Mutation Analysis (MutSig v2.0)
Glioblastoma Multiforme (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C12806ZG
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Analysis (MutSig v2.0). Broad Institute of MIT and Harvard. doi:10.7908/C12806ZG
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: GBM-TP

  • Number of patients in set: 283

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:GBM-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 71

  • Mutations seen in COSMIC: 458

  • Significantly mutated genes in COSMIC territory: 70

  • Significantly mutated genesets: 122

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

Mutation Preprocessing

  • Read 283 MAFs of type "maf1"

  • Total number of mutations in input MAFs: 21510

  • After removing 892 blacklisted mutations: 20618

  • After removing 536 noncoding mutations: 20082

Mutation Filtering

  • Number of mutations before filtering: 20082

  • After removing 958 mutations outside gene set: 19124

  • After removing 11 mutations outside category set: 19113

Results
Breakdown of Mutations by Type

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

type count
De_novo_Start_InFrame 9
De_novo_Start_OutOfFrame 22
Frame_Shift_Del 431
Frame_Shift_Ins 136
In_Frame_Del 125
In_Frame_Ins 19
Missense_Mutation 12215
Nonsense_Mutation 748
Nonstop_Mutation 10
Silent 4717
Splice_Site 672
Start_Codon_SNP 9
Total 19113
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 5042 471456280 0.000011 11 6.3 2.1
*Cp(A/C/T)->T 2178 3846173156 5.7e-07 0.57 0.33 1.7
A->G 1503 4141354039 3.6e-07 0.36 0.21 2.3
transver 3501 8458983475 4.1e-07 0.41 0.24 5
indel+null 2161 8458983475 2.6e-07 0.26 0.15 NaN
double_null 11 8458983475 1.3e-09 0.0013 0.00076 NaN
Total 14396 8458983475 1.7e-06 1.7 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: GBM-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: A->G

  • n4 = number of nonsilent mutations of type: transver

  • 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: 71. 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 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 668244 33 32 27 0 0 3 4 7 19 0 <1.00e-15 0.0059 4e-06 0.12 5.4e-06 <0.000 <0.000
2 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 337830 89 86 72 0 5 20 10 11 43 0 <1.00e-15 1.9e-09 0.0045 0.4 0.015 <5.55e-16 <3.02e-12
3 TP53 tumor protein p53 358092 97 80 60 0 29 15 11 23 19 0 <1.00e-15 8.1e-13 0 0 0 <1.00e-15 <3.02e-12
4 EGFR epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian) 1131442 92 74 44 7 10 43 2 32 5 0 4.55e-15 1.1e-08 0 0 0 <1.00e-15 <3.02e-12
5 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 361231 14 14 2 0 13 0 0 1 0 0 2.01e-14 0.0076 0 0.83 0 <1.00e-15 <3.02e-12
6 BRAF v-raf murine sarcoma viral oncogene homolog B1 631073 6 6 2 1 0 1 0 5 0 0 0.000183 0.62 0 0.08 0 <1.00e-15 <3.02e-12
7 RB1 retinoblastoma 1 (including osteosarcoma) 752003 24 24 22 1 0 0 0 0 23 1 1.44e-15 0.097 0.057 0.05 0.031 1.78e-15 4.53e-12
8 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 924699 33 30 28 0 5 7 7 6 8 0 4.44e-15 0.00033 0.012 0.11 0.012 2.00e-15 4.53e-12
9 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 2457594 30 29 30 1 0 2 1 1 21 5 7.77e-15 0.011 0.16 0.95 0.26 6.94e-14 1.40e-10
10 SPTA1 spectrin, alpha, erythrocytic 1 (elliptocytosis 2) 2111845 26 24 26 0 11 2 1 9 3 0 2.89e-14 0.0044 0.36 0.26 0.4 3.79e-13 6.87e-10
11 GABRA6 gamma-aminobutyric acid (GABA) A receptor, alpha 6 395438 11 11 10 1 4 1 1 4 1 0 9.16e-12 0.11 0.74 0.38 0.71 1.73e-10 2.86e-07
12 KEL Kell blood group, metallo-endopeptidase 639794 15 15 12 2 8 0 0 3 4 0 6.66e-11 0.19 0.45 0.92 0.62 1.03e-09 1.55e-06
13 CDH18 cadherin 18, type 2 682827 11 11 10 0 3 3 0 4 1 0 5.88e-09 0.048 0.073 0.71 0.14 1.81e-08 2.53e-05
14 SEMA3C sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3C 654951 11 11 11 1 3 0 2 4 2 0 3.30e-09 0.22 0.44 0.55 0.61 4.24e-08 5.49e-05
15 RPL5 ribosomal protein L5 261806 7 7 7 0 0 1 1 0 5 0 1.59e-08 0.28 0.44 0.072 0.19 6.17e-08 7.45e-05
16 TPTE2 transmembrane phosphoinositide 3-phosphatase and tensin homolog 2 464692 8 8 6 0 2 0 0 2 4 0 1.86e-07 0.1 0.026 0.032 0.022 8.46e-08 9.59e-05
17 OR5AR1 olfactory receptor, family 5, subfamily AR, member 1 264117 7 7 7 0 3 0 2 2 0 0 4.88e-08 0.18 0.38 0.077 0.18 1.73e-07 0.000185
18 OR8K3 olfactory receptor, family 8, subfamily K, member 3 265683 7 7 7 1 2 2 0 2 1 0 1.44e-08 0.32 0.48 0.93 0.66 1.87e-07 0.000188
19 STAG2 stromal antigen 2 1110411 12 12 12 0 0 0 0 1 11 0 3.02e-08 0.19 0.29 0.62 0.48 2.79e-07 0.000266
20 SEMG1 semenogelin I 395351 8 8 7 0 5 0 2 0 1 0 3.49e-08 0.11 0.27 0.42 0.5 3.31e-07 0.000300
21 PDGFRA platelet-derived growth factor receptor, alpha polypeptide 950298 13 11 12 1 0 5 2 4 2 0 2.84e-07 0.096 0.086 0.25 0.14 7.05e-07 0.000608
22 ADAM29 ADAM metallopeptidase domain 29 698127 9 9 8 1 6 0 0 2 1 0 2.08e-07 0.34 0.35 0.58 0.51 1.81e-06 0.00149
23 NLRP5 NLR family, pyrin domain containing 5 962338 12 12 11 2 9 0 1 2 0 0 1.69e-06 0.11 0.35 0.035 0.089 2.51e-06 0.00198
24 ABCC9 ATP-binding cassette, sub-family C (CFTR/MRP), member 9 1396433 14 11 14 1 4 0 3 5 2 0 4.15e-06 0.1 0.02 0.36 0.041 2.79e-06 0.00210
25 WNT2 wingless-type MMTV integration site family member 2 311956 5 5 5 0 3 0 0 1 1 0 0.000264 0.2 0.003 0.03 0.00066 2.90e-06 0.00210
26 SULT1B1 sulfotransferase family, cytosolic, 1B, member 1 257699 7 6 7 1 0 2 1 3 1 0 6.69e-07 0.36 0.16 0.86 0.27 3.02e-06 0.00211
27 LZTR1 leucine-zipper-like transcription regulator 1 647961 10 10 10 0 4 0 1 4 1 0 1.78e-06 0.08 0.29 0.18 0.24 6.78e-06 0.00455
28 UGT2A3 UDP glucuronosyltransferase 2 family, polypeptide A3 450901 6 6 6 0 1 0 2 1 2 0 1.05e-05 0.15 0.23 0.015 0.047 7.69e-06 0.00498
29 CALCR calcitonin receptor 424651 8 8 8 0 6 1 1 0 0 0 5.51e-07 0.051 0.84 0.78 1 8.49e-06 0.00531
30 QKI quaking homolog, KH domain RNA binding (mouse) 327911 5 5 5 0 0 0 0 2 3 0 2.54e-05 0.69 0.4 0.0052 0.028 1.08e-05 0.00653
31 IL18RAP interleukin 18 receptor accessory protein 520719 7 7 7 0 1 2 0 1 3 0 2.41e-06 0.095 0.84 0.078 0.32 1.15e-05 0.00675
32 COL1A2 collagen, type I, alpha 2 1162327 11 11 11 1 2 3 0 5 1 0 3.10e-06 0.2 0.31 0.26 0.31 1.42e-05 0.00803
33 OR5W2 olfactory receptor, family 5, subfamily W, member 2 264214 5 5 5 1 1 0 2 2 0 0 4.70e-06 0.62 0.19 0.4 0.25 1.75e-05 0.00943
34 ZPBP zona pellucida binding protein 282197 5 5 4 0 3 0 1 0 1 0 6.47e-05 0.28 0.0081 0.37 0.019 1.78e-05 0.00943
35 ABCB1 ATP-binding cassette, sub-family B (MDR/TAP), member 1 1117814 10 10 10 0 5 1 0 3 1 0 2.63e-06 0.12 0.88 0.14 0.47 1.82e-05 0.00943
PIK3R1

Figure S1.  This figure depicts the distribution of mutations and mutation types across the PIK3R1 significant gene.

PTEN

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

TP53

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

EGFR

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

IDH1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the IDH1 significant gene.

BRAF

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

RB1

Figure S7.  This figure depicts the distribution of mutations and mutation types across the RB1 significant gene.

PIK3CA

Figure S8.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

NF1

Figure S9.  This figure depicts the distribution of mutations and mutation types across the NF1 significant gene.

SPTA1

Figure S10.  This figure depicts the distribution of mutations and mutation types across the SPTA1 significant gene.

GABRA6

Figure S11.  This figure depicts the distribution of mutations and mutation types across the GABRA6 significant gene.

KEL

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

CDH18

Figure S13.  This figure depicts the distribution of mutations and mutation types across the CDH18 significant gene.

SEMA3C

Figure S14.  This figure depicts the distribution of mutations and mutation types across the SEMA3C significant gene.

RPL5

Figure S15.  This figure depicts the distribution of mutations and mutation types across the RPL5 significant gene.

TPTE2

Figure S16.  This figure depicts the distribution of mutations and mutation types across the TPTE2 significant gene.

OR5AR1

Figure S17.  This figure depicts the distribution of mutations and mutation types across the OR5AR1 significant gene.

OR8K3

Figure S18.  This figure depicts the distribution of mutations and mutation types across the OR8K3 significant gene.

STAG2

Figure S19.  This figure depicts the distribution of mutations and mutation types across the STAG2 significant gene.

SEMG1

Figure S20.  This figure depicts the distribution of mutations and mutation types across the SEMG1 significant gene.

PDGFRA

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

ADAM29

Figure S22.  This figure depicts the distribution of mutations and mutation types across the ADAM29 significant gene.

NLRP5

Figure S23.  This figure depicts the distribution of mutations and mutation types across the NLRP5 significant gene.

ABCC9

Figure S24.  This figure depicts the distribution of mutations and mutation types across the ABCC9 significant gene.

WNT2

Figure S25.  This figure depicts the distribution of mutations and mutation types across the WNT2 significant gene.

SULT1B1

Figure S26.  This figure depicts the distribution of mutations and mutation types across the SULT1B1 significant gene.

LZTR1

Figure S27.  This figure depicts the distribution of mutations and mutation types across the LZTR1 significant gene.

UGT2A3

Figure S28.  This figure depicts the distribution of mutations and mutation types across the UGT2A3 significant gene.

CALCR

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

QKI

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

IL18RAP

Figure S31.  This figure depicts the distribution of mutations and mutation types across the IL18RAP significant gene.

COL1A2

Figure S32.  This figure depicts the distribution of mutations and mutation types across the COL1A2 significant gene.

OR5W2

Figure S33.  This figure depicts the distribution of mutations and mutation types across the OR5W2 significant gene.

ZPBP

Figure S34.  This figure depicts the distribution of mutations and mutation types across the ZPBP 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: 70. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 TP53 tumor protein p53 97 356 93 100748 26307 0 0
2 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 89 767 87 217061 3053 0 0
3 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 14 5 14 1415 20888 1.6e-14 2.5e-11
4 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 33 33 13 9339 25 1.1e-13 1.2e-10
5 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 33 220 25 62260 5654 6.5e-13 4.8e-10
6 RB1 retinoblastoma 1 (including osteosarcoma) 24 267 15 75561 41 7.8e-13 4.8e-10
7 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 30 285 11 80655 23 8.2e-13 4.8e-10
8 EGFR epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian) 92 293 70 82919 1144 8.4e-13 4.8e-10
9 BRAF v-raf murine sarcoma viral oncogene homolog B1 6 89 6 25187 71896 8.6e-12 4.3e-09
10 ADAM29 ADAM metallopeptidase domain 29 9 5 3 1415 3 2.3e-09 9.5e-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: 122. 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 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 CRKL(1), DOCK1(2), ELK1(2), FOS(1), HGF(1), ITGA1(2), ITGB1(1), MAP2K1(1), MAP4K1(2), MAPK1(2), MAPK3(1), MET(3), PAK1(1), PIK3CA(33), PIK3R1(33), PTEN(89), PTK2B(3), PTPN11(4), PXN(1), RAF1(1), SOS1(3), SRC(2), STAT3(1) 18829475 190 148 162 5 20 35 25 35 75 0 <1.00e-15 <1.00e-15 <4.65e-14
2 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(4), ATR(4), BRCA1(4), BRCA2(4), CHEK1(3), FANCD2(2), MRE11A(1), RAD50(1), TP53(97) 18721101 120 99 83 4 34 19 17 29 21 0 1.08e-10 <1.00e-15 <4.65e-14
3 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(2), IFNGR1(1), IFNGR2(3), IKBKB(1), JAK2(1), NFKB1(1), NFKBIA(1), RB1(24), TNFRSF1A(1), TNFRSF1B(1), TP53(97), USH1C(1), WT1(2) 7833614 136 99 97 2 31 16 15 29 44 1 2.55e-15 <1.00e-15 <4.65e-14
4 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(10), AKT1(1), ATM(4), CDKN1A(1), CSNK1D(1), MDM2(2), TP53(97) 8872453 116 94 79 4 35 18 13 26 24 0 3.56e-11 <1.00e-15 <4.65e-14
5 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(1), EIF2S2(1), NFKB1(1), NFKBIA(1), TP53(97) 4216169 101 83 64 1 30 15 12 24 20 0 3.86e-12 <1.00e-15 <4.65e-14
6 ERBB3PATHWAY Neuregulins bind to the receptor tyrosine kinases ErbB3 and ErbB4, surface-localized receptors whose overexpression induces tumor formation. EGF, EGFR, ERBB3, NRG1, UBE2D1 5 EGF(3), EGFR(92), NRG1(2) 4366865 97 79 49 11 10 43 2 36 6 0 1.88e-05 <1.00e-15 <4.65e-14
7 TPOPATHWAY Thrombopoietin binds to its receptor and activates cell growth through the Erk and JNK MAP kinase pathways, protein kinase C, and JAK/STAT activation. CSNK2A1, FOS, GRB2, HRAS, JAK2, JUN, MAP2K1, MAPK3, MPL, PIK3CA, PIK3R1, PLCG1, PRKCA, PRKCB1, RAF1, RASA1, SHC1, SOS1, STAT1, STAT3, STAT5A, STAT5B, THPO 22 FOS(1), JAK2(1), MAP2K1(1), MAPK3(1), MPL(3), PIK3CA(33), PIK3R1(33), PLCG1(6), PRKCA(1), RAF1(1), SOS1(3), STAT1(2), STAT3(1), THPO(1) 12700557 88 79 77 3 11 13 14 20 30 0 6.82e-07 <1.00e-15 <4.65e-14
8 BADPATHWAY When phosphorylated, BAD is inhibited by sequestration; when non-phosphorylated, it promotes apoptosis by inactivating pro-survival BCL-XL and BCL-2. ADCY1, AKT1, BAD, BAX, BCL2, BCL2L1, CSF2RB, IGF1, IGF1R, IL3, IL3RA, KIT, KITLG, PIK3CA, PIK3R1, PRKACB, PRKACG, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, YWHAH 22 ADCY1(3), AKT1(1), BCL2(2), CSF2RB(4), IGF1(2), IGF1R(3), IL3(1), IL3RA(2), KIT(3), KITLG(1), PIK3CA(33), PIK3R1(33), PRKAR1B(1), PRKAR2B(1), YWHAH(1) 9316577 91 78 79 6 15 12 13 18 33 0 1.23e-05 <1.00e-15 <4.65e-14
9 CDC42RACPATHWAY PI3 kinase stimulates cell migration by activating cdc42, which activates ARP2/3, which in turn promotes formation of new actin fibers. ACTR2, ACTR3, ARHA, ARPC1A, ARPC1B, ARPC2, ARPC3, ARPC4, CDC42, PAK1, PDGFRA, PIK3CA, PIK3R1, RAC1, WASL 14 ARPC2(1), ARPC4(1), PAK1(1), PDGFRA(13), PIK3CA(33), PIK3R1(33) 5669242 82 72 70 2 5 15 13 17 32 0 1.78e-06 <1.00e-15 <4.65e-14
10 PAR1PATHWAY Activated extracellular thrombin cleaves and activates the G-protein coupled receptors PAR1 and PAR4, which activate platelets. ADCY1, ARHA, ARHGEF1, F2, F2R, F2RL3, GNA12, GNA13, GNAI1, GNAQ, GNB1, GNGT1, MAP3K7, PIK3CA, PIK3R1, PLCB1, PPP1R12B, PRKCA, PRKCB1, PTK2B, ROCK1 19 ADCY1(3), F2(1), F2RL3(2), GNB1(1), MAP3K7(3), PIK3CA(33), PIK3R1(33), PLCB1(1), PPP1R12B(1), PRKCA(1), PTK2B(3), ROCK1(2) 11028541 84 70 73 3 13 13 12 15 31 0 1.06e-06 <1.00e-15 <4.65e-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 IL17PATHWAY Activated T cells secrete IL-17, which stimulates fibroblasts and other cells to secrete inflammatory and hematopoietic cytokines. CD2, CD34, CD3D, CD3E, CD3G, CD3Z, CD4, CD58, CD8A, CSF3, IL17, IL3, IL6, IL8, KITLG, TRA@, TRB@ 13 CD2(1), CD3E(2), CD3G(1), CD4(1), CD58(1), CD8A(1), IL3(1), IL6(1), KITLG(1) 2644975 10 10 10 1 3 1 1 3 2 0 0.15 0.0047 0.93
2 IL18PATHWAY Pro-inflammatory IL-18 is activated in macrophages by caspase-1 cleavage and, in conjunction with IL-12, stimulates Th1 cell differentiation. CASP1, IFNG, IL12A, IL12B, IL18, IL2 6 CASP1(2), IFNG(2), IL12B(2) 1269839 6 6 6 1 2 1 1 0 2 0 0.34 0.0057 0.93
3 ACE_INHIBITOR_PATHWAY_PHARMGKB ACE, AGT, AGTR1, AGTR2, BDKRB2, KNG1, NOS3, REN 8 ACE(5), AGT(1), AGTR1(1), AGTR2(1), KNG1(2), NOS3(3), REN(5) 4284311 18 18 17 2 10 2 0 2 4 0 0.04 0.0063 0.93
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(1), CD3E(2), CD3G(1), CD4(1) 1364210 6 6 6 1 3 0 1 1 1 0 0.36 0.012 0.93
5 TH1TH2PATHWAY Helper T subtype Th1 produces pro-inflammatory cytokines that stimulate phagocytosis, while Th2 cells promote antibody production and activate eosinophils. CD28, CD86, HLA-DRA, HLA-DRB1, IFNG, IFNGR1, IFNGR2, IL12A, IL12B, IL12RB1, IL12RB2, IL18, IL18R1, IL2, IL2RA, IL4, IL4R, TNFRSF5, TNFSF5 16 CD28(1), CD86(2), HLA-DRA(1), IFNG(2), IFNGR1(1), IFNGR2(3), IL12B(2), IL12RB1(2), IL12RB2(2), IL18R1(2) 4681327 18 17 18 3 10 0 3 3 2 0 0.088 0.012 0.93
6 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 FABP6(1), LDLR(3), NR0B2(1), NR1H4(3), RXRA(3) 2319597 11 11 11 2 4 2 0 4 1 0 0.24 0.012 0.93
7 HSA00472_D_ARGININE_AND_D_ORNITHINE_METABOLISM Genes involved in D-arginine and D-ornithine metabolism DAO 1 DAO(3) 306178 3 3 3 0 2 1 0 0 0 0 0.3 0.013 0.93
8 IFNGPATHWAY IFN gamma signaling pathway IFNG, IFNGR1, IFNGR2, JAK1, JAK2, STAT1 6 IFNG(2), IFNGR1(1), IFNGR2(3), JAK1(2), JAK2(1), STAT1(2) 3472100 11 11 11 0 0 0 3 5 3 0 0.067 0.013 0.93
9 TCRMOLECULE T Cell Receptor and CD3 Complex CD3D, CD3E, CD3G, CD3Z, TRA@, TRB@ 3 CD3E(2), CD3G(1) 483380 3 3 3 0 0 0 1 1 1 0 0.42 0.014 0.93
10 EOSINOPHILSPATHWAY Recruitment of eosinophils in the inflammatory response observed in asthma occurs via the chemoattractant eotaxin binding to the CCR3 receptor. CCL11, CCL5, CCR3, CSF2, HLA-DRA, HLA-DRB1, IL3, IL5 8 CCR3(1), CSF2(2), HLA-DRA(1), IL3(1) 1289447 5 5 5 0 2 1 1 0 1 0 0.2 0.024 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)
Copyright © 2016 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle