Mutation Analysis (MutSig v1.5)
Rectum Adenocarcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1KH0MFS
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSig v1.5). Broad Institute of MIT and Harvard. doi:10.7908/C1KH0MFS
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 v1.5 was used to generate the results found in this report.

  • Working with individual set: READ-TP

  • Number of patients in set: 122

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:READ-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 78

  • Mutations seen in COSMIC: 1468

  • Significantly mutated genes in COSMIC territory: 626

  • Significantly mutated genesets: 24

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

Mutation Preprocessing

  • Read 38 MAFs of type "Broad"

  • Read 53 MAFs of type "Baylor-Illumina"

  • Read 35 MAFs of type "Baylor-SOLiD"

  • Total number of mutations in input MAFs: 41349

  • After removing 24 noncoding mutations: 41325

  • After collapsing adjacent/redundant mutations: 33492

Mutation Filtering

  • Number of mutations before filtering: 33492

  • After removing 702 mutations outside gene set: 32790

  • After removing 229 mutations outside category set: 32561

  • After removing 1520 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 30110

Results
Breakdown of Mutations by Type

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

type count
De_novo_Start_InFrame 4
De_novo_Start_OutOfFrame 30
Frame_Shift_Del 607
Frame_Shift_Ins 311
In_Frame_Del 247
In_Frame_Ins 119
Missense_Mutation 21318
Nonsense_Mutation 2157
Nonstop_Mutation 16
Silent 7617
Splice_Site 135
Total 32561
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 6436 157111396 0.000041 41 5.5 2.2
*Cp(A/C/T)->mut 7899 1417529885 5.6e-06 5.6 0.75 3.4
*CpG->(G/A) 309 157111396 2e-06 2 0.26 2.8
A->mut 5393 1580983253 3.4e-06 3.4 0.46 3.8
indel+null 3283 3155624534 1e-06 1 0.14 NaN
double_null 183 3155624534 5.8e-08 0.058 0.0078 NaN
Total 23503 3155624534 7.4e-06 7.4 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: READ-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)->mut

  • n3 = number of nonsilent mutations of type: *CpG->(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_ns_s = p-value for the observed nonsilent/silent ratio being elevated in this gene

  • 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: 78. 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_ns_s p q
1 TP53 tumor protein p53 147570 109 91 71 5 39 17 3 24 26 0 1.7e-07 1.1e-15 1.4e-11
2 MUC4 mucin 4, cell surface associated 357584 56 34 28 5 0 1 2 4 47 2 0.94 2e-15 1.4e-11
3 NEFH neurofilament, heavy polypeptide 200kDa 215689 20 20 5 2 0 0 0 0 20 0 0.99 2.4e-15 1.4e-11
4 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 85759 63 63 14 4 0 57 0 4 2 0 0.023 3.7e-15 1.5e-11
5 ORAI1 ORAI calcium release-activated calcium modulator 1 69705 14 14 1 0 0 0 0 0 14 0 1 5e-15 1.5e-11
6 VHL von Hippel-Lindau tumor suppressor 38565 22 17 22 4 2 5 0 14 1 0 0.24 6.9e-15 1.5e-11
7 GPRIN2 G protein regulated inducer of neurite outgrowth 2 80687 13 11 3 2 2 0 0 0 11 0 0.9 6.9e-15 1.5e-11
8 CRIPAK cysteine-rich PAK1 inhibitor 97079 28 20 14 2 2 5 0 8 13 0 0.15 7.4e-15 1.5e-11
9 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 71479 11 11 6 1 0 7 0 4 0 0 0.26 7.6e-15 1.5e-11
10 KRTAP4-1 keratin associated protein 4-1 24478 12 12 1 0 0 0 0 0 12 0 1 9.3e-15 1.6e-11
11 OR6C76 olfactory receptor, family 6, subfamily C, member 76 113109 13 13 1 0 0 0 0 0 13 0 1 1.1e-14 1.6e-11
12 KRTAP10-7 keratin associated protein 10-7 64599 17 18 2 0 0 0 0 0 17 0 1 1.1e-14 1.6e-11
13 RBM38 RNA binding motif protein 38 19136 8 8 2 0 1 0 0 0 7 0 0.58 3.5e-14 4.7e-11
14 HLA-DQA1 major histocompatibility complex, class II, DQ alpha 1 52538 8 7 2 0 0 0 0 0 6 2 1 1.8e-12 2.3e-09
15 TCF7L2 transcription factor 7-like 2 (T-cell specific, HMG-box) 213436 13 13 12 1 3 4 0 1 5 0 0.054 5.5e-12 6.5e-09
16 DEFB126 defensin, beta 126 41372 7 7 3 0 0 2 0 0 5 0 0.73 3.3e-10 3.6e-07
17 TNFAIP6 tumor necrosis factor, alpha-induced protein 6 103218 9 9 3 0 0 0 0 2 7 0 0.59 3.6e-10 3.8e-07
18 MUC2 mucin 2, oligomeric mucus/gel-forming 336936 16 13 8 1 2 3 0 2 9 0 0.21 1.3e-09 1.3e-06
19 ESRRA estrogen-related receptor alpha 99563 9 9 3 0 1 1 0 0 7 0 0.52 1.5e-09 1.4e-06
20 ADAM29 ADAM metallopeptidase domain 29 296713 13 10 6 0 7 1 0 0 5 0 0.13 9.1e-09 8e-06
21 APC adenomatous polyposis coli 1031596 153 108 120 25 1 10 0 39 64 39 0.062 9.6e-09 8.1e-06
22 KRTAP5-2 keratin associated protein 5-2 56623 6 6 1 0 0 0 0 0 6 0 1 2.2e-08 0.000018
23 ALDH3B1 aldehyde dehydrogenase 3 family, member B1 80008 7 8 4 0 0 0 1 1 5 0 0.67 2.6e-08 2e-05
24 DDHD1 DDHD domain containing 1 273284 12 11 3 0 0 0 0 1 11 0 0.54 5.1e-08 0.000038
25 FMN2 formin 2 424640 16 15 9 5 0 2 0 2 12 0 0.99 6.9e-08 0.000049
26 CASP5 caspase 5, apoptosis-related cysteine peptidase 160292 8 8 4 0 0 1 0 1 6 0 0.62 1.7e-07 0.00012
27 PRKRA protein kinase, interferon-inducible double stranded RNA dependent activator 112731 7 7 3 0 0 2 0 0 5 0 0.71 3.8e-07 0.00025
28 KRT4 keratin 4 209867 8 8 1 4 0 0 0 0 8 0 1 4.9e-07 0.0003
29 POLI polymerase (DNA directed) iota 139357 7 8 5 0 1 0 0 1 5 0 0.65 4.9e-07 0.0003
30 KIAA1804 281784 12 10 10 0 7 4 0 1 0 0 0.031 7.1e-07 0.00042
31 TGIF1 TGFB-induced factor homeobox 1 124906 6 6 3 0 0 0 1 0 5 0 0.63 1.9e-06 0.001
32 LCE4A late cornified envelope 4A 33027 4 4 3 1 0 1 0 0 3 0 0.96 1.9e-06 0.001
33 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 319017 12 12 12 2 3 0 0 7 2 0 0.27 2.1e-06 0.0011
34 FAM123B family with sequence similarity 123B 365009 13 10 13 1 0 2 0 3 8 0 0.27 2.6e-06 0.0014
35 TMEM37 transmembrane protein 37 61771 5 5 1 1 0 0 0 0 5 0 1 2.8e-06 0.0014
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: 626. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 APC adenomatous polyposis coli 153 839 127 102358 3336 0 0
2 TP53 tumor protein p53 109 824 109 100528 37961 0 0
3 LIG1 ligase I, DNA, ATP-dependent 9 2 9 244 9 1.3e-14 1.9e-11
4 RHPN2 rhophilin, Rho GTPase binding protein 2 8 4 7 488 12 2.6e-14 2.9e-11
5 ADAM29 ADAM metallopeptidase domain 29 13 5 6 610 7 3.3e-14 2.9e-11
6 TAF1L TAF1 RNA polymerase II, TATA box binding protein (TBP)-associated factor, 210kDa-like 16 11 6 1342 8 7.2e-14 5.4e-11
7 CSMD3 CUB and Sushi multiple domains 3 29 22 7 2684 7 1.4e-13 9e-11
8 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 11 33 10 4026 10215 2.1e-13 1.1e-10
9 SYNE1 spectrin repeat containing, nuclear envelope 1 40 22 6 2684 6 2.3e-13 1.1e-10
10 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 63 52 62 6344 593044 3.2e-13 1.4e-10

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: 24. 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 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 17 ABCB1(6), ATM(37), BAX(2), CPB2(1), CSNK1D(1), FHL2(1), HIF1A(2), IGFBP3(2), MAPK8(6), TP53(109) 3403276 167 98 127 17 48 36 6 43 32 2 4.4e-06 <1.00e-15 <1.71e-13
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(4), BAX(2), BIRC2(2), BIRC3(7), CASP3(1), CASP6(1), CASP8(1), CASP9(1), FAS(2), GZMB(1), MAP2K4(4), MAP3K1(3), MAPK10(4), NFKB1(3), PARP1(1), PRF1(1), RELA(1), TNF(1), TNFSF10(1), TP53(109), TRAF1(2) 6152577 152 95 112 15 50 30 4 31 37 0 3.8e-07 <1.00e-15 <1.71e-13
3 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 CDK4(1), CDKN1B(2), CDKN2A(2), PRB1(2), TP53(109) 1293032 116 94 78 10 41 17 3 25 30 0 3.7e-06 <1.00e-15 <1.71e-13
4 ST_GRANULE_CELL_SURVIVAL_PATHWAY The survival and differentiation of granule cells in the brain is controlled by pro-growth PACAP and pro-apoptotic ceramides. ADPRT, APC, ASAH1, CAMP, CASP3, CERK, CREB1, CREB3, CREB5, CXCL2, DAG1, EPHB2, FOS, GNAQ, IL8RB, ITPKA, ITPKB, JUN, MAP2K4, MAP2K7, MAPK1, MAPK10, MAPK8, MAPK8IP1, MAPK8IP2, MAPK8IP3, MAPK9, PACAP 24 APC(153), CASP3(1), CREB3(1), CREB5(3), DAG1(2), EPHB2(1), FOS(1), GNAQ(1), ITPKB(1), MAP2K4(4), MAP2K7(2), MAPK10(4), MAPK8(6), MAPK8IP1(1), MAPK8IP3(1), MAPK9(3) 4369054 185 110 152 30 10 18 2 46 70 39 0.0013 1.11e-15 1.71e-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 SP1(1), SP3(3), TP53(109), WT1(4) 1194152 117 92 79 7 42 18 3 27 27 0 2.1e-08 2.66e-15 2.85e-13
6 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 17 DNAJA3(1), IFNGR1(2), IFNGR2(1), IKBKB(2), JAK2(7), NFKB1(3), RB1(16), RELA(1), TNF(1), TP53(109), USH1C(4), WT1(4) 3076498 151 95 112 25 49 26 3 41 31 1 0.0023 2.78e-15 2.85e-13
7 CHEMICALPATHWAY DNA damage promotes Bid cleavage, which stimulates mitochondrial cytochrome c release and consequent caspase activation, resulting in apoptosis. ADPRT, AKT1, APAF1, ATM, BAD, BAX, BCL2, BCL2L1, BID, CASP3, CASP6, CASP7, CASP9, CYCS, EIF2S1, PRKCA, PRKCB1, PTK2, PXN, STAT1, TLN1, TP53 20 APAF1(4), ATM(37), BAX(2), CASP3(1), CASP6(1), CASP9(1), PRKCA(3), STAT1(2), TLN1(2), TP53(109) 4715253 162 97 121 17 43 36 5 43 33 2 7.2e-06 3.77e-15 3.04e-13
8 ATMPATHWAY The tumor-suppressing protein kinase ATM responds to radiation-induced DNA damage by blocking cell-cycle progression and activating DNA repair. ABL1, ATM, BRCA1, CDKN1A, CHEK1, CHEK2, GADD45A, JUN, MAPK8, MDM2, MRE11A, NBS1, NFKB1, NFKBIA, RAD50, RAD51, RBBP8, RELA, TP53, TP73 19 ABL1(4), ATM(37), BRCA1(3), CHEK1(1), CHEK2(3), MAPK8(6), MRE11A(5), NFKB1(3), RAD50(7), RBBP8(1), RELA(1), TP53(109) 5081623 180 99 138 25 50 41 7 50 30 2 0.00042 4.11e-15 3.04e-13
9 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(4), NFKB1(3), RELA(1), TP53(109) 1620736 117 93 79 10 43 18 3 27 26 0 0.000013 4.44e-15 3.04e-13
10 PMLPATHWAY Ring-shaped PML nuclear bodies regulate transcription and are required co-activators in p53- and DAXX-mediated apoptosis. CREBBP, DAXX, HRAS, PAX3, PML, PRAM-1, RARA, RB1, SIRT1, SP100, TNF, TNFRSF1A, TNFRSF1B, TNFRSF6, TNFSF6, TP53, UBL1 13 CREBBP(13), DAXX(4), PAX3(3), PML(1), RARA(1), RB1(16), SIRT1(1), SP100(1), TNF(1), TP53(109) 3084344 150 96 109 21 51 19 5 43 32 0 0.000096 5.11e-15 3.15e-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 ERBB4PATHWAY ErbB4 (aka HER4) is a receptor tyrosine kinase that binds neuregulins as well as members of the EGF family, which also target EGF receptors. ADAM17, ERBB4, NRG2, NRG3, PRKCA, PRKCB1, PSEN1 6 ADAM17(2), ERBB4(9), NRG2(2), NRG3(4), PRKCA(3), PSEN1(1) 1586122 21 16 21 3 6 7 2 3 3 0 0.12 0.03 1
2 HSA00472_D_ARGININE_AND_D_ORNITHINE_METABOLISM Genes involved in D-arginine and D-ornithine metabolism DAO 1 DAO(4) 129236 4 3 4 0 3 1 0 0 0 0 0.27 0.066 1
3 HSP27PATHWAY Hsp27 oligomers have molecular chaperone activity and protect heat-stressed cells against apoptosis. ACTA1, APAF1, BCL2, CASP3, CASP9, CYCS, DAXX, FAS, FASLG, HSPB1, HSPB2, IL1A, MAPKAPK2, MAPKAPK3, TNF, TNFRSF6 14 ACTA1(2), APAF1(4), CASP3(1), CASP9(1), DAXX(4), FAS(2), HSPB2(1), MAPKAPK2(1), TNF(1) 1838947 17 14 16 1 1 3 0 11 2 0 0.12 0.067 1
4 C21_STEROID_HORMONE_METABOLISM AKR1C4, AKR1D1, CYP11A1, CYP11B1, CYP11B2, CYP17A1, CYP21A2, HSD11B1, HSD11B2, HSD3B1, HSD3B2 11 AKR1C4(2), AKR1D1(1), CYP11A1(2), CYP11B1(4), CYP21A2(3), HSD3B1(1), HSD3B2(1) 1477777 14 13 13 3 3 5 0 2 4 0 0.45 0.087 1
5 HSA00140_C21_STEROID_HORMONE_METABOLISM Genes involved in C21-steroid hormone metabolism AKR1C4, AKR1D1, CYP11A1, CYP11B1, CYP11B2, CYP17A1, CYP21A2, HSD11B1, HSD11B2, HSD3B1, HSD3B2 11 AKR1C4(2), AKR1D1(1), CYP11A1(2), CYP11B1(4), CYP21A2(3), HSD3B1(1), HSD3B2(1) 1477777 14 13 13 3 3 5 0 2 4 0 0.45 0.087 1
6 GSPATHWAY Activated G-protein coupled receptors stimulate cAMP production and thus activate protein kinase A, involved in a number of signal transduction pathways. ADCY1, GNAS, GNB1, GNGT1, PRKACA, PRKAR1A 6 ADCY1(5), GNAS(4), GNGT1(1), PRKACA(1), PRKAR1A(5) 974544 16 14 16 3 5 4 2 3 2 0 0.24 0.092 1
7 PS1PATHWAY Presenilin is required for gamma-secretase activity to activate Notch signaling; presenilin also inhibits beta-catenin in the Wnt/Frizzled pathway. ADAM17, APC, AXIN1, BTRC, CTNNB1, DLL1, DVL1, FZD1, GSK3B, NOTCH1, PSEN1, RBPSUH, TCF1, WNT1 10 ADAM17(2), AXIN1(2), BTRC(4), CTNNB1(21), DLL1(4), FZD1(2), GSK3B(1), NOTCH1(1), PSEN1(1) 1902790 38 29 36 9 7 10 1 16 4 0 0.3 0.096 1
8 BLOOD_GROUP_GLYCOLIPID_BIOSYNTHESIS_LACTOSERIES ABO, FUT1, FUT2, FUT3, FUT5, FUT6, SIAT6, ST3GAL3 7 ABO(2), FUT1(3), FUT2(1), FUT3(2), FUT6(1) 660538 9 7 9 1 5 2 0 0 2 0 0.25 0.1 1
9 HSA00660_C5_BRANCHED_DIBASIC_ACID_METABOLISM Genes involved in C5-branched dibasic acid metabolism ILVBL, SUCLA2 2 ILVBL(1), SUCLA2(3) 352237 4 4 4 1 0 3 0 1 0 0 0.66 0.16 1
10 BOTULINPATHWAY Blockade of Neurotransmitter Relase by Botulinum Toxin CHRM1, CHRNA1, SNAP25, STX1A, VAMP2 5 CHRNA1(3), SNAP25(1), STX1A(1) 555266 5 5 5 1 0 3 0 2 0 0 0.57 0.16 1
Methods & Data
Methods

In brief, we tabulate the number of mutations and the number of covered bases for each gene. The counts are broken down by mutation context category: four context categories that are discovered by MutSig, and one for indel and 'null' mutations, which include indels, nonsense mutations, splice-site mutations, and non-stop (read-through) mutations. For each gene, we calculate the probability of seeing the observed constellation of mutations, i.e. the product P1 x P2 x ... x Pm, or a more extreme one, given the background mutation rates calculated across the dataset. [1]

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] TCGA, Integrated genomic analyses of ovarian carcinoma, Nature 474:609 - 615 (2011)
Copyright © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle