Mutation Analysis (MutSig v2.0)
Breast Invasive Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1C24VM3
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/C1C24VM3
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: BRCA-TP

  • Number of patients in set: 977

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:BRCA-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: 887

  • Significantly mutated genes in COSMIC territory: 19

  • Significantly mutated genesets: 111

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

Mutation Preprocessing

  • Read 977 MAFs of type "maf1"

  • Total number of mutations in input MAFs: 89929

  • After removing 39 mutations outside chr1-24: 89890

  • After removing 1732 blacklisted mutations: 88158

  • After removing 6950 noncoding mutations: 81208

  • After collapsing adjacent/redundant mutations: 78029

Mutation Filtering

  • Number of mutations before filtering: 78029

  • After removing 7326 mutations outside gene set: 70703

  • After removing 274 mutations outside category set: 70429

  • After removing 35 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 62794

Results
Breakdown of Mutations by Type

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

type count
De_novo_Start_InFrame 3
De_novo_Start_OutOfFrame 13
Frame_Shift_Del 2219
Frame_Shift_Ins 1579
In_Frame_Del 427
In_Frame_Ins 103
Missense_Mutation 44009
Nonsense_Mutation 3571
Nonstop_Mutation 52
Silent 16064
Splice_Site 2327
Start_Codon_Del 6
Start_Codon_Ins 4
Start_Codon_SNP 47
Stop_Codon_Del 2
Stop_Codon_Ins 3
Total 70429
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->mut 8387 1431991223 5.9e-06 5.9 3 2.5
Tp*Cp(A/C/T)->mut 15599 3343111034 4.7e-06 4.7 2.4 3.4
(A/C/G)p*Cp(A/C/T)->mut 9401 9075750236 1e-06 1 0.52 3.4
A->mut 10641 13648491865 7.8e-07 0.78 0.39 3.8
indel+null 10066 27499344358 3.7e-07 0.37 0.19 NaN
double_null 237 27499344358 8.6e-09 0.0086 0.0044 NaN
Total 54331 27499344358 2e-06 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: BRCA-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: *CpG->mut

  • n2 = number of nonsilent mutations of type: Tp*Cp(A/C/T)->mut

  • n3 = number of nonsilent mutations of type: (A/C/G)p*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_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 RUNX1 runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) 1051659 29 29 23 2 0 2 5 4 17 1 1.04e-14 0.232 0.0098 0.00031 0.0002 1.11e-16 1.88e-12
2 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3200341 351 316 53 6 5 134 16 183 13 0 1.78e-15 <1.00e-15 0 0 0 <1.00e-15 <2.36e-12
3 TP53 tumor protein p53 1238133 299 296 161 4 45 22 38 67 127 0 2.66e-15 <1.00e-15 0 0 0 <1.00e-15 <2.36e-12
4 GATA3 GATA binding protein 3 998421 100 97 56 2 1 1 2 5 90 1 2.22e-15 0.352 0 0.41 0 <1.00e-15 <2.36e-12
5 FOXA1 forkhead box A1 1009017 23 23 16 0 1 8 3 7 3 1 2.55e-15 0.00969 2e-07 0.051 0 <1.00e-15 <2.36e-12
6 SF3B1 splicing factor 3b, subunit 1, 155kDa 3939945 16 16 9 2 0 2 1 12 1 0 0.00228 0.173 0 0.026 0 <1.00e-15 <2.36e-12
7 TNFSF8 tumor necrosis factor (ligand) superfamily, member 8 699445 2 2 2 0 0 0 0 1 1 0 0.277 0.507 0.49 0 0 <1.00e-15 <2.36e-12
8 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1212494 35 35 32 0 0 3 3 7 21 1 5.11e-15 0.0215 0.0024 0.75 0.0053 1.11e-15 2.36e-12
9 CBFB core-binding factor, beta subunit 459284 23 23 21 1 1 0 6 4 12 0 4.33e-15 0.113 0.0057 0.12 0.0086 1.44e-15 2.72e-12
10 MAP2K4 mitogen-activated protein kinase kinase 4 1094202 32 32 28 0 3 2 5 2 20 0 <1.00e-15 0.0102 0.12 0.5 0.21 <7.77e-15 <1.32e-11
11 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 2544201 109 107 90 1 4 7 4 1 93 0 1.55e-15 6.69e-07 0.22 0.73 0.31 1.75e-14 2.65e-11
12 TBX3 T-box 3 (ulnar mammary syndrome) 1280215 27 27 26 1 4 2 1 1 19 0 7.77e-15 0.130 0.035 0.83 0.067 1.88e-14 2.65e-11
13 MAP3K1 mitogen-activated protein kinase kinase kinase 1 4026466 80 71 72 1 1 3 5 7 46 18 1.55e-15 0.0122 0.75 0.69 1 5.45e-14 7.12e-11
14 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 3493901 21 20 14 2 3 7 3 8 0 0 4.15e-06 0.0445 0.000023 0.0011 5.2e-06 5.51e-10 6.68e-07
15 CDKN1B cyclin-dependent kinase inhibitor 1B (p27, Kip1) 500131 10 10 9 1 0 0 0 0 8 2 2.80e-09 0.586 0.16 0.65 0.3 1.86e-08 2.10e-05
16 HIST1H3B histone cluster 1, H3b 380829 11 11 11 2 3 3 2 1 2 0 7.84e-08 0.242 0.022 0.18 0.019 3.23e-08 3.42e-05
17 NCOR1 nuclear receptor co-repressor 1 7287573 41 40 39 2 2 10 5 2 22 0 8.59e-09 0.0296 0.51 0.26 0.64 1.11e-07 0.000111
18 GPS2 G protein pathway suppressor 2 948419 10 10 10 1 0 0 0 0 10 0 4.02e-07 0.799 0.023 0.14 0.017 1.36e-07 0.000129
19 AQP12A aquaporin 12A 324535 6 6 4 0 0 1 2 3 0 0 4.66e-06 0.198 0.56 0.0032 0.013 1.05e-06 0.000938
20 ACTL6B actin-like 6B 1186698 10 10 6 0 2 0 1 0 7 0 1.13e-05 0.306 0.0038 0.98 0.0073 1.43e-06 0.00122
21 RB1 retinoblastoma 1 (including osteosarcoma) 2645506 22 19 21 3 1 1 1 2 17 0 1.25e-07 0.498 0.74 0.57 0.83 1.77e-06 0.00143
22 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 2281896 15 14 14 2 0 2 2 4 6 1 0.000659 0.619 0.000069 0.46 0.00019 2.16e-06 0.00166
23 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 689307 6 6 3 0 0 1 5 0 0 0 0.000135 0.402 0.0017 0.059 0.0014 3.16e-06 0.00233
24 ZFP36L2 zinc finger protein 36, C3H type-like 2 360184 7 7 7 1 3 0 1 0 3 0 2.45e-06 0.438 0.24 0.29 0.33 1.23e-05 0.00869
25 ZFP36L1 zinc finger protein 36, C3H type-like 1 908013 8 8 8 0 0 0 1 1 6 0 2.23e-05 0.505 0.017 0.54 0.045 1.48e-05 0.0101
26 FAM86B1 family with sequence similarity 86, member B1 335001 7 6 6 1 3 2 0 1 1 0 1.40e-06 0.333 0.49 0.48 0.82 1.67e-05 0.0109
27 FBXW7 F-box and WD repeat domain containing 7 2522313 15 15 13 1 5 4 1 3 2 0 1.70e-05 0.153 0.14 0.037 0.078 1.94e-05 0.0122
28 TBL1XR1 transducin (beta)-like 1 X-linked receptor 1 1550025 10 10 8 0 0 2 0 1 5 2 5.58e-06 0.352 0.27 0.81 0.45 3.46e-05 0.0210
29 MYB v-myb myeloblastosis viral oncogene homolog (avian) 2261316 12 12 12 0 1 0 2 1 8 0 4.37e-05 0.227 0.14 0.041 0.068 4.10e-05 0.0240
30 PTGER2 prostaglandin E receptor 2 (subtype EP2), 53kDa 738145 3 3 3 1 0 0 0 1 2 0 0.207 0.729 0.00021 0.67 0.000016 4.46e-05 0.0252
31 CTCF CCCTC-binding factor (zinc finger protein) 2152284 17 17 15 3 1 3 1 5 7 0 0.00516 0.527 0.0078 0.0007 0.00079 5.45e-05 0.0299
32 CASP8 caspase 8, apoptosis-related cysteine peptidase 1709033 10 10 10 1 1 1 1 3 4 0 0.000108 0.255 0.034 0.18 0.043 6.15e-05 0.0318
33 TCP10 t-complex 10 homolog (mouse) 729598 10 8 7 0 5 0 2 2 1 0 3.21e-05 0.0472 0.077 0.83 0.14 6.19e-05 0.0318
34 WSCD2 WSC domain containing 2 1546082 14 13 14 2 6 3 4 0 1 0 1.53e-05 0.131 0.7 0.14 0.32 6.48e-05 0.0323
35 ZP4 zona pellucida glycoprotein 4 1628063 13 13 13 1 0 5 3 1 4 0 1.04e-05 0.110 0.77 0.23 0.56 7.52e-05 0.0365
RUNX1

Figure S1.  This figure depicts the distribution of mutations and mutation types across the RUNX1 significant gene.

PIK3CA

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

TP53

Figure S3.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

GATA3

Figure S4.  This figure depicts the distribution of mutations and mutation types across the GATA3 significant gene.

FOXA1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the FOXA1 significant gene.

SF3B1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the SF3B1 significant gene.

TNFSF8

Figure S7.  This figure depicts the distribution of mutations and mutation types across the TNFSF8 significant gene.

PTEN

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

CBFB

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

MAP2K4

Figure S10.  This figure depicts the distribution of mutations and mutation types across the MAP2K4 significant gene.

CDH1

Figure S11.  This figure depicts the distribution of mutations and mutation types across the CDH1 significant gene.

TBX3

Figure S12.  This figure depicts the distribution of mutations and mutation types across the TBX3 significant gene.

MAP3K1

Figure S13.  This figure depicts the distribution of mutations and mutation types across the MAP3K1 significant gene.

ERBB2

Figure S14.  This figure depicts the distribution of mutations and mutation types across the ERBB2 significant gene.

CDKN1B

Figure S15.  This figure depicts the distribution of mutations and mutation types across the CDKN1B significant gene.

HIST1H3B

Figure S16.  This figure depicts the distribution of mutations and mutation types across the HIST1H3B significant gene.

NCOR1

Figure S17.  This figure depicts the distribution of mutations and mutation types across the NCOR1 significant gene.

GPS2

Figure S18.  This figure depicts the distribution of mutations and mutation types across the GPS2 significant gene.

AQP12A

Figure S19.  This figure depicts the distribution of mutations and mutation types across the AQP12A significant gene.

ACTL6B

Figure S20.  This figure depicts the distribution of mutations and mutation types across the ACTL6B significant gene.

RB1

Figure S21.  This figure depicts the distribution of mutations and mutation types across the RB1 significant gene.

PIK3R1

Figure S22.  This figure depicts the distribution of mutations and mutation types across the PIK3R1 significant gene.

KRAS

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

ZFP36L2

Figure S24.  This figure depicts the distribution of mutations and mutation types across the ZFP36L2 significant gene.

ZFP36L1

Figure S25.  This figure depicts the distribution of mutations and mutation types across the ZFP36L1 significant gene.

FAM86B1

Figure S26.  This figure depicts the distribution of mutations and mutation types across the FAM86B1 significant gene.

FBXW7

Figure S27.  This figure depicts the distribution of mutations and mutation types across the FBXW7 significant gene.

TBL1XR1

Figure S28.  This figure depicts the distribution of mutations and mutation types across the TBL1XR1 significant gene.

MYB

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

PTGER2

Figure S30.  This figure depicts the distribution of mutations and mutation types across the PTGER2 significant gene.

CTCF

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

TCP10

Figure S32.  This figure depicts the distribution of mutations and mutation types across the TCP10 significant gene.

WSCD2

Figure S33.  This figure depicts the distribution of mutations and mutation types across the WSCD2 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: 19. 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 351 220 325 214940 195319 0 0
2 GATA3 GATA binding protein 3 100 34 33 33218 234 0 0
3 TP53 tumor protein p53 299 356 279 347812 52914 0 0
4 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 21 42 13 41034 91 0 0
5 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 109 185 36 180745 67 0 0
6 RUNX1 runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) 29 178 13 173906 49 0 0
7 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 35 767 34 749359 393 0 0
8 MAP2K4 mitogen-activated protein kinase kinase 4 32 15 6 14655 10 1.3e-13 7.1e-11
9 RB1 retinoblastoma 1 (including osteosarcoma) 22 267 10 260859 23 2.2e-10 1.1e-07
10 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 6 52 6 50804 72781 1.3e-09 5.7e-07

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: 111. 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 HSA04620_TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY Genes involved in Toll-like receptor signaling pathway AKT1, AKT2, AKT3, CASP8, CCL3, CCL4, CCL5, CD14, CD40, CD80, CD86, CHUK, CXCL10, CXCL11, CXCL9, FADD, FOS, IFNA1, IFNA10, IFNA13, IFNA14, IFNA16, IFNA17, IFNA2, IFNA21, IFNA4, IFNA5, IFNA6, IFNA7, IFNA8, IFNAR1, IFNAR2, IFNB1, IKBKB, IKBKE, IKBKG, IL12A, IL12B, IL1B, IL6, IL8, IRAK1, IRAK4, IRF3, IRF5, IRF7, JUN, LBP, LY96, MAP2K1, MAP2K2, MAP2K3, MAP2K4, MAP2K6, MAP2K7, MAP3K7, MAP3K7IP1, MAP3K7IP2, MAP3K8, MAPK1, MAPK10, MAPK11, MAPK12, MAPK13, MAPK14, MAPK3, MAPK8, MAPK9, MYD88, NFKB1, NFKB2, NFKBIA, PIK3CA, PIK3CB, PIK3CD, PIK3CG, PIK3R1, PIK3R2, PIK3R3, PIK3R5, RAC1, RELA, RIPK1, SPP1, STAT1, TBK1, TICAM1, TICAM2, TIRAP, TLR1, TLR2, TLR3, TLR4, TLR5, TLR6, TLR7, TLR8, TLR9, TNF, TOLLIP, TRAF3, TRAF6 99 AKT1(2), AKT2(4), AKT3(5), CASP8(10), CD14(1), CD40(2), CD86(4), CHUK(7), CXCL10(1), IFNA1(1), IFNA10(3), IFNA13(3), IFNA14(3), IFNA16(2), IFNA17(3), IFNA2(3), IFNA21(4), IFNA4(2), IFNA6(1), IFNA7(2), IFNA8(2), IFNAR1(2), IFNAR2(1), IKBKB(5), IKBKE(6), IL12B(2), IL6(1), IRAK1(3), IRAK4(3), IRF3(6), IRF5(2), JUN(3), LBP(1), LY96(1), MAP2K1(5), MAP2K2(2), MAP2K3(3), MAP2K4(32), MAP2K6(1), MAP2K7(1), MAP3K7(3), MAP3K8(4), MAPK1(1), MAPK10(4), MAPK13(1), MAPK14(1), MAPK3(1), MAPK8(2), MAPK9(1), MYD88(1), NFKB1(5), NFKB2(5), NFKBIA(3), PIK3CA(351), PIK3CB(8), PIK3CD(6), PIK3CG(5), PIK3R1(15), PIK3R2(4), PIK3R3(3), PIK3R5(5), RELA(3), RIPK1(5), SPP1(2), STAT1(4), TBK1(1), TICAM1(2), TIRAP(1), TLR1(3), TLR2(3), TLR3(7), TLR4(15), TLR5(4), TLR7(9), TLR8(13), TLR9(2), TRAF3(3), TRAF6(4) 125201952 655 451 351 72 39 212 66 246 89 3 <1.00e-15 <1.00e-15 <1.87e-14
2 PPARAPATHWAY Peroxisome proliferators regulate gene expression via PPAR/RXR heterodimers which bind to peroxisome-proliferator response elements (PPREs). ACOX1, APOA1, APOA2, CD36, CITED2, CPT1B, CREBBP, DUSP1, DUT, EHHADH, EP300, FABP1, FAT, FRA8B, HSD17B4, HSPA1A, HSPCA, INS, JUN, LPL, MAPK1, MAPK3, ME1, MRPL11, MYC, NCOA1, NCOR1, NCOR2, NFKBIA, NOS2A, NR0B2, NR1H3, NR2F1, NRIP1, PDGFA, PIK3CA, PIK3R1, PPARA, PPARBP, PPARGC1, PRKACB, PRKACG, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, PRKCA, PRKCB1, PTGS2, RB1, RELA, RXRA, SP1, SRA1, STAT5A, STAT5B, TNF 49 ACOX1(3), APOA1(3), CD36(4), CITED2(2), CPT1B(4), CREBBP(15), DUSP1(1), DUT(1), EHHADH(4), EP300(13), FABP1(2), HSD17B4(8), JUN(3), LPL(1), MAPK1(1), MAPK3(1), MRPL11(1), MYC(1), NCOA1(5), NCOR1(41), NCOR2(8), NFKBIA(3), NR0B2(2), NR1H3(2), NR2F1(1), NRIP1(10), PIK3CA(351), PIK3R1(15), PPARA(4), PRKACB(1), PRKACG(3), PRKAR1A(4), PRKAR2A(2), PRKAR2B(2), PRKCA(4), PTGS2(6), RB1(22), RELA(3), RXRA(2), SP1(6), STAT5A(6), STAT5B(5) 88375670 576 422 274 52 34 180 46 217 97 2 <1.00e-15 <1.00e-15 <1.87e-14
3 APOPTOSIS_GENMAPP APAF1, BAK1, BCL2L7P1, BAX, BCL2, BCL2L1, BID, BIRC2, BIRC3, BIRC4, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9, CYCS, FADD, FAS, FASLG, GZMB, IKBKG, JUN, MAP2K4, MAP3K1, MAP3K14, MAPK10, MCL1, MDM2, MYC, NFKB1, NFKBIA, PARP1, PRF1, RELA, RIPK1, TNF, TNFRSF1A, TNFRSF1B, TNFSF10, TP53, TRADD, TRAF1, TRAF2 41 APAF1(11), BAK1(3), BAX(3), BCL2L1(1), BID(3), BIRC2(3), BIRC3(4), CASP2(3), CASP3(1), CASP6(2), CASP7(2), CASP8(10), CASP9(2), FAS(1), FASLG(2), JUN(3), MAP2K4(32), MAP3K1(80), MAPK10(4), MCL1(1), MDM2(3), MYC(1), NFKB1(5), NFKBIA(3), PARP1(6), PRF1(2), RELA(3), RIPK1(5), TNFRSF1A(4), TNFRSF1B(2), TP53(299), TRAF1(2), TRAF2(1) 52249364 507 420 356 22 66 60 64 87 212 18 <1.00e-15 <1.00e-15 <1.87e-14
4 SIG_PIP3_SIGNALING_IN_B_LYMPHOCYTES Genes related to PIP3 signaling in B lymphocytes AKT1, AKT2, AKT3, BCR, BTK, CD19, CDKN2A, DAPP1, FLOT1, FLOT2, FOXO3A, GAB1, ITPR1, ITPR2, ITPR3, LYN, NR0B2, P101-PI3K, PDK1, PHF11, PIK3CA, PITX2, PLCG2, PPP1R13B, PREX1, PSCD3, PTEN, PTPRC, RPS6KA1, RPS6KA2, RPS6KA3, RPS6KB1, SAG, SYK, TEC, VAV1 33 AKT1(2), AKT2(4), AKT3(5), BCR(3), BTK(7), CD19(6), CDKN2A(1), DAPP1(2), FLOT1(2), FLOT2(2), GAB1(2), ITPR1(15), ITPR2(18), ITPR3(14), LYN(6), NR0B2(2), PDK1(2), PHF11(3), PIK3CA(351), PITX2(5), PLCG2(8), PPP1R13B(4), PREX1(9), PTEN(35), PTPRC(8), RPS6KA1(6), RPS6KA2(5), RPS6KA3(8), RPS6KB1(4), SAG(3), SYK(4), TEC(6), VAV1(6) 79116874 558 414 255 45 41 179 43 231 63 1 <1.00e-15 <1.00e-15 <1.87e-14
5 EGFPATHWAY The epidermal growth factor (EGF) peptide stimulates the EGF receptor to promote cell proliferation via the MAP kinase and Ras pathways. CSNK2A1, EGF, EGFR, ELK1, FOS, GRB2, HRAS, JAK1, JUN, MAP2K1, MAP2K4, MAP3K1, MAPK3, MAPK8, PIK3CA, PIK3R1, PLCG1, PRKCA, PRKCB1, RAF1, RASA1, SHC1, SOS1, SRF, STAT1, STAT3, STAT5A 26 CSNK2A1(3), EGF(5), EGFR(5), ELK1(4), HRAS(2), JAK1(11), JUN(3), MAP2K1(5), MAP2K4(32), MAP3K1(80), MAPK3(1), MAPK8(2), PIK3CA(351), PIK3R1(15), PLCG1(8), PRKCA(4), RAF1(4), RASA1(2), SHC1(2), SOS1(5), SRF(1), STAT1(4), STAT3(5), STAT5A(6) 53850802 560 408 248 33 24 158 39 217 103 19 <1.00e-15 <1.00e-15 <1.87e-14
6 SIG_INSULIN_RECEPTOR_PATHWAY_IN_CARDIAC_MYOCYTES Genes related to the insulin receptor pathway AKT1, AKT2, AKT3, BRD4, CAP1, CBL, CDC42, CDKN2A, F2RL2, FLOT1, FLOT2, FOXO1A, GRB2, GSK3A, GSK3B, IGFBP1, INPPL1, IRS1, IRS2, IRS4, LNPEP, MAPK1, MAPK3, PARD3, PARD6A, PDK1, PIK3CA, PIK3CD, PIK3R1, PPYR1, PSCD3, PTEN, PTPN1, RAF1, RPS6KA1, RPS6KA2, RPS6KA3, RPS6KB1, SERPINB6, SFN, SHC1, SLC2A4, SORBS1, SOS1, SOS2, YWHAB, YWHAE, YWHAG, YWHAH, YWHAQ, YWHAZ 48 AKT1(2), AKT2(4), AKT3(5), BRD4(3), CAP1(2), CBL(4), CDKN2A(1), F2RL2(2), FLOT1(2), FLOT2(2), GSK3A(5), GSK3B(2), INPPL1(6), IRS1(4), IRS4(7), LNPEP(2), MAPK1(1), MAPK3(1), PARD3(8), PARD6A(1), PDK1(2), PIK3CA(351), PIK3CD(6), PIK3R1(15), PTEN(35), RAF1(4), RPS6KA1(6), RPS6KA2(5), RPS6KA3(8), RPS6KB1(4), SERPINB6(2), SFN(1), SHC1(2), SLC2A4(1), SORBS1(7), SOS1(5), SOS2(6), YWHAB(2), YWHAG(1), YWHAZ(3) 80302320 530 406 227 52 31 178 32 221 65 3 1.19e-14 <1.00e-15 <1.87e-14
7 METPATHWAY The hepatocyte growth factor receptor c-Met stimulates proliferation and alters cell motility and adhesion on binding the ligand HGF. ACTA1, CRK, CRKL, DOCK1, ELK1, FOS, GAB1, GRB2, GRF2, HGF, HRAS, ITGA1, ITGB1, JUN, MAP2K1, MAP2K2, MAP4K1, MAPK1, MAPK3, MAPK8, MET, PAK1, PIK3CA, PIK3R1, PTEN, PTK2, PTK2B, PTPN11, PXN, RAF1, RAP1A, RAP1B, RASA1, SOS1, SRC, STAT3 35 ACTA1(3), CRK(1), CRKL(1), DOCK1(8), ELK1(4), GAB1(2), HGF(5), HRAS(2), ITGA1(6), ITGB1(5), JUN(3), MAP2K1(5), MAP2K2(2), MAP4K1(6), MAPK1(1), MAPK3(1), MAPK8(2), MET(10), PAK1(3), PIK3CA(351), PIK3R1(15), PTEN(35), PTK2(7), PTK2B(6), PTPN11(1), RAF1(4), RAP1A(2), RAP1B(1), RASA1(2), SOS1(5), SRC(1), STAT3(5) 65385720 505 394 203 43 28 168 39 212 55 3 <1.00e-15 <1.00e-15 <1.87e-14
8 ST_B_CELL_ANTIGEN_RECEPTOR B cell receptors bind antigens and promote B cell activation. AKT1, AKT2, AKT3, BAD, BCR, BLNK, BTK, CD19, CSK, DAG1, EPHB2, GRB2, ITPKA, ITPKB, LYN, MAP2K1, MAP2K2, MAPK1, NFAT5, NFKB1, NFKB2, NFKBIA, NFKBIB, NFKBIE, NFKBIL1, NFKBIL2, PAG, PI3, PIK3CA, PIK3CD, PIK3R1, PLCG2, PPP1R13B, RAF1, SERPINA4, SHC1, SOS1, SOS2, SYK, VAV1 38 AKT1(2), AKT2(4), AKT3(5), BAD(1), BCR(3), BLNK(1), BTK(7), CD19(6), DAG1(2), EPHB2(5), ITPKA(1), ITPKB(8), LYN(6), MAP2K1(5), MAP2K2(2), MAPK1(1), NFAT5(7), NFKB1(5), NFKB2(5), NFKBIA(3), NFKBIB(1), NFKBIE(2), PI3(1), PIK3CA(351), PIK3CD(6), PIK3R1(15), PLCG2(8), PPP1R13B(4), RAF1(4), SERPINA4(4), SHC1(2), SOS1(5), SOS2(6), SYK(4), VAV1(6) 72377320 498 393 199 42 31 171 38 216 41 1 <1.00e-15 <1.00e-15 <1.87e-14
9 ST_PHOSPHOINOSITIDE_3_KINASE_PATHWAY The phosphoinositide-3 kinase pathway produces the lipid second messenger PIP3 and regulates cell growth, survival, and movement. A1BG, AKT1, AKT2, AKT3, BAD, BTK, CDKN2A, CSL4, DAF, DAPP1, FOXO1A, GRB2, GSK3A, GSK3B, IARS, IGFBP1, INPP5D, P14, PDK1, PIK3CA, PPP1R13B, PSCD3, PTEN, RPS6KA1, RPS6KA2, RPS6KA3, RPS6KB1, SFN, SHC1, SOS1, SOS2, TEC, YWHAB, YWHAE, YWHAG, YWHAH, YWHAQ, YWHAZ 33 AKT1(2), AKT2(4), AKT3(5), BAD(1), BTK(7), CDKN2A(1), DAPP1(2), GSK3A(5), GSK3B(2), IARS(8), INPP5D(4), PDK1(2), PIK3CA(351), PPP1R13B(4), PTEN(35), RPS6KA1(6), RPS6KA2(5), RPS6KA3(8), RPS6KB1(4), SFN(1), SHC1(2), SOS1(5), SOS2(6), TEC(6), YWHAB(2), YWHAG(1), YWHAZ(3) 51274772 482 379 180 27 23 165 31 205 57 1 <1.00e-15 <1.00e-15 <1.87e-14
10 INOSITOL_PHOSPHATE_METABOLISM IMPA1, INPP1, INPP4A, INPP4B, INPP5A, INPPL1, ITPKA, ITPKB, MIOX, OCRL, PIK3C2A, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3CG, PIK4CA, PIK4CA, LOC220686, PIP5K2B, PLCB1, PLCB2, PLCB3, PLCB4, PLCD1, PLCG1, PLCG2 23 INPP1(1), INPP4A(5), INPP4B(7), INPP5A(1), INPPL1(6), ITPKA(1), ITPKB(8), OCRL(8), PIK3C2A(5), PIK3C2B(12), PIK3C2G(8), PIK3CA(351), PIK3CB(8), PIK3CG(5), PLCB1(12), PLCB2(7), PLCB3(2), PLCB4(15), PLCD1(3), PLCG1(8), PLCG2(8) 64214840 481 376 182 33 26 172 33 204 45 1 <1.00e-15 <1.00e-15 <1.87e-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 TCRMOLECULE T Cell Receptor and CD3 Complex CD3D, CD3E, CD3G, CD3Z, TRA@, TRB@ 3 CD3E(3), CD3G(3) 1708219 6 6 6 0 0 2 1 2 1 0 0.27 0.096 1
2 HSA00660_C5_BRANCHED_DIBASIC_ACID_METABOLISM Genes involved in C5-branched dibasic acid metabolism ILVBL, SUCLA2 2 ILVBL(5), SUCLA2(1) 3044170 6 6 6 1 2 1 0 2 1 0 0.48 0.37 1
3 HSA00627_1,4_DICHLOROBENZENE_DEGRADATION Genes involved in 1,4-dichlorobenzene degradation CMBL 1 CMBL(1) 739599 1 1 1 0 0 0 0 1 0 0 0.8 0.39 1
4 TCAPOPTOSISPATHWAY HIV infection upregulates Fas ligand in macrophages and CD4 in helper T cells, leading to widespread Fas-induced T cell apoptosis. CCR5, CD28, CD3D, CD3E, CD3G, CD3Z, CD4, TNFRSF6, TNFSF6, TRA@, TRB@ 6 CCR5(1), CD28(2), CD3E(3), CD3G(3), CD4(4) 4752811 13 12 13 3 1 3 2 3 4 0 0.52 0.46 1
5 ST_PAC1_RECEPTOR_PATHWAY The signaling peptide PACAP binds to its receptor, PAC1R, which activates adenylyl cyclase and phospholipase C. ASAH1, CAMP, DAG1, GAS, GNAQ, ITPKA, ITPKB, PACAP 6 ASAH1(1), DAG1(2), GNAQ(1), ITPKA(1), ITPKB(8) 7947000 13 13 13 1 2 4 1 3 3 0 0.14 0.47 1
6 HSA00601_GLYCOSPHINGOLIPID_BIOSYNTHESIS_LACTOSERIES Genes involved in glycosphingolipid biosynthesis - lactoseries ABO, B3GALT1, B3GALT2, B3GALT5, B3GNT5, FUT1, FUT2, FUT3, ST3GAL3, ST3GAL4 9 B3GALT1(3), B3GALT2(2), B3GALT5(2), B3GNT5(4), FUT1(1), FUT3(3), ST3GAL3(4), ST3GAL4(2) 9411230 21 18 21 2 5 6 7 3 0 0 0.049 0.49 1
7 HSA00902_MONOTERPENOID_BIOSYNTHESIS Genes involved in monoterpenoid biosynthesis CYP2C19, CYP2C9 2 CYP2C19(6), CYP2C9(3) 2944424 9 9 9 2 1 4 2 2 0 0 0.49 0.51 1
8 FBW7PATHWAY Cyclin E interacts with cell cycle checkpoint kinase cdk2 to allow transcription of genes required for S phase, including transcription of additional cyclin E. CCNE1, CDC34, CDK2, CUL1, E2F1, FBXW7, RB1, SKP1A, TFDP1 6 CCNE1(2), CDC34(1), CDK2(2), CUL1(2), E2F1(5), TFDP1(3) 7066982 15 15 14 3 4 3 2 1 5 0 0.52 0.53 1
9 IFNGPATHWAY IFN gamma signaling pathway IFNG, IFNGR1, IFNGR2, JAK1, JAK2, STAT1 6 IFNG(2), IFNGR1(2), JAK1(11), JAK2(7), STAT1(4) 12001014 26 24 26 2 2 5 6 5 8 0 0.077 0.54 1
10 HSA00643_STYRENE_DEGRADATION Genes involved in styrene degradation FAH, GSTZ1, HGD 3 FAH(2), GSTZ1(1), HGD(4) 3252093 7 7 7 2 1 0 2 3 1 0 0.71 0.56 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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