Mutation Analysis (MutSig v1.5)
Ovarian Serous Cystadenocarcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1JM283N
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSig v1.5). Broad Institute of MIT and Harvard. doi:10.7908/C1JM283N
Overview
Introduction

This report serves to describe the mutational landscape and properties of a given individual set, as well as rank genes and genesets according to mutational significance. MutSig v1.5 was used to generate the results found in this report.

  • Working with individual set: OV-TP

  • Number of patients in set: 316

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:OV-TP.final_analysis_set.maf

  • Significantly mutated genes (q ≤ 0.1): 9

  • Mutations seen in COSMIC: 394

  • Significantly mutated genes in COSMIC territory: 39

  • Significantly mutated genesets: 30

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

Mutation Preprocessing

  • Read 148 MAFs of type "Broad"

  • Read 88 MAFs of type "WashU"

  • Read 80 MAFs of type "Baylor-SOLiD"

  • Total number of mutations in input MAFs: 20219

Mutation Filtering

  • Number of mutations before filtering: 20219

  • After removing 1 non-mutations: 20218

  • After removing 780 mutations outside gene set: 19438

  • After removing 9 mutations outside category set: 19429

  • After removing 7 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 18633

Results
Breakdown of Mutations by Type

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

type count
Frame_Shift_Del 441
Frame_Shift_Ins 155
In_Frame_Del 148
In_Frame_Ins 38
Indel 13
Missense_Mutation 13160
Nonsense_Mutation 801
Nonstop_Mutation 16
Silent 4231
Splice_Site_Del 33
Splice_Site_Ins 5
Splice_Site_SNP 388
Total 19429
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 1860 396631260 4.7e-06 4.7 2.5 2.2
*Cp(A/C/T)->T 2187 3659967421 6e-07 0.6 0.32 1.7
C->(G/A) 5207 4056598681 1.3e-06 1.3 0.69 5
A->mut 3901 4114782770 9.5e-07 0.95 0.51 3.8
indel+null 2031 8171381451 2.5e-07 0.25 0.13 NaN
double_null 7 8171381451 8.6e-10 0.00086 0.00046 NaN
Total 15193 8171381451 1.9e-06 1.9 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: OV-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: C->(G/A)

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

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

  • n6 = number of nonsilent mutations of type: double_null

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

  • p = p-value (overall)

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

Table 3.  Get Full Table A Ranked List of Significantly Mutated Genes. Number of significant genes found: 9. Number of genes displayed: 35. Click on a gene name to display its stick figure depicting the distribution of mutations and mutation types across the chosen gene (this feature may not be available for all significant genes).

rank gene description N n npat nsite nsil n1 n2 n3 n4 n5 n6 p_ns_s p q
1 TP53 tumor protein p53 401524 279 276 143 3 47 31 39 61 101 0 <1.00e-15 1.1e-15 1.9e-11
2 RB1 retinoblastoma 1 (including osteosarcoma) 826059 9 9 9 0 0 1 3 0 5 0 0.334 2.5e-06 0.021
3 BRCA1 breast cancer 1, early onset 1798045 12 12 12 0 0 0 1 0 11 0 0.319 7.2e-06 0.041
4 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 2619095 15 14 15 0 1 1 1 3 9 0 0.0634 0.000021 0.081
5 GABRA6 gamma-aminobutyric acid (GABA) A receptor, alpha 6 440417 6 6 6 1 1 3 1 1 0 0 0.366 0.000031 0.081
6 PECAM1 platelet/endothelial cell adhesion molecule (CD31 antigen) 9588 2 2 2 0 0 1 0 0 1 0 0.638 0.000032 0.081
7 FAT3 FAT tumor suppressor homolog 3 (Drosophila) 3706784 20 19 20 1 4 2 3 10 1 0 0.0208 0.000035 0.081
8 CHST2 carbohydrate (N-acetylglucosamine-6-O) sulfotransferase 2 172127 5 5 5 1 0 2 2 1 0 0 0.370 4e-05 0.081
9 CYP11B1 cytochrome P450, family 11, subfamily B, polypeptide 1 456589 7 7 6 0 1 0 3 3 0 0 0.217 0.000043 0.081
10 GLI2 GLI-Kruppel family member GLI2 947026 10 9 10 0 2 3 3 1 1 0 0.0284 0.000081 0.13
11 FAM171B family with sequence similarity 171, member B 744587 7 7 7 0 1 1 2 2 1 0 0.176 0.000086 0.13
12 C9orf171 chromosome 9 open reading frame 171 251675 5 5 5 0 2 0 2 0 1 0 0.318 0.00017 0.23
13 TACC3 transforming, acidic coiled-coil containing protein 3 311979 5 5 5 0 0 1 0 2 2 0 0.386 0.00019 0.23
14 SI sucrase-isomaltase (alpha-glucosidase) 1778977 10 10 10 0 1 1 4 4 0 0 0.126 0.0002 0.23
15 ZNF479 zinc finger protein 479 365518 5 5 5 0 0 2 2 1 0 0 0.300 0.0002 0.23
16 CDK12 cyclin-dependent kinase 12 1351667 9 9 9 0 0 0 1 3 5 0 0.154 0.00027 0.28
17 PAOX polyamine oxidase (exo-N4-amino) 205136 4 4 4 0 1 0 0 1 2 0 0.665 0.00029 0.28
18 DUSP19 dual specificity phosphatase 19 211393 4 4 4 0 0 0 1 3 0 0 0.393 0.0003 0.28
19 TBP TATA box binding protein 321652 4 4 2 0 0 0 0 0 4 0 1.000 0.00031 0.28
20 VN1R5 vomeronasal 1 receptor 5 296593 4 4 4 0 0 2 1 1 0 0 0.237 0.00032 0.28
21 OR5D16 olfactory receptor, family 5, subfamily D, member 16 306800 4 4 4 0 2 0 1 1 0 0 0.234 0.00036 0.28
22 SNTG1 syntrophin, gamma 1 505767 5 5 5 1 0 1 1 0 3 0 0.533 0.00036 0.28
23 SLCO1C1 solute carrier organic anion transporter family, member 1C1 717910 6 6 6 0 0 1 4 1 0 0 0.334 0.00037 0.28
24 HIST1H1C histone cluster 1, H1c 195033 4 4 4 0 0 1 2 0 1 0 0.322 0.00046 0.32
25 C1orf95 chromosome 1 open reading frame 95 81558 3 3 3 0 0 0 2 0 1 0 0.418 0.00047 0.32
26 MAS1L MAS1 oncogene-like 357306 4 4 4 1 1 2 1 0 0 0 0.468 0.00059 0.39
27 GAS2L1 growth arrest-specific 2 like 1 197396 4 4 4 0 0 0 3 1 0 0 0.361 0.00066 0.39
28 EPHA7 EPH receptor A7 941078 7 7 7 1 0 1 4 2 0 0 0.535 0.00067 0.39
29 C10orf140 chromosome 10 open reading frame 140 424809 5 5 3 1 0 0 2 0 3 0 0.939 0.00068 0.39
30 HAS2 hyaluronan synthase 2 527414 5 5 5 0 0 1 3 0 1 0 0.262 0.00069 0.39
31 GPR149 G protein-coupled receptor 149 595318 6 6 6 1 1 2 1 2 0 0 0.379 0.00071 0.39
32 PXN paxillin 321961 5 5 5 0 1 0 4 0 0 0 0.247 0.00079 0.42
33 C17orf63 chromosome 17 open reading frame 63 23190 2 2 2 0 0 0 2 0 0 0 0.775 0.00086 0.44
34 DEFB118 defensin, beta 118 119590 3 3 3 0 0 1 1 0 1 0 0.439 0.00086 0.44
35 LZTS2 leucine zipper, putative tumor suppressor 2 282513 4 4 4 0 0 0 1 2 1 0 0.445 0.00089 0.44
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: 39. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 TP53 tumor protein p53 279 824 278 260384 70907 0 0
2 RB1 retinoblastoma 1 (including osteosarcoma) 9 267 8 84372 16 7.3e-12 1.6e-08
3 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 15 285 6 90060 8 2.7e-08 0.000039
4 MYO3A myosin IIIA 7 14 3 4424 3 9.2e-08 0.0001
5 KLK10 kallikrein-related peptidase 10 2 2 2 632 2 6.9e-07 0.0006
6 CDC27 cell division cycle 27 homolog (S. cerevisiae) 4 3 2 948 2 1.5e-06 0.0011
7 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 4 42 3 13272 4 2.5e-06 0.0015
8 MLL4 myeloid/lymphoid or mixed-lineage leukemia 2 4 6 2 1896 2 6.2e-06 0.0034
9 NIPBL Nipped-B homolog (Drosophila) 5 7 2 2212 2 8.4e-06 0.0041
10 FBXW7 F-box and WD repeat domain containing 7 4 91 3 28756 118 0.000024 0.011

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: 30. 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 HSA04110_CELL_CYCLE Genes involved in cell cycle ABL1, ANAPC1, ANAPC10, ANAPC11, ANAPC2, ANAPC4, ANAPC5, ANAPC7, ATM, ATR, BUB1, BUB1B, BUB3, CCNA1, CCNA2, CCNB1, CCNB2, CCNB3, CCND1, CCND2, CCND3, CCNE1, CCNE2, CCNH, CDC14A, CDC14B, CDC16, CDC2, CDC20, CDC23, CDC25A, CDC25B, CDC25C, CDC26, CDC27, CDC45L, CDC6, CDC7, CDK2, CDK4, CDK6, CDK7, CDKN1A, CDKN1B, CDKN1C, CDKN2A, CDKN2B, CDKN2C, CDKN2D, CHEK1, CHEK2, CREBBP, CUL1, DBF4, E2F1, E2F2, E2F3, EP300, ESPL1, FZR1, GADD45A, GADD45B, GADD45G, GSK3B, hCG_1982709, HDAC1, HDAC2, LOC440917, LOC728919, MAD1L1, MAD2L1, MAD2L2, MCM2, MCM3, MCM4, MCM5, MCM6, MCM7, MDM2, ORC1L, ORC2L, ORC3L, ORC4L, ORC5L, ORC6L, PCNA, PKMYT1, PLK1, PRKDC, PTTG1, PTTG2, RB1, RBL1, RBL2, RBX1, SFN, SKP1, SKP2, SMAD2, SMAD3, SMAD4, SMC1A, SMC1B, TFDP1, TGFB1, TGFB2, TGFB3, TP53, WEE1, YWHAB, YWHAE, YWHAG, YWHAH, YWHAQ, YWHAZ 106 ABL1(1), ANAPC1(1), ANAPC2(1), ANAPC4(1), ANAPC5(1), ATM(5), ATR(2), BUB1(1), BUB1B(2), BUB3(1), CCNA1(2), CCNA2(1), CCNB3(3), CCNH(1), CDC20(1), CDC25B(2), CDC27(4), CDC6(1), CDC7(2), CDK2(1), CDKN1A(1), CDKN1B(1), CDKN2C(2), CDKN2D(1), CHEK2(1), CREBBP(7), E2F2(1), EP300(1), GADD45A(1), GADD45B(1), GSK3B(1), HDAC1(1), MAD2L2(1), MCM2(2), MCM3(1), MCM4(2), MCM5(2), ORC1L(1), ORC4L(1), PRKDC(8), RB1(9), RBL1(1), RBL2(3), SKP2(2), SMC1A(5), SMC1B(3), TFDP1(2), TP53(279), YWHAB(1), YWHAE(2), YWHAG(2) 59044600 380 287 244 28 60 44 71 84 121 0 2.00e-15 <1.00e-15 <5.13e-14
2 CELL_CYCLE_KEGG ABL1, ASK, ATM, BUB1, BUB1B, BUB3, CCNA1, CCNA2, CCNB1, CCNB2, CCNB3, CCND2, CCND3, CCNE1, CCNE2, CCNH, CDAN1, CDC14A, CDC14B, CDC14B, CDC14C, CDC2, CDC20, CDC25A, CDC25B, CDC25C, CDC45L, CDC6, CDC7, CDH1, CDK2, CDK4, CDKN1A, CDKN2A, CHEK1, CHEK2, DTX4, E2F1, E2F2, E2F3, E2F4, E2F5, E2F6, EP300, ESPL1, FLJ14001, GADD45A, GSK3B, HDAC1, HDAC2, HDAC3, HDAC4, HDAC5, HDAC6, HDAC7A, HDAC8, MAD1L1, MAD2L1, MAD2L2, MCM2, MCM3, MCM4, MCM5, MCM6, MCM7, MDM2, MPEG1, MPL, ORC1L, ORC2L, ORC3L, ORC4L, ORC5L, ORC6L, PCNA, PLK1, PRKDC, PTPRA, PTTG1, PTTG2, PTTG3, RB1, RBL1, SKP2, SMAD4, SMC1L1, TBC1D8, TFDP1, TGFB1, TP53, WEE1 82 ABL1(1), ATM(5), BUB1(1), BUB1B(2), BUB3(1), CCNA1(2), CCNA2(1), CCNB3(3), CCNH(1), CDAN1(2), CDC20(1), CDC25B(2), CDC6(1), CDC7(2), CDH1(2), CDK2(1), CDKN1A(1), CHEK2(1), E2F2(1), E2F5(1), EP300(1), GADD45A(1), GSK3B(1), HDAC1(1), HDAC3(2), HDAC4(2), HDAC5(1), HDAC6(2), MAD2L2(1), MCM2(2), MCM3(1), MCM4(2), MCM5(2), MPEG1(1), ORC1L(1), ORC4L(1), PRKDC(8), RB1(9), RBL1(1), SKP2(2), TBC1D8(3), TFDP1(2), TP53(279) 48735694 358 283 222 27 56 42 65 80 115 0 1.33e-15 <1.00e-15 <5.13e-14
3 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 EGFR(7), IGF1R(1), PPP2CA(2), PRKCA(1), RB1(9), TEP1(5), TERF1(1), TERT(1), TNKS(1), TP53(279) 12360237 307 281 171 10 50 33 49 65 110 0 <1.00e-15 <1.00e-15 <5.13e-14
4 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 40 APAF1(2), BAK1(1), BID(1), BIRC3(2), CASP2(1), CASP6(1), CASP7(1), CASP9(1), FADD(1), MAP2K4(1), MAP3K1(1), MAP3K14(1), NFKB1(1), PRF1(2), RELA(1), TNF(1), TNFRSF1A(1), TNFSF10(1), TP53(279), TRAF1(4), TRAF2(1) 16033981 305 280 169 10 50 36 48 67 104 0 <1.00e-15 <1.00e-15 <5.13e-14
5 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(5), ATR(2), BRCA1(12), BRCA2(11), CHEK2(1), FANCA(3), FANCC(2), FANCD2(1), FANCE(1), FANCG(1), HUS1(1), RAD1(1), RAD50(2), RAD51(1), TP53(279) 19936305 323 280 187 6 49 33 47 69 124 1 <1.00e-15 <1.00e-15 <5.13e-14
6 RBPATHWAY The ATM protein kinase recognizes DNA damage and blocks cell cycle progression by phosphorylating chk1 and p53, which normally inhibits Rb to allow G1/S transitions. ATM, CDC2, CDC25A, CDC25B, CDC25C, CDK2, CDK4, CHEK1, MYT1, RB1, TP53, WEE1, YWHAH 12 ATM(5), CDC25B(2), CDK2(1), MYT1(2), RB1(9), TP53(279) 7986534 298 279 162 5 47 33 47 64 107 0 <1.00e-15 <1.00e-15 <5.13e-14
7 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(1), CDKN1A(1), CDKN1B(1), E2F2(1), NXT1(1), PRB1(1), TP53(279) 3310452 285 279 149 8 49 33 40 62 101 0 <1.00e-15 <1.00e-15 <5.13e-14
8 P53PATHWAY p53 induces cell cycle arrest or apoptosis under conditions of DNA damage. APAF1, ATM, BAX, BCL2, CCND1, CCNE1, CDK2, CDK4, CDKN1A, E2F1, GADD45A, MDM2, PCNA, RB1, TIMP3, TP53 16 APAF1(2), ATM(5), CDK2(1), CDKN1A(1), GADD45A(1), RB1(9), TP53(279) 8056664 298 278 162 3 48 34 44 65 107 0 <1.00e-15 <1.00e-15 <5.13e-14
9 ST_JNK_MAPK_PATHWAY JNKs are MAP kinases regulated by several levels of kinases (MAPKK, MAPKKK) and phosphorylate transcription factors and regulatory proteins. AKT1, ATF2, CDC42, DLD, DUSP10, DUSP4, DUSP8, GAB1, GADD45A, GCK, IL1R1, JUN, MAP2K4, MAP2K5, MAP2K7, MAP3K1, MAP3K10, MAP3K11, MAP3K12, MAP3K13, MAP3K2, MAP3K3, MAP3K4, MAP3K5, MAP3K7, MAP3K7IP1, MAP3K7IP2, MAP3K9, MAPK10, MAPK7, MAPK8, MAPK9, MYEF2, NFATC3, NR2C2, PAPPA, SHC1, TP53, TRAF6, ZAK 36 GADD45A(1), MAP2K4(1), MAP3K1(1), MAP3K10(2), MAP3K12(1), MAP3K13(2), MAP3K2(2), MAP3K3(1), MAP3K4(2), MAP3K5(2), MAP3K7(3), MAP3K9(1), MAPK8(1), MAPK9(1), MYEF2(1), PAPPA(3), SHC1(1), TP53(279), TRAF6(2) 21375636 307 278 171 6 51 37 50 65 104 0 <1.00e-15 <1.00e-15 <5.13e-14
10 TIDPATHWAY On ligand binding, interferon gamma receptors stimulate JAK2 kinase to phosphorylate STAT transcription factors, which promote expression of interferon responsive genes. DNAJA3, HSPA1A, IFNG, IFNGR1, IFNGR2, IKBKB, JAK2, LIN7A, NFKB1, NFKBIA, RB1, RELA, TIP-1, TNF, TNFRSF1A, TNFRSF1B, TP53, USH1C, WT1 17 DNAJA3(2), IKBKB(1), JAK2(2), LIN7A(1), NFKB1(1), RB1(9), RELA(1), TNF(1), TNFRSF1A(1), TP53(279), USH1C(1) 7976204 299 278 163 5 49 35 45 63 107 0 <1.00e-15 <1.00e-15 <5.13e-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 FXRPATHWAY The nuclear receptor transcription factors FXR and LXR are activated by cholesterol metabolites and regulate cholesterol homeostasis. FABP6, LDLR, NR0B2, NR1H3, NR1H4, RXRA 6 LDLR(3), NR1H3(4), NR1H4(3), RXRA(2) 2264708 12 12 12 0 2 2 3 2 3 0 0.025 0.00088 0.54
2 HSA00902_MONOTERPENOID_BIOSYNTHESIS Genes involved in monoterpenoid biosynthesis CYP2C19, CYP2C9 2 CYP2C19(4), CYP2C9(1) 942461 5 5 5 1 0 0 1 4 0 0 0.6 0.028 1
3 FOLATE_BIOSYNTHESIS ALPI, ALPL, ALPP, ALPP, ALPPL2, ALPPL2, DHFR, FPGS, GCH1, GGH, SPR 9 ALPL(2), ALPP(2), FPGS(1), SPR(2) 2149074 7 7 7 0 2 4 0 0 1 0 0.04 0.056 1
4 AMIPATHWAY Endogenous anti-thrombosis pathways are overwhelmed in plaque-narrowed blood vessels, resulting in potentially lethal myocardial infarction. ADCY1, CD3D, CD3E, CD3G, CD3Z, CD4, CREBBP, CSK, GNAS, GNB1, GNGT1, HLA-DRA, HLA-DRB1, LCK, PRKACB, PRKACG, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, PTPRC, TRA@, TRB@, ZAP70 21 ADCY1(4), CD3E(2), CD4(1), CREBBP(7), CSK(1), GNAS(3), HLA-DRA(1), HLA-DRB1(1), LCK(1), PRKACG(1), PRKAR2A(1), PRKAR2B(1), PTPRC(3), ZAP70(1) 9488232 28 28 27 3 5 3 6 6 8 0 0.062 0.066 1
5 CSKPATHWAY Csk inhibits T-cell activation by phosphorylating Lck; Csk is regulated by cAMP-dependent kinases and is opposed by the T-cell activator CD45. ADCY1, CD3D, CD3E, CD3G, CD3Z, CD4, CREBBP, CSK, GNAS, GNB1, GNGT1, HLA-DRA, HLA-DRB1, LCK, PRKACB, PRKACG, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, PTPRC, TRA@, TRB@, ZAP70 21 ADCY1(4), CD3E(2), CD4(1), CREBBP(7), CSK(1), GNAS(3), HLA-DRA(1), HLA-DRB1(1), LCK(1), PRKACG(1), PRKAR2A(1), PRKAR2B(1), PTPRC(3), ZAP70(1) 9488232 28 28 27 3 5 3 6 6 8 0 0.062 0.066 1
6 LYSINE_BIOSYNTHESIS AADAT, AASDH, AASDHPPT, AASS, KARS 5 AASDH(5), AASDHPPT(1), AASS(2), KARS(1) 3198163 9 9 9 1 0 0 3 4 2 0 0.39 0.075 1
7 HSA00601_GLYCOSPHINGOLIPID_BIOSYNTHESIS_LACTOSERIES Genes involved in glycosphingolipid biosynthesis - lactoseries ABO, B3GALT1, B3GALT2, B3GALT5, B3GNT5, FUT1, FUT2, FUT3, ST3GAL3, ST3GAL4 10 ABO(1), B3GALT1(1), B3GALT2(2), B3GNT5(1), FUT1(1), ST3GAL3(3) 3040139 9 9 9 1 3 1 3 0 2 0 0.2 0.078 1
8 HSA00750_VITAMIN_B6_METABOLISM Genes involved in vitamin B6 metabolism AOX1, PDXK, PDXP, PNPO, PSAT1 5 AOX1(4), PDXK(1), PDXP(1), PSAT1(1) 2140556 7 7 7 1 0 1 3 2 1 0 0.49 0.078 1
9 SETPATHWAY Cytotoxic T cells release perforin, which to allow entry into target cells of granzyme B, which activates caspases, and granzyme A, which induces caspase-independent apoptosis. ANP32A, APEX1, CREBBP, DFFA, DFFB, GZMA, GZMB, HMGB2, NME1, PRF1, SET 11 APEX1(2), CREBBP(7), GZMA(2), PRF1(2) 4460489 13 13 13 2 2 1 0 5 5 0 0.37 0.091 1
10 BLOOD_GROUP_GLYCOLIPID_BIOSYNTHESIS_NEOLACTOSERIES ABO, B3GNT1, FUT1, FUT2, FUT9, GCNT2, ST8SIA1 7 ABO(1), FUT1(1), FUT9(1), GCNT2(2), ST8SIA1(2) 2495432 7 7 7 1 1 2 1 1 2 0 0.32 0.11 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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