Mutation Analysis (MutSig v2.0)
Head and Neck Squamous Cell Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1W0955V
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/C1W0955V
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: HNSC-TP

  • Number of patients in set: 510

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:HNSC-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 75

  • Mutations seen in COSMIC: 856

  • Significantly mutated genes in COSMIC territory: 16

  • Significantly mutated genesets: 59

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

Mutation Preprocessing

  • Read 510 MAFs of type "maf1"

  • Total number of mutations in input MAFs: 120562

  • After removing 8 mutations outside chr1-24: 120554

  • After removing 3459 blacklisted mutations: 117095

  • After removing 6176 noncoding mutations: 110919

  • After collapsing adjacent/redundant mutations: 100966

Mutation Filtering

  • Number of mutations before filtering: 100966

  • After removing 5396 mutations outside gene set: 95570

  • After removing 152 mutations outside category set: 95418

Results
Breakdown of Mutations by Type

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

type count
De_novo_Start_InFrame 50
De_novo_Start_OutOfFrame 80
Frame_Shift_Del 2032
Frame_Shift_Ins 824
In_Frame_Del 428
In_Frame_Ins 51
Missense_Mutation 60037
Nonsense_Mutation 4589
Nonstop_Mutation 86
Silent 24155
Splice_Site 2988
Start_Codon_Del 5
Start_Codon_Ins 2
Start_Codon_SNP 91
Total 95418
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 10551 837007747 0.000013 13 2.7 2.1
*Cp(A/C/T)->T 15586 6830387296 2.3e-06 2.3 0.48 1.7
C->(G/A) 22278 7667395043 2.9e-06 2.9 0.61 4.8
A->mut 11713 7359712076 1.6e-06 1.6 0.34 3.9
indel+null 10991 15027107119 7.3e-07 0.73 0.15 NaN
double_null 144 15027107119 9.6e-09 0.0096 0.002 NaN
Total 71263 15027107119 4.7e-06 4.7 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: HNSC-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: 75. 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 NSD1 nuclear receptor binding SET domain protein 1 4162026 73 63 72 2 2 9 13 5 38 6 4.11e-15 0.000300 5e-05 0.000056 5.2e-06 0.000 0.000
2 CASP8 caspase 8, apoptosis-related cysteine peptidase 890224 61 55 49 1 4 8 8 8 31 2 2.66e-15 1.55e-06 0.6 0.000013 0.00016 0.000 0.000
3 NOTCH1 Notch homolog 1, translocation-associated (Drosophila) 3182901 95 87 92 9 14 15 14 9 42 1 4.11e-15 1.19e-06 0.00022 0.2 0.00043 1.11e-16 6.70e-13
4 TP53 tumor protein p53 627695 426 359 209 5 72 45 61 67 171 10 <1.00e-15 <1.00e-15 0 0 0 <1.00e-15 <1.29e-12
5 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 432500 114 112 54 1 4 3 7 7 90 3 <1.00e-15 4.29e-11 0 0 0 <1.00e-15 <1.29e-12
6 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 1675015 96 94 35 1 2 55 6 30 3 0 1.11e-15 1.79e-11 0 0.00058 0 <1.00e-15 <1.29e-12
7 FBXW7 F-box and WD repeat domain containing 7 1269883 34 33 25 4 4 2 11 5 11 1 5.71e-10 0.272 2e-07 0.19 0 <1.00e-15 <1.29e-12
8 HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog 331344 32 29 9 0 7 3 18 3 1 0 4.33e-15 3.95e-05 0 5.4e-06 0 <1.00e-15 <1.29e-12
9 NFE2L2 nuclear factor (erythroid-derived 2)-like 2 911867 27 26 18 2 0 6 13 7 1 0 4.25e-13 0.0701 0 0 0 <1.00e-15 <1.29e-12
10 NRF1 nuclear respiratory factor 1 782181 5 5 5 0 0 1 2 0 2 0 0.165 0.310 0.75 0 0 <1.00e-15 <1.29e-12
11 C6orf136 chromosome 6 open reading frame 136 526964 2 2 2 0 0 0 0 1 1 0 0.549 0.598 0.079 0 0 <1.00e-15 <1.29e-12
12 GATA2 GATA binding protein 2 636913 2 2 2 1 0 0 0 0 2 0 0.762 0.659 0.049 0 0 <1.00e-15 <1.29e-12
13 NEDD8 neural precursor cell expressed, developmentally down-regulated 8 127942 2 2 2 0 0 0 1 0 1 0 0.0489 0.659 0.88 0 0 <1.00e-15 <1.29e-12
14 ULK2 unc-51-like kinase 2 (C. elegans) 1552074 2 2 2 2 0 0 1 0 1 0 0.995 0.943 0.82 0 0 <1.00e-15 <1.29e-12
15 FAT1 FAT tumor suppressor homolog 1 (Drosophila) 6963586 125 114 120 11 3 7 11 11 77 16 3.66e-15 5.83e-05 0.14 0.044 0.029 4.00e-15 4.83e-12
16 HLA-B major histocompatibility complex, class I, B 502645 26 24 21 1 0 1 8 2 15 0 1.14e-14 0.00659 0.033 0.064 0.034 1.41e-14 1.60e-11
17 TGFBR2 transforming growth factor, beta receptor II (70/80kDa) 878843 25 23 16 2 4 7 0 5 9 0 1.52e-11 0.00403 0.0052 0.11 0.0087 4.07e-12 4.34e-09
18 ZNF750 zinc finger protein 750 1111690 24 21 22 1 1 2 4 3 14 0 7.48e-08 0.0388 0.0013 0.00027 0.000016 3.49e-11 3.51e-08
19 EP300 E1A binding protein p300 3751833 39 39 32 1 5 10 7 7 10 0 2.16e-08 0.000293 0.00019 0.047 0.00029 1.70e-10 1.62e-07
20 OR2M5 olfactory receptor, family 2, subfamily M, member 5 480420 21 18 20 0 4 3 10 2 2 0 1.57e-11 0.00297 0.48 0.88 0.62 2.58e-10 2.34e-07
21 RAC1 ras-related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1) 317246 15 14 8 0 6 4 2 3 0 0 1.46e-10 0.00386 0.18 0.061 0.075 2.87e-10 2.47e-07
22 HLA-A major histocompatibility complex, class I, A 564314 24 22 21 4 1 0 2 6 15 0 4.85e-10 0.224 0.068 0.39 0.096 1.15e-09 9.47e-07
23 POM121L12 POM121 transmembrane nucleoporin-like 12 443503 24 22 24 4 6 2 9 4 3 0 1.17e-08 0.0642 0.1 0.15 0.12 2.89e-08 2.10e-05
24 EPHA2 EPH receptor A2 1449501 27 24 24 2 6 1 2 0 16 2 2.22e-07 0.0231 0.0069 0.22 0.0061 2.90e-08 2.10e-05
25 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 618847 14 14 14 0 1 1 2 2 8 0 2.74e-08 0.0580 0.21 0.015 0.05 2.90e-08 2.10e-05
26 MAPK1 mitogen-activated protein kinase 1 505899 9 9 2 0 7 1 0 0 1 0 2.84e-05 0.0486 4e-05 0.05 0.000053 3.21e-08 2.24e-05
27 AGTR1 angiotensin II receptor, type 1 552840 14 14 14 1 2 5 2 5 0 0 4.16e-08 0.0568 0.51 0.44 0.62 4.78e-07 0.000314
28 RHOA ras homolog gene family, member A 304980 10 10 7 1 0 1 6 2 1 0 5.97e-07 0.296 0.055 0.082 0.044 4.85e-07 0.000314
29 B2M beta-2-microglobulin 189710 10 9 9 0 0 2 2 0 6 0 3.94e-08 0.242 0.45 0.51 0.7 5.11e-07 0.000319
30 EPDR1 ependymin related protein 1 (zebrafish) 474660 10 10 10 1 1 1 2 5 1 0 0.000127 0.249 0.00091 0.0036 0.0012 2.50e-06 0.00151
31 DOK6 docking protein 6 494748 12 11 12 0 1 2 5 2 2 0 1.64e-05 0.0635 0.42 0.0023 0.014 3.78e-06 0.00221
32 CD1E CD1e molecule 605964 14 14 14 1 4 5 4 0 1 0 3.70e-07 0.0880 0.85 0.79 1 5.85e-06 0.00331
33 PSG8 pregnancy specific beta-1-glycoprotein 8 674216 17 17 16 2 1 5 8 2 1 0 5.41e-07 0.103 0.9 0.38 0.88 7.37e-06 0.00404
34 C6 complement component 6 1464933 25 23 25 2 1 5 6 4 9 0 2.26e-06 0.0821 0.51 0.17 0.36 1.23e-05 0.00653
35 ZNF99 zinc finger protein 99 1584967 26 21 26 3 0 5 11 4 6 0 0.000230 0.357 0.0052 0.023 0.0042 1.45e-05 0.00715
NSD1

Figure S1.  This figure depicts the distribution of mutations and mutation types across the NSD1 significant gene.

NOTCH1

Figure S2.  This figure depicts the distribution of mutations and mutation types across the NOTCH1 significant gene.

TP53

Figure S3.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

CDKN2A

Figure S4.  This figure depicts the distribution of mutations and mutation types across the CDKN2A significant gene.

PIK3CA

Figure S5.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

FBXW7

Figure S6.  This figure depicts the distribution of mutations and mutation types across the FBXW7 significant gene.

HRAS

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

NFE2L2

Figure S8.  This figure depicts the distribution of mutations and mutation types across the NFE2L2 significant gene.

NRF1

Figure S9.  This figure depicts the distribution of mutations and mutation types across the NRF1 significant gene.

C6orf136

Figure S10.  This figure depicts the distribution of mutations and mutation types across the C6orf136 significant gene.

GATA2

Figure S11.  This figure depicts the distribution of mutations and mutation types across the GATA2 significant gene.

NEDD8

Figure S12.  This figure depicts the distribution of mutations and mutation types across the NEDD8 significant gene.

ULK2

Figure S13.  This figure depicts the distribution of mutations and mutation types across the ULK2 significant gene.

FAT1

Figure S14.  This figure depicts the distribution of mutations and mutation types across the FAT1 significant gene.

HLA-B

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

TGFBR2

Figure S16.  This figure depicts the distribution of mutations and mutation types across the TGFBR2 significant gene.

ZNF750

Figure S17.  This figure depicts the distribution of mutations and mutation types across the ZNF750 significant gene.

EP300

Figure S18.  This figure depicts the distribution of mutations and mutation types across the EP300 significant gene.

OR2M5

Figure S19.  This figure depicts the distribution of mutations and mutation types across the OR2M5 significant gene.

RAC1

Figure S20.  This figure depicts the distribution of mutations and mutation types across the RAC1 significant gene.

HLA-A

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

POM121L12

Figure S22.  This figure depicts the distribution of mutations and mutation types across the POM121L12 significant gene.

EPHA2

Figure S23.  This figure depicts the distribution of mutations and mutation types across the EPHA2 significant gene.

PTEN

Figure S24.  This figure depicts the distribution of mutations and mutation types across the PTEN significant gene.

MAPK1

Figure S25.  This figure depicts the distribution of mutations and mutation types across the MAPK1 significant gene.

AGTR1

Figure S26.  This figure depicts the distribution of mutations and mutation types across the AGTR1 significant gene.

RHOA

Figure S27.  This figure depicts the distribution of mutations and mutation types across the RHOA significant gene.

B2M

Figure S28.  This figure depicts the distribution of mutations and mutation types across the B2M significant gene.

EPDR1

Figure S29.  This figure depicts the distribution of mutations and mutation types across the EPDR1 significant gene.

DOK6

Figure S30.  This figure depicts the distribution of mutations and mutation types across the DOK6 significant gene.

CD1E

Figure S31.  This figure depicts the distribution of mutations and mutation types across the CD1E significant gene.

PSG8

Figure S32.  This figure depicts the distribution of mutations and mutation types across the PSG8 significant gene.

C6

Figure S33.  This figure depicts the distribution of mutations and mutation types across the C6 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: 16. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 TP53 tumor protein p53 426 356 392 181560 76737 0 0
2 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 96 220 81 112200 38739 0 0
3 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 114 332 112 169320 4767 0 0
4 HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog 32 19 31 9690 7799 2.9e-13 3.3e-10
5 FGFR3 fibroblast growth factor receptor 3 (achondroplasia, thanatophoric dwarfism) 15 62 9 31620 5442 9.5e-13 8.6e-10
6 FBXW7 F-box and WD repeat domain containing 7 34 91 23 46410 520 1.2e-12 8.9e-10
7 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 10 33 6 16830 14 3.4e-10 2.2e-07
8 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 14 767 14 391170 527 1.2e-08 6.6e-06
9 RB1 retinoblastoma 1 (including osteosarcoma) 18 267 9 136170 24 3e-08 0.000015
10 SMAD4 SMAD family member 4 14 159 7 81090 17 1.8e-07 8e-05

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: 59. 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 G1_TO_S_CELL_CYCLE_REACTOME ATM, CCNA1, CCNB1, CCND1, CCND2, CCND3, CCNE1, CCNE2, CCNG2, CCNH, CDC25A, CDC45L, CDK2, CDK4, CDK7, CDKN1A, CDKN1B, CDKN1C, CDKN2A, CDKN2B, CDKN2C, CDKN2D, CREB3, CREB3L1, CREB3L3, CREB3L4, CREBL1, CREBL1, TNXB, E2F1, E2F2, E2F3, E2F4, E2F5, E2F6, FLJ14001, GADD45A, GBA2, MCM2, MCM3, MCM4, MCM5, MCM6, MCM7, MDM2, MNAT1, MYC, MYT1, NACA, NACA, FKSG17, ORC1L, ORC2L, ORC3L, ORC4L, ORC5L, ORC6L, PCNA, POLA2, POLE, POLE2, PRIM1, PRIM2A, RB1, RBL1, RPA1, RPA2, RPA3, TFDP1, TFDP2, TP53, WEE1 64 ATM(16), CCNA1(7), CCNB1(3), CCND1(4), CCND2(2), CCNE1(5), CCNE2(4), CCNG2(4), CDC25A(2), CDK4(5), CDKN1B(2), CDKN2A(114), CDKN2C(1), CREB3L1(4), CREB3L3(9), CREB3L4(2), E2F2(3), E2F3(3), E2F4(2), E2F5(6), E2F6(1), GADD45A(1), GBA2(6), MCM2(4), MCM3(4), MCM4(7), MCM5(2), MCM6(4), MCM7(9), MDM2(2), MNAT1(1), MYC(6), MYT1(12), NACA(25), PCNA(1), POLA2(2), POLE(11), POLE2(1), PRIM1(3), RB1(18), RBL1(11), RPA1(5), RPA2(1), TFDP1(2), TFDP2(1), TNXB(23), TP53(426), WEE1(1) 58754687 788 415 508 77 104 113 138 112 307 14 <1.00e-15 <1.00e-15 <5.93e-14
2 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(12), BAK1(1), BAX(1), BCL2(1), BCL2L1(2), BID(3), BIRC2(2), BIRC3(2), CASP2(3), CASP3(2), CASP6(5), CASP7(3), CASP8(61), CASP9(1), FADD(4), FAS(5), FASLG(1), GZMB(2), JUN(1), MAP2K4(2), MAP3K1(6), MAPK10(4), MCL1(3), MDM2(2), MYC(6), NFKB1(5), NFKBIA(1), PARP1(5), PRF1(4), RELA(2), RIPK1(2), TNFRSF1A(1), TNFRSF1B(2), TNFSF10(1), TP53(426), TRADD(1), TRAF2(2) 28538961 587 399 356 33 94 78 95 89 219 12 <1.00e-15 <1.00e-15 <5.93e-14
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(16), ATR(25), BRCA1(12), CCNB1(3), CDC25A(2), CDC25B(4), CDC25C(4), CDC34(4), CHEK1(1), CHEK2(5), EP300(39), GADD45A(1), MDM2(2), MYT1(12), PRKDC(28), RPS6KA1(10), TP53(426), WEE1(1), YWHAH(2), YWHAQ(3) 31890716 600 394 376 34 94 85 107 102 202 10 1.33e-15 <1.00e-15 <5.93e-14
4 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 DNAJA3(3), IFNG(2), IFNGR1(4), IFNGR2(5), IKBKB(12), JAK2(5), LIN7A(5), NFKB1(5), NFKBIA(1), RB1(18), RELA(2), TNFRSF1A(1), TNFRSF1B(2), TP53(426), USH1C(3), WT1(3) 13844325 497 384 279 27 78 57 84 75 193 10 <1.00e-15 <1.00e-15 <5.93e-14
5 P53PATHWAY p53 induces cell cycle arrest or apoptosis under conditions of DNA damage. APAF1, ATM, BAX, BCL2, CCND1, CCNE1, CDK2, CDK4, CDKN1A, E2F1, GADD45A, MDM2, PCNA, RB1, TIMP3, TP53 16 APAF1(12), ATM(16), BAX(1), BCL2(1), CCND1(4), CCNE1(5), CDK4(5), GADD45A(1), MDM2(2), PCNA(1), RB1(18), TP53(426) 13779449 492 379 275 19 76 61 71 80 194 10 <1.00e-15 <1.00e-15 <5.93e-14
6 SA_G1_AND_S_PHASES Cdk2, 4, and 6 bind cyclin D in G1, while cdk2/cyclin E promotes the G1/S transition. ARF1, ARF3, CCND1, CDK2, CDK4, CDKN1A, CDKN1B, CDKN2A, CFL1, E2F1, E2F2, MDM2, NXT1, PRB1, TP53 15 CCND1(4), CDK4(5), CDKN1B(2), CDKN2A(114), CFL1(2), E2F2(3), MDM2(2), PRB1(8), TP53(426) 6424705 566 372 289 17 77 55 77 79 265 13 <1.00e-15 <1.00e-15 <5.93e-14
7 RNAPATHWAY dsRNA-activated protein kinase phosphorylates elF2a, which generally inhibits translation, and activates NF-kB to provoke inflammation. CHUK, DNAJC3, EIF2S1, EIF2S2, MAP3K14, NFKB1, NFKBIA, PRKR, RELA, TP53 9 CHUK(6), DNAJC3(2), EIF2S1(2), EIF2S2(6), NFKB1(5), NFKBIA(1), RELA(2), TP53(426) 7476252 450 367 233 14 76 50 66 70 178 10 <1.00e-15 <1.00e-15 <5.93e-14
8 RACCYCDPATHWAY Ras, Rac, and Rho coordinate to induce cyclin D1 expression and activate cdk2 to promote the G1/S transition. AKT1, ARHA, CCND1, CCNE1, CDK2, CDK4, CDK6, CDKN1A, CDKN1B, E2F1, HRAS, MAPK1, MAPK3, NFKB1, NFKBIA, PAK1, PIK3CA, PIK3R1, RAC1, RAF1, RB1, RELA, TFDP1 22 AKT1(2), CCND1(4), CCNE1(5), CDK4(5), CDK6(2), CDKN1B(2), HRAS(32), MAPK1(9), NFKB1(5), NFKBIA(1), PAK1(3), PIK3CA(96), PIK3R1(10), RAC1(15), RAF1(3), RB1(18), RELA(2), TFDP1(2) 15389151 216 171 118 19 26 75 38 44 33 0 1.18e-13 <1.00e-15 <5.93e-14
9 INSULINPATHWAY Insulin regulates glucose levels via Ras-mediated transcriptional activation. CSNK2A1, ELK1, FOS, GRB2, HRAS, INS, INSR, IRS1, JUN, MAP2K1, MAPK3, MAPK8, PIK3CA, PIK3R1, PTPN11, RAF1, RASA1, SHC1, SLC2A4, SOS1, SRF 21 CSNK2A1(7), ELK1(2), FOS(6), HRAS(32), INSR(3), IRS1(4), JUN(1), MAP2K1(5), MAPK8(4), PIK3CA(96), PIK3R1(10), PTPN11(2), RAF1(3), RASA1(17), SHC1(3), SLC2A4(3), SOS1(9), SRF(1) 19385060 208 169 122 10 18 69 47 45 28 1 <1.00e-15 <1.00e-15 <5.93e-14
10 AKTPATHWAY Second messenger PIP3 promotes cell survival by activating the anti-apoptotic kinase AKT. AKT1, BAD, CASP9, CHUK, FOXO1A, FOXO3A, GH1, GHR, HSPCA, MLLT7, NFKB1, NFKBIA, PDPK1, PIK3CA, PIK3R1, PPP2CA, RELA, TNFSF6, YWHAH 14 AKT1(2), BAD(1), CASP9(1), CHUK(6), GH1(4), GHR(8), NFKB1(5), NFKBIA(1), PDPK1(4), PIK3CA(96), PIK3R1(10), RELA(2), YWHAH(2) 10744900 142 127 80 10 7 67 17 37 14 0 6.29e-11 1.11e-15 5.93e-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 HSA00627_1,4_DICHLOROBENZENE_DEGRADATION Genes involved in 1,4-dichlorobenzene degradation CMBL 1 CMBL(2) 386575 2 2 2 0 0 0 0 0 2 0 0.66 0.29 1
2 FBW7PATHWAY Cyclin E interacts with cell cycle checkpoint kinase cdk2 to allow transcription of genes required for S phase, including transcription of additional cyclin E. CCNE1, CDC34, CDK2, CUL1, E2F1, FBXW7, RB1, SKP1A, TFDP1 7 CCNE1(5), CDC34(4), CUL1(11), RB1(18), TFDP1(2) 4986845 40 35 40 7 3 12 5 3 17 0 0.042 0.3 1
3 SULFUR_METABOLISM BPNT1, PAPSS1, PAPSS2, SULT1A2, SULT1A3, SULT1A3, SULT1A4, SULT1E1, SULT2A1, SUOX 7 BPNT1(2), PAPSS1(6), PAPSS2(6), SULT1A2(1), SULT1E1(5), SULT2A1(2), SUOX(2) 4644911 24 22 24 2 5 7 5 5 2 0 0.031 0.46 1
4 HSA00472_D_ARGININE_AND_D_ORNITHINE_METABOLISM Genes involved in D-arginine and D-ornithine metabolism DAO 1 DAO(2) 549130 2 2 2 1 1 0 0 1 0 0 0.8 0.56 1
5 HSA00550_PEPTIDOGLYCAN_BIOSYNTHESIS Genes involved in peptidoglycan biosynthesis GLUL, PGLYRP2 2 GLUL(3), PGLYRP2(6) 1383171 9 9 9 2 1 3 2 2 1 0 0.35 0.6 1
6 RABPATHWAY Rab family GTPases regulate vesicle transport, endocytosis and exocytosis, and vesicle docking via interactions with the rabphilins. ACTA1, MEL, RAB11A, RAB1A, RAB2, RAB27A, RAB3A, RAB4A, RAB5A, RAB6A, RAB7, RAB9A 9 ACTA1(4), RAB11A(1), RAB1A(2), RAB27A(1), RAB3A(2), RAB4A(1), RAB5A(3) 3208378 14 14 14 1 5 4 2 2 1 0 0.034 0.65 1
7 HSA00902_MONOTERPENOID_BIOSYNTHESIS Genes involved in monoterpenoid biosynthesis CYP2C19, CYP2C9 2 CYP2C19(6), CYP2C9(7) 1537915 13 13 13 3 0 4 5 3 1 0 0.37 0.66 1
8 NUCLEOTIDE_SUGARS_METABOLISM GALE, GALT, TGDS, UGDH, UXS1 5 GALT(2), TGDS(3), UGDH(4), UXS1(2) 2921501 11 11 11 0 0 1 5 4 1 0 0.1 0.66 1
9 HSA00031_INOSITOL_METABOLISM Genes involved in inositol metabolism ALDH6A1, TPI1 2 ALDH6A1(2), TPI1(4) 1256585 6 6 6 2 1 3 2 0 0 0 0.65 0.7 1
10 HSA00430_TAURINE_AND_HYPOTAURINE_METABOLISM Genes involved in taurine and hypotaurine metabolism BAAT, CDO1, CSAD, GAD1, GAD2, GGT1, GGTL3, GGTL4 6 BAAT(2), CDO1(2), CSAD(5), GAD1(4), GAD2(7), GGT1(4) 4376205 24 24 24 4 4 7 6 2 5 0 0.065 0.72 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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