Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result)
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C16D5RC0
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result). Broad Institute of MIT and Harvard. doi:10.7908/C16D5RC0
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 and MutSigCV v0.9 merged result was used to generate the results found in this report.

  • Working with individual set: UCEC-TP

  • Number of patients in set: 248

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:UCEC-TP.final_analysis_set.maf

  • Significantly mutated genes (q ≤ 0.1): 33

  • Mutations seen in COSMIC: 1002

  • Significantly mutated genes in COSMIC territory: 46

  • Significantly mutated genesets: 68

Mutation Preprocessing

  • Read 248 MAFs of type "WashU"

  • Total number of mutations in input MAFs: 184861

  • After removing 118 mutations outside chr1-24: 184743

  • After removing 1940 noncoding mutations: 182803

  • After collapsing adjacent/redundant mutations: 182801

Mutation Filtering

  • Number of mutations before filtering: 182801

  • After removing 15963 mutations outside gene set: 166838

  • After removing 1023 mutations outside category set: 165815

  • After removing 29 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 148800

Results
Breakdown of Mutations by Type

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

type count
Frame_Shift_Del 1066
Frame_Shift_Ins 463
In_Frame_Del 738
In_Frame_Ins 140
Missense_Mutation 110598
Nonsense_Mutation 11525
Nonstop_Mutation 127
Silent 38582
Splice_Site 2576
Total 165815
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 37505 363920417 0.0001 100 5.6 2.2
*Cp(A/C/T)->mut 48870 3147487610 0.000016 16 0.85 3.4
A->mut 23156 3458111551 6.7e-06 6.7 0.37 3.8
*CpG->(G/A) 1051 363920417 2.9e-06 2.9 0.16 2.7
indel+null 15764 6969519578 2.3e-06 2.3 0.12 NaN
double_null 868 6969519578 1.2e-07 0.12 0.0068 NaN
Total 127214 6969519578 0.000018 18 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: UCEC-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)->mut

  • n3 = number of nonsilent mutations of type: A->mut

  • n4 = number of nonsilent mutations of type: *CpG->(G/A)

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

  • n6 = number of nonsilent mutations of type: double_null

  • 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: 33. 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_clust p_cons p_joint p_cv p q
1 PPP2R1A protein phosphatase 2 (formerly 2A), regulatory subunit A , alpha isoform 434902 30 27 18 3 10 17 1 0 2 0 0 0.14 0 9.7e-08 0 0
2 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 813637 172 132 76 3 31 61 68 3 9 0 0 0 0 2.4e-15 0 0
3 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 592248 80 74 25 7 5 61 14 0 0 0 0 0 0 4.9e-15 0 0
4 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 581125 100 83 77 2 4 4 13 1 63 15 0 0.44 0 2.8e-15 0 0
5 PRKAR1B protein kinase, cAMP-dependent, regulatory, type I, beta 242642 4 4 4 3 2 0 0 1 1 0 0.0051 0 0 0.56 0 0
6 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 306286 228 161 134 5 20 29 21 33 98 27 0 0.82 0 0 0 0
7 RPL22 ribosomal protein L22 97813 31 31 7 0 1 0 3 0 26 1 0 0.0075 0 1.3e-15 0 0
8 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 175322 53 53 11 2 1 47 4 1 0 0 0 0.00012 0 3.1e-12 0 0
9 TP53 tumor protein p53 317550 74 69 50 2 23 18 15 1 17 0 0 4e-07 0 5.3e-15 0 0
10 FBXW7 F-box and WD repeat domain containing 7 638720 46 39 30 1 22 12 2 1 8 1 0.0005 0.0086 0.00016 3.7e-14 2.2e-16 3.7e-13
11 CTCF CCCTC-binding factor (zinc finger protein) 547098 49 45 39 1 8 4 7 0 27 3 0.0086 0.065 0.014 6.2e-15 3.3e-15 5.1e-12
12 ARID1A AT rich interactive domain 1A (SWI-like) 1412380 94 83 79 5 2 7 5 1 65 14 0.94 0.84 1 1.1e-15 3.9e-14 5.5e-11
13 SPOP speckle-type POZ protein 287083 23 21 18 0 5 8 6 0 4 0 0.0034 0.035 0.0013 5.2e-09 1.9e-10 2.4e-07
14 ARID5B AT rich interactive domain 5B (MRF1-like) 886908 34 29 34 9 5 5 8 2 13 1 0.064 0.96 0.15 1.4e-09 4.6e-09 5.5e-06
15 FGFR2 fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome, Pfeiffer syndrome, Jackson-Weiss syndrome) 687457 34 31 19 3 3 5 13 9 3 1 0.022 0.3 0.045 1.2e-08 1.2e-08 0.000014
16 CCND1 cyclin D1 154874 15 15 13 1 1 7 3 0 4 0 0.00035 0.23 0.00062 1e-06 1.4e-08 0.000014
17 SMTNL2 smoothelin-like 2 189739 9 9 3 2 0 2 0 0 7 0 0.000037 0.62 0.000061 0.000012 1.6e-08 0.000016
18 NFE2L2 nuclear factor (erythroid-derived 2)-like 2 446498 15 15 12 0 2 3 5 3 2 0 0.000085 0.031 0.000083 0.000095 1.5e-07 0.00014
19 CHD4 chromodomain helicase DNA binding protein 4 1452335 43 35 38 2 16 16 8 0 2 1 0.014 0.59 0.034 1.1e-06 6.9e-07 0.00061
20 RBMX RNA binding motif protein, X-linked 309438 14 13 8 0 1 4 2 0 7 0 0.0054 0.17 0.0081 0.000022 2.9e-06 0.0024
21 FAM9A family with sequence similarity 9, member A 249932 20 14 20 1 2 12 1 0 4 1 0.12 0.99 0.2 1.6e-06 5.2e-06 0.0041
22 MORC4 MORC family CW-type zinc finger 4 675031 28 20 26 2 6 10 1 0 11 0 0.69 0.23 0.65 6.2e-07 6.3e-06 0.0048
23 HPD 4-hydroxyphenylpyruvate dioxygenase 304730 7 7 3 0 0 0 1 0 6 0 0.0076 0.00033 0.00018 0.0025 7e-06 0.0051
24 CASP8 caspase 8, apoptosis-related cysteine peptidase 434533 21 17 19 6 2 8 3 0 8 0 0.65 0.022 0.11 0.000015 0.000023 0.016
25 FOXA2 forkhead box A2 222670 13 12 13 1 1 3 3 0 6 0 0.1 0.23 0.16 0.000016 0.000036 0.024
26 ABI1 abl-interactor 1 390369 5 4 2 5 0 5 0 0 0 0 8e-07 1 2.8e-06 1 0.000039 0.025
27 DNER delta/notch-like EGF repeat containing 485604 21 18 20 1 4 9 5 0 3 0 0.28 0.0024 0.0053 0.00075 0.000054 0.033
28 BCOR BCL6 co-repressor 1283685 43 30 34 19 13 13 14 0 3 0 0.14 0.013 0.0077 0.00088 0.000087 0.052
29 BRS3 bombesin-like receptor 3 299754 17 15 17 2 1 11 1 0 4 0 0.14 0.17 0.2 0.000051 0.00013 0.074
30 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 145328 9 9 6 2 1 4 3 0 1 0 0.08 0.015 0.014 0.00084 0.00014 0.08
31 SGK1 serum/glucocorticoid regulated kinase 1 469608 16 15 14 3 2 5 1 1 7 0 0.39 0.0069 0.03 0.00039 0.00015 0.08
32 TIAL1 TIA1 cytotoxic granule-associated RNA binding protein-like 1 297036 15 11 15 2 5 4 1 0 5 0 0.035 0.4 0.063 0.00019 0.00015 0.08
33 RPL14 ribosomal protein L14 165361 8 7 4 0 0 1 0 2 5 0 0.0069 0.94 0.011 0.0012 0.00016 0.081
34 SIN3A SIN3 homolog A, transcription regulator (yeast) 966485 29 21 27 3 5 7 7 0 10 0 0.026 0.067 0.016 0.0013 0.00025 0.12
35 SLC48A1 solute carrier family 48 (heme transporter), member 1 64852 5 5 5 0 1 2 1 0 1 0 0.7 0.87 1 0.000025 0.00029 0.14
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: 46. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 100 33 39 8184 111 0 0
2 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 53 52 49 12896 556649 0 0
3 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 172 220 150 54560 35669 0 0
4 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 80 138 67 34224 23813 0 0
5 FGFR2 fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome, Pfeiffer syndrome, Jackson-Weiss syndrome) 34 51 24 12648 113 0 0
6 FBXW7 F-box and WD repeat domain containing 7 46 91 29 22568 852 0 0
7 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 228 767 219 190216 15125 0 0
8 TP53 tumor protein p53 74 356 72 88288 24186 0 0
9 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 9 33 6 8184 6490 1.4e-08 6.6e-06
10 RB1 retinoblastoma 1 (including osteosarcoma) 26 267 11 66216 30 6.7e-08 0.000029

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: 68. 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 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(4), EIF4A1(5), EIF4A2(9), EIF4B(7), EIF4E(2), EIF4G1(15), EIF4G2(7), EIF4G3(24), FKBP1A(1), MKNK1(3), PDK2(5), PDPK1(3), PIK3CA(172), PIK3R1(100), PPP2CA(6), PTEN(228), RPS6(7), RPS6KB1(5), TSC1(13), TSC2(15) 9925342 631 218 417 44 91 141 126 38 191 44 <1.00e-15 <1.00e-15 <3.96e-14
2 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(4), EIF2B5(5), EIF2S1(2), EIF2S2(9), EIF2S3(6), EIF4E(2), GSK3B(13), IGF1(5), IGF1R(13), INPPL1(19), PDK2(5), PDPK1(3), PIK3CA(172), PIK3R1(100), PPP2CA(6), PTEN(228), RPS6(7), RPS6KB1(5) 7138255 604 217 388 46 80 131 117 40 194 42 <1.00e-15 <1.00e-15 <3.96e-14
3 GSK3PATHWAY Bacterial lipopolysaccharide activates AKT to promote the survival and activation of macrophages and inhibits Gsk3-beta to promote beta-catenin accumulation in the nucleus. AKT1, APC, AXIN1, CCND1, CD14, CTNNB1, DVL1, FZD1, GJA1, GNAI1, GSK3B, IRAK1, LBP, LEF1, LY96, MYD88, NFKB1, PDPK1, PIK3CA, PIK3R1, PPP2CA, PRKR, RELA, TIRAP, TLR4, TOLLIP, WNT1 26 AKT1(4), APC(56), AXIN1(9), CCND1(15), CD14(2), CTNNB1(80), DVL1(3), FZD1(3), GJA1(5), GNAI1(5), GSK3B(13), IRAK1(4), LBP(3), LEF1(9), LY96(4), MYD88(3), NFKB1(10), PDPK1(3), PIK3CA(172), PIK3R1(100), PPP2CA(6), RELA(6), TIRAP(2), TLR4(17), TOLLIP(1), WNT1(1) 10634125 536 210 356 58 77 201 122 6 110 20 2.06e-13 <1.00e-15 <3.96e-14
4 CREBPATHWAY CREB is a transcription factor that binds to cAMP-responsive elements (CREs) to activate transcription in response to extracellular signaling. ADCY1, AKT1, CAMK2A, CAMK2B, CAMK2D, CAMK2G, CREB1, GNAS, GRB2, HRAS, MAPK1, MAPK14, MAPK3, PIK3CA, PIK3R1, PRKACB, PRKACG, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, PRKCA, PRKCB1, RAC1, RPS6KA1, RPS6KA5, SOS1 26 ADCY1(21), AKT1(4), CAMK2A(9), CAMK2B(3), CAMK2D(5), CAMK2G(5), CREB1(6), GNAS(24), GRB2(3), HRAS(1), MAPK1(2), MAPK14(4), MAPK3(3), PIK3CA(172), PIK3R1(100), PRKACB(3), PRKACG(9), PRKAR1A(4), PRKAR1B(4), PRKAR2A(4), PRKAR2B(3), PRKCA(10), RAC1(1), RPS6KA1(1), RPS6KA5(11), SOS1(13) 10330216 425 200 300 54 88 113 111 5 92 16 4.56e-08 <1.00e-15 <3.96e-14
5 IL7PATHWAY IL-7 is required for B and T cell development and proliferation and may contribute to activation of VDJ recombination. BCL2, CREBBP, EP300, FYN, IL2RG, IL7, IL7R, JAK1, JAK3, LCK, NMI, PIK3CA, PIK3R1, PTK2B, STAT5A, STAT5B 16 CREBBP(32), EP300(32), FYN(7), IL2RG(13), IL7(1), IL7R(12), JAK1(20), JAK3(10), LCK(5), NMI(3), PIK3CA(172), PIK3R1(100), PTK2B(16), STAT5A(5), STAT5B(7) 10213532 435 200 312 56 77 121 115 6 100 16 3.60e-08 <1.00e-15 <3.96e-14
6 EGFPATHWAY The epidermal growth factor (EGF) peptide stimulates the EGF receptor to promote cell proliferation via the MAP kinase and Ras pathways. CSNK2A1, EGF, EGFR, ELK1, FOS, GRB2, HRAS, JAK1, JUN, MAP2K1, MAP2K4, MAP3K1, MAPK3, MAPK8, PIK3CA, PIK3R1, PLCG1, PRKCA, PRKCB1, RAF1, RASA1, SHC1, SOS1, SRF, STAT1, STAT3, STAT5A 26 CSNK2A1(9), EGF(20), EGFR(12), ELK1(3), FOS(3), GRB2(3), HRAS(1), JAK1(20), JUN(1), MAP2K1(2), MAP2K4(9), MAP3K1(30), MAPK3(3), MAPK8(10), PIK3CA(172), PIK3R1(100), PLCG1(12), PRKCA(10), RAF1(8), RASA1(30), SHC1(7), SOS1(13), SRF(3), STAT1(15), STAT3(10), STAT5A(5) 13675029 511 199 380 61 99 144 131 4 116 17 8.39e-11 <1.00e-15 <3.96e-14
7 ERK5PATHWAY Signaling between a tissue and its innervating axon stimulates retrograde transport via Trk receptors, which activate Erk5, which induces transcription of anti-apoptotic factors. AKT1, CREB1, GRB2, HRAS, MAPK1, MAPK3, MAPK7, MEF2A, MEF2B, MEF2C, MEF2D, NTRK1, PIK3CA, PIK3R1, PLCG1, RPS6KA1, SHC1 17 AKT1(4), CREB1(6), GRB2(3), HRAS(1), MAPK1(2), MAPK3(3), MAPK7(14), MEF2A(3), MEF2B(1), MEF2C(11), MEF2D(7), NTRK1(13), PIK3CA(172), PIK3R1(100), PLCG1(12), RPS6KA1(1), SHC1(7) 7017966 360 199 240 37 64 100 96 6 79 15 1.94e-09 <1.00e-15 <3.96e-14
8 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(21), ARHGEF1(12), F2(8), F2R(6), GNA12(8), GNA13(7), GNAI1(5), GNAQ(3), GNB1(2), GNGT1(1), MAP3K7(9), PIK3CA(172), PIK3R1(100), PLCB1(5), PPP1R12B(20), PRKCA(10), PTK2B(16), ROCK1(22) 9289594 427 199 304 63 96 111 112 7 84 17 2.95e-06 <1.00e-15 <3.96e-14
9 GCRPATHWAY Corticosteroids activate the glucocorticoid receptor (GR), which inhibits NF-kB and activates Annexin-1, thus inhibiting the inflammatory response. ADRB2, AKT1, ANXA1, CALM1, CALM2, CALM3, CRN, GNAS, GNB1, GNGT1, HSPCA, NFKB1, NOS3, NPPA, NR3C1, PIK3CA, PIK3R1, RELA, SYT1 17 ADRB2(7), AKT1(4), ANXA1(5), CALM1(2), CALM2(4), GNAS(24), GNB1(2), GNGT1(1), NFKB1(10), NOS3(11), NPPA(2), NR3C1(18), PIK3CA(172), PIK3R1(100), RELA(6), SYT1(4) 6288912 372 197 249 26 79 99 91 4 84 15 9.69e-14 <1.00e-15 <3.96e-14
10 RASPATHWAY Ras activation stimulates many signaling cascades, including PI3K/AKT activation to inhibit apoptosis. AKT1, ARHA, BAD, BCL2L1, CASP9, CDC42, CHUK, ELK1, H2AFX, HRAS, MAP2K1, MAPK3, MLLT7, NFKB1, PIK3CA, PIK3R1, RAC1, RAF1, RALA, RALBP1, RALGDS, RELA, RHOA 21 AKT1(4), BAD(2), BCL2L1(4), CASP9(3), CDC42(4), CHUK(8), ELK1(3), H2AFX(1), HRAS(1), MAP2K1(2), MAPK3(3), NFKB1(10), PIK3CA(172), PIK3R1(100), RAC1(1), RAF1(8), RALA(6), RALBP1(6), RALGDS(9), RELA(6), RHOA(3) 6942603 356 197 235 28 56 106 95 4 78 17 4.18e-12 <1.00e-15 <3.96e-14
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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