Mutation Analysis (MutSig v2.0)
Breast Invasive Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C11N8039
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSig v2.0). Broad Institute of MIT and Harvard. doi:10.7908/C11N8039
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: 977

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

  • Blacklist used for this analysis: pancan_mutation_blacklist.v14.hg19.txt

  • Significantly mutated genes (q ≤ 0.1): 47

  • Mutations seen in COSMIC: 895

  • Significantly mutated genes in COSMIC territory: 19

  • Significantly mutated genesets: 110

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

Mutation Preprocessing

  • Read 977 MAFs of type "WashU"

  • Total number of mutations in input MAFs: 90172

  • After removing 39 mutations outside chr1-24: 90133

  • After removing 1739 blacklisted mutations: 88394

  • After removing 4275 noncoding mutations: 84119

  • After collapsing adjacent/redundant mutations: 80843

Mutation Filtering

  • Number of mutations before filtering: 80843

  • After removing 6768 mutations outside gene set: 74075

  • After removing 291 mutations outside category set: 73784

  • After removing 36 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 66689

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 47985
Nonsense_Mutation 4196
Nonstop_Mutation 113
Silent 15401
Splice_Site 1324
Total 73784
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 1431991223 6.4e-06 6.4 3 2.5
Tp*Cp(A/C/T)->mut 16622 3343111034 5e-06 5 2.3 3.4
(A/C/G)p*Cp(A/C/T)->mut 10474 9075750236 1.2e-06 1.2 0.54 3.4
A->mut 11685 13648491865 8.6e-07 0.86 0.4 3.8
indel+null 10137 27499344358 3.7e-07 0.37 0.17 NaN
double_null 253 27499344358 9.2e-09 0.0092 0.0043 NaN
Total 58347 27499344358 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). 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: 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_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: 47. 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 RUNX1 runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) 1051659 29 29 23 2 0 2 5 4 17 1 5.44e-15 0.232 0.0098 0.00031 0.0002 0.000 0.000
2 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3200341 352 316 53 6 5 134 21 185 7 0 <1.00e-15 <1.00e-15 0 0 0 <1.00e-15 <2.69e-12
3 TP53 tumor protein p53 1238133 298 295 160 5 45 22 39 69 123 0 <1.00e-15 <1.00e-15 0 0 0 <1.00e-15 <2.69e-12
4 GATA3 GATA binding protein 3 998421 100 97 56 2 1 1 2 5 90 1 <1.00e-15 0.352 0 0.38 0 <1.00e-15 <2.69e-12
5 FOXA1 forkhead box A1 1009017 23 23 16 0 1 8 3 7 3 1 5.11e-15 0.00969 2e-07 0.051 0 <1.00e-15 <2.69e-12
6 SF3B1 splicing factor 3b, subunit 1, 155kDa 3939945 16 16 9 2 0 2 1 12 1 0 0.00419 0.173 0 0.026 0 <1.00e-15 <2.69e-12
7 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1212494 36 35 33 0 0 4 3 8 20 1 3.33e-15 0.0193 0.0044 0.73 0.0084 1.11e-15 2.69e-12
8 CBFB core-binding factor, beta subunit 459284 24 23 22 1 1 0 8 4 11 0 5.00e-15 0.0963 0.0031 0.081 0.0067 1.33e-15 2.83e-12
9 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 2544201 108 105 89 3 4 8 6 1 89 0 <1.00e-15 3.86e-05 0.22 0.67 0.31 <1.14e-14 <2.16e-11
10 TBX3 T-box 3 (ulnar mammary syndrome) 1280215 27 27 26 1 4 2 1 1 19 0 8.99e-15 0.130 0.035 0.83 0.067 2.16e-14 3.68e-11
11 MAP2K4 mitogen-activated protein kinase kinase 4 1094202 32 32 28 0 3 2 5 2 20 0 3.11e-15 0.0102 0.11 0.47 0.23 2.55e-14 3.94e-11
12 MAP3K1 mitogen-activated protein kinase kinase kinase 1 4026466 79 70 71 3 1 3 6 7 45 17 1.11e-15 0.126 0.73 0.72 1 3.93e-14 5.56e-11
13 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 3493901 23 21 15 1 4 7 3 8 1 0 9.16e-07 0.00866 6.2e-06 0.0044 5.6e-06 1.39e-10 1.81e-07
14 MLL3 myeloid/lymphoid or mixed-lineage leukemia 3 14460562 78 69 77 6 0 14 8 7 44 5 3.05e-11 0.0962 0.42 0.14 0.48 3.79e-10 4.60e-07
15 NCOR1 nuclear receptor co-repressor 1 7287573 43 41 41 1 2 10 6 3 22 0 2.30e-10 0.00546 0.44 0.32 0.45 2.49e-09 2.82e-06
16 FAM86B2 family with sequence similarity 86, member B2 268870 9 9 6 1 6 0 0 1 2 0 1.45e-09 0.252 0.57 0.85 1 3.10e-08 3.29e-05
17 CDKN1B cyclin-dependent kinase inhibitor 1B (p27, Kip1) 500131 10 10 9 1 0 0 0 0 8 2 5.21e-09 0.586 0.16 0.65 0.3 3.36e-08 3.35e-05
18 HIST1H3B histone cluster 1, H3b 380829 11 11 11 2 3 3 2 1 2 0 1.74e-07 0.242 0.022 0.18 0.019 6.91e-08 6.28e-05
19 THEM5 thioesterase superfamily member 5 748996 11 11 8 1 0 3 0 1 7 0 2.46e-08 0.721 0.25 0.063 0.14 7.03e-08 6.28e-05
20 FAM86B1 family with sequence similarity 86, member B1 335001 9 8 8 0 4 0 0 2 3 0 5.37e-09 0.0666 0.51 0.7 0.74 8.09e-08 6.87e-05
21 GPS2 G protein pathway suppressor 2 948419 11 11 11 0 0 1 0 0 10 0 1.01e-07 0.383 0.12 0.18 0.16 3.03e-07 0.000245
22 AQP12A aquaporin 12A 324535 6 6 4 0 0 1 2 3 0 0 9.33e-06 0.198 0.56 0.0032 0.013 2.02e-06 0.00156
23 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 2281896 16 15 15 2 0 3 2 4 6 1 0.000269 0.574 0.00024 0.44 0.0005 2.27e-06 0.00167
24 ACTL6B actin-like 6B 1186698 10 10 6 0 2 0 1 0 7 0 2.28e-05 0.306 0.0038 0.98 0.0073 2.77e-06 0.00196
25 ZFP36L1 zinc finger protein 36, C3H type-like 1 908013 9 9 9 0 0 0 1 2 6 0 4.79e-06 0.431 0.017 0.54 0.045 3.50e-06 0.00238
26 RB1 retinoblastoma 1 (including osteosarcoma) 2645506 22 19 21 3 1 1 1 2 17 0 2.84e-07 0.498 0.74 0.57 0.83 3.82e-06 0.00247
27 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 689307 6 6 3 0 0 1 5 0 0 0 0.000170 0.402 0.0017 0.059 0.0014 3.93e-06 0.00247
28 EPDR1 ependymin related protein 1 (zebrafish) 622213 5 5 3 0 1 0 1 0 3 0 0.00192 0.482 0.000047 0.96 0.00022 6.75e-06 0.00409
29 C1QTNF5 C1q and tumor necrosis factor related protein 5 299012 6 7 6 0 0 2 1 1 2 0 7.25e-06 0.239 NaN NaN NaN 7.25e-06 0.00424
30 ZFP36L2 zinc finger protein 36, C3H type-like 2 360184 7 7 7 1 3 0 1 0 3 0 3.59e-06 0.438 0.24 0.29 0.33 1.76e-05 0.00993
31 CTCF CCCTC-binding factor (zinc finger protein) 2152284 17 17 15 3 1 3 1 5 7 0 0.00163 0.527 0.0078 0.00087 0.00086 2.04e-05 0.0112
32 ASB10 ankyrin repeat and SOCS box-containing 10 887382 8 8 2 0 0 0 0 1 7 0 3.09e-05 0.796 NaN NaN NaN 3.09e-05 0.0164
33 FBXW7 F-box and WD repeat domain containing 7 2522313 15 15 13 1 5 4 1 3 2 0 3.85e-05 0.153 0.14 0.037 0.078 4.12e-05 0.0212
34 RPGR retinitis pigmentosa GTPase regulator 3589713 21 18 19 1 3 7 2 1 8 0 0.000113 0.208 0.064 0.082 0.032 4.93e-05 0.0246
35 MYB v-myb myeloblastosis viral oncogene homolog (avian) 2261316 12 12 12 0 1 0 2 2 7 0 8.65e-05 0.227 0.15 0.021 0.044 5.10e-05 0.0248
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 214940 195973 0 0
2 TP53 tumor protein p53 298 356 278 347812 52912 0 0
3 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 108 185 36 180745 67 0 0
4 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 36 767 35 749359 395 0 0
5 GATA3 GATA binding protein 3 100 34 33 33218 234 1.7e-12 1.3e-09
6 MAP2K4 mitogen-activated protein kinase kinase 4 32 15 6 14655 10 2e-12 1.3e-09
7 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 23 42 14 41034 99 2e-12 1.3e-09
8 RUNX1 runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) 29 178 13 173906 49 6.5e-12 3.5e-09
9 RB1 retinoblastoma 1 (including osteosarcoma) 22 267 10 260859 23 4.6e-10 2.2e-07
10 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 6 52 6 50804 72781 2e-09 8.7e-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) 118964074 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) 27727729 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) 88375670 586 425 283 49 35 184 53 222 90 2 <1.00e-15 <1.00e-15 <1.71e-14
4 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) 79116874 563 415 259 43 42 178 50 236 56 1 <1.00e-15 <1.00e-15 <1.71e-14
5 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) 103329099 565 412 264 60 42 187 51 244 40 1 1.22e-15 <1.00e-15 <1.71e-14
6 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) 50158644 558 407 246 31 24 162 43 216 95 18 <1.00e-15 <1.00e-15 <1.71e-14
7 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) 71686075 526 400 220 42 41 166 45 215 58 1 <1.00e-15 <1.00e-15 <1.71e-14
8 ST_B_CELL_ANTIGEN_RECEPTOR B cell receptors bind antigens and promote B cell activation. AKT1, AKT2, AKT3, BAD, BCR, BLNK, BTK, CD19, CSK, DAG1, EPHB2, GRB2, ITPKA, ITPKB, LYN, MAP2K1, MAP2K2, MAPK1, NFAT5, NFKB1, NFKB2, NFKBIA, NFKBIB, NFKBIE, NFKBIL1, NFKBIL2, PAG, PI3, PIK3CA, PIK3CD, PIK3R1, PLCG2, PPP1R13B, RAF1, SERPINA4, SHC1, SOS1, SOS2, SYK, VAV1 38 AKT1(2), AKT2(5), AKT3(4), BAD(1), BCR(3), BLNK(1), BTK(7), CD19(6), DAG1(2), EPHB2(5), ITPKA(1), ITPKB(8), LYN(6), MAP2K1(5), MAP2K2(2), MAPK1(1), NFAT5(7), NFKB1(5), NFKB2(5), NFKBIA(3), NFKBIB(2), NFKBIE(2), PI3(1), PIK3CA(352), PIK3CD(6), PIK3R1(16), PLCG2(8), PPP1R13B(4), RAF1(4), SERPINA4(4), SHC1(2), SOS1(5), SOS2(6), SYK(4), VAV1(6) 72377320 501 395 201 41 31 172 42 220 35 1 <1.00e-15 <1.00e-15 <1.71e-14
9 HCMVPATHWAY Cytomegalovirus activates MAP kinase pathways in the host cell, inducing transcription of viral genes. AKT1, CREB1, MAP2K1, MAP2K2, MAP2K3, MAP2K6, MAP3K1, MAPK1, MAPK14, MAPK3, NFKB1, PIK3CA, PIK3R1, RB1, RELA, SP1 16 AKT1(2), CREB1(2), MAP2K1(5), MAP2K2(2), MAP2K3(3), MAP2K6(1), MAP3K1(79), MAPK1(1), MAPK14(2), MAPK3(1), NFKB1(5), PIK3CA(352), PIK3R1(16), RB1(22), RELA(3), SP1(6) 28315648 502 394 193 20 13 153 36 205 77 18 <1.00e-15 <1.00e-15 <1.71e-14
10 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 ACTA1(3), AGT(3), AKT1(2), CALM2(2), CALR(1), CAMK1(1), CAMK1G(5), CAMK4(5), CREBBP(15), CSNK1A1(4), EDN1(5), F2(2), GATA4(2), GSK3B(2), HAND2(4), HRAS(2), LIF(2), MAP2K1(5), MAPK1(1), MAPK14(2), MAPK3(1), MAPK8(2), MEF2C(4), MYH2(9), NFATC1(5), NFATC2(4), NFATC3(9), NFATC4(8), NKX2-5(1), NPPA(1), PIK3CA(352), PIK3R1(16), PPP3CA(5), PPP3CB(3), PPP3CC(1), PRKACB(1), PRKACG(3), PRKAR1A(6), PRKAR2A(3), PRKAR2B(2), RAF1(4), RPS6KB1(4), SYT1(2) 70947847 514 392 214 51 27 176 53 223 33 2 2.55e-15 <1.00e-15 <1.71e-14

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 GSPATHWAY Activated G-protein coupled receptors stimulate cAMP production and thus activate protein kinase A, involved in a number of signal transduction pathways. ADCY1, GNAS, GNB1, GNGT1, PRKACA, PRKAR1A 6 ADCY1(5), GNAS(11), GNB1(2), PRKACA(4), PRKAR1A(6) 8511207 28 26 28 3 9 3 4 5 7 0 0.094 0.043 1
2 TCRMOLECULE T Cell Receptor and CD3 Complex CD3D, CD3E, CD3G, CD3Z, TRA@, TRB@ 3 CD3E(3), CD3G(3) 1708219 6 6 6 0 0 2 1 2 1 0 0.27 0.12 1
3 SA_DIACYLGLYCEROL_SIGNALING DAG (diacylglycerol) signaling activity ESR1, ESR2, ITPKA, PDE1A, PDE1B, PLCB1, PLCB2, PRL, TRH, VIP 10 ESR1(7), ESR2(5), ITPKA(1), PDE1A(5), PDE1B(5), PLCB1(13), PLCB2(7), TRH(1), VIP(1) 15499132 45 34 45 2 6 11 9 9 10 0 0.0017 0.23 1
4 TUBBYPATHWAY Tubby is activated by phospholipase C activity and hydrolysis of PIP2, after which it enters the nucleus and regulates transcription. CHRM1, GNAQ, GNB1, GNGT1, HTR2C, PLCB1, TUB 7 CHRM1(4), GNAQ(1), GNB1(2), HTR2C(6), PLCB1(13), TUB(4) 10158106 30 25 30 3 8 5 4 7 6 0 0.04 0.29 1
5 HSA00643_STYRENE_DEGRADATION Genes involved in styrene degradation FAH, GSTZ1, HGD 3 FAH(3), GSTZ1(1), HGD(4) 3252093 8 8 8 1 1 1 2 3 1 0 0.4 0.3 1
6 HSA00950_ALKALOID_BIOSYNTHESIS_I Genes involved in alkaloid biosynthesis I DDC, GOT1, GOT2, TAT, TYR 5 DDC(4), GOT1(2), GOT2(1), TAT(3), TYR(5) 6781413 15 14 15 2 4 2 5 2 2 0 0.27 0.32 1
7 PEPIPATHWAY Proepithelin (PEPI) induces epithelial cells to secrete IL-8, which promotes elastase secretion by neutrophils. ELA1, ELA2, ELA2A, ELA2B, ELA3B, GRN, IL8, SLPI 3 GRN(4), IL8(1), SLPI(1) 2295771 6 6 6 1 0 0 3 2 1 0 0.59 0.34 1
8 SA_PROGRAMMED_CELL_DEATH Programmed cell death, or apoptosis, eliminates damaged or unneeded cells. APAF1, BAD, BAK1, BAX, BCL10, BCL2, BCL2L1, BCL2L11, BID, CASP8AP2, CASP9, CES1 11 APAF1(11), BAD(1), BAK1(3), BAX(3), BCL10(3), BCL2L1(1), BCL2L11(5), BID(4), CASP9(3), CES1(4) 10869574 38 31 38 4 7 16 5 5 5 0 0.036 0.4 1
9 MSPPATHWAY Macrophage stimulating protein is synthesized as pro-MSP by the liver and, on proteolysis, binds to monocyte receptor kinase RON to induce macrophage development. CCL2, CSF1, IL1B, MST1, MST1R, TNF 6 CCL2(1), CSF1(5), MST1(7), MST1R(8) 8256780 21 17 20 2 4 8 4 2 3 0 0.06 0.42 1
10 PTC1PATHWAY The binding of extracellular signaling protein Sonic hedgehog to the Patched receptor (Ptc1) allows progression through G1 and may inhibit the G2/M transition. CCNB1, CCNH, CDC2, CDC25A, CDC25B, CDC25C, CDK7, MNAT1, PTCH, SHH, XPO1 9 CCNB1(3), CCNH(2), CDC25A(1), CDC25B(3), CDC25C(6), CDK7(3), MNAT1(2), SHH(1), XPO1(15) 12412367 36 30 35 3 8 17 4 2 5 0 0.023 0.42 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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