Mutation Analysis (MutSig v1.5)
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1KS6PWG
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Analysis (MutSig v1.5). Broad Institute of MIT and Harvard. doi:10.7908/C1KS6PWG
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: LAML-TB

  • Number of patients in set: 197

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:LAML-TB.final_analysis_set.maf

  • Significantly mutated genes (q ≤ 0.1): 28

  • Mutations seen in COSMIC: 239

  • Significantly mutated genes in COSMIC territory: 19

  • Genes with clustered mutations (≤ 3 aa apart): 18

  • Significantly mutated genesets: 64

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

Mutation Preprocessing

  • Read 197 MAFs of type "WashU"

  • Total number of mutations in input MAFs: 2585

  • After removing 41 mutations outside chr1-24: 2544

  • After removing 104 noncoding mutations: 2440

Mutation Filtering

  • Number of mutations before filtering: 2440

  • After removing 204 mutations outside gene set: 2236

  • After removing 14 mutations outside category set: 2222

  • After removing 1 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 2003

Results
Breakdown of Mutations by Type

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

type count
Frame_Shift_Del 51
Frame_Shift_Ins 110
In_Frame_Del 8
In_Frame_Ins 43
Missense_Mutation 1401
Nonsense_Mutation 108
Silent 451
Splice_Site 50
Total 2222
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 508 272168305 1.9e-06 1.9 5.8 2.2
*Cp(A/C/T)->T 312 2466260336 1.3e-07 0.13 0.39 1.7
A->G 183 2743656430 6.7e-08 0.067 0.21 2.3
transver 398 5482085071 7.3e-08 0.073 0.22 5.1
indel+null 357 5482085071 6.5e-08 0.065 0.2 NaN
double_null 13 5482085071 2.4e-09 0.0024 0.0073 NaN
Total 1771 5482085071 3.2e-07 0.32 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).

Figure 1. 

Distribution of Mutation Counts, Coverage, and Mutation Rates Across Samples

Figure 2.  Patients counts and rates file used to generate this plot: LAML-TB.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_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: 28. 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 NPM1 nucleophosmin (nucleolar phosphoprotein B23, numatrin) 183407 54 54 7 0 0 0 1 0 52 1 0.63 <1.00e-15 <2.88e-12
2 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 251569 19 19 2 0 17 0 0 2 0 0 0.0015 <1.00e-15 <2.88e-12
3 RUNX1 runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) 145189 20 18 16 0 2 3 2 2 10 1 0.0061 <1.00e-15 <2.88e-12
4 TET2 tet oncogene family member 2 689894 21 17 20 0 0 2 0 2 11 6 0.02 <1.00e-15 <2.88e-12
5 TP53 tumor protein p53 258267 19 15 19 1 3 2 4 2 8 0 0.024 <1.00e-15 <2.88e-12
6 WT1 Wilms tumor 1 164692 12 12 10 0 1 1 0 0 9 1 0.23 <1.00e-15 <2.88e-12
7 DNMT3A DNA (cytosine-5-)-methyltransferase 3 alpha 512791 57 51 29 0 34 2 3 4 14 0 6e-09 2.89e-15 7.14e-12
8 FLT3 fms-related tyrosine kinase 3 597107 56 56 30 0 0 0 1 16 39 0 0.053 3.44e-15 7.44e-12
9 IDH2 isocitrate dehydrogenase 2 (NADP+), mitochondrial 230293 20 20 2 0 16 3 0 1 0 0 0.00013 5.44e-15 1.05e-11
10 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 115442 15 15 6 0 0 8 1 6 0 0 0.0096 7.88e-15 1.24e-11
11 PTPN11 protein tyrosine phosphatase, non-receptor type 11 (Noonan syndrome 1) 359328 9 9 9 0 0 3 2 4 0 0 0.075 7.88e-15 1.24e-11
12 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 139279 8 8 6 0 0 4 1 3 0 0 0.12 1.77e-14 2.38e-11
13 U2AF1 U2 small nuclear RNA auxiliary factor 1 153266 8 8 2 0 0 5 0 3 0 0 0.054 1.79e-14 2.38e-11
14 KIT v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog 585287 9 8 5 0 1 0 0 6 1 1 0.22 1.02e-11 1.27e-08
15 PHF6 PHD finger protein 6 244083 6 6 6 0 0 1 0 1 4 0 0.49 4.20e-11 4.84e-08
16 SMC3 structural maintenance of chromosomes 3 738947 7 7 7 0 1 1 1 2 2 0 0.17 1.55e-09 1.67e-06
17 SMC1A structural maintenance of chromosomes 1A 744857 7 7 7 0 3 0 1 2 1 0 0.32 4.88e-09 4.96e-06
18 RAD21 RAD21 homolog (S. pombe) 383756 5 5 5 0 0 0 0 0 5 0 0.74 6.37e-08 6.12e-05
19 STAG2 stromal antigen 2 775983 6 6 6 0 0 0 0 0 6 0 0.19 8.09e-08 7.36e-05
20 FAM5C family with sequence similarity 5, member C 458813 6 5 6 0 4 1 0 1 0 0 0.18 2.24e-07 0.000194
21 CBFB core-binding factor, beta subunit 87862 2 2 2 0 0 0 0 0 2 0 0.85 8.69e-06 0.00716
22 ASXL1 additional sex combs like 1 (Drosophila) 903245 5 5 5 0 0 0 0 0 5 0 0.38 1.14e-05 0.00899
23 SUZ12 suppressor of zeste 12 homolog (Drosophila) 395182 3 3 3 0 1 1 0 0 1 0 0.62 8.86e-05 0.0666
24 DIS3 DIS3 mitotic control homolog (S. cerevisiae) 537613 3 3 3 0 1 1 1 0 0 0 0.36 0.000104 0.0745
25 PHACTR1 phosphatase and actin regulator 1 323868 3 3 2 0 0 0 0 1 2 0 0.84 0.000112 0.0745
26 EZH2 enhancer of zeste homolog 2 (Drosophila) 459010 3 3 3 0 1 0 0 0 1 1 0.53 0.000114 0.0745
27 GBP4 guanylate binding protein 4 387499 3 3 3 0 0 2 0 1 0 0 0.31 0.000116 0.0745
28 BAGE4 B melanoma antigen family, member 4 24822 1 1 1 0 1 0 0 0 0 0 0.5 0.000133 0.0823
29 CALR calreticulin 218670 2 2 2 0 0 0 0 0 2 0 1 0.000186 0.111
30 CLEC18B C-type lectin domain family 18, member B 209805 2 2 1 0 0 0 2 0 0 0 0.66 0.000232 0.134
31 FAM57B family with sequence similarity 57, member B 77618 2 2 2 0 1 0 0 0 1 0 0.35 0.000249 0.139
32 ABTB1 ankyrin repeat and BTB (POZ) domain containing 1 176512 2 2 1 0 0 0 0 0 2 0 1 0.000301 0.156
33 KCNK13 potassium channel, subfamily K, member 13 176709 2 2 2 0 0 0 0 1 1 0 0.83 0.000304 0.156
34 GRIK2 glutamate receptor, ionotropic, kainate 2 560071 3 3 3 0 1 1 1 0 0 0 0.33 0.000307 0.156
35 MTA2 metastasis associated 1 family, member 2 403259 2 2 2 0 0 0 2 0 0 0 0.49 0.000345 0.171
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 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 19 5 19 985 28348 0 0
2 IDH2 isocitrate dehydrogenase 2 (NADP+), mitochondrial 20 6 20 1182 2000 0 0
3 NPM1 nucleophosmin (nucleolar phosphoprotein B23, numatrin) 54 41 53 8077 112668 0 0
4 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 15 33 15 6501 11840 0 0
5 FLT3 fms-related tyrosine kinase 3 56 124 52 24428 6301 0 0
6 PTPN11 protein tyrosine phosphatase, non-receptor type 11 (Noonan syndrome 1) 9 32 8 6304 216 0 0
7 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 8 52 7 10244 46308 0 0
8 RUNX1 runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) 20 178 18 35066 93 0 0
9 WT1 Wilms tumor 1 12 185 9 36445 444 0 0
10 KIT v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog 9 240 8 47280 4044 0 0

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)

Clustered Mutations

Table 5.  Get Full Table Genes with Clustered Mutations

num gene desc n mindist nmuts0 nmuts3 nmuts12 npairs0 npairs3 npairs12
337 DNMT3A DNA (cytosine-5-)-methyltransferase 3 alpha 57 0 408 410 427 408 410 427
535 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 19 0 171 171 171 171 171 171
536 IDH2 isocitrate dehydrogenase 2 (NADP+), mitochondrial 20 0 139 139 139 139 139 139
429 FLT3 fms-related tyrosine kinase 3 55 0 120 120 136 120 120 136
752 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 15 0 31 51 51 31 51 51
955 RUNX1 runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) 20 0 16 21 26 16 21 26
589 KIT v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog 9 0 10 10 10 10 10 10
598 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 8 0 3 7 7 3 7 7
902 PTPN11 protein tyrosine phosphatase, non-receptor type 11 (Noonan syndrome 1) 9 0 1 3 10 1 3 10
1109 TET2 tet oncogene family member 2 21 0 1 1 2 1 1 2

Note:

n - number of mutations in this gene in the individual set.

mindist - distance (in aa) between closest pair of mutations in this gene

npairs3 - how many pairs of mutations are within 3 aa of each other.

npairs12 - how many pairs of mutations are within 12 aa of each other.

Geneset Analyses

Table 6.  Get Full Table A Ranked List of Significantly Mutated Genesets. (Source: MSigDB GSEA Cannonical Pathway Set).Number of significant genesets found: 64. 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 HSA00271_METHIONINE_METABOLISM Genes involved in methionine metabolism AHCY, AMD1, BHMT, CBS, CTH, DNMT1, DNMT3A, DNMT3B, KIAA0828, MARS, MARS2, MAT1A, MAT2B, MTAP, MTFMT, MTR, SRM, TAT 17 DNMT1(1), DNMT3A(57), DNMT3B(1), MAT1A(1) 5703150 60 53 32 0 36 2 3 5 14 0 1.2e-09 1.1e-15 2.7e-13
2 ERYTHPATHWAY Erythropoietin selectively stimulates erythrocyte differentiation from CFU-GEMM cells in bone marrow. CCL3, CSF2, CSF3, EPO, FLT3, IGF1, IL11, IL1A, IL3, IL6, IL9, KITLG, TGFB1, TGFB2, TGFB3 14 FLT3(56) 2377790 56 56 30 0 0 0 1 16 39 0 0.048 2.4e-15 2.7e-13
3 HSA00720_REDUCTIVE_CARBOXYLATE_CYCLE Genes involved in reductive carboxylate cycle (CO2 fixation) ACLY, ACO1, ACO2, ACSS1, ACSS2, FH, IDH1, IDH2, LOC441996, MDH1, MDH2, SUCLA2 11 IDH1(19), IDH2(20) 3866716 39 38 4 0 33 3 0 3 0 0 2e-07 2.8e-15 2.7e-13
4 GLUTATHIONE_METABOLISM ANPEP, G6PD, GCLC, GCLM, GGT1, GPX1, GPX2, GPX3, GPX4, GPX5, GSS, GSTA1, GSTA2, GSTA3, GSTA4, GSTM1, GSTM2, GSTM3, GSTM4, GSTM5, GSTO2, GSTP1, GSTT1, GSTT2, GSTZ1, IDH1, IDH2, MGST1, MGST2, MGST3, PGD 29 GCLM(1), GPX2(1), GSTM3(1), IDH1(19), IDH2(20) 5170462 42 40 7 0 33 4 0 5 0 0 1.7e-07 2.9e-15 2.7e-13
5 KREBPATHWAY The Krebs (citric acid) cycle takes place in mitochondria, where it extracts energy in the form of electron carriers NADH and FADH2, which drive the electron transport chain. ACO2, CS, FH, IDH2, MDH1, OGDH, SDHA, SUCLA2 8 IDH2(20) 2741649 20 20 2 0 16 3 0 1 0 0 0.00031 3e-15 2.7e-13
6 NUCLEAR_RECEPTORS ALK, AR, ESR1, ESR2, ESRRA, HNF4A, NPM1, NR0B1, NR1D2, NR1H2, NR1H3, NR1I2, NR1I3, NR2C2, NR2E1, NR2F1, NR2F2, NR2F6, NR3C1, NR4A1, NR4A2, NR5A1, NR5A2, PGR, PPARA, PPARD, PPARG, RARA, RARB, RARG, ROR1, RORA, RORC, RXRA, RXRB, RXRG, THRA, THRA, NR1D1, THRB, VDR 38 NPM1(54), NR2E1(2), THRB(1) 11589707 57 56 10 0 1 1 1 1 52 1 0.25 3.8e-15 2.7e-13
7 CITRATE_CYCLE_TCA_CYCLE ACO1, ACO2, CS, DLD, DLST, DLSTP, FH, IDH1, IDH2, IDH3A, IDH3B, IDH3G, MDH1, MDH2, PC, PCK1, SDHA, SDHA, SDHAL2, SDHB, SUCLA2, SUCLG1, SUCLG2 20 DLD(1), IDH1(19), IDH2(20) 5929109 40 39 5 1 33 4 0 3 0 0 1.5e-06 4.1e-15 2.7e-13
8 REDUCTIVE_CARBOXYLATE_CYCLE_CO2_FIXATION ACO1, ACO2, FH, IDH1, IDH2, MDH1, MDH2, SDHB, SUCLA2 9 IDH1(19), IDH2(20) 2582079 39 38 4 0 33 3 0 3 0 0 2.8e-07 4.1e-15 2.7e-13
9 HSA04060_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION Genes involved in cytokine-cytokine receptor interaction ACVR1, ACVR1B, ACVR2A, ACVR2B, AMH, AMHR2, BMP2, BMP7, BMPR1A, BMPR1B, BMPR2, CCL1, CCL11, CCL13, CCL14, CCL15, CCL16, CCL17, CCL18, CCL19, CCL2, CCL20, CCL21, CCL22, CCL23, CCL24, CCL25, CCL26, CCL27, CCL28, CCL3, CCL4, CCL5, CCL7, CCL8, CCR1, CCR2, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, CCR9, CD27, CD40, CD40LG, CD70, CLCF1, CNTF, CNTFR, CRLF2, CSF1, CSF1R, CSF2, CSF2RA, CSF2RB, CSF3, CSF3R, CTF1, CX3CL1, CX3CR1, CXCL1, CXCL10, CXCL11, CXCL12, CXCL13, CXCL14, CXCL16, CXCL2, CXCL3, CXCL5, CXCL6, CXCL9, CXCR3, CXCR4, CXCR6, EDA, EDA2R, EDAR, EGF, EGFR, EPO, EPOR, FAS, FASLG, FLJ78302, FLT1, FLT3, FLT3LG, FLT4, GDF5, GH1, GH2, GHR, HGF, IFNA1, IFNA10, IFNA13, IFNA14, IFNA16, IFNA17, IFNA2, IFNA21, IFNA4, IFNA5, IFNA6, IFNA7, IFNA8, IFNAR1, IFNAR2, IFNB1, IFNE1, IFNG, IFNGR1, IFNGR2, IFNK, IFNW1, IL10, IL10RA, IL10RB, IL11, IL11RA, IL12A, IL12B, IL12RB1, IL12RB2, IL13, IL13RA1, IL15, IL15RA, IL17A, IL17B, IL17RA, IL17RB, IL18, IL18R1, IL18RAP, IL19, IL1A, IL1B, IL1R1, IL1R2, IL1RAP, IL2, IL20, IL20RA, IL21, IL21R, IL22, IL22RA1, IL22RA2, IL23A, IL23R, IL24, IL25, IL26, IL28A, IL28B, IL28RA, IL29, IL2RA, IL2RB, IL2RG, IL3, IL3RA, IL4, IL4R, IL5, IL5RA, IL6, IL6R, IL6ST, IL7, IL7R, IL8, IL8RA, IL8RB, IL9, IL9R, INHBA, INHBB, INHBC, INHBE, KDR, KIT, KITLG, LEP, LEPR, LIF, LIFR, LOC728045, LTA, LTB, LTBR, MET, MPL, NGFR, OSM, OSMR, PDGFB, PDGFC, PDGFRA, PDGFRB, PF4, PF4V1, PLEKHO2, PPBP, PRL, PRLR, RELT, TGFB1, TGFB2, TGFB3, TGFBR1, TGFBR2, TNF, TNFRSF10A, TNFRSF10B, TNFRSF10C, TNFRSF10D, TNFRSF11A, TNFRSF11B, TNFRSF12A, TNFRSF13B, TNFRSF13C, TNFRSF14, TNFRSF17, TNFRSF18, TNFRSF19, TNFRSF1A, TNFRSF1B, TNFRSF21, TNFRSF25, TNFRSF4, TNFRSF6B, TNFRSF8, TNFRSF9, TNFSF10, TNFSF11, TNFSF12, TNFSF13, TNFSF13B, TNFSF14, TNFSF15, TNFSF18, TNFSF4, TNFSF8, TNFSF9, TPO, TSLP, VEGFA, VEGFB, VEGFC, XCL1, XCL2, XCR1 242 ACVR2B(1), CCL11(1), CCL16(1), CCL20(1), CCL21(1), CD70(1), CSF3R(1), EGFR(2), FLT1(1), FLT3(56), IL1R1(1), KDR(2), KIT(9), MPL(1), PDGFRA(1), PDGFRB(1), TPO(1) 49587264 82 78 52 4 7 5 2 27 40 1 0.014 4.3e-15 2.7e-13
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 JAK2(1), TP53(19), WT1(12) 5112938 32 28 30 2 4 3 4 3 17 1 0.02 4.3e-15 2.7e-13

Table 7.  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 ERKPATHWAY Cell growth is promoted by Ras activation of the anti-apoptotic p44/42 MAP kinase pathway. DPM2, EGFR, ELK1, GNAS, GNB1, GNGT1, GRB2, HRAS, IGF1R, ITGB1, KLK2, MAP2K1, MAP2K2, MAPK1, MAPK3, MKNK1, MKNK2, MYC, NGFB, NGFR, PDGFRA, PPP2CA, PTPRR, RAF1, RPS6KA1, RPS6KA5, SHC1, SOS1, SRC, STAT3 29 EGFR(2), GNB1(1), MAPK1(1), MYC(1), PDGFRA(1), SHC1(1), SOS1(1) 9119524 8 8 8 1 3 1 0 3 1 0 0.37 0.006 1
2 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 CCL11(1), HLA-DRB1(1) 893001 2 2 2 0 0 1 0 1 0 0 0.47 0.015 1
3 HSA00480_GLUTATHIONE_METABOLISM Genes involved in glutathione metabolism ANPEP, G6PD, GCLC, GCLM, GGT1, GGTL3, GGTL4, GPX1, GPX2, GPX3, GPX4, GPX5, GPX6, GPX7, GSR, GSS, GSTA1, GSTA2, GSTA3, GSTA4, GSTA5, GSTK1, GSTM1, GSTM2, GSTM3, GSTM4, GSTM5, GSTO2, GSTP1, GSTT1, GSTT2, GSTZ1, IDH1, IDH2, MGST1, MGST2, MGST3, OPLAH, TXNDC12 32 GCLM(1), GPX2(1), GSTK1(2), GSTM3(1) 5289056 5 5 5 0 1 1 0 3 0 0 0.23 0.018 1
4 CBLPATHWAY Activated EGF receptors undergo endocytosis into clathrin-coated vesicles, where they are recycled to the membrane or ubiquitinated by Cbl. CBL, CSF1R, EGF, EGFR, GRB2, MET, PDGFRA, PRKCA, PRKCB1, SH3GLB1, SH3GLB2, SH3KBP1, SRC 12 CBL(2), EGFR(2), PDGFRA(1) 5655476 5 5 5 1 1 0 1 2 1 0 0.55 0.026 1
5 CLASSICPATHWAY The classic complement pathway is initiated by antibodies and promotes phagocytosis and lysis of foreign cells as well as activating the inflammatory response. C1QA, C1QB, C1QG, C1R, C1S, C2, C3, C4A, C4B, C5, C6, C7, C8A, C9 11 C2(1), C3(1), C5(1), C7(1) 5092056 4 4 4 1 2 0 0 1 1 0 0.76 0.026 1
6 ALTERNATIVEPATHWAY The alternative complement pathway is an antibody-independent mechanism of immune activation that results in cell lysis via the membrane attack complex. BF, C3, C5, C6, C7, C8A, C9, DF, PFC 6 C3(1), C5(1), C7(1) 3564715 3 3 3 1 1 0 0 1 1 0 0.89 0.027 1
7 SPRYPATHWAY Four members of the Sprouty protein family block proliferative EGF signals by binding Grb-2, preventing Ras and MAP kinase activation. CBL, EGF, EGFR, GRB2, HRAS, MAP2K1, MAPK1, MAPK3, PTPRB, RAF1, RASA1, SHC1, SOS1, SPRY1, SPRY2, SPRY3, SPRY4, SRC 18 CBL(2), EGFR(2), MAPK1(1), SHC1(1), SOS1(1) 7219656 7 7 7 2 2 1 1 1 2 0 0.67 0.029 1
8 LAIRPATHWAY The local acute inflammatory response is mediated by activated macrophages and mast cells or by complement activation. BDK, C3, C5, C6, C7, ICAM1, IL1A, IL6, IL8, ITGA4, ITGAL, ITGB1, ITGB2, SELP, SELPLG, TNF, VCAM1 16 C3(1), C5(1), C7(1), SELPLG(1), VCAM1(1) 6976558 5 5 5 1 2 0 0 2 1 0 0.66 0.03 1
9 STAT3PATHWAY The STAT transcription factors are phosphorylated and activated by JAK kinases in response to cytokine signaling. FRAP1, JAK1, JAK2, JAK3, MAPK1, MAPK3, STAT3, TYK2 7 JAK2(1), JAK3(1), MAPK1(1) 3335407 3 3 3 0 1 1 0 1 0 0 0.35 0.032 1
10 BIOPEPTIDESPATHWAY Extracellular signaling peptides exert biological effects via G-protein coupled receptors (GPCRs), which activate intracellular GTPases. AGT, AGTR2, BDK, CALM1, CALM2, CALM3, CAMK2A, CAMK2B, CAMK2D, CAMK2G, CDK5, F2, FYN, GNA11, GNAI1, GNB1, GNGT1, GRB2, HRAS, JAK2, MAP2K1, MAP2K2, MAPK1, MAPK14, MAPK3, MAPK8, MAPT, MYLK, PLCG1, PRKCA, PRKCB1, PTK2B, RAF1, SHC1, SOS1, STAT1, STAT3, STAT5A, SYT1 37 GNB1(1), JAK2(1), MAPK1(1), MYLK(1), SHC1(1), SOS1(1), SYT1(1) 11743170 7 7 7 1 2 1 0 3 1 0 0.48 0.039 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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