Mutation Analysis (MutSig v2.0)
Sarcoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C17080QP
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/C17080QP
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: SARC-TP

  • Number of patients in set: 245

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:SARC-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 36

  • Mutations seen in COSMIC: 149

  • Significantly mutated genes in COSMIC territory: 6

  • Significantly mutated genesets: 33

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

Mutation Preprocessing

  • Read 245 MAFs of type "maf1"

  • Total number of mutations in input MAFs: 26359

  • After removing 65 mutations outside chr1-24: 26294

  • After removing 792 blacklisted mutations: 25502

  • After removing 5238 noncoding mutations: 20264

  • After collapsing adjacent/redundant mutations: 20262

Mutation Filtering

  • Number of mutations before filtering: 20262

  • After removing 1431 mutations outside gene set: 18831

  • After removing 48 mutations outside category set: 18783

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 6
Frame_Shift_Del 660
Frame_Shift_Ins 159
In_Frame_Del 252
In_Frame_Ins 81
Missense_Mutation 11514
Nonsense_Mutation 795
Nonstop_Mutation 19
Silent 4565
Splice_Site 699
Start_Codon_Del 6
Start_Codon_Ins 2
Start_Codon_SNP 17
Stop_Codon_Del 5
Total 18783
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 2084 421585855 4.9e-06 4.9 2.6 2.1
*Cp(A/C/T)->T 3701 3420700969 1.1e-06 1.1 0.57 1.7
C->(G/A) 3497 3842286824 9.1e-07 0.91 0.48 4.7
A->mut 2249 3666407405 6.1e-07 0.61 0.32 3.9
indel+null 2643 7508694229 3.5e-07 0.35 0.19 NaN
double_null 44 7508694229 5.9e-09 0.0059 0.0031 NaN
Total 14218 7508694229 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: SARC-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->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_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: 36. 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 RB1 retinoblastoma 1 (including osteosarcoma) 585385 25 24 25 0 0 0 2 0 21 2 <1.00e-15 0.038 0.0079 0.6 0.02 <7.77e-16 <3.08e-12
2 TP53 tumor protein p53 269597 89 84 75 3 10 11 10 18 40 0 <1.00e-15 5.6e-07 0 0 0 <1.00e-15 <3.08e-12
3 NUMBL numb homolog (Drosophila)-like 317270 9 9 1 0 0 0 0 0 9 0 6.74e-09 1 0 0.9 0 <1.00e-15 <3.08e-12
4 MSH3 mutS homolog 3 (E. coli) 847520 8 7 4 2 0 0 0 0 7 1 2.82e-05 1 0 1 0 <1.00e-15 <3.08e-12
5 WNK1 WNK lysine deficient protein kinase 1 1865549 6 6 2 2 0 0 1 0 5 0 0.0621 0.99 0.0017 0.000024 0 <1.00e-15 <3.08e-12
6 LMTK2 lemur tyrosine kinase 2 1050704 2 2 2 2 1 0 0 0 1 0 0.819 0.85 1 0 0 <1.00e-15 <3.08e-12
7 ATRX alpha thalassemia/mental retardation syndrome X-linked (RAD54 homolog, S. cerevisiae) 1709368 40 37 40 0 0 1 4 3 32 0 7.66e-15 0.0076 0.046 0.36 0.077 2.13e-14 5.63e-11
8 LOR loricrin 73051 6 6 2 2 0 0 0 0 6 0 1.77e-10 1 2e-07 1 0.000017 1.04e-13 2.40e-10
9 KRTAP2-2 keratin associated protein 2-2 45502 4 4 1 0 0 0 0 0 4 0 4.65e-08 1 1e-06 0.8 0.000023 3.06e-11 6.28e-08
10 KIAA0040 KIAA0040 73441 5 5 2 0 0 0 0 0 5 0 3.34e-09 1 0.0002 0.32 0.00046 4.36e-11 8.07e-08
11 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 289394 8 7 8 0 1 1 0 4 2 0 1.21e-08 0.19 0.026 0.037 0.0091 2.63e-09 4.42e-06
12 KRTAP5-5 keratin associated protein 5-5 170570 8 7 7 1 0 2 1 0 4 1 1.28e-09 0.6 0.14 0.48 0.23 6.83e-09 1.05e-05
13 ANP32E acidic (leucine-rich) nuclear phosphoprotein 32 family, member E 203879 4 4 1 0 0 0 0 0 4 0 4.67e-05 1 1e-06 1 0.000024 2.42e-08 3.44e-05
14 AR androgen receptor (dihydrotestosterone receptor; testicular feminization; spinal and bulbar muscular atrophy; Kennedy disease) 599982 5 5 4 1 0 0 1 3 1 0 0.00151 0.67 6.4e-06 1 0.000043 1.13e-06 0.00150
15 EOMES eomesodermin homolog (Xenopus laevis) 384227 6 6 2 0 0 0 1 0 5 0 1.62e-05 0.81 0.0023 1 0.0049 1.37e-06 0.00169
16 LTBP3 latent transforming growth factor beta binding protein 3 674927 5 5 2 0 0 0 0 0 5 0 0.0115 1 0 0.8 9.4e-06 1.85e-06 0.00213
17 PRB3 proline-rich protein BstNI subfamily 3 216943 6 6 6 0 2 1 0 1 2 0 3.00e-07 0.25 0.5 0.62 0.64 3.18e-06 0.00346
18 EFCAB2 EF-hand calcium binding domain 2 119874 4 4 1 0 0 0 0 0 4 0 3.93e-06 1 NaN NaN NaN 3.93e-06 0.00402
19 DCDC1 doublecortin domain containing 1 260902 6 6 6 0 1 2 0 1 2 0 4.14e-06 0.16 NaN NaN NaN 4.14e-06 0.00402
20 CYLC2 cylicin, basic protein of sperm head cytoskeleton 2 222005 5 5 4 0 0 2 0 1 2 0 1.72e-05 0.4 0.021 1 0.045 1.16e-05 0.0107
21 FOXD2 forkhead box D2 120791 3 3 1 0 0 0 0 0 3 0 0.000487 1 0.00054 1 0.0027 1.91e-05 0.0168
22 MMP3 matrix metallopeptidase 3 (stromelysin 1, progelatinase) 354235 6 6 5 1 1 0 2 0 3 0 1.51e-06 0.58 0.8 0.99 1 2.17e-05 0.0182
23 PKD2 polycystic kidney disease 2 (autosomal dominant) 603162 6 6 3 0 0 1 1 0 4 0 0.000373 0.52 0.0017 0.52 0.0043 2.31e-05 0.0186
24 USP8 ubiquitin specific peptidase 8 804369 4 2 2 0 2 0 2 0 0 0 0.126 0.56 3.6e-06 0.8 0.000016 2.78e-05 0.0214
25 SPHKAP SPHK1 interactor, AKAP domain containing 1165604 12 11 12 1 0 2 6 3 1 0 4.74e-06 0.22 0.29 0.65 0.51 3.39e-05 0.0250
26 LHCGR luteinizing hormone/choriogonadotropin receptor 503006 6 6 4 0 0 1 0 0 5 0 9.93e-05 0.51 0.017 1 0.037 4.94e-05 0.0351
27 C14orf39 chromosome 14 open reading frame 39 440909 5 5 5 0 1 1 0 1 2 0 0.000163 0.26 0.097 0.049 0.03 6.46e-05 0.0435
28 CCDC7 coiled-coil domain containing 7 348400 5 5 5 1 0 2 0 1 2 0 6.60e-05 0.69 NaN NaN NaN 6.60e-05 0.0435
29 OR4S1 olfactory receptor, family 4, subfamily S, member 1 221458 5 5 5 0 0 1 1 2 1 0 1.21e-05 0.29 0.33 0.98 0.48 7.56e-05 0.0481
30 SCN2A sodium channel, voltage-gated, type II, alpha subunit 1497363 11 11 11 0 3 3 1 3 1 0 5.99e-06 0.039 0.87 0.74 1 7.80e-05 0.0481
31 CABLES1 Cdk5 and Abl enzyme substrate 1 299085 3 3 1 0 0 0 0 0 3 0 0.00464 1 0.00015 0.84 0.0015 9.20e-05 0.0548
32 EGF epidermal growth factor (beta-urogastrone) 903860 4 1 4 1 0 2 1 0 1 0 0.500 0.62 0.00025 0.0012 0.000019 0.000120 0.0696
33 OR8D1 olfactory receptor, family 8, subfamily D, member 1 224050 5 5 5 0 0 2 3 0 0 0 1.70e-05 0.14 0.43 0.82 0.63 0.000134 0.0751
34 COPS4 COP9 constitutive photomorphogenic homolog subunit 4 (Arabidopsis) 304448 4 4 4 1 0 1 1 1 1 0 0.000565 0.69 0.018 0.35 0.021 0.000145 0.0789
35 DOCK3 dedicator of cytokinesis 3 1485260 7 7 7 0 1 2 0 2 2 0 0.0313 0.12 0.00027 0.13 0.00042 0.000159 0.0841
RB1

Figure S1.  This figure depicts the distribution of mutations and mutation types across the RB1 significant gene.

TP53

Figure S2.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

NUMBL

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

MSH3

Figure S4.  This figure depicts the distribution of mutations and mutation types across the MSH3 significant gene.

WNK1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the WNK1 significant gene.

LMTK2

Figure S6.  This figure depicts the distribution of mutations and mutation types across the LMTK2 significant gene.

ATRX

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

LOR

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

KRTAP2-2

Figure S9.  This figure depicts the distribution of mutations and mutation types across the KRTAP2-2 significant gene.

KIAA0040

Figure S10.  This figure depicts the distribution of mutations and mutation types across the KIAA0040 significant gene.

PTEN

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

KRTAP5-5

Figure S12.  This figure depicts the distribution of mutations and mutation types across the KRTAP5-5 significant gene.

ANP32E

Figure S13.  This figure depicts the distribution of mutations and mutation types across the ANP32E significant gene.

AR

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

EOMES

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

LTBP3

Figure S16.  This figure depicts the distribution of mutations and mutation types across the LTBP3 significant gene.

PRB3

Figure S17.  This figure depicts the distribution of mutations and mutation types across the PRB3 significant gene.

EFCAB2

Figure S18.  This figure depicts the distribution of mutations and mutation types across the EFCAB2 significant gene.

DCDC1

Figure S19.  This figure depicts the distribution of mutations and mutation types across the DCDC1 significant gene.

CYLC2

Figure S20.  This figure depicts the distribution of mutations and mutation types across the CYLC2 significant gene.

FOXD2

Figure S21.  This figure depicts the distribution of mutations and mutation types across the FOXD2 significant gene.

MMP3

Figure S22.  This figure depicts the distribution of mutations and mutation types across the MMP3 significant gene.

PKD2

Figure S23.  This figure depicts the distribution of mutations and mutation types across the PKD2 significant gene.

USP8

Figure S24.  This figure depicts the distribution of mutations and mutation types across the USP8 significant gene.

SPHKAP

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

LHCGR

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

C14orf39

Figure S27.  This figure depicts the distribution of mutations and mutation types across the C14orf39 significant gene.

CCDC7

Figure S28.  This figure depicts the distribution of mutations and mutation types across the CCDC7 significant gene.

OR4S1

Figure S29.  This figure depicts the distribution of mutations and mutation types across the OR4S1 significant gene.

SCN2A

Figure S30.  This figure depicts the distribution of mutations and mutation types across the SCN2A significant gene.

CABLES1

Figure S31.  This figure depicts the distribution of mutations and mutation types across the CABLES1 significant gene.

EGF

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

OR8D1

Figure S33.  This figure depicts the distribution of mutations and mutation types across the OR8D1 significant gene.

COPS4

Figure S34.  This figure depicts the distribution of mutations and mutation types across the COPS4 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: 6. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 RB1 retinoblastoma 1 (including osteosarcoma) 25 267 16 65415 40 5.9e-13 1.7e-09
2 TP53 tumor protein p53 89 356 82 87220 13230 7.5e-13 1.7e-09
3 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 8 767 8 187915 265 4.6e-09 7e-06
4 CCNL2 cyclin L2 2 1 2 245 2 1.1e-07 0.00012
5 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 6 220 4 53900 2167 4.2e-06 0.0038
6 PTPN11 protein tyrosine phosphatase, non-receptor type 11 (Noonan syndrome 1) 3 32 2 7840 4 0.00011 0.082
7 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 2 52 2 12740 13156 0.00029 0.12
8 AP1M1 adaptor-related protein complex 1, mu 1 subunit 1 1 1 245 1 0.00046 0.12
9 GTF2B general transcription factor IIB 1 1 1 245 1 0.00046 0.12
10 LENG8 leukocyte receptor cluster (LRC) member 8 2 1 1 245 1 0.00046 0.12

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: 33. 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 P53HYPOXIAPATHWAY Hypoxia induces p53 accumulation and consequent apoptosis with p53-mediated cell cycle arrest, which is present under conditions of DNA damage. ABCB1, AKT1, ATM, BAX, CDKN1A, CPB2, CSNK1A1, CSNK1D, FHL2, GADD45A, HIC1, HIF1A, HSPA1A, HSPCA, IGFBP3, MAPK8, MDM2, NFKBIB, NQO1, TP53 19 ABCB1(1), ATM(5), CDKN1A(2), HIC1(1), HIF1A(1), MDM2(1), TP53(89) 7512770 100 90 86 12 11 13 14 21 41 0 0.0025 <1.00e-15 <2.18e-13
2 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 ARF1(1), CCND1(1), CDK2(1), CDK4(1), CDKN1A(2), CDKN2A(2), MDM2(1), TP53(89) 2970476 98 90 84 4 12 12 11 20 43 0 8e-08 <1.00e-15 <2.18e-13
3 PLK3PATHWAY Active Plk3 phosphorylates CDC25c, blocking the G2/M transition, and phosphorylates p53 to induce apoptosis. ATM, ATR, CDC25C, CHEK1, CHEK2, CNK, TP53, YWHAH 7 ATM(5), ATR(4), CHEK2(2), TP53(89) 5707218 100 87 86 6 10 14 12 21 43 0 0.0001 1.11e-15 2.18e-13
4 ARFPATHWAY Cyclin-dependent kinase inhibitor 2A is a tumor suppressor that induces G1 arrest and can activate the p53 pathway, leading to G2/M arrest. ABL1, CDKN2A, E2F1, MDM2, MYC, PIK3CA, PIK3R1, POLR1A, POLR1B, POLR1C, POLR1D, RAC1, RB1, TBX2, TP53, TWIST1 16 ABL1(2), CDKN2A(2), MDM2(1), MYC(3), PIK3CA(6), PIK3R1(1), POLR1A(1), RAC1(1), RB1(25), TP53(89), TWIST1(1) 7224205 132 103 117 7 13 13 13 23 68 2 6.8e-08 1.89e-15 2.18e-13
5 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 18 IFNG(1), LIN7A(1), NFKB1(1), RB1(25), RELA(2), TNF(1), TNFRSF1A(1), TP53(89), USH1C(2) 6645294 123 99 109 7 14 14 13 19 61 2 1.9e-07 2.66e-15 2.18e-13
6 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(3), IGF1R(2), MYC(3), PRKCA(1), RB1(25), TEP1(3), TERF1(3), TERT(1), TP53(89) 10130981 130 103 116 6 14 14 14 19 67 2 7.5e-09 2.78e-15 2.18e-13
7 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 ATM(5), CCND1(1), CDK2(1), CDK4(1), CDKN1A(2), MDM2(1), RB1(25), TP53(89) 6521754 125 101 111 7 12 13 15 21 62 2 1.2e-06 3.11e-15 2.18e-13
8 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 EIF2S2(1), NFKB1(1), RELA(2), TP53(89) 3632340 93 87 79 6 12 13 10 18 40 0 0.000028 3.11e-15 2.18e-13
9 TERTPATHWAY hTERC, the RNA subunit of telomerase, and hTERT, the catalytic protein subunit, are required for telomerase activity and are overexpressed in many cancers. HDAC1, MAX, MYC, SP1, SP3, TP53, WT1, ZNF42 7 MYC(3), SP1(2), SP3(2), TP53(89) 2470479 96 88 82 3 11 13 11 19 42 0 7.5e-08 3.33e-15 2.18e-13
10 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(1), CDK2(1), CDK4(1), MYT1(3), RB1(25), TP53(89), WEE1(1) 6298210 126 99 112 10 10 16 17 19 62 2 4e-05 3.89e-15 2.18e-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 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 PIK3CA(6), PIK3R1(1), PLCB1(5), PLCG1(1), PRKCA(1), VAV1(3) 4699509 17 15 16 1 2 3 1 8 3 0 0.051 0.01 1
2 FIBRINOLYSISPATHWAY Thrombin cleavage of fibrinogen results in rapid formation of fibrin threads that form a mesh to capture platelets and other blood cells into a clot. CPB2, F13A1, F2, F2R, FGA, FGB, FGG, PLAT, PLAU, PLG, SERPINB2, SERPINE1 12 F13A1(3), F2(3), F2R(1), FGA(2), FGB(2), FGG(2), PLG(1) 4869769 14 14 14 1 1 3 6 3 1 0 0.11 0.012 1
3 ST_JAK_STAT_PATHWAY The Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway transduces extracellular signals to promote gene activation. CISH, JAK1, JAK2, JAK3, PIAS1, PIAS3, PTPRU, REG1A, SOAT1 9 CISH(2), JAK1(1), JAK3(2), PIAS1(3), PTPRU(8), SOAT1(1) 5161931 17 16 17 2 4 2 5 4 2 0 0.13 0.015 1
4 PANTOTHENATE_AND_COA_BIOSYNTHESIS BCAT1, COASY, DPYD, DPYS, ENPP1, ENPP3, PANK1, PANK2, PANK3, PANK4, PPCS, UPB1 12 BCAT1(1), COASY(1), DPYD(6), ENPP1(3), ENPP3(2), PANK1(2), PANK3(1), PANK4(1), PPCS(1) 5105361 18 16 18 1 0 6 4 2 6 0 0.026 0.018 1
5 HSA00950_ALKALOID_BIOSYNTHESIS_I Genes involved in alkaloid biosynthesis I DDC, GOT1, GOT2, TAT, TYR 5 DDC(2), TAT(2), TYR(2) 1661167 6 6 6 0 2 2 1 0 1 0 0.16 0.018 1
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 14 ARF1(1), CCND1(1), CDK2(1), CDK4(1), CDKN1A(2), CDKN2A(2), MDM2(1) 2700879 9 9 9 1 2 1 1 2 3 0 0.18 0.023 1
7 ST_INTERFERON_GAMMA_PATHWAY The interferon gamma pathway resembles the JAK-STAT pathway and activates STAT transcription factors. CISH, IFNG, IFNGR1, JAK1, JAK2, PLA2G2A, PTPRU, REG1A, STAT1, STATIP1 9 CISH(2), IFNG(1), JAK1(1), PTPRU(8), STAT1(2) 4202463 14 13 14 2 3 3 3 4 1 0 0.14 0.037 1
8 TRKAPATHWAY Nerve growth factor (NGF) promotes neuronal survival and proliferation by binding its receptor TrkA, which activates PI3K/AKT, Ras, and the MAP kinase pathway. AKT1, DPM2, GRB2, HRAS, KLK2, NGFB, NTRK1, PIK3CA, PIK3R1, PLCG1, PRKCA, PRKCB1, SHC1, SOS1 12 DPM2(1), NTRK1(3), PIK3CA(6), PIK3R1(1), PLCG1(1), PRKCA(1), SOS1(2) 5667907 15 15 14 1 4 1 3 4 3 0 0.1 0.04 1
9 IL10PATHWAY The cytokine IL-10 inhibits the inflammatory response by macrophages via activation of heme oxygenase 1. BLVRA, BLVRB, HMOX1, IL10, IL10RA, IL10RB, IL1A, IL6, JAK1, STAT1, STAT3, STAT5A, TNF 13 BLVRB(2), HMOX1(1), IL10RA(2), IL1A(1), JAK1(1), STAT1(2), STAT3(1), STAT5A(1), TNF(1) 4447478 12 12 12 0 3 1 3 2 3 0 0.025 0.042 1
10 ST_STAT3_PATHWAY The transcription factor STAT3 is involved in cell growth regulation and is commonly upregulated in tumors. CISH, IL6, IL6R, JAK1, JAK2, JAK3, PIAS3, PTPRU, REG1A, SRC, STAT3 11 CISH(2), IL6R(2), JAK1(1), JAK3(2), PTPRU(8), STAT3(1) 5659860 16 15 16 2 4 2 4 6 0 0 0.12 0.049 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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