damage and promote double-stranded break repair; mutations induce breast cancer susceptibility. ATM, ATR, BRCA1, BRCA2, CHEK1, CHEK2, FANCA, FANCC, FANCD2, FANCE, FANCF, FANCG, HUS1, MRE11A, NBS1, RAD1, RAD17, RAD50, RAD51, RAD9A, TP53, TREX1 21 ATM(37), ATR(17), BRCA1(6), BRCA2(22), CHEK1(1), CHEK2(1), FANCA(7), FANCC(3), FANCD2(11), FANCF(2), FANCG(4), HUS1(1), MRE11A(8), RAD1(1), RAD17(4), RAD50(11), TP53(121), TREX1(2) 14298275 259 148 206 21 80 75 40 2 60 2 6.60e-10 <1.00e-15 <8.55e-14
5 ATMPATHWAY The tumor-suppressing protein kinase ATM responds to radiation-induced DNA damage by blocking cell-cycle progression and activating DNA repair. ABL1, ATM, BRCA1, CDKN1A, CHEK1, CHEK2, GADD45A, JUN, MAPK8, MDM2, MRE11A, NBS1, NFKB1, NFKBIA, RAD50, RAD51, RBBP8, RELA, TP53, TP73 19 ABL1(4), ATM(37), BRCA1(6), CDKN1A(1), CHEK1(1), CHEK2(1), MAPK8(7), MDM2(4), MRE11A(8), NFKB1(6), RAD50(11), RBBP8(2), RELA(2), TP53(121) 9687085 211 145 158 16 67 58 31 3 50 2 1.04e-09 <1.00e-15 <8.55e-14
6 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 15 CDK2(2), CDK4(3), CDKN1A(1), CDKN1B(3), CDKN2A(1), CFL1(1), E2F2(1), MDM2(4), NXT1(1), PRB1(1), TP53(121) 2480575 139 127 87 7 50 26 18 3 42 0 1.61e-10 <1.00e-15 <8.55e-14
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 CHUK(6), DNAJC3(1), EIF2S2(1), MAP3K14(3), NFKB1(6), RELA(2), TP53(121) 3081784 140 125 88 9 58 24 16 2 40 0 6.41e-08 <1.00e-15 <8.55e-14
8 TELPATHWAY Telomerase is a ribonucleotide protein that adds telomeric repeats to the 3' ends of chromosomes. AKT1, BCL2, EGFR, G22P1, HSPCA, IGF1R, KRAS2, MYC, POLR2A, PPP2CA, PRKCA, RB1, TEP1, TERF1, TERT, TNKS, TP53, XRCC5 15 AKT1(2), BCL2(1), EGFR(11), IGF1R(8), POLR2A(6), PPP2CA(1), PRKCA(4), RB1(7), TEP1(7), TERF1(1), TERT(2), TNKS(4), TP53(121), XRCC5(6) 9118297 181 143 129 22 74 38 23 3 42 1 6.62e-08 1.11e-15 8.55e-14
9 ST_GRANULE_CELL_SURVIVAL_PATHWAY The survival and differentiation of granule cells in the brain is controlled by pro-growth PACAP and pro-apoptotic ceramides. ADPRT, APC, ASAH1, CAMP, CASP3, CERK, CREB1, CREB3, CREB5, CXCL2, DAG1, EPHB2, FOS, GNAQ, IL8RB, ITPKA, ITPKB, JUN, MAP2K4, MAP2K7, MAPK1, MAPK10, MAPK8, MAPK8IP1, MAPK8IP2, MAPK8IP3, MAPK9, PACAP 25 APC(187), ASAH1(3), CASP3(2), CERK(1), CREB1(1), CREB3(3), CREB5(4), DAG1(5), EPHB2(8), FOS(1), GNAQ(4), ITPKB(4), MAP2K4(11), MAPK1(2), MAPK10(8), MAPK8(7), MAPK8IP1(2), MAPK8IP3(2), MAPK9(5) 9092442 260 170 199 25 26 36 28 2 116 52 1.65e-11 1.33e-15 9.12e-14
10 ST_WNT_BETA_CATENIN_PATHWAY Beta-catenin is degraded in the absence of Wnt signaling; when extracellular Wnt binds Frizzled receptors, beta-catenin accumulates in the nucleus and may promote cell survival. AKT1, AKT2, AKT3, ANKRD6, APC, AXIN1, AXIN2, C22orf2, CER1, CSNK1A1, CTNNB1, DACT1, DKK1, DKK2, DKK3, DKK4, DVL1, FRAT1, FSTL1, GSK3A, GSK3B, IDAX, LAMR1, LRP1, MVP, NKD1, NKD2, PIN1, PSEN1, PTPRA, SENP2, SFRP1, TSHB, WIF1 30 AKT1(2), AKT2(6), AKT3(1), ANKRD6(5), APC(187), AXIN1(2), AXIN2(9), CER1(3), CSNK1A1(2), CTNNB1(12), DACT1(6), DKK1(5), DKK2(6), DKK3(2), DKK4(7), FSTL1(6), GSK3A(2), GSK3B(8), LRP1(17), MVP(4), NKD1(3), NKD2(1), PIN1(1), PSEN1(3), PTPRA(2), SENP2(3), SFRP1(3), TSHB(1), WIF1(2) 12599173 311 185 249 37 60 43 39 1 116 52 1.12e-10 1.55e-15 9.33e-14

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 HSA00472_D_ARGININE_AND_D_ORNITHINE_METABOLISM Genes involved in D-arginine and D-ornithine metabolism DAO 1 DAO(6) 239708 6 5 6 0 4 1 0 0 1 0 0.15 0.043 1
2 SA_FAS_SIGNALING The TNF-type receptor Fas induces apoptosis on ligand binding. BCL2, CASP3, CASP8, CFL1, CFLAR, P11, PDE6D, TNFRSF6, TNFSF6 6 BCL2(1), CASP3(2), CASP8(11), CFL1(1), CFLAR(3), PDE6D(1) 1235944 19 17 18 2 3 7 5 0 4 0 0.11 0.072 1
3 INOSITOL_METABOLISM ALDH6A1, ALDOA, ALDOB, ALDOC, TPI1 5 ALDH6A1(4), ALDOA(1), ALDOB(5), ALDOC(3), TPI1(1) 1203129 14 13 14 1 4 7 3 0 0 0 0.077 0.13 1
4 NUCLEOTIDE_GPCRS ADORA1, ADORA2A, ADORA2B, ADORA3, GPR23, LTB4R, P2RY1, P2RY2, P2RY5, P2RY6 8 ADORA1(8), ADORA2A(5), ADORA2B(1), ADORA3(3), LTB4R(1), P2RY1(6), P2RY2(3), P2RY6(1) 1769574 28 20 28 4 12 11 3 1 1 0 0.011 0.28 1
5 HSA00902_MONOTERPENOID_BIOSYNTHESIS Genes involved in monoterpenoid biosynthesis CYP2C19, CYP2C9 2 CYP2C19(4), CYP2C9(7) 660768 11 9 11 2 3 3 4 0 1 0 0.51 0.32 1
6 CDC25PATHWAY The protein phosphatase Cdc25 is phosphorylated by Chk1 and activates Cdc2 to stimulate eukaryotic cells into M phase. ATM, CDC2, CDC25A, CDC25B, CDC25C, CHEK1, MYT1, WEE1, YWHAH 8 ATM(37), CDC25A(2), CDC25B(5), CDC25C(4), CHEK1(1), MYT1(13), WEE1(3), YWHAH(1) 4495368 66 37 64 7 13 30 10 1 10 2 0.0065 0.34 1
7 HSA00785_LIPOIC_ACID_METABOLISM Genes involved in lipoic acid metabolism LIAS, LIPT1, LOC387787 2 LIAS(4), LIPT1(4) 492074 8 6 8 1 0 6 1 0 1 0 0.58 0.35 1
8 HSA00830_RETINOL_METABOLISM Genes involved in retinol metabolism ALDH1A1, ALDH1A2, BCMO1, RDH5 4 ALDH1A1(4), ALDH1A2(6), BCMO1(4), RDH5(1) 1251009 15 13 15 2 6 4 4 0 1 0 0.16 0.35 1
9 HSA00031_INOSITOL_METABOLISM Genes involved in inositol metabolism ALDH6A1, TPI1 2 ALDH6A1(4), TPI1(1) 498985 5 5 5 0 0 2 3 0 0 0 0.27 0.38 1
10 ERBB4PATHWAY ErbB4 (aka HER4) is a receptor tyrosine kinase that binds neuregulins as well as members of the EGF family, which also target EGF receptors. ADAM17, ERBB4, NRG2, NRG3, PRKCA, PRKCB1, PSEN1 6 ADAM17(7), ERBB4(21), NRG2(6), NRG3(7), PRKCA(4), PSEN1(3) 2961197 48 31 47 9 11 19 9 2 7 0 0.074 0.43 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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