Mutation Analysis (MutSig v2.0)
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
22 February 2013  |  analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C14F1P0F
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Analysis (MutSig v2.0). Broad Institute of MIT and Harvard. doi:10.7908/C14F1P0F
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

  • Significantly mutated genes (q ≤ 0.1): 32

  • Mutations seen in COSMIC: 1001

  • Significantly mutated genes in COSMIC territory: 46

  • Genes with clustered mutations (≤ 3 aa apart): 4304

  • Significantly mutated genesets: 68

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

Mutation Preprocessing

  • Read 248 MAFs of type "WashU"

  • Total number of mutations in input MAFs: 184824

  • After removing 118 mutations outside chr1-24: 184706

  • After removing 211 blacklisted mutations: 184495

  • After removing 1932 noncoding mutations: 182563

  • After collapsing adjacent/redundant mutations: 182561

Mutation Filtering

  • Number of mutations before filtering: 182561

  • After removing 15927 mutations outside gene set: 166634

  • After removing 1021 mutations outside category set: 165613

  • After removing 27 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 148638

Results
Breakdown of Mutations by Type

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

type count
Frame_Shift_Del 1025
Frame_Shift_Ins 447
In_Frame_Del 686
In_Frame_Ins 130
Missense_Mutation 110554
Nonsense_Mutation 11525
Nonstop_Mutation 127
Silent 38550
Splice_Site 2569
Total 165613
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 37482 363920417 0.0001 100 5.7 2.2
*Cp(A/C/T)->mut 48855 3147487610 0.000016 16 0.85 3.4
A->mut 23150 3458111551 6.7e-06 6.7 0.37 3.8
*CpG->(G/A) 1051 363920417 2.9e-06 2.9 0.16 2.7
indel+null 15642 6969519578 2.2e-06 2.2 0.12 NaN
double_null 866 6969519578 1.2e-07 0.12 0.0068 NaN
Total 127046 6969519578 0.000018 18 1 3.5
Target Coverage for Each Individual

The x axis represents the samples. The y axis represents the exons, one row per exon, and they are sorted by average coverage across samples. For exons with exactly the same average coverage, they are sorted next by the %GC of the exon. (The secondary sort is especially useful for the zero-coverage exons at the bottom).

Figure 1. 

Distribution of Mutation Counts, Coverage, and Mutation Rates Across Samples

Figure 2.  Patients counts and rates file used to generate this plot: UCEC-TP.patients.counts_and_rates.txt

CoMut Plot

Figure 3.  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)->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: 32. 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_cons p_joint p q
1 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 306286 228 161 134 5 20 29 21 33 98 27 <1.00e-15 3.7e-12 0.9 1e-06 <0.000 <0.000
2 FBXW7 F-box and WD repeat domain containing 7 638720 46 39 30 1 22 12 2 1 8 1 7.88e-15 0.00048 0.015 0.00081 2.22e-16 1.70e-12
3 SPOP speckle-type POZ protein 287083 23 21 18 0 5 8 6 0 4 0 7.33e-15 0.0041 0.05 0.0015 4.44e-16 1.70e-12
4 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 813637 172 132 76 3 31 61 68 3 9 0 2.00e-15 2.1e-11 8e-07 0 <1.00e-15 <1.70e-12
5 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 581125 100 83 77 2 4 4 13 1 63 15 <1.00e-15 0.0021 0.58 0 <1.00e-15 <1.70e-12
6 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 592248 80 74 25 7 5 61 14 0 0 0 6.22e-15 0.0034 2e-07 0 <1.00e-15 <1.70e-12
7 TP53 tumor protein p53 317550 74 69 50 2 23 18 15 1 17 0 4.00e-15 2.1e-06 2.6e-06 0 <1.00e-15 <1.70e-12
8 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 175322 53 53 11 2 1 47 4 1 0 0 5.55e-15 0.0059 0.00019 0 <1.00e-15 <1.70e-12
9 PPP2R1A protein phosphatase 2 (formerly 2A), regulatory subunit A , alpha isoform 434902 30 27 18 3 10 17 1 0 2 0 3.44e-14 0.0026 0.23 0 <1.00e-15 <1.70e-12
10 PRKAR1B protein kinase, cAMP-dependent, regulatory, type I, beta 242642 4 4 4 3 2 0 0 1 1 0 0.987 0.8 0 0 <1.00e-15 <1.70e-12
11 CTCF CCCTC-binding factor (zinc finger protein) 547098 49 45 39 1 8 4 7 0 27 3 3.00e-15 0.00095 0.1 0.021 2.44e-15 3.77e-12
12 FGFR2 fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome, Pfeiffer syndrome, Jackson-Weiss syndrome) 687457 34 31 19 3 3 5 13 9 3 1 1.03e-14 0.0097 0.38 0.059 2.20e-14 3.11e-11
13 P2RY11 purinergic receptor P2Y, G-protein coupled, 11 4342 7 9 9 1 1 4 0 1 1 0 5.27e-14 0.19 NaN NaN 5.27e-14 6.89e-11
14 ARID1A AT rich interactive domain 1A (SWI-like) 1412380 93 83 78 5 2 7 5 1 64 14 3.33e-15 0.00015 0.88 1 1.14e-13 1.39e-10
15 CCND1 cyclin D1 154874 15 15 13 1 1 7 3 0 4 0 1.16e-11 0.083 0.28 0.001 4.00e-13 4.53e-10
16 CHD4 chromodomain helicase DNA binding protein 4 1452335 43 35 38 2 16 16 8 0 2 1 3.80e-11 0.0045 0.7 0.046 4.90e-11 5.20e-08
17 NFE2L2 nuclear factor (erythroid-derived 2)-like 2 446498 15 15 12 0 2 3 5 3 2 0 3.32e-08 0.018 0.039 0.000082 7.53e-11 7.52e-08
18 SOX17 SRY (sex determining region Y)-box 17 77226 7 7 3 0 0 6 0 0 1 0 5.85e-08 0.13 0.079 0.02 2.53e-08 2.39e-05
19 FOXA2 forkhead box A2 222670 13 12 13 1 1 3 3 0 6 0 2.59e-08 0.26 0.28 0.21 1.08e-07 9.68e-05
20 FAM9A family with sequence similarity 9, member A 249932 20 14 20 1 2 12 1 0 4 1 1.06e-07 0.1 1 0.26 5.11e-07 0.000434
21 SMTNL2 smoothelin-like 2 189739 9 9 3 2 0 2 0 0 7 0 0.000887 0.37 0.67 0.000094 1.44e-06 0.00117
22 RBMX RNA binding motif protein, X-linked 309438 14 13 8 0 1 4 2 0 7 0 4.37e-05 0.3 0.2 0.014 9.24e-06 0.00713
23 MAX MYC associated factor X 205913 12 11 8 0 3 3 5 0 1 0 1.55e-05 0.05 0.017 0.048 1.13e-05 0.00831
24 ING1 inhibitor of growth family, member 1 255446 13 13 10 2 4 1 0 1 7 0 0.000678 0.082 0.082 0.0027 2.60e-05 0.0184
25 RPL14 ribosomal protein L14 165361 8 7 4 0 0 1 0 2 5 0 0.000172 0.36 0.95 0.016 3.85e-05 0.0252
26 ABI1 abl-interactor 1 390369 5 4 2 5 0 5 0 0 0 0 1.000 1 1 2.8e-06 3.86e-05 0.0252
27 DNER delta/notch-like EGF repeat containing 485604 21 18 20 1 4 9 5 0 3 0 0.000229 0.011 0.0045 0.016 4.87e-05 0.0306
28 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 145328 9 9 6 2 1 4 3 0 1 0 0.000366 0.48 0.015 0.014 6.60e-05 0.0400
29 HPD 4-hydroxyphenylpyruvate dioxygenase 304730 7 7 3 0 0 0 1 0 6 0 0.0398 0.64 0.00056 0.00021 0.000104 0.0609
30 RASA1 RAS p21 protein activator (GTPase activating protein) 1 776972 30 22 28 3 5 9 4 0 11 1 0.000178 0.094 0.053 0.05 0.000113 0.0641
31 ZNF267 zinc finger protein 267 555952 22 16 19 1 10 5 3 1 3 0 2.07e-05 0.13 0.74 0.5 0.000130 0.0709
32 SGK1 serum/glucocorticoid regulated kinase 1 469608 16 15 14 3 2 5 1 1 7 0 0.000296 0.52 0.011 0.049 0.000177 0.0939
33 ARID5B AT rich interactive domain 5B (MRF1-like) 886908 34 29 34 9 5 5 8 2 13 1 0.000104 0.57 0.98 0.17 0.000207 0.106
34 CYLC1 cylicin, basic protein of sperm head cytoskeleton 1 477704 27 18 27 2 0 16 7 0 4 0 2.77e-05 0.13 0.25 0.75 0.000245 0.122
35 DYRK1A dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A 599522 12 12 11 1 5 2 1 1 2 1 0.00640 0.22 0.1 0.0033 0.000251 0.122
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: 46. 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) 100 33 39 8184 111 0 0
2 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 53 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) 34 51 24 12648 113 0 0
6 FBXW7 F-box and WD repeat domain containing 7 46 91 29 22568 852 0 0
7 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 228 767 219 190216 15125 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 1.3e-08 6.5e-06
10 RB1 retinoblastoma 1 (including osteosarcoma) 26 267 11 66216 30 6.6e-08 0.000029

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)

Clustered Mutations

Table 5.  Get Full Table Genes with Clustered Mutations

num gene desc n mindist nmuts0 nmuts3 nmuts12 npairs0 npairs3 npairs12
10506 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 228 0 1624 2112 3003 1624 2112 3003
6933 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 53 0 669 993 1039 669 993 1039
9757 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 172 0 573 1075 1553 573 1075 1553
2897 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 80 0 434 1165 2176 434 1165 2176
13869 TP53 tumor protein p53 74 0 89 167 417 89 167 417
4594 FGFR2 fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome, Pfeiffer syndrome, Jackson-Weiss syndrome) 34 0 56 56 78 56 56 78
4506 FBXW7 F-box and WD repeat domain containing 7 46 0 50 53 68 50 53 68
10022 POLE polymerase (DNA directed), epsilon 47 0 39 42 60 39 42 60
10189 PPP2R1A protein phosphatase 2 (formerly 2A), regulatory subunit A , alpha isoform 30 0 37 48 77 37 48 77
14206 TTN titin 525 0 36 54 105 36 54 105

Note:

n - number of mutations in this gene in the individual set.

mindist - distance (in aa) between closest pair of mutations in this gene

npairs3 - how many pairs of mutations are within 3 aa of each other.

npairs12 - how many pairs of mutations are within 12 aa of each other.

Geneset Analyses

Table 6.  Get Full Table A Ranked List of Significantly Mutated Genesets. (Source: MSigDB GSEA Cannonical Pathway Set).Number of significant genesets found: 68. 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 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(8), CDKN2A(2), E2F1(9), MDM2(4), MYC(8), PIK3CA(172), PIK3R1(100), POLR1A(16), POLR1B(16), POLR1C(2), POLR1D(3), RAC1(1), RB1(26), TBX2(2), TP53(74), TWIST1(1) 7080267 444 223 300 50 87 115 119 6 99 18 4.87e-10 <1.00e-15 <4.88e-14
2 IGF1MTORPATHWAY Growth factor IGF-1 activates AKT, Gsk3-beta, and mTOR to promote muscle hypertrophy. AKT1, EIF2B5, EIF2S1, EIF2S2, EIF2S3, EIF4E, EIF4EBP1, FRAP1, GSK3B, IGF1, IGF1R, INPPL1, PDK2, PDPK1, PIK3CA, PIK3R1, PPP2CA, PTEN, RPS6, RPS6KB1 19 AKT1(4), EIF2B5(5), EIF2S1(2), EIF2S2(9), EIF2S3(6), EIF4E(2), GSK3B(13), IGF1(5), IGF1R(13), INPPL1(18), PDK2(5), PDPK1(3), PIK3CA(172), PIK3R1(100), PPP2CA(6), PTEN(228), RPS6(7), RPS6KB1(5) 7138255 603 217 387 46 80 131 117 40 193 42 <1.00e-15 <1.00e-15 <4.88e-14
3 METPATHWAY The hepatocyte growth factor receptor c-Met stimulates proliferation and alters cell motility and adhesion on binding the ligand HGF. ACTA1, CRK, CRKL, DOCK1, ELK1, FOS, GAB1, GRB2, GRF2, HGF, HRAS, ITGA1, ITGB1, JUN, MAP2K1, MAP2K2, MAP4K1, MAPK1, MAPK3, MAPK8, MET, PAK1, PIK3CA, PIK3R1, PTEN, PTK2, PTK2B, PTPN11, PXN, RAF1, RAP1A, RAP1B, RASA1, SOS1, SRC, STAT3 35 ACTA1(5), CRK(2), CRKL(6), DOCK1(24), ELK1(3), FOS(3), GAB1(8), GRB2(3), HGF(14), HRAS(1), ITGA1(17), ITGB1(8), JUN(1), MAP2K1(2), MAP2K2(5), MAP4K1(11), MAPK1(2), MAPK3(3), MAPK8(10), MET(17), PAK1(3), PIK3CA(172), PIK3R1(100), PTEN(228), PTK2(12), PTK2B(16), PTPN11(8), PXN(6), RAF1(8), RAP1A(4), RAP1B(2), RASA1(30), SOS1(13), SRC(3), STAT3(10) 16563754 760 217 538 93 135 188 148 37 208 44 7.33e-15 <1.00e-15 <4.88e-14
4 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(15), CD14(2), CTNNB1(80), DVL1(3), FZD1(3), GJA1(5), GNAI1(5), GSK3B(13), IRAK1(4), LBP(3), LEF1(9), LY96(4), MYD88(3), NFKB1(10), PDPK1(3), PIK3CA(172), PIK3R1(100), PPP2CA(6), RELA(6), TIRAP(2), TLR4(17), TOLLIP(1), WNT1(1) 10634125 536 210 356 58 77 201 122 6 110 20 2.06e-13 <1.00e-15 <4.88e-14
5 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(9), ELK1(3), FOS(3), GRB2(3), HRAS(1), JAK1(20), JUN(1), MAP2K1(2), MAP2K4(9), MAP3K1(30), MAPK3(3), MAPK8(10), PDGFA(3), PDGFRA(24), PIK3CA(172), PIK3R1(100), PLCG1(12), PRKCA(10), RAF1(8), RASA1(30), SHC1(7), SOS1(13), SRF(3), STAT1(15), STAT3(10), STAT5A(5) 12730148 506 201 375 56 94 144 134 4 112 18 5.97e-12 <1.00e-15 <4.88e-14
6 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(7), IL2RG(13), IL7(1), IL7R(12), JAK1(20), JAK3(10), LCK(5), NMI(3), PIK3CA(172), PIK3R1(100), PTK2B(16), STAT5A(5), STAT5B(7) 10213532 435 200 312 56 77 121 115 6 100 16 3.60e-08 <1.00e-15 <4.88e-14
7 GCRPATHWAY Corticosteroids activate the glucocorticoid receptor (GR), which inhibits NF-kB and activates Annexin-1, thus inhibiting the inflammatory response. ADRB2, AKT1, ANXA1, CALM1, CALM2, CALM3, CRN, GNAS, GNB1, GNGT1, HSPCA, NFKB1, NOS3, NPPA, NR3C1, PIK3CA, PIK3R1, RELA, SYT1 17 ADRB2(7), AKT1(4), ANXA1(5), CALM1(2), CALM2(4), GNAS(24), GNB1(2), GNGT1(1), NFKB1(10), NOS3(11), NPPA(2), NR3C1(18), PIK3CA(172), PIK3R1(100), RELA(6), SYT1(4) 6288912 372 197 249 26 79 99 91 4 84 15 9.69e-14 <1.00e-15 <4.88e-14
8 RASPATHWAY Ras activation stimulates many signaling cascades, including PI3K/AKT activation to inhibit apoptosis. AKT1, ARHA, BAD, BCL2L1, CASP9, CDC42, CHUK, ELK1, H2AFX, HRAS, MAP2K1, MAPK3, MLLT7, NFKB1, PIK3CA, PIK3R1, RAC1, RAF1, RALA, RALBP1, RALGDS, RELA, RHOA 21 AKT1(4), BAD(2), BCL2L1(4), CASP9(3), CDC42(4), CHUK(8), ELK1(3), H2AFX(1), HRAS(1), MAP2K1(2), MAPK3(3), NFKB1(10), PIK3CA(172), PIK3R1(100), RAC1(1), RAF1(8), RALA(6), RALBP1(6), RALGDS(9), RELA(6), RHOA(3) 6942603 356 197 235 28 56 106 95 4 78 17 4.18e-12 <1.00e-15 <4.88e-14
9 TPOPATHWAY Thrombopoietin binds to its receptor and activates cell growth through the Erk and JNK MAP kinase pathways, protein kinase C, and JAK/STAT activation. CSNK2A1, FOS, GRB2, HRAS, JAK2, JUN, MAP2K1, MAPK3, MPL, PIK3CA, PIK3R1, PLCG1, PRKCA, PRKCB1, RAF1, RASA1, SHC1, SOS1, STAT1, STAT3, STAT5A, STAT5B, THPO 22 CSNK2A1(9), FOS(3), GRB2(3), HRAS(1), JAK2(17), JUN(1), MAP2K1(2), MAPK3(3), MPL(7), PIK3CA(172), PIK3R1(100), PLCG1(12), PRKCA(10), RAF1(8), RASA1(30), SHC1(7), SOS1(13), STAT1(15), STAT3(10), STAT5A(5), STAT5B(7), THPO(7) 10932923 442 196 313 50 83 126 118 4 92 19 2.33e-09 <1.00e-15 <4.88e-14
10 CDC42RACPATHWAY PI3 kinase stimulates cell migration by activating cdc42, which activates ARP2/3, which in turn promotes formation of new actin fibers. ACTR2, ACTR3, ARHA, ARPC1A, ARPC1B, ARPC2, ARPC3, ARPC4, CDC42, PAK1, PDGFRA, PIK3CA, PIK3R1, RAC1, WASL 14 ACTR2(6), ACTR3(1), ARPC1A(6), ARPC1B(5), ARPC2(1), ARPC3(1), ARPC4(1), CDC42(4), PAK1(3), PDGFRA(24), PIK3CA(172), PIK3R1(100), RAC1(1), WASL(9) 4979142 334 195 214 16 51 90 92 4 81 16 7.55e-15 <1.00e-15 <4.88e-14

Table 7.  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 0.21 1
2 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 3 16 0 0 7 0 0.16 0.25 1
3 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), IL8(3), SLPI(2) 572860 10 10 9 0 2 5 2 0 1 0 0.057 0.27 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 0.35 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 0.44 1
6 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(6), LPL(16) 1914084 39 23 39 7 10 15 9 0 5 0 0.057 0.46 1
7 IL17PATHWAY Activated T cells secrete IL-17, which stimulates fibroblasts and other cells to secrete inflammatory and hematopoietic cytokines. CD2, CD34, CD3D, CD3E, CD3G, CD3Z, CD4, CD58, CD8A, CSF3, IL17, IL3, IL6, IL8, KITLG, TRA@, TRB@ 13 CD2(4), CD34(1), CD3D(6), CD3E(2), CD3G(3), CD4(6), CD58(2), CD8A(2), CSF3(1), IL3(2), IL6(5), IL8(3), KITLG(3) 2309359 40 27 40 9 6 20 4 0 10 0 0.18 0.48 1
8 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 0.51 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 0.57 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 7 6 5 1 0 0 0.088 0.64 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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] TCGA, Integrated genomic analyses of ovarian carcinoma, Nature 474:609 - 615 (2011)
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