Mutation Analysis (MutSig v2.0)
Sarcoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C17080WX
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Analysis (MutSig v2.0). Broad Institute of MIT and Harvard. doi:10.7908/C17080WX
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: SARC-TP

  • Number of patients in set: 247

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:SARC-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 22

  • Mutations seen in COSMIC: 138

  • Significantly mutated genes in COSMIC territory: 5

  • Significantly mutated genesets: 39

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

Mutation Preprocessing

  • Read 247 MAFs of type "maf1"

  • Total number of mutations in input MAFs: 20347

  • After removing 65 mutations outside chr1-24: 20282

  • After removing 46 blacklisted mutations: 20236

  • After removing 3517 noncoding mutations: 16719

Mutation Filtering

  • Number of mutations before filtering: 16719

  • After removing 1169 mutations outside gene set: 15550

  • After removing 42 mutations outside category set: 15508

Results
Breakdown of Mutations by Type

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

type count
De_novo_Start_InFrame 2
De_novo_Start_OutOfFrame 4
Frame_Shift_Del 422
Frame_Shift_Ins 109
In_Frame_Del 166
In_Frame_Ins 57
Missense_Mutation 9651
Nonsense_Mutation 599
Nonstop_Mutation 15
Silent 3907
Splice_Site 552
Start_Codon_Del 4
Start_Codon_Ins 1
Start_Codon_SNP 16
Stop_Codon_Del 3
Total 15508
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 1897 425051677 4.5e-06 4.5 2.9 2.1
*Cp(A/C/T)->T 3358 3449001683 9.7e-07 0.97 0.64 1.7
C->(G/A) 2395 3874053360 6.2e-07 0.62 0.4 4.7
A->mut 2017 3696709882 5.5e-07 0.55 0.36 3.9
indel+null 1896 7570763242 2.5e-07 0.25 0.16 NaN
double_null 38 7570763242 5e-09 0.005 0.0033 NaN
Total 11601 7570763242 1.5e-06 1.5 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: SARC-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: 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: 22. 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 TP53 tumor protein p53 271997 90 85 75 3 10 12 10 19 39 0 2.1e-15 3.7e-07 0 0 0 <1.00e-15 <4.62e-12
2 NUMBL numb homolog (Drosophila)-like 320036 8 8 1 0 0 0 0 0 8 0 1.6e-08 1 0 0.89 0 <1.00e-15 <4.62e-12
3 MSH3 mutS homolog 3 (E. coli) 854445 8 7 4 2 0 0 0 0 7 1 4.8e-06 1 0 1 0 <1.00e-15 <4.62e-12
4 LMTK2 lemur tyrosine kinase 2 1059292 2 2 2 2 1 0 0 0 1 0 0.74 0.85 1 0 0 <1.00e-15 <4.62e-12
5 RB1 retinoblastoma 1 (including osteosarcoma) 590566 24 24 24 0 0 0 1 0 21 2 4.2e-15 0.031 0.0066 0.37 0.011 1.89e-15 6.98e-12
6 ATRX alpha thalassemia/mental retardation syndrome X-linked (RAD54 homolog, S. cerevisiae) 1724523 38 36 38 0 0 1 3 3 31 0 5e-15 0.0083 0.049 0.33 0.067 1.23e-14 3.80e-11
7 LOR loricrin 73402 6 6 2 2 0 0 0 0 6 0 2.3e-11 1 4e-07 1 0.000021 1.73e-14 4.57e-11
8 KRTAP2-2 keratin associated protein 2-2 45865 4 4 1 0 0 0 0 0 4 0 1.1e-08 1 1e-06 0.8 0.000024 7.66e-12 1.77e-08
9 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 291864 8 7 8 0 1 1 0 4 2 0 2.4e-09 0.19 0.026 0.037 0.0092 5.69e-10 1.17e-06
10 KRTAP5-5 keratin associated protein 5-5 171941 8 7 7 1 0 2 1 0 4 1 1.3e-10 0.6 0.14 0.48 0.23 7.45e-10 1.38e-06
11 EOMES eomesodermin homolog (Xenopus laevis) 387327 5 5 1 0 0 0 0 0 5 0 2e-05 1 0 1 7.6e-06 3.52e-09 5.92e-06
12 LTBP3 latent transforming growth factor beta binding protein 3 680652 5 5 2 0 0 0 0 0 5 0 0.004 1 0 0.8 9.8e-06 7.10e-07 0.00109
13 SCN2A sodium channel, voltage-gated, type II, alpha subunit 1509786 11 11 11 0 3 3 1 3 1 0 4.6e-07 0.039 0.87 0.74 1 7.21e-06 0.0103
14 C14orf39 chromosome 14 open reading frame 39 444568 5 5 5 0 1 1 0 1 2 0 0.000045 0.26 0.097 0.049 0.031 2.00e-05 0.0264
15 AR androgen receptor (dihydrotestosterone receptor; testicular feminization; spinal and bulbar muscular atrophy; Kennedy disease) 605355 4 4 3 1 0 0 1 3 0 0 0.0044 0.67 0.00018 1 0.00035 2.23e-05 0.0275
16 DCDC1 doublecortin domain containing 1 263029 5 5 5 0 1 1 0 1 2 0 0.000025 0.22 NaN NaN NaN 2.51e-05 0.0290
17 CABLES1 Cdk5 and Abl enzyme substrate 1 301344 3 3 1 0 0 0 0 0 3 0 0.0015 1 0.00016 0.84 0.0015 3.17e-05 0.0345
18 OR8D1 olfactory receptor, family 8, subfamily D, member 1 225910 5 5 5 0 0 2 3 0 0 0 5.3e-06 0.14 0.43 0.82 0.63 4.61e-05 0.0473
19 COPS4 COP9 constitutive photomorphogenic homolog subunit 4 (Arabidopsis) 306948 4 4 4 0 0 1 1 1 1 0 0.00024 0.37 0.018 0.35 0.02 6.28e-05 0.0611
20 SPHKAP SPHK1 interactor, AKAP domain containing 1175566 9 9 9 1 0 0 5 3 1 0 0.000013 0.44 0.27 0.85 0.44 7.35e-05 0.0680
21 EGF epidermal growth factor (beta-urogastrone) 911287 4 1 4 1 0 2 1 0 1 0 0.35 0.62 0.00025 0.0012 0.000019 8.64e-05 0.0761
22 MEGF9 multiple EGF-like-domains 9 340159 3 3 1 0 0 0 0 0 3 0 0.006 1 0.00012 0.97 0.0012 9.24e-05 0.0777
23 PRDM9 PR domain containing 9 672679 4 4 4 0 0 0 3 0 1 0 0.01 0.37 0.0022 0.049 0.00096 0.000126 0.101
24 CALN1 calneuron 1 198312 4 4 4 0 2 0 0 2 0 0 0.000043 0.32 0.28 0.42 0.38 0.000197 0.147
25 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 793887 6 6 5 0 1 0 1 3 1 0 0.000091 0.36 0.11 0.28 0.18 0.000198 0.147
26 ZNF268 zinc finger protein 268 117705 3 3 3 1 0 1 2 0 0 0 0.00023 0.85 NaN NaN NaN 0.000233 0.165
27 OR6C76 olfactory receptor, family 6, subfamily C, member 76 217112 3 3 3 0 1 1 0 1 0 0 0.0015 0.24 0.0055 0.16 0.014 0.000245 0.167
28 TRDN triadin 562227 5 5 5 0 1 2 2 0 0 0 0.000053 0.32 0.86 0.16 0.42 0.000262 0.173
29 DOCK3 dedicator of cytokinesis 3 1497579 6 6 6 0 1 2 0 1 2 0 0.044 0.15 0.00053 0.1 0.00054 0.000276 0.176
30 CDH12 cadherin 12, type 2 (N-cadherin 2) 598162 6 6 6 1 3 2 0 0 1 0 0.00011 0.34 0.81 0.052 0.23 0.000292 0.180
31 CCDC7 coiled-coil domain containing 7 351298 4 4 4 1 0 2 0 0 2 0 0.00035 0.79 NaN NaN NaN 0.000349 0.204
32 LHCGR luteinizing hormone/choriogonadotropin receptor 507112 5 5 4 0 0 1 0 0 4 0 0.00033 0.51 0.041 0.99 0.094 0.000353 0.204
33 TRAF7 TNF receptor-associated factor 7 441634 4 4 3 0 0 0 1 1 2 0 0.00044 0.69 0.31 0.015 0.082 0.000400 0.224
34 HSF2BP heat shock transcription factor 2 binding protein 245275 3 3 3 0 2 0 1 0 0 0 0.0011 0.43 0.023 0.76 0.034 0.000424 0.231
35 PRB3 proline-rich protein BstNI subfamily 3 218748 4 4 4 0 2 1 0 1 0 0 0.000068 0.25 0.76 0.27 0.68 0.000507 0.268
TP53

Figure S1.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

NUMBL

Figure S2.  This figure depicts the distribution of mutations and mutation types across the NUMBL significant gene.

MSH3

Figure S3.  This figure depicts the distribution of mutations and mutation types across the MSH3 significant gene.

LMTK2

Figure S4.  This figure depicts the distribution of mutations and mutation types across the LMTK2 significant gene.

RB1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the RB1 significant gene.

ATRX

Figure S6.  This figure depicts the distribution of mutations and mutation types across the ATRX significant gene.

LOR

Figure S7.  This figure depicts the distribution of mutations and mutation types across the LOR significant gene.

KRTAP2-2

Figure S8.  This figure depicts the distribution of mutations and mutation types across the KRTAP2-2 significant gene.

PTEN

Figure S9.  This figure depicts the distribution of mutations and mutation types across the PTEN significant gene.

KRTAP5-5

Figure S10.  This figure depicts the distribution of mutations and mutation types across the KRTAP5-5 significant gene.

EOMES

Figure S11.  This figure depicts the distribution of mutations and mutation types across the EOMES significant gene.

LTBP3

Figure S12.  This figure depicts the distribution of mutations and mutation types across the LTBP3 significant gene.

SCN2A

Figure S13.  This figure depicts the distribution of mutations and mutation types across the SCN2A significant gene.

C14orf39

Figure S14.  This figure depicts the distribution of mutations and mutation types across the C14orf39 significant gene.

AR

Figure S15.  This figure depicts the distribution of mutations and mutation types across the AR significant gene.

DCDC1

Figure S16.  This figure depicts the distribution of mutations and mutation types across the DCDC1 significant gene.

CABLES1

Figure S17.  This figure depicts the distribution of mutations and mutation types across the CABLES1 significant gene.

OR8D1

Figure S18.  This figure depicts the distribution of mutations and mutation types across the OR8D1 significant gene.

COPS4

Figure S19.  This figure depicts the distribution of mutations and mutation types across the COPS4 significant gene.

SPHKAP

Figure S20.  This figure depicts the distribution of mutations and mutation types across the SPHKAP significant gene.

EGF

Figure S21.  This figure depicts the distribution of mutations and mutation types across the EGF significant gene.

MEGF9

Figure S22.  This figure depicts the distribution of mutations and mutation types across the MEGF9 significant gene.

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: 5. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 RB1 retinoblastoma 1 (including osteosarcoma) 24 267 15 65949 37 2.9e-12 8.6e-09
2 TP53 tumor protein p53 90 356 83 87932 13509 3.8e-12 8.6e-09
3 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 8 767 8 189449 265 9.7e-10 1.5e-06
4 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 6 220 4 54340 2167 1.9e-06 0.0021
5 PTPN11 protein tyrosine phosphatase, non-receptor type 11 (Noonan syndrome 1) 3 32 2 7904 4 0.000073 0.066
6 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 2 52 2 12844 13156 0.00019 0.13
7 AP1M1 adaptor-related protein complex 1, mu 1 subunit 1 1 1 247 1 0.00038 0.13
8 CCNL2 cyclin L2 1 1 1 247 1 0.00038 0.13
9 GTF2B general transcription factor IIB 1 1 1 247 1 0.00038 0.13
10 LENG8 leukocyte receptor cluster (LRC) member 8 2 1 1 247 1 0.00038 0.13

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: 39. 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 G1PATHWAY CDK4/6-cyclin D and CDK2-cyclin E phosphorylate Rb, which allows the transcription of genes needed for the G1/S cell cycle transition. ABL1, ATM, ATR, CCNA1, CCND1, CCNE1, CDC2, CDC25A, CDK2, CDK4, CDK6, CDKN1A, CDKN1B, CDKN2A, CDKN2B, DHFR, E2F1, GSK3B, HDAC1, MADH3, MADH4, RB1, SKP2, TFDP1, TGFB1, TGFB2, TGFB3, TP53 25 ABL1(2), ATM(5), ATR(4), CCNA1(1), CCND1(1), CDK4(1), CDKN1A(2), CDKN2A(1), RB1(24), TFDP1(1), TGFB1(1), TP53(90) 10950702 133 106 118 7 13 13 16 24 65 2 8.9e-08 <1.00e-15 <1.03e-13
2 RBPATHWAY The ATM protein kinase recognizes DNA damage and blocks cell cycle progression by phosphorylating chk1 and p53, which normally inhibits Rb to allow G1/S transitions. ATM, CDC2, CDC25A, CDC25B, CDC25C, CDK2, CDK4, CHEK1, MYT1, RB1, TP53, WEE1, YWHAH 12 ATM(5), CDC25B(1), CDK4(1), MYT1(2), RB1(24), TP53(90) 6350812 123 100 108 8 10 16 14 20 61 2 5.7e-06 <1.00e-15 <1.03e-13
3 G2PATHWAY Activated Cdc2-cyclin B kinase regulates the G2/M transition; DNA damage stimulates the DNA-PK/ATM/ATR kinases, which inactivate Cdc2. ATM, ATR, BRCA1, CCNB1, CDC2, CDC25A, CDC25B, CDC25C, CDC34, CDKN1A, CDKN2D, CHEK1, CHEK2, EP300, GADD45A, MDM2, MYT1, PLK, PRKDC, RPS6KA1, TP53, WEE1, YWHAH, YWHAQ 22 ATM(5), ATR(4), CDC25B(1), CDKN1A(2), CDKN2D(1), CHEK2(2), EP300(2), MDM2(1), MYT1(2), PRKDC(5), RPS6KA1(1), TP53(90) 15759879 116 95 101 13 11 20 14 26 45 0 0.00044 <1.00e-15 <1.03e-13
4 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 20 ATM(5), PRKCA(1), PTK2(3), STAT1(2), TLN1(2), TP53(90) 10014396 103 90 88 6 11 14 14 21 43 0 4.6e-06 <1.00e-15 <1.03e-13
5 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 19 ATM(5), CDKN1A(2), HIC1(1), MDM2(1), TP53(90) 7574022 99 89 84 11 11 14 12 22 40 0 0.0012 <1.00e-15 <1.03e-13
6 PLK3PATHWAY Active Plk3 phosphorylates CDC25c, blocking the G2/M transition, and phosphorylates p53 to induce apoptosis. ATM, ATR, CDC25C, CHEK1, CHEK2, CNK, TP53, YWHAH 7 ATM(5), ATR(4), CHEK2(2), TP53(90) 5754609 101 88 86 5 10 15 12 22 42 0 0.000024 <1.00e-15 <1.03e-13
7 TIDPATHWAY On ligand binding, interferon gamma receptors stimulate JAK2 kinase to phosphorylate STAT transcription factors, which promote expression of interferon responsive genes. DNAJA3, HSPA1A, IFNG, IFNGR1, IFNGR2, IKBKB, JAK2, LIN7A, NFKB1, NFKBIA, RB1, RELA, TIP-1, TNF, TNFRSF1A, TNFRSF1B, TP53, USH1C, WT1 18 IFNG(1), LIN7A(1), NFKB1(1), RB1(24), RELA(2), TNF(1), TNFRSF1A(1), TP53(90), USH1C(2) 6700890 123 100 108 6 14 15 12 20 60 2 2.3e-08 1.55e-15 1.23e-13
8 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 42 FAS(1), MAP2K4(2), MAPK10(2), MDM2(1), MYC(3), NFKB1(1), RELA(2), RIPK1(2), TNF(1), TNFRSF1A(1), TP53(90), TRAF2(2) 13731663 108 92 93 11 14 18 11 22 43 0 5e-05 1.78e-15 1.23e-13
9 ATRBRCAPATHWAY BRCA1 and 2 block cell cycle progression in response to DNA damage and promote double-stranded break repair; mutations induce breast cancer susceptibility. ATM, ATR, BRCA1, BRCA2, CHEK1, CHEK2, FANCA, FANCC, FANCD2, FANCE, FANCF, FANCG, HUS1, MRE11A, NBS1, RAD1, RAD17, RAD50, RAD51, RAD9A, TP53, TREX1 21 ATM(5), ATR(4), BRCA2(2), CHEK2(2), FANCC(2), RAD17(1), RAD50(2), TP53(90) 15765036 108 94 93 7 10 17 12 24 44 1 0.000031 1.89e-15 1.23e-13
10 PMLPATHWAY Ring-shaped PML nuclear bodies regulate transcription and are required co-activators in p53- and DAXX-mediated apoptosis. CREBBP, DAXX, HRAS, PAX3, PML, PRAM-1, RARA, RB1, SIRT1, SP100, TNF, TNFRSF1A, TNFRSF1B, TNFRSF6, TNFSF6, TP53, UBL1 13 CREBBP(4), PML(1), RARA(1), RB1(24), SIRT1(1), TNF(1), TNFRSF1A(1), TP53(90) 6775135 123 98 108 5 14 13 11 20 63 2 7.2e-09 2.00e-15 1.23e-13

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 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(6), PIK3R1(1), PLCB1(4), PLCG1(2), PRKCA(1), VAV1(3) 4737732 17 15 16 1 2 2 1 8 4 0 0.065 0.0006 0.37
2 ACE2PATHWAY Angiotensin-converting enzyme 2 (ACE2) digests the blood-pressure regulator angiotensin II (AGT) ultimately to the vasodilator AGT1-7. ACE2, AGT, AGTR1, AGTR2, CMA1, COL4A1, COL4A2, COL4A3, COL4A4, COL4A5, COL4A6, REN 12 AGT(1), AGTR1(1), AGTR2(1), COL4A1(5), COL4A2(2), COL4A3(6), COL4A4(4), COL4A5(3), COL4A6(4), REN(1) 9388634 28 22 27 1 0 11 5 4 8 0 0.0031 0.0025 0.58
3 CXCR4PATHWAY CXCR4 is a G-protein coupled receptor that responds to the ligand SDF-1 by activating Ras and PI3 kinase to promote lymphocyte chemotaxis. BCAR1, CRK, CXCL12, CXCR4, GNAI1, GNAQ, GNB1, GNGT1, HRAS, MAP2K1, MAPK1, MAPK3, NFKB1, PIK3C2G, PIK3CA, PIK3R1, PLCG1, PRKCA, PRKCB1, PTK2, PTK2B, PXN, RAF1, RELA 23 BCAR1(1), MAPK1(1), NFKB1(1), PIK3C2G(7), PIK3CA(6), PIK3R1(1), PLCG1(2), PRKCA(1), PTK2(3), PTK2B(2), RELA(2) 10391282 27 26 26 2 2 4 5 8 8 0 0.029 0.0028 0.58
4 HSA00950_ALKALOID_BIOSYNTHESIS_I Genes involved in alkaloid biosynthesis I DDC, GOT1, GOT2, TAT, TYR 5 DDC(2), TAT(2), TYR(2) 1674708 6 6 6 0 2 2 1 0 1 0 0.16 0.0045 0.69
5 HSA00902_MONOTERPENOID_BIOSYNTHESIS Genes involved in monoterpenoid biosynthesis CYP2C19, CYP2C9 2 CYP2C19(2), CYP2C9(2) 744539 4 4 4 1 1 1 2 0 0 0 0.6 0.0078 0.89
6 ST_JAK_STAT_PATHWAY The Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway transduces extracellular signals to promote gene activation. CISH, JAK1, JAK2, JAK3, PIAS1, PIAS3, PTPRU, REG1A, SOAT1 9 CISH(2), JAK1(1), JAK3(2), PIAS1(2), PTPRU(7), SOAT1(1) 5204455 15 14 15 2 3 2 4 4 2 0 0.19 0.0087 0.89
7 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 DPM2(1), NTRK1(3), PIK3CA(6), PIK3R1(1), PLCG1(2), PRKCA(1) 5714646 14 14 13 1 4 1 1 4 4 0 0.12 0.013 1
8 FIBRINOLYSISPATHWAY Thrombin cleavage of fibrinogen results in rapid formation of fibrin threads that form a mesh to capture platelets and other blood cells into a clot. CPB2, F13A1, F2, F2R, FGA, FGB, FGG, PLAT, PLAU, PLG, SERPINB2, SERPINE1 12 F13A1(3), F2R(1), FGA(2), FGB(2), FGG(2), PLG(1) 4909993 11 11 11 1 1 2 4 3 1 0 0.21 0.013 1
9 ST_INTERFERON_GAMMA_PATHWAY The interferon gamma pathway resembles the JAK-STAT pathway and activates STAT transcription factors. CISH, IFNG, IFNGR1, JAK1, JAK2, PLA2G2A, PTPRU, REG1A, STAT1, STATIP1 9 CISH(2), IFNG(1), JAK1(1), PTPRU(7), STAT1(2) 4237234 13 12 13 2 2 3 3 4 1 0 0.18 0.019 1
10 FLUMAZENILPATHWAY Flumazenil is a benzodiazepine receptor antagonist that may induce protective preconditioning in ischemic cardiomyocytes. GABRA1, GABRA2, GABRA3, GABRA4, GABRA5, GABRA6, GPX1, PRKCE, SOD1 9 GABRA1(1), GABRA2(2), GABRA4(1), GABRA5(2), GABRA6(2), PRKCE(2) 2956453 10 9 10 1 2 4 1 1 2 0 0.13 0.021 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)
Copyright © 2016 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle