Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result)
Breast Invasive Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1F47MMN
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result). Broad Institute of MIT and Harvard. doi:10.7908/C1F47MMN
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: BRCA-TP

  • Number of patients in set: 976

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): 53

  • Mutations seen in COSMIC: 875

  • Significantly mutated genes in COSMIC territory: 19

  • Significantly mutated genesets: 110

Mutation Preprocessing

  • Read 976 MAFs of type "WashU"

  • Total number of mutations in input MAFs: 90092

  • After removing 39 mutations outside chr1-24: 90053

  • After removing 1737 blacklisted mutations: 88316

  • After removing 4275 noncoding mutations: 84041

  • After collapsing adjacent/redundant mutations: 80786

Mutation Filtering

  • Number of mutations before filtering: 80786

  • After removing 6766 mutations outside gene set: 74020

  • After removing 291 mutations outside category set: 73729

  • After removing 36 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 66636

Results
Breakdown of Mutations by Type

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

type count
Frame_Shift_Del 2469
Frame_Shift_Ins 1733
In_Frame_Del 450
In_Frame_Ins 113
Missense_Mutation 47984
Nonsense_Mutation 4196
Nonstop_Mutation 113
Silent 15401
Splice_Site 1270
Total 73729
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->mut 9176 1430399757 6.4e-06 6.4 3 2.5
Tp*Cp(A/C/T)->mut 16622 3339563136 5e-06 5 2.3 3.4
(A/C/G)p*Cp(A/C/T)->mut 10473 9065964566 1.2e-06 1.2 0.54 3.4
A->mut 11685 13634047637 8.6e-07 0.86 0.4 3.8
indel+null 10083 27469975096 3.7e-07 0.37 0.17 NaN
double_null 253 27469975096 9.2e-09 0.0092 0.0043 NaN
Total 58292 27469975096 2.1e-06 2.1 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

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->mut

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

  • n3 = number of nonsilent mutations of type: (A/C/G)p*Cp(A/C/T)->mut

  • 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_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: 53. 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 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3197054 352 316 53 6 5 134 21 185 7 0 0 0 0 0 0 0
2 CBFB core-binding factor, beta subunit 458797 24 23 22 1 1 0 8 4 11 0 0.0031 0.081 0.0065 0 0 0
3 GATA3 GATA binding protein 3 997288 80 77 55 2 1 1 2 5 70 1 0 0.38 0 1.1e-14 0 0
4 FOXA1 forkhead box A1 1007827 23 23 16 0 1 8 3 7 3 1 2e-07 0.051 0 6.3e-13 0 0
5 SF3B1 splicing factor 3b, subunit 1, 155kDa 3935906 16 16 9 2 0 2 1 12 1 0 0 0.026 0 0.82 0 0
6 TP53 tumor protein p53 1236852 298 295 160 5 45 22 39 69 123 0 0 0 0 7.3e-15 0 0
7 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 2541544 108 105 89 3 4 8 6 1 89 0 0.22 0.67 0.31 0 0 0
8 RUNX1 runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) 1050415 29 29 23 2 0 2 5 4 17 1 0.0098 0.00031 0.00017 1.1e-14 1.1e-16 2.3e-13
9 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1211246 36 35 33 0 0 4 3 8 20 1 0.0044 0.73 0.0084 7.7e-15 2.4e-15 4.5e-12
10 TBX3 T-box 3 (ulnar mammary syndrome) 1278815 27 27 26 1 4 2 1 1 19 0 0.036 0.83 0.076 1.2e-14 3.2e-14 5.4e-11
11 MAP2K4 mitogen-activated protein kinase kinase 4 1093077 32 32 28 0 3 2 5 2 20 0 0.11 0.47 0.23 9.8e-15 7.8e-14 1.2e-10
12 MLL3 myeloid/lymphoid or mixed-lineage leukemia 3 14445628 78 69 77 6 0 14 8 7 44 5 0.42 0.14 0.48 4.9e-15 8.1e-14 1.2e-10
13 MAP3K1 mitogen-activated protein kinase kinase kinase 1 4022333 79 70 71 3 1 3 6 7 45 17 0.73 0.72 1 7.4e-15 2.5e-13 3.2e-10
14 NCOR1 nuclear receptor co-repressor 1 7280070 43 41 41 1 2 10 6 3 22 0 0.44 0.32 0.45 2.4e-14 3.7e-13 4.4e-10
15 RB1 retinoblastoma 1 (including osteosarcoma) 2642756 22 19 21 3 1 1 1 2 17 0 0.74 0.57 0.74 2.9e-14 7e-13 7.8e-10
16 CTCF CCCTC-binding factor (zinc finger protein) 2150060 17 17 15 3 1 3 1 5 7 0 0.0078 0.00087 0.00086 9.2e-11 2.5e-12 2.6e-09
17 THEM5 thioesterase superfamily member 5 748228 11 11 8 1 0 3 0 1 7 0 0.25 0.063 0.14 3.4e-11 1.3e-10 1.3e-07
18 GPS2 G protein pathway suppressor 2 947396 10 10 10 0 0 1 0 0 9 0 0.082 0.22 0.12 1.7e-10 5e-10 4.7e-07
19 RPGR retinitis pigmentosa GTPase regulator 3585906 21 18 19 1 3 7 2 1 8 0 0.064 0.082 0.032 3.9e-09 3e-09 2.7e-06
20 DNAH12 dynein, axonemal, heavy chain 12 8188396 20 17 20 3 3 4 4 6 3 0 0.13 0.52 0.2 2.8e-09 1.3e-08 0.000011
21 CDKN1B cyclin-dependent kinase inhibitor 1B (p27, Kip1) 499594 10 10 9 1 0 0 0 0 8 2 0.16 0.65 0.3 2.2e-09 1.5e-08 0.000012
22 ZFP36L1 zinc finger protein 36, C3H type-like 1 906988 9 9 9 0 0 0 1 2 6 0 0.017 0.54 0.045 2.9e-08 2.8e-08 0.000022
23 ASB10 ankyrin repeat and SOCS box-containing 10 886035 8 8 2 0 0 0 0 1 7 0 NaN NaN NaN 3e-08 3e-08 0.000022
24 HIST1H3B histone cluster 1, H3b 380414 11 11 11 2 3 3 2 1 2 0 0.022 0.18 0.023 1.3e-07 6.4e-08 0.000045
25 FAM86B1 family with sequence similarity 86, member B1 334382 9 8 8 0 4 0 0 2 3 0 0.51 0.7 0.74 4.7e-09 7.1e-08 0.000047
26 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 2279536 16 15 15 2 0 3 2 4 6 1 0.00024 0.44 0.0005 9.9e-06 1e-07 0.000064
27 MYH9 myosin, heavy chain 9, non-muscle 5465560 22 19 21 5 6 2 1 3 10 0 0.007 0.7 0.012 5.1e-07 1.2e-07 0.000074
28 CASP8 caspase 8, apoptosis-related cysteine peptidase 1707280 12 12 12 1 1 2 1 4 4 0 0.34 0.26 0.34 8.7e-08 5.5e-07 0.00033
29 COL6A5 collagen, type VI, alpha 5 6740607 29 25 29 8 5 7 9 4 3 1 0.25 0.99 0.34 1.7e-07 1e-06 0.00058
30 TBL1XR1 transducin (beta)-like 1 X-linked receptor 1 1548424 10 10 8 0 0 2 0 1 5 2 0.27 0.83 0.41 1.5e-07 1.1e-06 0.00059
31 ASXL2 additional sex combs like 2 (Drosophila) 4207311 16 16 16 4 1 6 3 1 4 1 0.000096 0.086 0.000083 0.0011 1.6e-06 0.00087
32 ARID1A AT rich interactive domain 1A (SWI-like) 5494607 28 27 26 2 1 1 4 4 17 1 0.65 0.77 1 1.8e-07 3e-06 0.0015
33 AQP12A aquaporin 12A 324130 6 6 4 0 0 1 2 3 0 0 0.56 0.0032 0.012 0.000042 8.2e-06 0.0041
34 EPDR1 ependymin related protein 1 (zebrafish) 621523 5 5 3 0 1 0 1 0 3 0 0.000047 0.96 0.00022 0.0024 8.3e-06 0.0041
35 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 3490149 23 21 15 1 4 7 3 8 1 0 6.2e-06 0.0044 5.6e-06 0.15 0.000012 0.0059
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 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 352 220 326 214720 195973 0 0
2 TP53 tumor protein p53 298 356 278 347456 52912 0 0
3 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 108 185 36 180560 67 0 0
4 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 36 767 35 748592 395 0 0
5 GATA3 GATA binding protein 3 80 34 13 33184 54 1.6e-12 1.2e-09
6 MAP2K4 mitogen-activated protein kinase kinase 4 32 15 6 14640 10 2e-12 1.2e-09
7 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 23 42 14 40992 99 2e-12 1.2e-09
8 RUNX1 runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) 29 178 13 173728 49 6.4e-12 3.5e-09
9 RB1 retinoblastoma 1 (including osteosarcoma) 22 267 10 260592 23 4.5e-10 2.2e-07
10 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 6 52 6 50752 72781 2e-09 8.6e-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: 110. 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 HSA04210_APOPTOSIS Genes involved in apoptosis AIFM1, AKT1, AKT2, AKT3, APAF1, ATM, BAD, BAX, BCL2, BCL2L1, BID, BIRC2, BIRC3, BIRC4, CAPN1, CAPN2, CASP10, CASP3, CASP6, CASP7, CASP8, CASP9, CFLAR, CHP, CHUK, CSF2RB, CYCS, DFFA, DFFB, ENDOG, FADD, FAS, FASLG, IKBKB, IKBKG, IL1A, IL1B, IL1R1, IL1RAP, IL3, IL3RA, IRAK1, IRAK2, IRAK3, IRAK4, MAP3K14, MYD88, NFKB1, NFKB2, NFKBIA, NGFB, NTRK1, PIK3CA, PIK3CB, PIK3CD, PIK3CG, PIK3R1, PIK3R2, PIK3R3, PIK3R5, PPP3CA, PPP3CB, PPP3CC, PPP3R1, PPP3R2, PRKACA, PRKACB, PRKACG, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, RELA, RIPK1, TNF, TNFRSF10A, TNFRSF10B, TNFRSF10C, TNFRSF10D, TNFRSF1A, TNFSF10, TP53, TRADD, TRAF2 81 AIFM1(3), AKT1(2), AKT2(5), AKT3(4), APAF1(11), ATM(23), BAD(1), BAX(3), BCL2L1(1), BID(4), BIRC2(3), BIRC3(4), CAPN1(8), CAPN2(8), CASP10(2), CASP3(1), CASP6(2), CASP7(2), CASP8(12), CASP9(3), CFLAR(1), CHUK(8), CSF2RB(4), DFFA(4), DFFB(2), FAS(1), FASLG(2), IKBKB(6), IL1A(1), IL1R1(1), IL1RAP(10), IL3(1), IL3RA(3), IRAK1(5), IRAK2(4), IRAK3(2), IRAK4(3), MYD88(1), NFKB1(5), NFKB2(5), NFKBIA(3), NTRK1(8), PIK3CA(352), PIK3CB(8), PIK3CD(6), PIK3CG(5), PIK3R1(16), PIK3R2(4), PIK3R3(3), PIK3R5(5), PPP3CA(5), PPP3CB(3), PPP3CC(1), PPP3R1(1), PRKACA(4), PRKACB(1), PRKACG(3), PRKAR1A(6), PRKAR2A(3), PRKAR2B(2), RELA(3), RIPK1(5), TNFRSF10A(1), TNFRSF10B(1), TNFRSF10D(1), TNFRSF1A(4), TP53(298), TRAF2(1) 118836923 924 613 483 61 86 233 107 303 194 1 <1.00e-15 <1.00e-15 <1.71e-14
2 ARFPATHWAY Cyclin-dependent kinase inhibitor 2A is a tumor suppressor that induces G1 arrest and can activate the p53 pathway, leading to G2/M arrest. ABL1, CDKN2A, E2F1, MDM2, MYC, PIK3CA, PIK3R1, POLR1A, POLR1B, POLR1C, POLR1D, RAC1, RB1, TBX2, TP53, TWIST1 16 ABL1(7), CDKN2A(1), E2F1(5), MDM2(3), MYC(1), PIK3CA(352), PIK3R1(16), POLR1A(10), POLR1B(4), POLR1C(2), POLR1D(4), RB1(22), TBX2(1), TP53(298), TWIST1(2) 27698505 728 568 289 25 58 169 67 269 164 1 <1.00e-15 <1.00e-15 <1.71e-14
3 PPARAPATHWAY Peroxisome proliferators regulate gene expression via PPAR/RXR heterodimers which bind to peroxisome-proliferator response elements (PPREs). ACOX1, APOA1, APOA2, CD36, CITED2, CPT1B, CREBBP, DUSP1, DUT, EHHADH, EP300, FABP1, FAT, FRA8B, HSD17B4, HSPA1A, HSPCA, INS, JUN, LPL, MAPK1, MAPK3, ME1, MRPL11, MYC, NCOA1, NCOR1, NCOR2, NFKBIA, NOS2A, NR0B2, NR1H3, NR2F1, NRIP1, PDGFA, PIK3CA, PIK3R1, PPARA, PPARBP, PPARGC1, PRKACB, PRKACG, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, PRKCA, PRKCB1, PTGS2, RB1, RELA, RXRA, SP1, SRA1, STAT5A, STAT5B, TNF 49 ACOX1(4), APOA1(3), CD36(5), CITED2(2), CPT1B(4), CREBBP(15), DUSP1(1), DUT(1), EHHADH(4), EP300(13), FABP1(2), HSD17B4(8), JUN(3), LPL(1), MAPK1(1), MAPK3(1), MRPL11(2), MYC(1), NCOA1(5), NCOR1(43), NCOR2(8), NFKBIA(3), NR0B2(2), NR1H3(2), NR2F1(1), NRIP1(10), PIK3CA(352), PIK3R1(16), PPARA(4), PRKACB(1), PRKACG(3), PRKAR1A(6), PRKAR2A(3), PRKAR2B(2), PRKCA(4), PTGS2(5), RB1(22), RELA(3), RXRA(2), SP1(6), SRA1(1), STAT5A(6), STAT5B(5) 88281122 586 425 283 49 35 184 53 222 90 2 <1.00e-15 <1.00e-15 <1.71e-14
4 APOPTOSIS_GENMAPP APAF1, BAK1, BCL2L7P1, BAX, BCL2, BCL2L1, BID, BIRC2, BIRC3, BIRC4, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9, CYCS, FADD, FAS, FASLG, GZMB, IKBKG, JUN, MAP2K4, MAP3K1, MAP3K14, MAPK10, MCL1, MDM2, MYC, NFKB1, NFKBIA, PARP1, PRF1, RELA, RIPK1, TNF, TNFRSF1A, TNFRSF1B, TNFSF10, TP53, TRADD, TRAF1, TRAF2 41 APAF1(11), BAK1(3), BAX(3), BCL2L1(1), BID(4), BIRC2(3), BIRC3(4), CASP2(3), CASP3(1), CASP6(2), CASP7(2), CASP8(12), CASP9(3), FAS(1), FASLG(2), JUN(3), MAP2K4(32), MAP3K1(79), MAPK10(4), MCL1(1), MDM2(3), MYC(1), NFKB1(5), NFKBIA(3), PARP1(6), PRF1(2), RELA(3), RIPK1(5), TNFRSF1A(4), TNFRSF1B(2), TP53(298), TRAF1(2), TRAF2(1) 52194389 509 420 358 24 67 61 66 89 209 17 <1.00e-15 <1.00e-15 <1.71e-14
5 SIG_CHEMOTAXIS Genes related to chemotaxis ACTR2, ACTR3, AKT1, AKT2, AKT3, ANGPTL2, ARHGAP1, ARHGAP4, ARHGEF11, BTK, CDC42, CFL1, CFL2, GDI1, GDI2, INPPL1, ITPR1, ITPR2, ITPR3, LIMK1, MYLK, MYLK2, P101-PI3K, PAK1, PAK2, PAK3, PAK4, PAK6, PAK7, PDK1, PIK3CA, PIK3CD, PIK3CG, PIK3R1, PITX2, PPP1R13B, PTEN, RACGAP1, RHO, ROCK1, ROCK2, RPS4X, SAG, WASF1, WASL 44 ACTR2(2), ACTR3(2), AKT1(2), AKT2(5), AKT3(4), ANGPTL2(1), ARHGAP1(4), ARHGAP4(6), ARHGEF11(6), BTK(7), CFL1(1), CFL2(1), GDI1(3), GDI2(2), INPPL1(6), ITPR1(15), ITPR2(18), ITPR3(14), LIMK1(8), MYLK(11), MYLK2(5), PAK1(4), PAK2(2), PAK3(5), PAK4(3), PAK6(5), PAK7(4), PDK1(2), PIK3CA(352), PIK3CD(6), PIK3CG(5), PIK3R1(16), PITX2(5), PPP1R13B(4), PTEN(36), RACGAP1(3), RHO(2), ROCK1(8), ROCK2(9), RPS4X(2), SAG(3), WASF1(6), WASL(4) 100200544 609 418 304 71 42 192 61 241 70 3 4.57e-13 <1.00e-15 <1.71e-14
6 SIG_PIP3_SIGNALING_IN_B_LYMPHOCYTES Genes related to PIP3 signaling in B lymphocytes AKT1, AKT2, AKT3, BCR, BTK, CD19, CDKN2A, DAPP1, FLOT1, FLOT2, FOXO3A, GAB1, ITPR1, ITPR2, ITPR3, LYN, NR0B2, P101-PI3K, PDK1, PHF11, PIK3CA, PITX2, PLCG2, PPP1R13B, PREX1, PSCD3, PTEN, PTPRC, RPS6KA1, RPS6KA2, RPS6KA3, RPS6KB1, SAG, SYK, TEC, VAV1 33 AKT1(2), AKT2(5), AKT3(4), BCR(3), BTK(7), CD19(6), CDKN2A(1), DAPP1(2), FLOT1(2), FLOT2(3), GAB1(2), ITPR1(15), ITPR2(18), ITPR3(14), LYN(6), NR0B2(2), PDK1(2), PHF11(3), PIK3CA(352), PITX2(5), PLCG2(8), PPP1R13B(4), PREX1(10), PTEN(36), PTPRC(8), RPS6KA1(7), RPS6KA2(5), RPS6KA3(8), RPS6KB1(4), SAG(3), SYK(4), TEC(6), VAV1(6) 79033718 563 415 259 43 42 178 50 236 56 1 <1.00e-15 <1.00e-15 <1.71e-14
7 SIG_BCR_SIGNALING_PATHWAY Members of the BCR signaling pathway AKT1, AKT2, AKT3, BAD, BCL2, BCR, BLNK, BTK, CD19, CD22, CD81, CR2, CSK, DAG1, FLOT1, FLOT2, GRB2, GSK3A, GSK3B, INPP5D, ITPR1, ITPR2, ITPR3, LYN, MAP4K1, MAPK1, MAPK3, NFATC1, NFATC2, NR0B2, PDK1, PIK3CA, PIK3CD, PIK3R1, PLCG2, PPP1R13B, PPP3CA, PPP3CB, PPP3CC, PTPRC, RAF1, SHC1, SOS1, SOS2, SYK, VAV1 46 AKT1(2), AKT2(5), AKT3(4), BAD(1), BCR(3), BLNK(1), BTK(7), CD19(6), CD22(5), CR2(5), DAG1(2), FLOT1(2), FLOT2(3), GSK3A(5), GSK3B(2), INPP5D(4), ITPR1(15), ITPR2(18), ITPR3(14), LYN(6), MAP4K1(10), MAPK1(1), MAPK3(1), NFATC1(5), NFATC2(4), NR0B2(2), PDK1(2), PIK3CA(352), PIK3CD(6), PIK3R1(16), PLCG2(8), PPP1R13B(4), PPP3CA(5), PPP3CB(3), PPP3CC(1), PTPRC(8), RAF1(4), SHC1(2), SOS1(5), SOS2(6), SYK(4), VAV1(6) 103219435 565 412 264 60 42 187 51 244 40 1 <1.00e-15 <1.00e-15 <1.71e-14
8 PDGFPATHWAY Platelet-derived growth factor (PDGF) receptor is phosphorylated on ligand binding and promotes cell proliferation. CSNK2A1, ELK1, FOS, GRB2, HRAS, JAK1, JUN, MAP2K1, MAP2K4, MAP3K1, MAPK3, MAPK8, PDGFA, PDGFRA, PIK3CA, PIK3R1, PLCG1, PRKCA, PRKCB1, RAF1, RASA1, SHC1, SOS1, SRF, STAT1, STAT3, STAT5A 26 CSNK2A1(3), ELK1(4), FOS(1), HRAS(2), JAK1(11), JUN(3), MAP2K1(5), MAP2K4(32), MAP3K1(79), MAPK3(1), MAPK8(2), PDGFRA(6), PIK3CA(352), PIK3R1(16), PLCG1(8), PRKCA(4), RAF1(4), RASA1(2), SHC1(2), SOS1(5), SRF(1), STAT1(4), STAT3(5), STAT5A(6) 50105993 558 407 246 31 24 162 43 216 95 18 <1.00e-15 <1.00e-15 <1.71e-14
9 ST_DIFFERENTIATION_PATHWAY_IN_PC12_CELLS Rat-derived PC12 cells respond to nerve growth factor (NGF) and PACAP to differentiate into neuronal cells. AKT1, ASAH1, ATF1, BRAF, CAMP, CREB1, CREB3, CREB5, CREBBP, CRKL, DAG1, EGR1, EGR2, EGR3, EGR4, ELK1, FRS2, GAS, GNAQ, GRF2, JUN, MAP1B, MAP2K4, MAP2K7, MAPK1, MAPK10, MAPK3, MAPK8, MAPK8IP1, MAPK8IP2, MAPK8IP3, MAPK9, NTRK1, OPN1LW, PACAP, PIK3C2G, PIK3CA, PIK3CD, PIK3R1, PTPN11, RPS6KA3, SH2B, SHC1, SRC, TERF2IP, TH, TUBA3 42 AKT1(2), ASAH1(1), ATF1(3), BRAF(5), CREB1(2), CREB3(1), CREB5(4), CREBBP(15), CRKL(1), DAG1(2), EGR1(3), EGR2(3), EGR3(4), ELK1(4), FRS2(1), GNAQ(1), JUN(3), MAP1B(8), MAP2K4(32), MAP2K7(1), MAPK1(1), MAPK10(4), MAPK3(1), MAPK8(2), MAPK8IP1(1), MAPK8IP2(2), MAPK8IP3(8), MAPK9(1), NTRK1(8), OPN1LW(3), PIK3C2G(8), PIK3CA(352), PIK3CD(6), PIK3R1(16), PTPN11(1), RPS6KA3(8), SHC1(2), SRC(1), TERF2IP(3), TH(2) 71608009 526 400 220 42 41 166 45 215 58 1 <1.00e-15 <1.00e-15 <1.71e-14
10 HSA04150_MTOR_SIGNALING_PATHWAY Genes involved in mTOR signaling pathway AKT1, AKT2, AKT3, BRAF, CAB39, DDIT4, EIF4B, EIF4EBP1, FIGF, FRAP1, GBL, HIF1A, IGF1, INS, KIAA1303, LYK5, MAPK1, MAPK3, PDPK1, PGF, PIK3CA, PIK3CB, PIK3CD, PIK3CG, PIK3R1, PIK3R2, PIK3R3, PIK3R5, PRKAA1, PRKAA2, RHEB, RICTOR, RPS6, RPS6KA1, RPS6KA2, RPS6KA3, RPS6KA6, RPS6KB1, RPS6KB2, STK11, TSC1, TSC2, ULK1, ULK2, ULK3, VEGFA, VEGFB, VEGFC 44 AKT1(2), AKT2(5), AKT3(4), BRAF(5), CAB39(1), DDIT4(1), EIF4B(2), FIGF(3), HIF1A(4), MAPK1(1), MAPK3(1), PDPK1(2), PGF(5), PIK3CA(352), PIK3CB(8), PIK3CD(6), PIK3CG(5), PIK3R1(16), PIK3R2(4), PIK3R3(3), PIK3R5(5), PRKAA1(3), PRKAA2(2), RHEB(1), RICTOR(7), RPS6(4), RPS6KA1(7), RPS6KA2(5), RPS6KA3(8), RPS6KA6(6), RPS6KB1(4), RPS6KB2(5), STK11(2), TSC1(5), TSC2(8), ULK1(5), ULK2(6), ULK3(5), VEGFA(1), VEGFC(2) 73740492 521 396 220 42 30 185 45 215 45 1 <1.00e-15 <1.00e-15 <1.71e-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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