Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result)
Stomach Adenocarcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C17P8WRS
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result). Broad Institute of MIT and Harvard. doi:10.7908/C17P8WRS
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 and MutSigCV v0.9 merged result was used to generate the results found in this report.

  • Working with individual set: STAD-TP

  • Number of patients in set: 221

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:STAD-TP.final_analysis_set.maf

  • Significantly mutated genes (q ≤ 0.1): 22

  • Mutations seen in COSMIC: 504

  • Significantly mutated genes in COSMIC territory: 30

  • Significantly mutated genesets: 18

Mutation Preprocessing

  • Read 221 MAFs of type "Broad"

  • Total number of mutations in input MAFs: 112521

  • After removing 38 mutations outside chr1-24: 112483

  • After removing 2156 noncoding mutations: 110327

Mutation Filtering

  • Number of mutations before filtering: 110327

  • After removing 1499 mutations outside gene set: 108828

  • After removing 117 mutations outside category set: 108711

  • After removing 3 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 107092

Results
Breakdown of Mutations by Type

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

type count
Frame_Shift_Del 2300
Frame_Shift_Ins 513
In_Frame_Del 204
In_Frame_Ins 10
Missense_Mutation 70797
Nonsense_Mutation 3553
Nonstop_Mutation 72
Silent 29494
Splice_Site 1570
Translation_Start_Site 198
Total 108711
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 25194 355853720 0.000071 71 5.8 2.1
*Np(A/C/T)->transit 22532 5121780072 4.4e-06 4.4 0.36 2
*ApG->G 3518 992607213 3.5e-06 3.5 0.29 2.1
transver 19746 6470241005 3.1e-06 3.1 0.25 5
indel+null 8115 6470241005 1.3e-06 1.3 0.1 NaN
double_null 110 6470241005 1.7e-08 0.017 0.0014 NaN
Total 79215 6470241005 0.000012 12 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: STAD-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: *Np(A/C/T)->transit

  • n3 = number of nonsilent mutations of type: *ApG->G

  • 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_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: 22. 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_clust p_cons p_joint p_cv p q
1 TRIM48 tripartite motif-containing 48 135763 14 14 2 1 0 13 0 1 0 0 0 0.94 0 1 0 0
2 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 723978 62 48 31 2 10 40 3 8 1 0 0.0003 0.0051 0.000018 1.5e-14 0 0
3 PGM5 phosphoglucomutase 5 305048 25 22 7 1 4 18 0 1 2 0 0 1 0 4.2e-09 0 0
4 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 153529 25 25 6 0 0 20 0 5 0 0 0 0.000083 0 0.000027 0 0
5 CBWD1 COBW domain containing 1 210334 30 28 3 0 0 1 0 1 28 0 0 0 0 0 0 0
6 TP53 tumor protein p53 272497 103 99 66 1 28 21 2 14 37 1 0 0 0 2.7e-15 0 0
7 ARID1A AT rich interactive domain 1A (SWI-like) 1285806 46 42 46 2 6 5 1 4 28 2 0.42 0.46 0.65 9.7e-15 2.1e-13 5.4e-10
8 SMAD4 SMAD family member 4 374970 21 19 18 1 5 6 0 6 3 1 6.2e-06 0.15 0.000014 1.6e-08 6.7e-12 1.5e-08
9 RHOA ras homolog gene family, member A 132155 14 13 10 0 1 5 0 8 0 0 4e-07 0.55 8.6e-06 7.5e-08 1.9e-11 3.7e-08
10 MXRA8 matrix-remodelling associated 8 171805 13 11 6 2 3 1 0 0 9 0 0.01 0.6 0.028 1.7e-09 1.2e-09 2.1e-06
11 IRF2 interferon regulatory factor 2 238325 17 15 16 2 5 2 0 5 4 1 0.0087 0.0016 0.00041 1.2e-07 1.2e-09 2.1e-06
12 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 561265 19 18 18 5 1 6 1 7 4 0 0.00089 0.00038 8.8e-06 0.000013 2.8e-09 4.2e-06
13 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 266766 18 14 16 4 1 4 0 6 7 0 0.1 0.84 0.18 1.1e-09 4.4e-09 6.2e-06
14 FBXW7 F-box and WD repeat domain containing 7 548512 20 19 13 1 11 4 0 0 5 0 0.0012 0.12 0.0018 7.9e-06 2.7e-07 0.00035
15 B2M beta-2-microglobulin 82186 8 8 8 0 0 2 0 2 3 1 0.25 0.12 0.18 8.1e-08 2.8e-07 0.00035
16 PTH2 parathyroid hormone 2 47536 4 4 1 0 0 0 0 4 0 0 2e-06 0.083 0.000028 0.011 5.1e-06 0.0057
17 FAM46D family with sequence similarity 46, member D 255109 6 6 3 0 0 0 0 5 1 0 5.6e-06 0.097 0.000014 0.054 0.000011 0.012
18 APC adenomatous polyposis coli 1887815 35 33 32 5 8 6 1 4 14 2 0.13 0.95 0.18 4.8e-06 0.000013 0.013
19 RNF43 ring finger protein 43 479231 10 9 10 2 2 2 0 0 6 0 0.011 0.021 0.0088 0.00011 0.000014 0.013
20 MAP2K7 mitogen-activated protein kinase kinase 7 219244 20 14 20 0 10 4 0 3 3 0 0.4 0.4 0.55 6.6e-06 0.000049 0.044
21 WSB2 WD repeat and SOCS box-containing 2 272621 7 7 7 1 2 2 0 2 1 0 0.11 0.17 0.16 0.000034 0.000073 0.062
22 BCOR BCL6 co-repressor 1054518 18 16 18 3 2 4 0 2 10 0 0.51 0.3 0.53 0.000017 0.00012 0.095
23 TRPS1 trichorhinophalangeal syndrome I 862345 34 30 34 12 6 11 4 8 5 0 0.71 0.47 0.77 0.000016 0.00015 0.12
24 C13orf33 chromosome 13 open reading frame 33 168686 6 6 2 1 0 0 0 0 6 0 0.0034 0.53 0.0081 0.0015 0.00015 0.12
25 HRCT1 histidine rich carboxyl terminus 1 64764 4 4 1 0 0 0 0 4 0 0 6e-07 1 0.000012 1 0.00015 0.12
26 IAPP islet amyloid polypeptide 61356 4 4 3 0 0 0 0 4 0 0 0.0009 0.97 0.0024 0.0094 0.00027 0.19
27 XPOT exportin, tRNA (nuclear export receptor for tRNAs) 655614 9 8 4 19 1 7 0 1 0 0 6.6e-06 0.96 0.000026 1 0.0003 0.2
28 MAGEE2 melanoma antigen family E, 2 340871 7 7 5 0 1 1 0 2 3 0 0.00023 0.8 0.00059 0.053 0.00035 0.23
29 LEMD1 LEM domain containing 1 74907 3 2 2 1 0 0 1 1 1 0 0.0025 0.0054 0.00052 0.11 0.00061 0.38
30 EDNRB endothelin receptor type B 303308 20 18 17 4 8 3 0 5 4 0 0.043 0.84 0.12 0.00047 0.00063 0.38
31 BCL7C B-cell CLL/lymphoma 7C 149528 7 7 7 0 2 2 0 0 3 0 0.69 0.81 1 0.000066 0.0007 0.41
32 KCNMB2 potassium large conductance calcium-activated channel, subfamily M, beta member 2 159602 6 6 4 2 0 1 0 5 0 0 0.000099 1 0.00033 0.24 0.00084 0.47
33 LRIT1 leucine-rich repeat, immunoglobulin-like and transmembrane domains 1 347905 9 9 8 1 2 0 1 3 3 0 0.026 1 0.058 0.0016 0.00094 0.51
34 OR51G1 olfactory receptor, family 51, subfamily G, member 1 213431 6 6 6 2 2 2 0 2 0 0 0.000095 0.78 0.00046 0.23 0.0011 0.58
35 CBLN3 cerebellin 3 precursor 98729 4 4 3 2 0 0 0 1 3 0 0.34 0.11 0.29 0.00039 0.0011 0.59
TRIM48

Figure S1.  This figure depicts the distribution of mutations and mutation types across the TRIM48 significant gene.

PIK3CA

Figure S2.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

PGM5

Figure S3.  This figure depicts the distribution of mutations and mutation types across the PGM5 significant gene.

KRAS

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

CBWD1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the CBWD1 significant gene.

TP53

Figure S6.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

ARID1A

Figure S7.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

SMAD4

Figure S8.  This figure depicts the distribution of mutations and mutation types across the SMAD4 significant gene.

RHOA

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

MXRA8

Figure S10.  This figure depicts the distribution of mutations and mutation types across the MXRA8 significant gene.

IRF2

Figure S11.  This figure depicts the distribution of mutations and mutation types across the IRF2 significant gene.

CDH1

Figure S12.  This figure depicts the distribution of mutations and mutation types across the CDH1 significant gene.

PTEN

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

FBXW7

Figure S14.  This figure depicts the distribution of mutations and mutation types across the FBXW7 significant gene.

B2M

Figure S15.  This figure depicts the distribution of mutations and mutation types across the B2M significant gene.

FAM46D

Figure S16.  This figure depicts the distribution of mutations and mutation types across the FAM46D significant gene.

APC

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

RNF43

Figure S18.  This figure depicts the distribution of mutations and mutation types across the RNF43 significant gene.

MAP2K7

Figure S19.  This figure depicts the distribution of mutations and mutation types across the MAP2K7 significant gene.

WSB2

Figure S20.  This figure depicts the distribution of mutations and mutation types across the WSB2 significant gene.

BCOR

Figure S21.  This figure depicts the distribution of mutations and mutation types across the BCOR 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: 30. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 ERBB3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) 29 6 6 1326 6 0 0
2 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 25 52 24 11492 166293 0 0
3 TP53 tumor protein p53 103 356 96 78676 26850 0 0
4 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 62 220 54 48620 19016 0 0
5 FBXW7 F-box and WD repeat domain containing 7 20 91 13 20111 681 0 0
6 SMAD4 SMAD family member 4 21 159 17 35139 66 0 0
7 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 19 185 11 40885 35 6.7e-12 4.3e-09
8 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 18 767 18 169507 551 1e-11 5.8e-09
9 APC adenomatous polyposis coli 35 839 18 185419 374 4.6e-11 2.3e-08
10 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 12 42 6 9282 75 2.7e-09 1.2e-06

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: 18. 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 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 AKT1(3), APAF1(5), ATM(30), BAD(3), BAX(1), BCL2(2), BID(2), CASP3(2), CASP6(1), CASP7(2), CASP9(2), EIF2S1(2), PRKCA(3), PTK2(10), PXN(3), STAT1(7), TLN1(14), TP53(103) 8999666 195 131 156 21 57 46 11 30 48 3 1.2e-09 1.6e-15 2.9e-13
2 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 HDAC1(3), MAX(3), MYC(3), SP1(6), SP3(3), TP53(103), WT1(3) 2316024 124 111 87 11 34 29 2 18 40 1 1.6e-08 2.4e-15 2.9e-13
3 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(30), BRCA1(14), CDKN1A(1), CHEK1(4), CHEK2(7), JUN(3), MAPK8(5), MDM2(3), MRE11A(2), NFKB1(7), NFKBIA(1), RAD50(9), RAD51(1), RBBP8(6), RELA(3), TP53(103), TP73(2) 9791154 205 137 167 22 51 47 8 44 52 3 7.8e-09 2.6e-15 2.9e-13
4 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 ABCB1(18), AKT1(3), ATM(30), BAX(1), CDKN1A(1), CPB2(6), CSNK1A1(3), CSNK1D(2), FHL2(2), HIC1(5), HIF1A(5), HSPA1A(1), IGFBP3(5), MAPK8(5), MDM2(3), NFKBIB(5), NQO1(2), TP53(103) 6815341 200 134 162 20 56 47 6 33 55 3 1.2e-10 2.6e-15 2.9e-13
5 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 ARF1(4), CCND1(1), CDK2(3), CDKN1A(1), CDKN2A(8), CFL1(1), E2F1(2), E2F2(3), MDM2(3), NXT1(2), PRB1(2), TP53(103) 2761021 133 114 96 12 38 31 3 16 44 1 6.1e-08 3.2e-15 2.9e-13
6 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(27), DAXX(9), PAX3(7), PML(7), RARA(2), RB1(6), SIRT1(4), SP100(10), TNF(1), TNFRSF1A(3), TNFRSF1B(2), TP53(103) 6230239 181 122 143 25 57 43 4 29 47 1 1.3e-07 3.3e-15 2.9e-13
7 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(4), CDKN2A(8), E2F1(2), MDM2(3), MYC(3), PIK3CA(62), PIK3R1(8), POLR1A(12), POLR1B(4), POLR1C(2), POLR1D(1), RB1(6), TBX2(3), TP53(103), TWIST1(1) 6653717 222 147 153 31 56 78 6 31 50 1 3.4e-09 3.8e-15 2.9e-13
8 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(30), CDC25A(3), CDC25B(4), CDC25C(7), CDK2(3), CHEK1(4), MYT1(14), RB1(6), TP53(103), WEE1(2), YWHAH(3) 5794957 179 127 139 14 48 41 6 34 47 3 7e-10 3.8e-15 2.9e-13
9 TIDPATHWAY On ligand binding, interferon gamma receptors stimulate JAK2 kinase to phosphorylate STAT transcription factors, which promote expression of interferon responsive genes. DNAJA3, HSPA1A, IFNG, IFNGR1, IFNGR2, IKBKB, JAK2, LIN7A, NFKB1, NFKBIA, RB1, RELA, TIP-1, TNF, TNFRSF1A, TNFRSF1B, TP53, USH1C, WT1 18 DNAJA3(2), HSPA1A(1), IFNG(2), IFNGR1(5), IFNGR2(1), IKBKB(7), JAK2(10), LIN7A(7), NFKB1(7), NFKBIA(1), RB1(6), RELA(3), TNF(1), TNFRSF1A(3), TNFRSF1B(2), TP53(103), USH1C(2), WT1(3) 5958723 166 118 127 26 42 40 3 36 44 1 0.000017 5e-15 3.4e-13
10 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 DNAJC3(3), EIF2S1(2), EIF2S2(3), MAP3K14(3), NFKB1(7), NFKBIA(1), RELA(3), TP53(103) 3207445 125 106 88 7 34 29 2 20 39 1 3.6e-09 5.7e-15 3.5e-13
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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