Mutation Analysis (MutSig v2.0)
Breast Invasive Carcinoma (Primary solid tumor)
22 February 2013  |  analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C1H1306W
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Analysis (MutSig v2.0). Broad Institute of MIT and Harvard. doi:10.7908/C1H1306W
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: BRCA-TP

  • Number of patients in set: 4

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:BRCA-TP.final_analysis_set.maf

  • Significantly mutated genes (q ≤ 0.1): 2

  • Mutations seen in COSMIC: 8

  • Significantly mutated genes in COSMIC territory: 2

  • Genes with clustered mutations (≤ 3 aa apart): 1

  • Significantly mutated genesets: 106

  • Significantly mutated genesets: (excluding sig. mutated genes):1

Mutation Preprocessing

  • Read 772 MAFs of type "WashU"

  • Total number of mutations in input MAFs: 47116

  • After removing 330 mutations outside chr1-24: 46786

  • After removing 559 blacklisted mutations: 46227

  • After removing 1703 noncoding mutations: 44524

  • After collapsing adjacent/redundant mutations: 44522

Mutation Filtering

  • Number of mutations before filtering: 44522

  • After removing 44387 mutations outside patient set: 135

  • After removing 2 mutations outside gene set: 133

Results
Breakdown of Mutations by Type

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

type count
Frame_Shift_Del 2
Frame_Shift_Ins 2
In_Frame_Del 1
Missense_Mutation 101
Nonsense_Mutation 5
Silent 17
Splice_Site 5
Total 133
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 28 6601072 4.2e-06 4.2 4.5 2.1
*Cp(A/C/T)->T 18 55704172 3.2e-07 0.32 0.34 1.7
C->(G/A) 32 62305244 5.1e-07 0.51 0.54 4.8
A->mut 23 60393368 3.8e-07 0.38 0.4 3.9
indel+null 15 122698612 1.2e-07 0.12 0.13 NaN
double_null 0 122698612 0 0 0 NaN
Total 116 122698612 9.5e-07 0.95 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: BRCA-TP.patients.counts_and_rates.txt

CoMut Plot

Figure 3.  Get High-res Image The matrix in the center of the figure represents individual mutations in patient samples, color-coded by type of mutation, for the significantly mutated genes. The rate of synonymous and non-synonymous mutations is displayed at the top of the matrix. The barplot on the left of the matrix shows the number of mutations in each gene. The percentages represent the fraction of tumors with at least one mutation in the specified gene. The barplot to the right of the matrix displays the q-values for the most significantly mutated genes. The purple boxplots below the matrix (only displayed if required columns are present in the provided MAF) represent the distributions of allelic fractions observed in each sample. The plot at the bottom represents the base substitution distribution of individual samples, using the same categories that were used to calculate significance.

Significantly Mutated Genes

Column Descriptions:

  • N = number of sequenced bases in this gene across the individual set

  • n = number of (nonsilent) mutations in this gene across the individual set

  • npat = number of patients (individuals) with at least one nonsilent mutation

  • nsite = number of unique sites having a non-silent mutation

  • nsil = number of silent mutations in this gene across the individual set

  • n1 = number of nonsilent mutations of type: *CpG->T

  • n2 = number of nonsilent mutations of type: *Cp(A/C/T)->T

  • n3 = number of nonsilent mutations of type: C->(G/A)

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

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

  • n6 = number of nonsilent mutations of type: double_null

  • p_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: 2. 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_cons p_joint p q
1 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 13148 3 3 2 0 0 0 0 3 0 0 5.4e-08 0.45 NaN NaN 5.4e-08 0.00098
2 TP53 tumor protein p53 5124 2 2 2 0 0 0 0 2 0 0 2.8e-06 0.63 NaN NaN 2.8e-06 0.025
3 DGKG diacylglycerol kinase, gamma 90kDa 9860 1 1 1 0 0 0 0 0 1 0 0.00011 0.84 NaN NaN 0.00011 0.68
4 ETS1 v-ets erythroblastosis virus E26 oncogene homolog 1 (avian) 6320 1 1 1 0 0 1 0 0 0 0 0.00027 0.62 NaN NaN 0.00027 1
5 TNK2 tyrosine kinase, non-receptor, 2 8412 1 1 1 0 0 1 0 0 0 0 0.00042 0.55 NaN NaN 0.00042 1
6 LIPF lipase, gastric 4932 1 1 1 0 0 0 0 0 1 0 0.00058 0.88 NaN NaN 0.00058 1
7 SPHKAP SPHK1 interactor, AKAP domain containing 20600 1 1 1 0 0 0 1 0 0 0 0.0006 0.84 NaN NaN 0.0006 1
8 TSEN15 tRNA splicing endonuclease 15 homolog (S. cerevisiae) 1652 1 1 1 0 0 0 1 0 0 0 0.00069 0.87 NaN NaN 0.00069 1
9 ZNF626 zinc finger protein 626 6244 1 1 1 0 0 0 0 0 1 0 0.00069 1 NaN NaN 0.00069 1
10 BCAT1 branched chain aminotransferase 1, cytosolic 4848 1 1 1 0 1 0 0 0 0 0 0.00089 0.7 NaN NaN 0.00089 1
11 ZNF829 zinc finger protein 829 5612 1 1 1 0 0 0 0 0 1 0 0.00095 1 NaN NaN 0.00095 1
12 MUC1 mucin 1, cell surface associated 3524 1 1 1 0 0 0 0 1 0 0 0.00097 0.83 NaN NaN 0.00097 1
13 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 15008 1 1 1 0 0 0 1 0 0 0 0.0011 0.78 NaN NaN 0.0011 1
14 CRTAP cartilage associated protein 3036 1 1 1 0 0 1 0 0 0 0 0.0011 0.59 NaN NaN 0.0011 1
15 BCL2L11 BCL2-like 11 (apoptosis facilitator) 2952 1 1 1 0 0 1 0 0 0 0 0.0011 0.68 NaN NaN 0.0011 1
16 CSMD3 CUB and Sushi multiple domains 3 45880 1 1 1 0 1 0 0 0 0 0 0.0013 0.73 NaN NaN 0.0013 1
17 PDHB pyruvate dehydrogenase (lipoamide) beta 4480 1 1 1 0 0 0 0 1 0 0 0.0013 0.75 NaN NaN 0.0013 1
18 SRGN serglycin 1956 1 1 1 0 1 0 0 0 0 0 0.0014 0.71 NaN NaN 0.0014 1
19 NR1H2 nuclear receptor subfamily 1, group H, member 2 4976 1 1 1 0 0 0 0 1 0 0 0.0014 0.84 NaN NaN 0.0014 1
20 OR8D1 olfactory receptor, family 8, subfamily D, member 1 3720 1 1 1 0 0 1 0 0 0 0 0.0014 0.55 NaN NaN 0.0014 1
21 LHFPL4 lipoma HMGIC fusion partner-like 4 3024 1 1 1 0 0 0 1 0 0 0 0.0015 0.73 NaN NaN 0.0015 1
22 ZNF675 zinc finger protein 675 6892 1 1 1 0 0 0 0 0 1 0 0.0016 0.8 NaN NaN 0.0016 1
23 PIWIL2 piwi-like 2 (Drosophila) 12040 1 1 1 0 0 0 0 0 1 0 0.0017 1 NaN NaN 0.0017 1
24 NPAS4 neuronal PAS domain protein 4 9508 1 1 1 0 0 0 0 1 0 0 0.0017 0.78 NaN NaN 0.0017 1
25 TTC9C tetratricopeptide repeat domain 9C 2112 1 1 1 0 1 0 0 0 0 0 0.0018 0.77 NaN NaN 0.0018 1
26 FBXL17 F-box and leucine-rich repeat protein 17 4580 1 1 1 0 0 0 0 0 1 0 0.002 0.68 NaN NaN 0.002 1
27 SCAI suppressor of cancer cell invasion 7636 1 1 1 0 1 0 0 0 0 0 0.002 0.82 NaN NaN 0.002 1
28 PIK3R3 phosphoinositide-3-kinase, regulatory subunit 3 (gamma) 5704 1 1 1 0 0 0 0 1 0 0 0.0021 0.8 NaN NaN 0.0021 1
29 ATL3 atlastin GTPase 3 6712 1 1 1 0 0 0 0 1 0 0 0.0021 0.78 NaN NaN 0.0021 1
30 SSX3 synovial sarcoma, X breakpoint 3 2568 1 1 1 0 0 0 1 0 0 0 0.0021 0.86 NaN NaN 0.0021 1
31 FGFR2 fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome, Pfeiffer syndrome, Jackson-Weiss syndrome) 11160 1 1 1 0 0 0 0 1 0 0 0.0021 0.81 NaN NaN 0.0021 1
32 DSP desmoplakin 34308 1 1 1 0 0 1 0 0 0 0 0.0022 0.57 NaN NaN 0.0022 1
33 CRIPAK cysteine-rich PAK1 inhibitor 5300 1 1 1 0 1 0 0 0 0 0 0.0023 0.66 NaN NaN 0.0023 1
34 CHI3L1 chitinase 3-like 1 (cartilage glycoprotein-39) 4768 1 1 1 0 0 0 1 0 0 0 0.0023 0.8 NaN NaN 0.0023 1
35 SLC16A1 solute carrier family 16, member 1 (monocarboxylic acid transporter 1) 6076 1 1 1 0 0 1 0 0 0 0 0.0024 0.69 NaN NaN 0.0024 1
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: 2. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3 220 3 880 1967 9.6e-11 4.3e-07
2 TP53 tumor protein p53 2 356 2 1424 574 9e-07 0.002
3 CSMD3 CUB and Sushi multiple domains 3 1 22 1 88 1 0.000083 0.13
4 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 1 42 1 168 3 0.00016 0.17
5 FGFR2 fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome, Pfeiffer syndrome, Jackson-Weiss syndrome) 1 51 1 204 5 0.00019 0.17
6 A4GNT alpha-1,4-N-acetylglucosaminyltransferase 0 0 0 0 0 1 1
7 AACS acetoacetyl-CoA synthetase 0 0 0 0 0 1 1
8 ABCA9 ATP-binding cassette, sub-family A (ABC1), member 9 0 0 0 0 0 1 1
9 ABCC10 ATP-binding cassette, sub-family C (CFTR/MRP), member 10 0 0 0 0 0 1 1
10 ABCF2 ATP-binding cassette, sub-family F (GCN20), member 2 0 0 0 0 0 1 1

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
71 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3 0 1 1 1 1 1 1
92 TEX15 testis expressed 15 3 22 0 0 0 0 0 0
95 TP53 tumor protein p53 2 54 0 0 0 0 0 0

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: 106. 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 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 F2(1), PIK3CA(3) 153848 4 4 3 0 0 0 1 3 0 0 0.44 1.1e-06 0.00066
2 LONGEVITYPATHWAY Caloric restriction in animals often increases lifespan, which may occur via decreased IGF receptor expression and consequent expression of stress-resistance proteins. AKT1, CAT, FOXO3A, GH1, GHR, HRAS, IGF1, IGF1R, PIK3CA, PIK3R1, SHC1, SOD1, SOD2, SOD3 12 PIK3CA(3) 78608 3 3 2 0 0 0 0 3 0 0 0.52 8.9e-06 0.0009
3 CTLA4PATHWAY T cell activation requires interaction with an antigen-MHC-I complex on an antigen-presenting cell (APC), as well as CD28 interaction with the APC's CD80 or 86. CD28, CD3D, CD3E, CD3G, CD3Z, CD80, CD86, CTLA4, GRB2, HLA-DRA, HLA-DRB1, ICOS, ICOSL, IL2, ITK, LCK, PIK3CA, PIK3R1, PTPN11, TRA@, TRB@ 16 PIK3CA(3) 72992 3 3 2 0 0 0 0 3 0 0 0.5 9.1e-06 0.0009
4 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(3) 78008 3 3 2 0 0 0 0 3 0 0 0.54 9.7e-06 0.0009
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 14 PIK3CA(3) 81180 3 3 2 0 0 0 0 3 0 0 0.48 0.000012 0.0009
6 AKTPATHWAY Second messenger PIP3 promotes cell survival by activating the anti-apoptotic kinase AKT. AKT1, BAD, CASP9, CHUK, FOXO1A, FOXO3A, GH1, GHR, HSPCA, MLLT7, NFKB1, NFKBIA, PDPK1, PIK3CA, PIK3R1, PPP2CA, RELA, TNFSF6, YWHAH 14 PIK3CA(3) 85460 3 3 2 0 0 0 0 3 0 0 0.49 0.000013 0.0009
7 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 PIK3CA(3) 98576 3 3 2 0 0 0 0 3 0 0 0.54 0.000014 0.0009
8 ACHPATHWAY Nicotinic acetylcholine receptors are ligand-gated ion channels that primarily mediate neuromuscular signaling and may inhibit neuronal apoptosis via the AKT pathway. AKT1, BAD, CHRNB1, CHRNG, FOXO3A, MUSK, PIK3CA, PIK3R1, PTK2, PTK2B, RAPSN, SRC, TERT, TNFSF6, YWHAH 13 PIK3CA(3) 101872 3 3 2 0 0 0 0 3 0 0 0.56 0.000015 0.0009
9 NFATPATHWAY Cardiac hypertrophy is induced by NF-ATc4 and GATA4, which are stimulated through calcineurin activated by CaMK. ACTA1, AGT, AKT1, CALM1, CALM2, CALM3, CALR, CAMK1, CAMK1G, CAMK4, CREBBP, CSNK1A1, CTF1, DTR, EDN1, ELSPBP1, F2, FGF2, FKBP1A, GATA4, GSK3B, HAND1, HAND2, HRAS, IGF1, LIF, MAP2K1, MAPK1, MAPK14, MAPK3, MAPK8, MEF2C, MYH2, NFATC1, NFATC2, NFATC3, NFATC4, NKX2-5, NPPA, PIK3CA, PIK3R1, PPP3CA, PPP3CB, PPP3CC, PRKACB, PRKACG, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, RAF1, RPS6KB1, SYT1 52 F2(1), NFATC4(1), PIK3CA(3) 300744 5 4 4 0 0 0 1 3 1 0 0.43 0.000016 0.0009
10 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 PIK3CA(3) 93892 3 3 2 0 0 0 0 3 0 0 0.52 0.000016 0.0009

Table 7.  Get Full Table A Ranked List of Significantly Mutated Genesets (Excluding Significantly Mutated Genes). Number of significant genesets found: 1. 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 HSA04320_DORSO_VENTRAL_AXIS_FORMATION Genes involved in dorso-ventral axis formation BRAF, CPEB1, EGFR, ERBB2, ERBB4, ETS1, ETS2, ETV6, ETV7, FMN2, GRB2, KRAS, MAP2K1, MAPK1, MAPK3, NOTCH1, NOTCH2, NOTCH3, NOTCH4, PIWIL1, PIWIL2, PIWIL3, PIWIL4, RAF1, SOS1, SOS2, SPIRE1, SPIRE2 27 ERBB2(1), ETS1(1), PIWIL2(1) 293236 3 3 3 1 0 1 1 0 1 0 0.79 0.000024 0.015
2 VALINE_LEUCINE_AND_ISOLEUCINE_BIOSYNTHESIS BCAT1, IARS, LARS, LARS2, PDHA1, PDHA2, PDHB 7 BCAT1(1), PDHB(1) 60328 2 2 2 0 1 0 0 1 0 0 0.55 0.00034 0.1
3 GLYCEROLIPID_METABOLISM ADH1A, ADH1A, ADH1B, ADH1C, ADH1B, ADH1C, ADH4, ADH6, ADH7, ADHFE1, AGPAT1, AGPAT2, AGPAT3, AGPAT4, AKR1A1, AKR1B1, ALDH1A1, ALDH1A2, ALDH1A3, ALDH1B1, ALDH2, ALDH3A1, ALDH3A2, ALDH9A1, CEL, DGAT1, DGKA, DGKB, DGKD, DGKE, DGKG, DGKH, DGKQ, DGKZ, GK, GLA, GLB1, LCT, LIPC, LIPF, LIPG, LPL, PNLIP, PNLIPRP1, PNLIPRP2, PPAP2A, PPAP2B, PPAP2C 44 DGKG(1), LIPF(1) 292672 2 2 2 0 0 0 0 0 2 0 0.66 0.0011 0.17
4 HSA00290_VALINE_LEUCINE_AND_ISOLEUCINE_BIOSYNTHESIS Genes involved in valine, leucine and isoleucine biosynthesis BCAT1, BCAT2, IARS, IARS2, ILVBL, LARS, LARS2, PDHA1, PDHA2, PDHB, VARS, VARS2 12 BCAT1(1), PDHB(1) 93956 2 2 2 0 1 0 0 1 0 0 0.55 0.0011 0.17
5 HSA00561_GLYCEROLIPID_METABOLISM Genes involved in glycerolipid metabolism ADH1A, ADH1B, ADH1C, ADH4, ADH5, ADH6, ADH7, ADHFE1, AGK, AGPAT1, AGPAT2, AGPAT3, AGPAT4, AGPAT6, AKR1A1, AKR1B1, ALDH1A3, ALDH1B1, ALDH2, ALDH3A1, ALDH3A2, ALDH7A1, ALDH9A1, CEL, DAK, DGAT1, DGAT2, DGKA, DGKB, DGKD, DGKE, DGKG, DGKH, DGKI, DGKQ, DGKZ, GK, GK2, GLA, GLB1, GPAM, LCT, LIPA, LIPC, LIPF, LIPG, LPL, LYCAT, MGLL, PNLIP, PNLIPRP1, PNLIPRP2, PNPLA3, PPAP2A, PPAP2B, PPAP2C, UGCGL1, UGCGL2 54 DGKG(1), LIPF(1) 356400 2 2 2 0 0 0 0 0 2 0 0.66 0.0019 0.23
6 HSA04012_ERBB_SIGNALING_PATHWAY Genes involved in ErbB signaling pathway ABL1, ABL2, AKT1, AKT2, AKT3, ARAF, AREG, BAD, BRAF, BTC, CAMK2A, CAMK2B, CAMK2D, CAMK2G, CBL, CBLB, CBLC, CDKN1A, CDKN1B, CRK, CRKL, EGF, EGFR, EIF4EBP1, ELK1, ERBB2, ERBB3, ERBB4, EREG, FRAP1, GAB1, GRB2, GSK3B, HBEGF, HRAS, JUN, KRAS, MAP2K1, MAP2K2, MAP2K4, MAP2K7, MAPK1, MAPK10, MAPK3, MAPK8, MAPK9, MYC, NCK1, NCK2, NRAS, NRG1, NRG2, NRG3, NRG4, PAK1, PAK2, PAK3, PAK4, PAK6, PAK7, PIK3CA, PIK3CB, PIK3CD, PIK3CG, PIK3R1, PIK3R2, PIK3R3, PIK3R5, PLCG1, PLCG2, PRKCA, PRKCB1, PRKCG, PTK2, RAF1, RPS6KB1, RPS6KB2, SHC1, SHC2, SHC3, SHC4, SOS1, SOS2, SRC, STAT5A, STAT5B, TGFA 84 ERBB2(1), PIK3R3(1) 601936 2 2 2 0 0 0 1 1 0 0 0.66 0.011 1
7 ETSPATHWAY The Ets transcription factors are activated by Ras and promote macrophage differentiation. CSF1, CSF1R, DDX20, E2F1, E2F4, ETS1, ETS2, ETV3, FOS, HDAC2, HDAC5, HRAS, JUN, NCOR2, RBL1, RBL2, SIN3A, SIN3B 18 ETS1(1) 166252 1 1 1 0 0 1 0 0 0 0 0.6 0.013 1
8 HSA04070_PHOSPHATIDYLINOSITOL_SIGNALING_SYSTEM Genes involved in phosphatidylinositol signaling system CALM1, CALM2, CALM3, CALML3, CALML6, CARKL, CDIPT, CDS1, CDS2, DGKA, DGKB, DGKD, DGKE, DGKG, DGKH, DGKI, DGKQ, DGKZ, FN3K, IMPA1, IMPA2, INPP1, INPP4A, INPP4B, INPP5A, INPP5B, INPP5D, INPP5E, INPPL1, ITGB1BP3, ITPK1, ITPKA, ITPKB, ITPR1, ITPR2, ITPR3, OCRL, PI4KA, PI4KB, PIB5PA, PIK3C2A, PIK3C2B, PIK3C2G, PIK3C3, PIK3CA, PIK3CB, PIK3CD, PIK3CG, PIK3R1, PIK3R2, PIK3R3, PIK3R5, PIP4K2A, PIP4K2B, PIP4K2C, PIP5K1A, PIP5K1B, PIP5K1C, PIP5K3, PLCB1, PLCB2, PLCB3, PLCB4, PLCD1, PLCD3, PLCD4, PLCE1, PLCG1, PLCG2, PLCZ1, PRKCA, PRKCB1, PRKCG, PTEN, PTPMT1, SKIP, SYNJ1, SYNJ2 72 DGKG(1), PIK3R3(1) 754748 2 2 2 0 0 0 0 1 1 0 0.67 0.016 1
9 NFATPATHWAY Cardiac hypertrophy is induced by NF-ATc4 and GATA4, which are stimulated through calcineurin activated by CaMK. ACTA1, AGT, AKT1, CALM1, CALM2, CALM3, CALR, CAMK1, CAMK1G, CAMK4, CREBBP, CSNK1A1, CTF1, DTR, EDN1, ELSPBP1, F2, FGF2, FKBP1A, GATA4, GSK3B, HAND1, HAND2, HRAS, IGF1, LIF, MAP2K1, MAPK1, MAPK14, MAPK3, MAPK8, MEF2C, MYH2, NFATC1, NFATC2, NFATC3, NFATC4, NKX2-5, NPPA, PIK3CA, PIK3R1, PPP3CA, PPP3CB, PPP3CC, PRKACB, PRKACG, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, RAF1, RPS6KB1, SYT1 51 F2(1), NFATC4(1) 287596 2 2 2 0 0 0 1 0 1 0 0.83 0.017 1
10 CHEMICALPATHWAY DNA damage promotes Bid cleavage, which stimulates mitochondrial cytochrome c release and consequent caspase activation, resulting in apoptosis. ADPRT, AKT1, APAF1, ATM, BAD, BAX, BCL2, BCL2L1, BID, CASP3, CASP6, CASP7, CASP9, CYCS, EIF2S1, PRKCA, PRKCB1, PTK2, PXN, STAT1, TLN1, TP53 19 TLN1(1) 162092 1 1 1 0 1 0 0 0 0 0 0.7 0.018 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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] TCGA, Integrated genomic analyses of ovarian carcinoma, Nature 474:609 - 615 (2011)
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