Mutation Analysis (MutSig v2.0)
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C16D5RFW
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSig v2.0). Broad Institute of MIT and Harvard. doi:10.7908/C16D5RFW
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: KIRC-TP

  • Number of patients in set: 417

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:KIRC-TP.final_analysis_set.maf

  • Significantly mutated genes (q ≤ 0.1): 15

  • Mutations seen in COSMIC: 440

  • Significantly mutated genes in COSMIC territory: 102

  • Significantly mutated genesets: 8

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

Mutation Preprocessing

  • Read 183 MAFs of type "Broad"

  • Read 169 MAFs of type "Baylor-Illumina"

  • Read 120 MAFs of type "Baylor-SOLiD"

  • Total number of mutations in input MAFs: 28827

  • After removing 2242 blacklisted mutations: 26585

  • After removing 539 noncoding mutations: 26046

  • After collapsing adjacent/redundant mutations: 26045

Mutation Filtering

  • Number of mutations before filtering: 26045

  • After removing 420 mutations outside gene set: 25625

  • After removing 25 mutations outside category set: 25600

  • After removing 8 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 25147

Results
Breakdown of Mutations by Type

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

type count
Frame_Shift_Del 1388
Frame_Shift_Ins 505
In_Frame_Del 275
In_Frame_Ins 41
Missense_Mutation 15886
Nonsense_Mutation 1121
Nonstop_Mutation 2
Silent 5850
Splice_Site 517
Translation_Start_Site 15
Total 25600
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 1484 645639745 2.3e-06 2.3 1.4 2.2
*Cp(A/C/T)->T 2864 5584464449 5.1e-07 0.51 0.32 1.7
A->G 2631 6109862055 4.3e-07 0.43 0.27 2.3
transver 8917 12339966249 7.2e-07 0.72 0.45 5.1
indel+null 3825 12339966249 3.1e-07 0.31 0.19 NaN
double_null 23 12339966249 1.9e-09 0.0019 0.0012 NaN
Total 19744 12339966249 1.6e-06 1.6 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: KIRC-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: 15. 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 VHL von Hippel-Lindau tumor suppressor 155991 231 218 134 7 8 11 24 62 125 1 <1.00e-15 4.3e-13 0.00063 0.011 0.00037 <0.000 <0.000
2 PBRM1 polybromo 1 2090589 139 137 129 4 0 2 5 17 114 1 <1.00e-15 0.000017 0.022 0.026 0.0059 <2.22e-16 <2.03e-12
3 SETD2 SET domain containing 2 2968715 52 48 50 1 3 3 4 10 31 1 6.00e-15 0.00059 0.18 0.0021 0.003 6.66e-16 4.07e-12
4 BAP1 BRCA1 associated protein-1 (ubiquitin carboxy-terminal hydrolase) 867094 43 42 39 1 1 2 5 9 25 1 6.44e-15 0.00069 0.0083 0.25 0.009 2.22e-15 1.02e-11
5 KDM5C lysine (K)-specific demethylase 5C 1842978 27 27 27 1 1 1 1 7 17 0 4.55e-15 0.059 0.094 0.33 0.16 2.54e-14 9.31e-11
6 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 519386 20 18 20 0 1 1 0 3 14 1 5.88e-15 0.044 0.18 0.73 0.37 7.55e-14 2.30e-10
7 HGC6.3 104325 9 7 9 0 0 2 3 3 1 0 1.41e-11 0.076 0.089 0.93 0.17 6.58e-11 1.72e-07
8 TP53 tumor protein p53 538821 10 9 10 1 2 1 2 3 2 0 1.98e-08 0.2 0.00062 0.053 0.00092 4.70e-10 1.08e-06
9 MTOR mechanistic target of rapamycin (serine/threonine kinase) 3273597 26 25 22 3 0 3 4 19 0 0 2.23e-06 0.077 0.000078 0.8 0.00023 1.15e-08 2.34e-05
10 TCEB1 transcription elongation factor B (SIII), polypeptide 1 (15kDa, elongin C) 145454 4 3 3 0 0 0 0 3 1 0 0.000375 0.52 2.2e-06 0.52 0.000013 9.95e-08 0.000182
11 MUC4 mucin 4, cell surface associated 1332950 66 50 60 16 6 8 13 23 16 0 1.27e-07 0.49 NaN NaN NaN 1.27e-07 0.000212
12 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 1343040 12 12 9 0 0 5 2 4 1 0 8.08e-07 0.034 0.085 0.75 0.14 1.90e-06 0.00290
13 TSPAN19 tetraspanin 19 277310 5 5 5 0 0 1 0 2 2 0 3.83e-05 0.54 0.0016 0.24 0.0046 2.93e-06 0.00413
14 OR4C45 olfactory receptor, family 4, subfamily C, member 45 233459 4 4 3 0 3 0 1 0 0 0 4.15e-06 0.49 0.22 0.77 0.29 1.76e-05 0.0231
15 RHEB Ras homolog enriched in brain 223852 4 4 2 0 0 0 1 3 0 0 0.000302 0.41 0.0031 0.33 0.0051 2.22e-05 0.0271
16 NEFH neurofilament, heavy polypeptide 200kDa 900889 6 6 5 0 0 0 1 0 5 0 0.00125 0.73 0.0071 0.12 0.0057 9.12e-05 0.103
17 NFE2L2 nuclear factor (erythroid-derived 2)-like 2 745921 6 6 6 0 0 1 0 4 1 0 0.000539 0.32 0.015 0.11 0.014 9.59e-05 0.103
18 TMEM229A transmembrane protein 229A 172692 4 4 4 0 0 1 0 2 1 0 0.000192 0.33 0.029 0.94 0.073 0.000171 0.161
19 DIO2 deiodinase, iodothyronine, type II 380718 4 4 4 1 1 0 0 2 1 0 0.00146 0.79 0.0077 0.14 0.01 0.000177 0.161
20 FGGY FGGY carbohydrate kinase domain containing 745802 2 2 2 0 0 0 0 1 1 0 0.172 0.69 0.0095 0.00038 0.000087 0.000182 0.161
21 WASL Wiskott-Aldrich syndrome-like 625870 6 6 6 1 0 2 0 4 0 0 0.000428 0.49 0.054 0.065 0.038 0.000197 0.161
22 CD300E CD300e molecule 263865 5 5 5 0 0 3 1 1 0 0 2.69e-05 0.13 0.71 0.22 0.62 0.000201 0.161
23 C4orf47 chromosome 4 open reading frame 47 228003 2 1 2 0 0 0 0 1 1 0 0.162 0.85 0.016 0.000097 0.0001 0.000202 0.161
24 RALGAPA1 Ral GTPase activating protein, alpha subunit 1 (catalytic) 2656129 8 8 8 0 0 1 1 1 5 0 0.0288 0.22 0.001 0.11 0.00086 0.000288 0.217
25 PSMA6 proteasome (prosome, macropain) subunit, alpha type, 6 320673 3 3 3 0 0 0 1 1 1 0 0.00506 0.45 0.0036 0.5 0.0051 0.000296 0.217
26 NPNT nephronectin 701325 6 6 6 0 2 0 0 3 1 0 0.000383 0.3 0.18 0.076 0.078 0.000342 0.241
27 PDE4C phosphodiesterase 4C, cAMP-specific (phosphodiesterase E1 dunce homolog, Drosophila) 781898 6 6 6 1 2 1 0 2 1 0 0.000914 0.47 0.12 0.029 0.036 0.000368 0.249
28 KHK ketohexokinase (fructokinase) 432140 4 4 4 1 0 0 1 2 1 0 0.00409 0.64 0.0047 0.55 0.0089 0.000410 0.266
29 FAM200A family with sequence similarity 200, member A 447769 6 5 6 0 0 2 1 3 0 0 0.000277 0.21 0.41 0.037 0.14 0.000421 0.266
30 ARID1A AT rich interactive domain 1A (SWI-like) 2379893 12 12 12 1 1 2 1 1 7 0 0.000147 0.27 0.71 0.089 0.27 0.000447 0.271
31 ATP2A1 ATPase, Ca++ transporting, cardiac muscle, fast twitch 1 1279378 6 6 5 1 1 0 0 5 0 0 0.0251 0.55 0.00089 0.57 0.0016 0.000458 0.271
32 ULK3 unc-51-like kinase 3 (C. elegans) 445384 3 3 3 0 0 0 0 2 1 0 0.0361 0.69 0.00069 0.11 0.0012 0.000477 0.273
33 GUSB glucuronidase, beta 766407 6 4 4 1 1 1 2 2 0 0 0.0104 0.35 0.0019 0.79 0.0044 0.000498 0.276
34 FCRL5 Fc receptor-like 5 1223003 6 6 6 0 2 2 0 1 1 0 0.00397 0.16 0.011 0.11 0.016 0.000665 0.358
35 TP53I3 tumor protein p53 inducible protein 3 417702 4 2 4 0 0 0 2 1 1 0 0.0582 0.33 0.00024 0.74 0.0013 0.000773 0.384
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: 102. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 VHL von Hippel-Lindau tumor suppressor 231 541 227 225597 5472 0 0
2 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 12 220 9 91740 3320 0 0
3 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 20 767 20 319839 547 0 0
4 TP53 tumor protein p53 10 356 9 148452 1652 0 0
5 EGFR epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian) 7 293 6 122181 30 6.6e-08 0.000059
6 CSF1R colony stimulating factor 1 receptor, formerly McDonough feline sarcoma viral (v-fms) oncogene homolog 5 14 3 5838 9 1.3e-07 0.0001
7 STXBP3 syntaxin binding protein 3 3 1 2 417 2 2.2e-07 0.00013
8 TMEM47 transmembrane protein 47 2 1 2 417 2 2.2e-07 0.00013
9 KLK10 kallikrein-related peptidase 10 3 2 2 834 2 8.9e-07 0.00037
10 TRIM58 tripartite motif-containing 58 2 2 2 834 2 8.9e-07 0.00037

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: 8. 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 HIFPATHWAY Under normal conditions, hypoxia inducible factor HIF-1 is degraded; under hypoxic conditions, it activates transcription of genes controlled by hpoxic response elements (HREs). ARNT, ASPH, COPS5, CREB1, EDN1, EP300, EPO, HIF1A, HSPCA, JUN, LDHA, NOS3, P4HB, VEGF, VHL 13 ARNT(1), ASPH(3), COPS5(1), EP300(6), HIF1A(3), JUN(2), NOS3(2), VHL(231) 10473682 249 228 152 15 8 11 29 71 129 1 1.9e-08 <1.00e-15 <5.93e-13
2 VEGFPATHWAY Vascular endothelial growth factor (VEGF) is upregulated by hypoxic conditions and promotes normal blood vessel formation and angiogenesis related to tumor growth or cardiac disease. ARNT, EIF1, EIF1A, EIF2B1, EIF2B2, EIF2B3, EIF2B4, EIF2B5, EIF2S1, EIF2S2, EIF2S3, ELAVL1, FLT1, FLT4, HIF1A, HRAS, KDR, NOS3, PIK3CA, PIK3R1, PLCG1, PRKCA, PRKCB1, PTK2, PXN, SHC1, VEGF, VHL 25 ARNT(1), EIF1(1), EIF2B1(2), EIF2B2(1), EIF2B3(1), EIF2B5(1), EIF2S2(1), FLT1(4), FLT4(6), HIF1A(3), HRAS(1), KDR(5), NOS3(2), PIK3CA(12), PIK3R1(2), PTK2(1), SHC1(2), VHL(231) 21044758 277 238 177 21 13 20 30 81 132 1 2.8e-08 2.33e-15 5.93e-13
3 HSA04120_UBIQUITIN_MEDIATED_PROTEOLYSIS Genes involved in ubiquitin mediated proteolysis ANAPC1, ANAPC10, ANAPC11, ANAPC2, ANAPC4, ANAPC5, ANAPC7, BTRC, CDC16, CDC20, CDC23, CDC26, CDC27, CUL1, CUL2, CUL3, FBXW11, FBXW7, FZR1, ITCH, LOC728919, RBX1, SKP1, SKP2, SMURF1, SMURF2, TCEB1, TCEB2, UBA1, UBE2C, UBE2D1, UBE2D2, UBE2D3, UBE2D4, UBE2E1, UBE2E2, UBE2E3, VHL, WWP1, WWP2 39 ANAPC1(4), ANAPC11(1), ANAPC2(1), ANAPC4(1), ANAPC5(1), ANAPC7(2), BTRC(2), CDC16(2), CDC20(1), CDC23(2), CDC27(2), CUL1(3), CUL2(1), CUL3(5), FBXW11(1), FBXW7(1), FZR1(4), SKP2(1), SMURF2(3), TCEB1(4), TCEB2(1), UBA1(1), UBE2D1(2), UBE2D3(1), UBE2E2(1), VHL(231), WWP2(2) 25294181 281 241 181 20 10 16 29 87 138 1 8.3e-08 2.89e-15 5.93e-13
4 SA_PTEN_PATHWAY PTEN is a tumor suppressor that dephosphorylates the lipid messenger phosphatidylinositol triphosphate. AKT1, AKT2, AKT3, BPNT1, GRB2, ILK, MAPK1, MAPK3, PDK1, PIK3CA, PIK3CD, PIP3-E, PTEN, PTK2B, RBL2, SHC1, SOS1 16 AKT1(2), AKT2(3), AKT3(2), GRB2(1), ILK(1), MAPK3(1), PDK1(1), PIK3CA(12), PTEN(20), PTK2B(2), RBL2(2), SHC1(2), SOS1(6) 12480838 55 50 52 3 3 13 5 17 16 1 0.00026 1.57e-08 2.43e-06
5 SA_TRKA_RECEPTOR The TrkA receptor binds nerve growth factor to activate MAP kinase pathways and promote cell growth. AKT1, AKT2, AKT3, ARHA, CDKN1A, ELK1, GRB2, HRAS, MAP2K1, MAP2K2, NGFB, NGFR, NTRK1, PIK3CA, PIK3CD, SHC1, SOS1 15 AKT1(2), AKT2(3), AKT3(2), CDKN1A(1), ELK1(1), GRB2(1), HRAS(1), NGFR(1), NTRK1(4), PIK3CA(12), SHC1(2), SOS1(6) 9746239 36 35 33 1 1 11 5 17 2 0 0.00027 5.19e-06 0.000640
6 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 ACTR2(1), ARPC1B(1), ARPC2(1), CDC42(1), PAK1(1), PDGFRA(7), PIK3CA(12), PIK3R1(2), RAC1(1), WASL(6) 8358184 33 32 30 2 0 9 3 17 4 0 0.0081 2.71e-05 0.00278
7 PTENPATHWAY PTEN suppresses AKT-induced cell proliferation and antagonizes the action of PI3K. AKT1, BCAR1, CDKN1B, FOXO3A, GRB2, ILK, ITGB1, MAPK1, MAPK3, PDK2, PDPK1, PIK3CA, PIK3R1, PTEN, PTK2, SHC1, SOS1, TNFSF6 16 AKT1(2), BCAR1(3), GRB2(1), ILK(1), ITGB1(3), MAPK3(1), PDK2(1), PIK3CA(12), PIK3R1(2), PTEN(20), PTK2(1), SHC1(2), SOS1(6) 11771993 55 50 52 6 3 11 4 19 17 1 0.018 3.87e-05 0.00341
8 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 17 ABCB1(4), AKT1(2), ATM(15), CDKN1A(1), CSNK1A1(1), FHL2(1), HIF1A(3), IGFBP3(1), NFKBIB(1), TP53(10) 12308769 39 35 39 2 2 5 7 13 12 0 0.0037 0.000750 0.0578
9 MTORPATHWAY Mammalian target of rapamycin (mTOR) senses mitogenic factors and nutrients, including ATP, and induces cell proliferation. AKT1, EIF3S10, EIF4A1, EIF4A2, EIF4B, EIF4E, EIF4EBP1, EIF4G1, EIF4G2, EIF4G3, FKBP1A, FRAP1, MKNK1, PDK2, PDPK1, PIK3CA, PIK3R1, PPP2CA, PTEN, RPS6, RPS6KB1, TSC1, TSC2 21 AKT1(2), EIF4A2(3), EIF4E(1), EIF4G1(2), EIF4G2(3), EIF4G3(3), MKNK1(1), PDK2(1), PIK3CA(12), PIK3R1(2), PPP2CA(1), PTEN(20), RPS6KB1(1), TSC1(2), TSC2(4) 17176831 58 54 55 6 2 10 5 20 20 1 0.0071 0.00572 0.392
10 IGF1MTORPATHWAY Growth factor IGF-1 activates AKT, Gsk3-beta, and mTOR to promote muscle hypertrophy. AKT1, EIF2B5, EIF2S1, EIF2S2, EIF2S3, EIF4E, EIF4EBP1, FRAP1, GSK3B, IGF1, IGF1R, INPPL1, PDK2, PDPK1, PIK3CA, PIK3R1, PPP2CA, PTEN, RPS6, RPS6KB1 19 AKT1(2), EIF2B5(1), EIF2S2(1), EIF4E(1), INPPL1(2), PDK2(1), PIK3CA(12), PIK3R1(2), PPP2CA(1), PTEN(20), RPS6KB1(1) 12147172 44 42 41 5 1 9 2 13 18 1 0.024 0.00815 0.472

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 SA_TRKA_RECEPTOR The TrkA receptor binds nerve growth factor to activate MAP kinase pathways and promote cell growth. AKT1, AKT2, AKT3, ARHA, CDKN1A, ELK1, GRB2, HRAS, MAP2K1, MAP2K2, NGFB, NGFR, NTRK1, PIK3CA, PIK3CD, SHC1, SOS1 14 AKT1(2), AKT2(3), AKT3(2), CDKN1A(1), ELK1(1), GRB2(1), HRAS(1), NGFR(1), NTRK1(4), SHC1(2), SOS1(6) 8403199 24 24 24 1 1 6 3 13 1 0 0.009 0.0038 1
2 HSA00750_VITAMIN_B6_METABOLISM Genes involved in vitamin B6 metabolism AOX1, PDXK, PDXP, PNPO, PSAT1 5 AOX1(7), PDXK(1), PDXP(1), PSAT1(1) 2946049 10 10 10 0 1 1 0 6 2 0 0.14 0.016 1
3 EEA1PATHWAY The FYVE-finger proteins EEA1 and HRS are localized to endosome membranes and regulate sorting and ubiquitination in the vesicle transport system. EEA1, EGF, EGFR, HGS, RAB5A, TF, TFRC 7 EEA1(5), EGF(4), EGFR(7), HGS(2), RAB5A(1) 7898260 19 19 18 0 3 1 5 5 5 0 0.01 0.022 1
4 IFNGPATHWAY IFN gamma signaling pathway IFNG, IFNGR1, IFNGR2, JAK1, JAK2, STAT1 6 IFNGR1(2), IFNGR2(1), JAK1(4), JAK2(5), STAT1(4) 5136758 16 16 16 2 1 0 1 10 4 0 0.4 0.025 1
5 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 13 ACTR2(1), ARPC1B(1), ARPC2(1), CDC42(1), PAK1(1), PDGFRA(7), PIK3R1(2), RAC1(1), WASL(6) 7015144 21 20 21 2 0 4 1 13 3 0 0.12 0.027 1
6 STILBENE_COUMARINE_AND_LIGNIN_BIOSYNTHESIS EPX, GBA3, LPO, MPO, PRDX1, PRDX2, PRDX5, PRDX6, TPO, TYR 10 EPX(1), GBA3(2), MPO(4), PRDX1(1), PRDX5(1), TPO(3), TYR(3) 5954630 15 15 15 1 1 2 5 5 2 0 0.077 0.032 1
7 HSA00950_ALKALOID_BIOSYNTHESIS_I Genes involved in alkaloid biosynthesis I DDC, GOT1, GOT2, TAT, TYR 5 DDC(2), GOT1(2), GOT2(2), TYR(3) 2929848 9 9 9 1 0 0 3 2 4 0 0.44 0.033 1
8 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 16 ABCB1(4), AKT1(2), ATM(15), CDKN1A(1), CSNK1A1(1), FHL2(1), HIF1A(3), IGFBP3(1), NFKBIB(1) 11769948 29 26 29 1 0 4 5 10 10 0 0.0094 0.041 1
9 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(4), EGFR(7), ERBB3(3), NRG1(4), UBE2D1(2) 6368227 20 20 19 3 2 1 4 8 5 0 0.24 0.045 1
10 EGFR_SMRTEPATHWAY EGF receptor activation inhibits SMRT, a transcriptional co-repressor that interacts with transcription factor complexes and gene silencers. EGF, EGFR, MAP2K1, MAP3K1, MAPK14, NCOR2, RARA, RXRA, THRA, THRB, ZNF145 10 EGF(4), EGFR(7), MAP3K1(4), MAPK14(1), NCOR2(4), RARA(4), THRA(2) 10677896 26 26 25 2 1 3 7 8 7 0 0.034 0.05 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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