Mutation Analysis (MutSig v2.0)
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C16M3650
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/C16M3650
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: UCEC-TP

  • Number of patients in set: 248

Input

The input for this pipeline is a set of individuals with the following files associated for each:

  1. An annotated .maf file describing the mutations called for the respective individual, and their properties.

  2. A .wig file that contains information about the coverage of the sample.

Summary

  • MAF used for this analysis:UCEC-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 42

  • Mutations seen in COSMIC: 989

  • Significantly mutated genes in COSMIC territory: 41

  • Significantly mutated genesets: 70

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

Mutation Preprocessing

  • Read 248 MAFs of type "maf1"

  • Total number of mutations in input MAFs: 184731

  • After removing 118 mutations outside chr1-24: 184613

  • After removing 1169 blacklisted mutations: 183444

  • After removing 9575 noncoding mutations: 173869

  • After collapsing adjacent/redundant mutations: 173868

Mutation Filtering

  • Number of mutations before filtering: 173868

  • After removing 15013 mutations outside gene set: 158855

  • After removing 1077 mutations outside category set: 157778

  • After removing 26 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 141662

Results
Breakdown of Mutations by Type

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

type count
De_novo_Start_InFrame 11
De_novo_Start_OutOfFrame 27
Frame_Shift_Del 846
Frame_Shift_Ins 367
In_Frame_Del 571
In_Frame_Ins 76
Missense_Mutation 103531
Nonsense_Mutation 10343
Nonstop_Mutation 96
Silent 37001
Splice_Site 4822
Start_Codon_Del 3
Start_Codon_SNP 80
Stop_Codon_Del 3
Stop_Codon_Ins 1
Total 157778
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 35144 363920417 0.000097 97 5.6 2.2
*Cp(A/C/T)->mut 45813 3147487610 0.000015 15 0.84 3.4
A->mut 21640 3458111551 6.3e-06 6.3 0.36 3.8
*CpG->(G/A) 999 363920417 2.7e-06 2.7 0.16 2.7
indel+null 16225 6969519578 2.3e-06 2.3 0.13 NaN
double_null 940 6969519578 1.3e-07 0.13 0.0078 NaN
Total 120761 6969519578 0.000017 17 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: UCEC-TP.patients.counts_and_rates.txt

Lego Plots

The mutation spectrum is depicted in the lego plots below in which the 96 possible mutation types are subdivided into six large blocks, color-coded to reflect the base substitution type. Each large block is further subdivided into the 16 possible pairs of 5' and 3' neighbors, as listed in the 4x4 trinucleotide context legend. The height of each block corresponds to the mutation frequency for that kind of mutation (counts of mutations normalized by the base coverage in a given bin). The shape of the spectrum is a signature for dominant mutational mechanisms in different tumor types.

Figure 3.  Get High-res Image SNV Mutation rate lego plot for entire set. Each bin is normalized by base coverage for that bin. Colors represent the six SNV types on the upper right. The three-base context for each mutation is labeled in the 4x4 legend on the lower right. The fractional breakdown of SNV counts is shown in the pie chart on the upper left. If this figure is blank, not enough information was provided in the MAF to generate it.

Figure 4.  Get High-res Image SNV Mutation rate lego plots for 4 slices of mutation allele fraction (0<=AF<0.1, 0.1<=AF<0.25, 0.25<=AF<0.5, & 0.5<=AF) . The color code and three-base context legends are the same as the previous figure. If this figure is blank, not enough information was provided in the MAF to generate it.

Significantly Mutated Genes

Column Descriptions:

  • N = number of sequenced bases in this gene across the individual set

  • n = number of (nonsilent) mutations in this gene across the individual set

  • npat = number of patients (individuals) with at least one nonsilent mutation

  • nsite = number of unique sites having a non-silent mutation

  • nsil = number of silent mutations in this gene across the individual set

  • n1 = number of nonsilent mutations of type: *CpG->T

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

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

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

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

  • n6 = number of nonsilent mutations of type: double_null

  • p_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: 42. 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 FBXW7 F-box and WD repeat domain containing 7 638720 44 38 28 1 20 12 2 1 8 1 4.11e-15 0.00073 0.000065 0.0057 0.000035 0.000 0.000
2 SPOP speckle-type POZ protein 287083 23 21 18 0 5 8 6 0 4 0 6.00e-15 0.0041 0.0034 0.035 0.0013 3.33e-16 1.21e-12
3 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 306286 227 161 134 5 19 25 21 33 102 27 <1.00e-15 4.8e-12 0 0.86 0 <1.00e-15 <1.21e-12
4 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 813637 172 132 76 3 31 55 68 3 15 0 1.78e-15 2.1e-11 0 0 0 <1.00e-15 <1.21e-12
5 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 581125 99 82 76 1 4 4 12 1 63 15 4.33e-15 0.00051 0 0.54 0 <1.00e-15 <1.21e-12
6 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 592248 80 74 25 7 5 61 14 0 0 0 3.22e-15 0.0034 0 0 0 <1.00e-15 <1.21e-12
7 TP53 tumor protein p53 317550 74 69 50 2 23 18 15 1 17 0 <1.00e-15 2.1e-06 0 4e-07 0 <1.00e-15 <1.21e-12
8 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 175322 52 52 11 1 2 45 4 1 0 0 2.78e-15 0.0014 0 0.0011 0 <1.00e-15 <1.21e-12
9 PPP2R1A protein phosphatase 2 (formerly 2A), regulatory subunit A , alpha isoform 434902 31 28 19 2 10 18 1 0 2 0 1.28e-14 0.00052 0 0.12 0 <1.00e-15 <1.21e-12
10 SNAP25 synaptosomal-associated protein, 25kDa 188638 7 7 7 2 3 1 2 0 1 0 0.457 0.64 0.59 0 0 <1.00e-15 <1.21e-12
11 FAM118B family with sequence similarity 118, member B 267757 3 3 3 0 0 1 1 0 1 0 0.589 0.42 0.7 0 0 <1.00e-15 <1.21e-12
12 TMEM55A transmembrane protein 55A 195925 3 3 3 0 0 2 0 0 1 0 0.321 0.52 0.66 0 0 <1.00e-15 <1.21e-12
13 GIPC1 GIPC PDZ domain containing family, member 1 168127 2 2 2 1 1 0 0 0 1 0 0.503 0.7 0.92 0 0 <1.00e-15 <1.21e-12
14 PGRMC2 progesterone receptor membrane component 2 89028 2 2 2 0 1 0 0 0 1 0 0.366 0.45 0.14 0.24 0 <1.00e-15 <1.21e-12
15 FGFR2 fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome, Pfeiffer syndrome, Jackson-Weiss syndrome) 687457 33 31 18 3 3 5 12 9 3 1 2.55e-15 0.011 0.011 0.19 0.016 1.55e-15 1.76e-12
16 CTCF CCCTC-binding factor (zinc finger protein) 547098 48 44 38 1 8 4 7 0 27 2 5.55e-15 0.00095 0.012 0.08 0.022 4.66e-15 4.95e-12
17 ARID1A AT rich interactive domain 1A (SWI-like) 1412380 93 83 78 5 2 7 5 1 64 14 2.00e-15 0.00015 0.97 0.82 1 6.96e-14 6.95e-11
18 P2RY11 purinergic receptor P2Y, G-protein coupled, 11 4342 7 9 9 2 1 4 0 1 1 0 7.02e-13 0.33 NaN NaN NaN 7.02e-13 6.62e-10
19 CCND1 cyclin D1 154874 14 14 12 1 1 7 3 0 3 0 9.02e-11 0.083 0.0009 0.15 0.0017 4.60e-12 4.11e-09
20 CHD4 chromodomain helicase DNA binding protein 4 1452335 43 35 38 2 16 16 7 0 3 1 1.56e-11 0.0045 0.013 0.59 0.033 1.51e-11 1.28e-08
21 NFE2L2 nuclear factor (erythroid-derived 2)-like 2 446498 14 14 11 0 2 4 5 3 0 0 6.00e-08 0.024 0.000092 0.025 0.000084 1.36e-10 1.10e-07
22 SOX17 SRY (sex determining region Y)-box 17 77226 7 7 3 1 0 6 0 0 1 0 3.68e-08 0.36 0.13 0.069 0.02 1.63e-08 1.25e-05
23 FOXA2 forkhead box A2 222670 12 12 12 1 1 2 2 0 7 0 1.37e-08 0.27 0.14 0.29 0.25 7.04e-08 5.19e-05
24 FAM9A family with sequence similarity 9, member A 249932 20 14 20 1 2 12 1 0 4 1 6.78e-08 0.1 0.12 0.99 0.2 2.58e-07 0.000182
25 LIMK2 LIM domain kinase 2 575544 13 12 11 1 5 3 1 0 4 0 0.0197 0.07 0.000037 0.092 0.000017 5.43e-06 0.00369
26 ING1 inhibitor of growth family, member 1 255446 12 12 9 2 4 1 0 1 6 0 0.00229 0.1 0.00016 0.022 0.0002 7.08e-06 0.00462
27 DNER delta/notch-like EGF repeat containing 485604 21 18 20 0 4 8 5 0 4 0 0.000107 0.0019 0.29 0.0026 0.0052 8.66e-06 0.00535
28 MAX MYC associated factor X 205913 9 9 5 0 3 1 4 0 1 0 0.000145 0.1 0.11 0.0017 0.004 8.83e-06 0.00535
29 ABI1 abl-interactor 1 390369 5 4 2 5 0 5 0 0 0 0 1.000 1 8e-07 1 2.8e-06 3.86e-05 0.0226
30 ZNF286A zinc finger protein 286A 389712 16 12 14 1 3 6 6 0 1 0 0.000886 0.12 0.0015 0.62 0.0039 4.73e-05 0.0268
31 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 145328 9 9 6 2 1 4 3 0 1 0 0.000277 0.48 0.08 0.015 0.014 5.12e-05 0.0280
32 TPTE transmembrane phosphatase with tensin homology 431520 32 15 28 3 8 10 8 1 5 0 0.000287 0.032 0.017 0.047 0.017 6.59e-05 0.0349
33 ZNF267 zinc finger protein 267 555952 22 16 19 1 10 5 3 1 3 0 1.26e-05 0.13 0.29 0.65 0.5 8.22e-05 0.0423
34 RASA1 RAS p21 protein activator (GTPase activating protein) 1 776972 29 21 27 3 5 9 3 0 11 1 0.000538 0.11 0.048 0.013 0.013 8.90e-05 0.0444
35 CYLC1 cylicin, basic protein of sperm head cytoskeleton 1 477704 27 18 27 2 0 16 7 0 4 0 1.52e-05 0.13 0.79 0.13 0.48 9.31e-05 0.0451
FBXW7

Figure S1.  This figure depicts the distribution of mutations and mutation types across the FBXW7 significant gene.

SPOP

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

PTEN

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

PIK3CA

Figure S4.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

PIK3R1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the PIK3R1 significant gene.

CTNNB1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the CTNNB1 significant gene.

TP53

Figure S7.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

KRAS

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

PPP2R1A

Figure S9.  This figure depicts the distribution of mutations and mutation types across the PPP2R1A significant gene.

SNAP25

Figure S10.  This figure depicts the distribution of mutations and mutation types across the SNAP25 significant gene.

FAM118B

Figure S11.  This figure depicts the distribution of mutations and mutation types across the FAM118B significant gene.

TMEM55A

Figure S12.  This figure depicts the distribution of mutations and mutation types across the TMEM55A significant gene.

GIPC1

Figure S13.  This figure depicts the distribution of mutations and mutation types across the GIPC1 significant gene.

PGRMC2

Figure S14.  This figure depicts the distribution of mutations and mutation types across the PGRMC2 significant gene.

FGFR2

Figure S15.  This figure depicts the distribution of mutations and mutation types across the FGFR2 significant gene.

CTCF

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

ARID1A

Figure S17.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

P2RY11

Figure S18.  This figure depicts the distribution of mutations and mutation types across the P2RY11 significant gene.

CCND1

Figure S19.  This figure depicts the distribution of mutations and mutation types across the CCND1 significant gene.

CHD4

Figure S20.  This figure depicts the distribution of mutations and mutation types across the CHD4 significant gene.

NFE2L2

Figure S21.  This figure depicts the distribution of mutations and mutation types across the NFE2L2 significant gene.

SOX17

Figure S22.  This figure depicts the distribution of mutations and mutation types across the SOX17 significant gene.

FOXA2

Figure S23.  This figure depicts the distribution of mutations and mutation types across the FOXA2 significant gene.

FAM9A

Figure S24.  This figure depicts the distribution of mutations and mutation types across the FAM9A significant gene.

LIMK2

Figure S25.  This figure depicts the distribution of mutations and mutation types across the LIMK2 significant gene.

ING1

Figure S26.  This figure depicts the distribution of mutations and mutation types across the ING1 significant gene.

DNER

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

MAX

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

ABI1

Figure S29.  This figure depicts the distribution of mutations and mutation types across the ABI1 significant gene.

ZNF286A

Figure S30.  This figure depicts the distribution of mutations and mutation types across the ZNF286A significant gene.

NRAS

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

TPTE

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

ZNF267

Figure S33.  This figure depicts the distribution of mutations and mutation types across the ZNF267 significant gene.

RASA1

Figure S34.  This figure depicts the distribution of mutations and mutation types across the RASA1 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: 41. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 99 33 39 8184 111 0 0
2 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 52 52 49 12896 556649 0 0
3 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 172 220 150 54560 35669 0 0
4 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 80 138 67 34224 23813 0 0
5 FGFR2 fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome, Pfeiffer syndrome, Jackson-Weiss syndrome) 33 51 24 12648 113 0 0
6 FBXW7 F-box and WD repeat domain containing 7 44 91 29 22568 852 0 0
7 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 227 767 218 190216 14940 0 0
8 TP53 tumor protein p53 74 356 72 88288 24186 0 0
9 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 9 33 6 8184 6490 1e-08 4.8e-06
10 RB1 retinoblastoma 1 (including osteosarcoma) 26 267 11 66216 30 4e-08 0.000017

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: 70. Number of genesets displayed: 10

rank geneset description genes N_genes mut_tally N n npat nsite nsil n1 n2 n3 n4 n5 n6 p_ns_s p q
1 MTORPATHWAY Mammalian target of rapamycin (mTOR) senses mitogenic factors and nutrients, including ATP, and induces cell proliferation. AKT1, EIF3S10, EIF4A1, EIF4A2, EIF4B, EIF4E, EIF4EBP1, EIF4G1, EIF4G2, EIF4G3, FKBP1A, FRAP1, MKNK1, PDK2, PDPK1, PIK3CA, PIK3R1, PPP2CA, PTEN, RPS6, RPS6KB1, TSC1, TSC2 21 AKT1(4), EIF4A1(5), EIF4A2(8), EIF4B(7), EIF4E(1), EIF4G1(16), EIF4G2(7), EIF4G3(23), FKBP1A(1), MKNK1(3), PDK2(4), PDPK1(3), PIK3CA(172), PIK3R1(99), PPP2CA(6), PTEN(227), RPS6(7), RPS6KB1(3), TSC1(12), TSC2(14) 9925342 622 218 409 44 89 126 123 38 202 44 <1.00e-15 <1.00e-15 <3.87e-14
2 GSK3PATHWAY Bacterial lipopolysaccharide activates AKT to promote the survival and activation of macrophages and inhibits Gsk3-beta to promote beta-catenin accumulation in the nucleus. AKT1, APC, AXIN1, CCND1, CD14, CTNNB1, DVL1, FZD1, GJA1, GNAI1, GSK3B, IRAK1, LBP, LEF1, LY96, MYD88, NFKB1, PDPK1, PIK3CA, PIK3R1, PPP2CA, PRKR, RELA, TIRAP, TLR4, TOLLIP, WNT1 26 AKT1(4), APC(56), AXIN1(9), CCND1(14), CD14(2), CTNNB1(80), DVL1(3), FZD1(3), GJA1(5), GNAI1(5), GSK3B(13), IRAK1(4), LBP(3), LEF1(8), LY96(4), MYD88(2), NFKB1(10), PDPK1(3), PIK3CA(172), PIK3R1(99), PPP2CA(6), RELA(6), TIRAP(2), TLR4(17), TOLLIP(1), WNT1(1) 10634125 532 210 352 57 77 194 121 6 115 19 1.24e-13 <1.00e-15 <3.87e-14
3 SA_PTEN_PATHWAY PTEN is a tumor suppressor that dephosphorylates the lipid messenger phosphatidylinositol triphosphate. AKT1, AKT2, AKT3, BPNT1, GRB2, ILK, MAPK1, MAPK3, PDK1, PIK3CA, PIK3CD, PIP3-E, PTEN, PTK2B, RBL2, SHC1, SOS1 16 AKT1(4), AKT2(6), AKT3(11), BPNT1(3), GRB2(3), ILK(5), MAPK1(2), MAPK3(3), PDK1(2), PIK3CA(172), PIK3CD(12), PTEN(227), PTK2B(15), RBL2(19), SHC1(6), SOS1(13) 7243620 503 208 305 39 89 116 107 36 127 28 <1.00e-15 <1.00e-15 <3.87e-14
4 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 51 ACTA1(5), AGT(8), AKT1(4), CALM1(2), CALM2(3), CALR(3), CAMK1(3), CAMK1G(5), CAMK4(5), CREBBP(32), CSNK1A1(3), EDN1(4), ELSPBP1(5), F2(8), FKBP1A(1), GATA4(3), GSK3B(13), HAND1(6), HRAS(1), IGF1(5), LIF(3), MAP2K1(2), MAPK1(2), MAPK14(4), MAPK3(3), MAPK8(11), MEF2C(10), MYH2(41), NFATC1(10), NFATC2(13), NFATC3(11), NFATC4(5), NKX2-5(2), NPPA(2), PIK3CA(172), PIK3R1(99), PPP3CA(4), PPP3CB(6), PPP3CC(6), PRKACB(3), PRKACG(9), PRKAR1A(4), PRKAR1B(4), PRKAR2A(4), PRKAR2B(3), RAF1(9), RPS6KB1(3), SYT1(4) 18041266 568 206 439 96 127 166 134 5 116 20 3.96e-08 <1.00e-15 <3.87e-14
5 IL2RBPATHWAY The beta subunit of the IL-2 receptor is required for IL-2 and IL-15 signal recognition and activates JAK kinase on ligand binding. AKT1, BAD, BCL2, BCL2L1, CBL, CFLAR, CRKL, E2F1, FOS, GRB2, HRAS, IL2RA, IL2RB, IL2RG, IRS1, JAK1, JAK3, MAPK1, MAPK3, MYC, NMI, PIK3CA, PIK3R1, PPIA, PTPN6, RAF1, RPS6KB1, SHC1, SOCS1, SOCS3, SOS1, STAT5A, STAT5B, SYK, TNFRSF6, TNFSF6, ZNFN1A3 33 AKT1(4), BAD(1), BCL2L1(4), CBL(12), CFLAR(2), CRKL(6), E2F1(9), FOS(3), GRB2(3), HRAS(1), IL2RA(4), IL2RB(3), IL2RG(11), IRS1(13), JAK1(20), JAK3(10), MAPK1(2), MAPK3(3), MYC(8), NMI(3), PIK3CA(172), PIK3R1(99), PPIA(1), PTPN6(5), RAF1(9), RPS6KB1(3), SHC1(6), SOS1(13), STAT5A(5), STAT5B(7), SYK(4) 12777577 446 203 323 55 81 126 119 5 100 15 8.83e-10 <1.00e-15 <3.87e-14
6 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(9), ELK1(3), FOS(3), GRB2(3), HRAS(1), INS(1), INSR(18), IRS1(13), JUN(1), MAP2K1(2), MAPK3(3), MAPK8(11), PIK3CA(172), PIK3R1(99), PTPN11(8), RAF1(9), RASA1(29), SHC1(6), SLC2A4(9), SOS1(13), SRF(3) 9128952 416 202 289 36 79 113 106 5 97 16 4.30e-14 <1.00e-15 <3.87e-14
7 EDG1PATHWAY The lipid S1P is an EDG1 ligand promoting chemotaxis via Rac1 and cell survival and proliferation via ERK activation. ADCY1, AKT1, ARHA, ASAH1, EDG1, GNAI1, GNB1, GNGT1, ITGAV, ITGB3, MAPK1, MAPK3, PDGFA, PDGFRA, PIK3CA, PIK3R1, PLCB1, PRKCA, PRKCB1, PTK2, RAC1, SKIP, SMPD1, SMPD2, SPHK1, SRC 22 ADCY1(21), AKT1(4), ASAH1(6), GNAI1(5), GNB1(2), GNGT1(1), ITGAV(10), ITGB3(11), MAPK1(2), MAPK3(3), PDGFA(3), PDGFRA(24), PIK3CA(172), PIK3R1(99), PLCB1(5), PRKCA(11), PTK2(12), RAC1(1), SMPD1(4), SMPD2(6), SPHK1(2), SRC(3) 9845923 407 201 287 58 68 105 113 6 98 17 1.81e-06 <1.00e-15 <3.87e-14
8 CREBPATHWAY CREB is a transcription factor that binds to cAMP-responsive elements (CREs) to activate transcription in response to extracellular signaling. ADCY1, AKT1, CAMK2A, CAMK2B, CAMK2D, CAMK2G, CREB1, GNAS, GRB2, HRAS, MAPK1, MAPK14, MAPK3, PIK3CA, PIK3R1, PRKACB, PRKACG, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, PRKCA, PRKCB1, RAC1, RPS6KA1, RPS6KA5, SOS1 26 ADCY1(21), AKT1(4), CAMK2A(9), CAMK2B(4), CAMK2D(5), CAMK2G(5), CREB1(5), GNAS(24), GRB2(3), HRAS(1), MAPK1(2), MAPK14(4), MAPK3(3), PIK3CA(172), PIK3R1(99), PRKACB(3), PRKACG(9), PRKAR1A(4), PRKAR1B(4), PRKAR2A(4), PRKAR2B(3), PRKCA(11), RAC1(1), RPS6KA1(1), RPS6KA5(11), SOS1(13) 10330216 425 200 300 50 88 106 110 5 100 16 3.43e-09 <1.00e-15 <3.87e-14
9 IL7PATHWAY IL-7 is required for B and T cell development and proliferation and may contribute to activation of VDJ recombination. BCL2, CREBBP, EP300, FYN, IL2RG, IL7, IL7R, JAK1, JAK3, LCK, NMI, PIK3CA, PIK3R1, PTK2B, STAT5A, STAT5B 16 CREBBP(32), EP300(32), FYN(5), IL2RG(11), IL7(1), IL7R(12), JAK1(20), JAK3(10), LCK(4), NMI(3), PIK3CA(172), PIK3R1(99), PTK2B(15), STAT5A(5), STAT5B(7) 10213532 428 200 305 53 74 113 113 6 106 16 1.41e-08 <1.00e-15 <3.87e-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(4), BAD(1), CASP9(3), CHUK(8), GH1(2), GHR(8), NFKB1(10), NFKBIA(3), PDPK1(3), PIK3CA(172), PIK3R1(99), PPP2CA(6), RELA(6), YWHAH(3) 5135005 328 199 209 21 48 86 91 4 84 15 7.89e-12 <1.00e-15 <3.87e-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(3) 187727 3 3 3 1 0 1 0 1 1 0 0.81 0.2 1
2 LDLPATHWAY Low density lipoproteins (LDL) are present in blood plasma, contain cholesterol and triglycerides, and contribute to atherogenic plaque formation. ACAT1, CCL2, CSF1, IL6, LDLR, LPL 6 ACAT1(6), CSF1(6), IL6(5), LDLR(7), LPL(16) 1914084 40 24 40 6 10 15 9 0 6 0 0.025 0.25 1
3 TCAPOPTOSISPATHWAY HIV infection upregulates Fas ligand in macrophages and CD4 in helper T cells, leading to widespread Fas-induced T cell apoptosis. CCR5, CD28, CD3D, CD3E, CD3G, CD3Z, CD4, TNFRSF6, TNFSF6, TRA@, TRB@ 6 CCR5(6), CD28(3), CD3D(6), CD3E(2), CD3G(3), CD4(6) 1210424 26 16 26 5 2 17 0 0 7 0 0.16 0.26 1
4 HSA00472_D_ARGININE_AND_D_ORNITHINE_METABOLISM Genes involved in D-arginine and D-ornithine metabolism DAO 1 DAO(8) 264662 8 6 8 1 2 4 2 0 0 0 0.3 0.31 1
5 TCRMOLECULE T Cell Receptor and CD3 Complex CD3D, CD3E, CD3G, CD3Z, TRA@, TRB@ 3 CD3D(6), CD3E(2), CD3G(3) 433293 11 7 11 1 1 7 0 0 3 0 0.18 0.4 1
6 P27PATHWAY p27 blocks the G1/S transition by inhibiting the checkpoint kinase cdk2/cyclin E and is inhibited by cdk2-mediated ubiquitination. CCNE1, CDK2, CDKN1B, CKS1B, CUL1, E2F1, NEDD8, RB1, RBX1, SKP1A, SKP2, TFDP1, UBE2M 12 CCNE1(6), CDK2(2), CDKN1B(7), CKS1B(1), CUL1(13), E2F1(9), RB1(26), RBX1(1), SKP2(6), TFDP1(13) 3169643 84 38 82 17 26 25 17 0 13 3 0.029 0.43 1
7 HSA00643_STYRENE_DEGRADATION Genes involved in styrene degradation FAH, GSTZ1, HGD 3 FAH(6), GSTZ1(5), HGD(4) 822142 15 12 15 2 5 4 4 0 2 0 0.11 0.46 1
8 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(5), SLPI(2) 572860 7 7 6 0 1 4 2 0 0 0 0.15 0.52 1
9 HSA00031_INOSITOL_METABOLISM Genes involved in inositol metabolism ALDH6A1, TPI1 2 ALDH6A1(6), TPI1(2) 582980 8 7 8 1 0 3 1 0 4 0 0.27 0.54 1
10 HSA00401_NOVOBIOCIN_BIOSYNTHESIS Genes involved in novobiocin biosynthesis GOT1, GOT2, TAT 3 GOT1(9), GOT2(3), TAT(7) 959929 19 13 19 2 6 6 5 1 1 0 0.088 0.56 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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