Mutation Analysis (MutSig v2.0)
Kidney Chromophobe (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1BZ65F0
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/C1BZ65F0
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: KICH-TP

  • Number of patients in set: 66

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:KICH-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 28

  • Mutations seen in COSMIC: 42

  • Significantly mutated genes in COSMIC territory: 2

  • Significantly mutated genesets: 35

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

Mutation Preprocessing

  • Read 66 MAFs of type "maf1"

  • Total number of mutations in input MAFs: 7556

  • After removing 34 mutations outside chr1-24: 7522

  • After removing 519 blacklisted mutations: 7003

  • After removing 304 noncoding mutations: 6699

  • After collapsing adjacent/redundant mutations: 6686

Mutation Filtering

  • Number of mutations before filtering: 6686

  • After removing 435 mutations outside gene set: 6251

  • After removing 86 mutations outside category set: 6165

  • After removing 1 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 5643

Results
Breakdown of Mutations by Type

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

type count
De_novo_Start_OutOfFrame 2
Frame_Shift_Del 166
Frame_Shift_Ins 214
In_Frame_Del 51
In_Frame_Ins 16
Missense_Mutation 3690
Nonsense_Mutation 161
Nonstop_Mutation 9
Silent 1658
Splice_Site 188
Start_Codon_SNP 6
Stop_Codon_Del 2
Stop_Codon_Ins 2
Total 6165
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 1095 113882076 9.6e-06 9.6 4.4 2.1
A->G 1105 1020035596 1.1e-06 1.1 0.5 2.3
*Cp(A/C/T)->T 557 947255610 5.9e-07 0.59 0.27 1.7
transver 939 2081173282 4.5e-07 0.45 0.21 5
indel+null 732 2081173282 3.5e-07 0.35 0.16 NaN
double_null 78 2081173282 3.7e-08 0.037 0.017 NaN
Total 4506 2081173282 2.2e-06 2.2 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: KICH-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: A->G

  • n3 = number of nonsilent mutations of type: *Cp(A/C/T)->T

  • n4 = number of nonsilent mutations of type: transver

  • 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: 28. 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 85573 28 22 26 0 3 3 4 7 9 2 4.9e-15 0.0003 0.000078 0.0011 0.000017 0.000 0.000
2 MUC6 mucin 6, oligomeric mucus/gel-forming 475534 37 21 35 15 2 5 15 14 1 0 0.00058 0.54 0 1 0 <1.00e-15 <4.65e-12
3 MUC2 mucin 2, oligomeric mucus/gel-forming 541641 11 9 10 9 4 0 3 2 2 0 0.18 0.96 0 1 0 <1.00e-15 <4.65e-12
4 ZNF814 zinc finger protein 814 170280 5 3 4 0 0 1 3 1 0 0 0.0005 0.2 0 0.64 0 <1.00e-15 <4.65e-12
5 MUC4 mucin 4, cell surface associated 233000 101 44 97 28 20 24 25 31 1 0 5.4e-15 0.073 NaN NaN NaN 5.44e-15 2.02e-11
6 FRG1 FSHD region gene 1 53658 14 12 13 3 1 3 5 1 3 1 1.1e-14 0.25 0.009 0.23 0.021 8.22e-15 2.55e-11
7 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 79261 7 6 7 0 0 1 0 1 3 2 3.9e-10 0.49 0.0068 0.54 0.014 1.44e-10 3.83e-07
8 GOLGA6L6 golgin A6 family-like 6 64194 6 6 5 0 1 0 1 3 1 0 2.1e-09 0.34 0.006 0.86 0.022 1.18e-09 2.74e-06
9 PABPC1 poly(A) binding protein, cytoplasmic 1 129689 7 7 6 0 1 1 2 0 1 2 3.3e-10 0.23 0.22 0.42 0.32 2.57e-09 5.31e-06
10 TAS2R43 taste receptor, type 2, member 43 51426 5 5 5 0 1 2 0 2 0 0 7.1e-09 0.24 0.021 0.44 0.044 7.10e-09 1.32e-05
11 PABPC3 poly(A) binding protein, cytoplasmic 3 125375 9 7 9 1 3 3 2 1 0 0 7e-09 0.14 0.044 1 0.084 1.30e-08 2.19e-05
12 CDC27 cell division cycle 27 homolog (S. cerevisiae) 154221 7 6 7 1 1 1 3 1 1 0 4.9e-08 0.28 0.12 0.85 0.19 1.80e-07 0.000279
13 MAGEC1 melanoma antigen family C, 1 224484 6 5 6 1 0 0 2 4 0 0 0.000012 0.51 0.00031 1 0.0011 2.71e-07 0.000387
14 HLA-A major histocompatibility complex, class I, A 70491 5 5 5 0 0 0 1 3 1 0 1.2e-07 0.26 0.3 1 0.59 1.29e-06 0.00172
15 SDHA succinate dehydrogenase complex, subunit A, flavoprotein (Fp) 131182 6 5 6 0 1 0 3 2 0 0 2.4e-06 0.1 0.037 0.2 0.054 2.14e-06 0.00265
16 OR1S2 olfactory receptor, family 1, subfamily S, member 2 64746 4 4 4 1 0 1 3 0 0 0 1e-06 0.33 0.11 0.72 0.26 4.29e-06 0.00499
17 OR5M9 olfactory receptor, family 5, subfamily M, member 9 61710 4 4 4 0 1 1 0 2 0 0 2.2e-06 0.3 0.22 0.45 0.47 1.51e-05 0.0165
18 FAM86B1 family with sequence similarity 86, member B1 44150 3 3 3 0 2 0 0 0 1 0 0.000011 0.27 0.17 0.68 0.22 3.41e-05 0.0352
19 AQP7 aquaporin 7 69551 6 3 6 1 0 0 0 5 1 0 0.00019 0.43 0.007 0.49 0.014 3.77e-05 0.0355
20 OR13C2 olfactory receptor, family 13, subfamily C, member 2 63360 3 3 3 1 0 0 2 0 1 0 0.000038 0.67 0.13 0.12 0.073 3.87e-05 0.0355
21 CDKN1A cyclin-dependent kinase inhibitor 1A (p21, Cip1) 29948 2 2 2 0 0 0 0 0 1 1 0.000078 0.8 0.49 0.043 0.037 4.00e-05 0.0355
22 UBXN11 UBX domain protein 11 99875 3 3 3 0 0 0 2 1 0 0 0.0005 0.27 0.0012 0.95 0.0094 6.30e-05 0.0533
23 PRSS3 protease, serine, 3 57102 6 6 4 3 1 1 0 1 3 0 0.000048 0.67 0.046 0.81 0.11 6.85e-05 0.0554
24 OR5H1 olfactory receptor, family 5, subfamily H, member 1 62304 3 2 2 1 0 0 0 3 0 0 0.0026 0.8 0.00014 0.75 0.0022 7.32e-05 0.0567
25 NBPF10 neuroblastoma breakpoint family, member 10 350825 6 6 5 2 0 1 0 5 0 0 0.00038 0.75 0.01 0.53 0.018 8.84e-05 0.0654
26 MUC5B mucin 5B, oligomeric mucus/gel-forming 1118646 12 12 12 2 1 1 2 3 5 0 0.000019 0.38 0.25 0.61 0.37 9.14e-05 0.0654
27 GFM1 G elongation factor, mitochondrial 1 153603 3 3 2 0 0 0 1 0 2 0 0.0017 0.71 0.0021 0.98 0.0046 0.000100 0.0692
28 MUC17 mucin 17, cell surface associated 893222 9 5 9 3 1 2 1 5 0 0 0.0058 0.47 0.00085 1 0.0021 0.000150 0.0996
29 TEKT4 tektin 4 56304 3 3 3 0 0 2 0 0 1 0 0.000019 0.65 0.85 0.8 0.9 0.000209 0.127
30 NASP nuclear autoantigenic sperm protein (histone-binding) 165161 3 2 3 0 0 0 2 1 0 0 0.023 0.45 0.00031 0.24 0.00077 0.000209 0.127
31 POTEE POTE ankyrin domain family, member E 216226 5 5 5 1 1 1 0 2 1 0 0.000058 0.59 0.31 0.32 0.33 0.000222 0.127
32 HSP90AB1 heat shock protein 90kDa alpha (cytosolic), class B member 1 144891 4 4 4 0 0 1 1 2 0 0 0.000083 0.31 0.16 0.63 0.23 0.000224 0.127
33 ADAM21 ADAM metallopeptidase domain 21 143418 3 3 3 0 1 0 0 1 1 0 0.0016 0.4 0.044 0.11 0.012 0.000226 0.127
34 ZNF799 zinc finger protein 799 128554 5 4 5 0 1 1 2 1 0 0 0.000083 0.22 0.41 0.13 0.26 0.000253 0.138
35 PCDHB10 protocadherin beta 10 126521 5 4 4 0 1 1 1 2 0 0 0.000097 0.16 0.14 0.55 0.24 0.000267 0.142
TP53

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

MUC6

Figure S2.  This figure depicts the distribution of mutations and mutation types across the MUC6 significant gene.

MUC2

Figure S3.  This figure depicts the distribution of mutations and mutation types across the MUC2 significant gene.

ZNF814

Figure S4.  This figure depicts the distribution of mutations and mutation types across the ZNF814 significant gene.

MUC4

Figure S5.  This figure depicts the distribution of mutations and mutation types across the MUC4 significant gene.

FRG1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the FRG1 significant gene.

PTEN

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

GOLGA6L6

Figure S8.  This figure depicts the distribution of mutations and mutation types across the GOLGA6L6 significant gene.

PABPC1

Figure S9.  This figure depicts the distribution of mutations and mutation types across the PABPC1 significant gene.

TAS2R43

Figure S10.  This figure depicts the distribution of mutations and mutation types across the TAS2R43 significant gene.

PABPC3

Figure S11.  This figure depicts the distribution of mutations and mutation types across the PABPC3 significant gene.

CDC27

Figure S12.  This figure depicts the distribution of mutations and mutation types across the CDC27 significant gene.

MAGEC1

Figure S13.  This figure depicts the distribution of mutations and mutation types across the MAGEC1 significant gene.

HLA-A

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

OR1S2

Figure S15.  This figure depicts the distribution of mutations and mutation types across the OR1S2 significant gene.

OR5M9

Figure S16.  This figure depicts the distribution of mutations and mutation types across the OR5M9 significant gene.

FAM86B1

Figure S17.  This figure depicts the distribution of mutations and mutation types across the FAM86B1 significant gene.

AQP7

Figure S18.  This figure depicts the distribution of mutations and mutation types across the AQP7 significant gene.

OR13C2

Figure S19.  This figure depicts the distribution of mutations and mutation types across the OR13C2 significant gene.

CDKN1A

Figure S20.  This figure depicts the distribution of mutations and mutation types across the CDKN1A significant gene.

UBXN11

Figure S21.  This figure depicts the distribution of mutations and mutation types across the UBXN11 significant gene.

PRSS3

Figure S22.  This figure depicts the distribution of mutations and mutation types across the PRSS3 significant gene.

OR5H1

Figure S23.  This figure depicts the distribution of mutations and mutation types across the OR5H1 significant gene.

NBPF10

Figure S24.  This figure depicts the distribution of mutations and mutation types across the NBPF10 significant gene.

MUC5B

Figure S25.  This figure depicts the distribution of mutations and mutation types across the MUC5B significant gene.

MUC17

Figure S26.  This figure depicts the distribution of mutations and mutation types across the MUC17 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: 2. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 TP53 tumor protein p53 28 356 24 23496 2567 1.1e-12 5e-09
2 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 7 767 7 50622 36 3.6e-11 8.3e-08
3 RCN1 reticulocalbin 1, EF-hand calcium binding domain 1 1 1 66 1 0.00014 0.22
4 ALPK2 alpha-kinase 2 1 3 1 198 1 0.00043 0.39
5 SMARCC2 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily c, member 2 2 3 1 198 1 0.00043 0.39
6 DDR2 discoidin domain receptor tyrosine kinase 2 1 4 1 264 1 0.00057 0.43
7 RB1 retinoblastoma 1 (including osteosarcoma) 2 267 2 17622 4 0.00071 0.43
8 DCLK3 doublecortin-like kinase 3 1 6 1 396 1 0.00086 0.43
9 FLT4 fms-related tyrosine kinase 4 4 6 1 396 1 0.00086 0.43
10 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 1 33 1 2178 1298 0.0047 1

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: 35. 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 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 ARF3(1), CDKN1A(2), CDKN1B(1), TP53(28) 805992 32 24 30 1 3 3 4 7 12 3 0.00097 1.6e-15 4e-13
2 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 PAX3(1), RB1(2), SIRT1(2), TP53(28) 1877723 33 25 31 3 4 3 4 7 11 4 0.018 2.3e-15 4e-13
3 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(1), ATM(3), ATR(2), CDK6(1), CDKN1A(2), CDKN1B(1), RB1(2), TGFB2(1), TP53(28) 2965387 41 27 39 1 6 6 4 8 12 5 0.00012 4.1e-15 4e-13
4 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(1), TP53(28) 1018591 29 22 27 0 3 3 4 7 10 2 0.00021 4.6e-15 4e-13
5 TERTPATHWAY hTERC, the RNA subunit of telomerase, and hTERT, the catalytic protein subunit, are required for telomerase activity and are overexpressed in many cancers. HDAC1, MAX, MYC, SP1, SP3, TP53, WT1, ZNF42 7 MYC(1), TP53(28) 697656 29 23 27 0 3 3 4 7 10 2 0.00017 5.1e-15 4e-13
6 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(3), CDKN1A(2), NFKBIB(1), TP53(28) 2134520 34 24 32 0 6 3 4 8 10 3 0.000058 5.2e-15 4e-13
7 HSA04115_P53_SIGNALING_PATHWAY Genes involved in p53 signaling pathway APAF1, ATM, ATR, BAI1, BAX, BBC3, BID, CASP3, CASP8, CASP9, CCNB1, CCNB2, CCNB3, CCND1, CCND2, CCND3, CCNE1, CCNE2, CCNG1, CCNG2, CD82, CDC2, CDK2, CDK4, CDK6, CDKN1A, CDKN2A, CHEK1, CHEK2, CYCS, DDB2, EI24, FAS, GADD45A, GADD45B, GADD45G, GTSE1, IGF1, IGFBP3, LRDD, MDM2, MDM4, P53AIP1, PERP, PMAIP1, PPM1D, PTEN, RCHY1, RFWD2, RPRM, RRM2, RRM2B, SCOTIN, SERPINB5, SERPINE1, SESN1, SESN2, SESN3, SFN, SIAH1, STEAP3, THBS1, TNFRSF10B, TP53, TP53I3, TP73, TSC2, ZMAT3 65 ATM(3), ATR(2), BAI1(3), CCNE2(1), CDK6(1), CDKN1A(2), CHEK2(1), FAS(1), PERP(1), PTEN(7), SERPINB5(1), SESN1(1), TNFRSF10B(1), TP53(28), TP73(1), TSC2(3) 6545390 57 35 55 1 12 8 7 11 14 5 1.2e-06 6.4e-15 4e-13
8 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 IKBKB(1), JAK2(1), RB1(2), TP53(28) 1928715 32 23 30 0 4 4 4 7 9 4 0.000083 6.8e-15 4e-13
9 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(3), CDC25C(1), MYT1(1), RB1(2), TP53(28), WEE1(1) 1768224 36 25 34 0 5 4 5 9 9 4 8e-05 7e-15 4e-13
10 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(1), MYC(1), PIK3R1(1), POLR1A(1), POLR1D(1), RB1(2), TP53(28) 1963704 35 24 33 0 4 4 5 8 10 4 0.000058 7e-15 4e-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 SPPAPATHWAY Thrombin cleaves protease-activated receptors PAR1 and PAR4 to induce calcium influx and activate platelet aggregation, a process inhibited by aspirin. F2, F2R, F2RL3, GNAI1, GNB1, GNGT1, HRAS, ITGA1, ITGB1, MAP2K1, MAPK1, MAPK3, PLA2G4A, PLCB1, PRKCA, PRKCB1, PTGS1, PTK2, RAF1, SRC, SYK, TBXAS1 21 F2R(1), F2RL3(1), GNAI1(1), ITGB1(1), MAPK3(2), PLCB1(1), PTK2(1), RAF1(1), SRC(1), SYK(1) 2414919 11 10 11 0 2 3 3 1 2 0 0.023 0.0031 0.74
2 HSA00300_LYSINE_BIOSYNTHESIS Genes involved in lysine biosynthesis AADAT, AASDHPPT, AASS, KARS 4 AADAT(1), AASDHPPT(1), AASS(2) 471045 4 4 4 0 0 2 1 1 0 0 0.25 0.004 0.74
3 IL3PATHWAY IL-3 promotes proliferation and differentiation of hematopoietic cells via a heterodimeric receptor that activates the Stat5 and MAP kinase pathways. CSF2RB, FOS, GRB2, HRAS, IL3, IL3RA, JAK2, MAP2K1, MAPK3, PTPN6, RAF1, SHC1, SOS1, STAT5A, STAT5B 15 CSF2RB(3), JAK2(1), MAPK3(2), RAF1(1), SHC1(1), STAT5B(1) 1807799 9 9 9 0 2 4 0 3 0 0 0.074 0.0059 0.74
4 PTENPATHWAY PTEN suppresses AKT-induced cell proliferation and antagonizes the action of PI3K. AKT1, BCAR1, CDKN1B, FOXO3A, GRB2, ILK, ITGB1, MAPK1, MAPK3, PDK2, PDPK1, PIK3CA, PIK3R1, PTEN, PTK2, SHC1, SOS1, TNFSF6 15 BCAR1(2), CDKN1B(1), ITGB1(1), MAPK3(2), PDK2(1), PIK3R1(1), PTK2(1), SHC1(1) 1878331 10 9 10 1 2 1 1 2 3 1 0.27 0.0062 0.74
5 OXIDATIVE_PHOSPHORYLATION ATP12A, ATP4B, ATP5E, ATP5O, ATP6AP1, ATP6V0A1, ATP6V0A4, ATP6V0B, ATP6V0C, ATP6V0C, SHMT1, ATP6V0D1, ATP6V0E, ATP6V1A, ATP6V1B1, ATP6V1B2, ATP6V1C1, ATP6V1C2, ATP6V1D, ATP6V1E1, ATP6V1F, ATP6V1G1, ATP6V1G2, ATP6V1G3, ATP6V1H, ATP7A, ATP7B, COX10, COX4I1, COX5A, COX5B, COX6A1, COX6A2, COX6B1, COX6C, COX7A1, COX7A2, COX7B, COX7C, COX8A, NDUFA1, NDUFA10, NDUFA11, NDUFA4, NDUFA5, NDUFA8, NDUFB2, NDUFB4, NDUFB5, NDUFB6, NDUFB7, NDUFS1, NDUFS2, NDUFV1, NDUFV2, PP, PPA2, SDHA, SDHA, SDHAL2, SDHB, UQCRB, UQCRC1, UQCRFS1, UQCRH 59 ATP12A(1), ATP5E(1), ATP6AP1(1), ATP6V0A1(1), ATP6V0A4(2), ATP6V1A(1), ATP6V1B1(1), ATP6V1B2(1), ATP7A(1), COX7A1(1), NDUFB5(1), NDUFS1(2), UQCRFS1(1) 3683151 15 14 15 1 2 7 0 4 1 1 0.066 0.0063 0.74
6 ASBCELLPATHWAY B cells require interaction with helper T cells to produce antigen-specific immunoglobulins as a key element of the human immune response. CD28, CD4, CD80, HLA-DRA, HLA-DRB1, IL10, IL2, IL4, TNFRSF5, TNFRSF6, TNFSF5, TNFSF6 8 CD80(1), HLA-DRB1(5) 391735 6 5 6 2 1 1 2 2 0 0 0.57 0.0088 0.74
7 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 11 ATM(3), CDC25C(1), MYT1(1), RB1(2), WEE1(1) 1682651 8 7 8 0 2 1 1 2 0 2 0.17 0.01 0.74
8 HSA00531_GLYCOSAMINOGLYCAN_DEGRADATION Genes involved in glycosaminoglycan degradation ARSB, GALNS, GLB1, GNS, GUSB, HEXA, HEXB, HGSNAT, HPSE, HPSE2, HYAL1, HYAL2, IDS, IDUA, LCT, NAGLU, SPAM1 17 GALNS(4), GNS(2), HEXA(1), HGSNAT(1), HPSE2(1), LCT(2), SPAM1(1) 2085688 12 9 12 1 4 3 2 0 3 0 0.11 0.014 0.74
9 LYSINE_BIOSYNTHESIS AADAT, AASDH, AASDHPPT, AASS, KARS 5 AADAT(1), AASDHPPT(1), AASS(2) 692071 4 4 4 0 0 2 1 1 0 0 0.25 0.016 0.74
10 CELL2CELLPATHWAY Epithelial cell adhesion proteins such as cadherins transduce signals into the cell via catenins, which alter cell shape and motility. ACTN1, ACTN2, ACTN3, BCAR1, CSK, CTNNA1, CTNNA2, CTNNB1, PECAM1, PTK2, PXN, SRC, VCL 13 BCAR1(2), CSK(1), CTNNA2(2), CTNNB1(1), PTK2(1), PXN(1), SRC(1) 1936893 9 8 9 1 2 1 1 2 2 1 0.29 0.016 0.74
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