Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result)
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C16H4FRV
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/C16H4FRV
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: LAML-TB

- Number of patients in set: 197

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:LAML-TB.final_analysis_set.maf

- Significantly mutated genes (q ≤ 0.1): 6

- Mutations seen in COSMIC: 239

- Significantly mutated genes in COSMIC territory: 19

- Significantly mutated genesets: 64

Mutation Preprocessing

- Read 197 MAFs of type "WashU"

- Total number of mutations in input MAFs: 2585

- After removing 41 mutations outside chr1-24: 2544

- After removing 104 noncoding mutations: 2440

Mutation Filtering

- Number of mutations before filtering: 2440

- After removing 204 mutations outside gene set: 2236

- After removing 14 mutations outside category set: 2222

- After removing 1 "impossible" mutations in

- gene-patient-category bins of zero coverage: 2003

Results
Breakdown of Mutations by Type

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

type | count
--- | ---
Frame_Shift_Del | 51
Frame_Shift_Ins | 110
In_Frame_Del | 8
In_Frame_Ins | 43
Missense_Mutation | 1401
Nonsense_Mutation | 108
Silent | 451
Splice_Site | 50
Total | 2222

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 | 508 | 272168305 | 1.9e-06 | 1.9 | 5.8 | 2.2
*Cp(A/C/T)->T | 312 | 2466260336 | 1.3e-07 | 0.13 | 0.39 | 1.7
A->G | 183 | 2743656430 | 6.7e-08 | 0.067 | 0.21 | 2.3
transver | 398 | 5482085071 | 7.3e-08 | 0.073 | 0.22 | 5.1
indel+null | 357 | 5482085071 | 6.5e-08 | 0.065 | 0.2 | NaN
double_null | 13 | 5482085071 | 2.4e-09 | 0.0024 | 0.0073 | NaN
Total | 1771 | 5482085071 | 3.2e-07 | 0.32 | 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: LAML-TB.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)->T

- n3 = number of nonsilent mutations of type: A->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: 6. 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 | DNMT3A | DNA (cytosine-5-)-methyltransferase 3 alpha | 512791 | 57 | 51 | 29 | 0 | 34 | 2 | 3 | 4 | 14 | 0 | 0 | 0 | 0 | 1 | 0 | 0
2 | U2AF1 | U2 small nuclear RNA auxiliary factor 1 | 153266 | 8 | 8 | 2 | 0 | 0 | 5 | 0 | 3 | 0 | 0 | 1e-05 | 0.000096 | 0 | 1 | 0 | 0
3 | FLT3 | fms-related tyrosine kinase 3 | 597107 | 56 | 56 | 30 | 0 | 0 | 0 | 1 | 16 | 39 | 0 | 0 | 1.2e-06 | 0 | 1 | 0 | 0
4 | IDH2 | isocitrate dehydrogenase 2 (NADP+), mitochondrial | 230293 | 20 | 20 | 2 | 0 | 16 | 3 | 0 | 1 | 0 | 0 | 0 | 0.66 | 0 | 1 | 0 | 0
5 | IDH1 | isocitrate dehydrogenase 1 (NADP+), soluble | 251569 | 19 | 19 | 2 | 0 | 17 | 0 | 0 | 2 | 0 | 0 | 0 | 0.97 | 0 | 1 | 0 | 0
6 | NPM1 | nucleophosmin (nucleolar phosphoprotein B23, numatrin) | 183407 | 54 | 54 | 7 | 0 | 0 | 0 | 1 | 0 | 52 | 1 | 0 | 0 | 0 | 1 | 0 | 0
7 | NRAS | neuroblastoma RAS viral (v-ras) oncogene homolog | 115442 | 15 | 15 | 6 | 0 | 0 | 8 | 1 | 6 | 0 | 0 | 6e-07 | 0.24 | 4.2e-06 | 1 | 0.000056 | 0.14
8 | WT1 | Wilms tumor 1 | 164692 | 12 | 12 | 10 | 0 | 1 | 1 | 0 | 0 | 9 | 1 | 0.00012 | 0.99 | 0.00022 | 1 | 0.002 | 1
9 | RUNX1 | runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) | 145189 | 20 | 18 | 16 | 0 | 2 | 3 | 2 | 2 | 10 | 1 | 0.026 | 0.01 | 0.0032 | 1 | 0.021 | 1
10 | SMG1 | smg-1 homolog, phosphatidylinositol 3-kinase-related kinase (C. elegans) | 2065151 | 3 | 3 | 3 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0.0053 | 0.1 | 0.0033 | 1 | 0.022 | 1
11 | KIT | v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog | 585287 | 9 | 8 | 5 | 0 | 1 | 0 | 0 | 6 | 1 | 1 | 0.011 | 0.084 | 0.015 | 1 | 0.079 | 1
12 | TP53 | tumor protein p53 | 258267 | 19 | 15 | 19 | 1 | 3 | 2 | 4 | 2 | 8 | 0 | 0.0077 | 0.26 | 0.02 | 1 | 0.097 | 1
13 | EZH2 | enhancer of zeste homolog 2 (Drosophila) | 459010 | 3 | 3 | 3 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0.018 | 0.81 | 0.028 | 1 | 0.13 | 1
14 | GSTK1 | glutathione S-transferase kappa 1 | 172769 | 2 | 2 | 2 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0.024 | 0.22 | 0.031 | 1 | 0.14 | 1
15 | GATA2 | GATA binding protein 2 | 159964 | 2 | 2 | 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0.46 | 0.025 | 0.035 | 1 | 0.15 | 1
16 | FCGBP | Fc fragment of IgG binding protein | 2259984 | 3 | 3 | 3 | 2 | 1 | 1 | 0 | 1 | 0 | 0 | 0.047 | 0.075 | 0.038 | 1 | 0.16 | 1
17 | KRAS | v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog | 139279 | 8 | 8 | 6 | 0 | 0 | 4 | 1 | 3 | 0 | 0 | 0.023 | 0.64 | 0.039 | 1 | 0.16 | 1
18 | DOCK2 | dedicator of cytokinesis 2 | 1113838 | 2 | 2 | 2 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0.077 | 0.0046 | 0.043 | 1 | 0.18 | 1
19 | PTPN11 | protein tyrosine phosphatase, non-receptor type 11 (Noonan syndrome 1) | 359328 | 9 | 9 | 9 | 0 | 0 | 3 | 2 | 4 | 0 | 0 | 0.045 | 0.26 | 0.06 | 1 | 0.23 | 1
20 | PKD1L2 | polycystic kidney disease 1-like 2 | 1191259 | 2 | 2 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0.28 | 0.066 | 0.064 | 1 | 0.24 | 1
21 | TRPM3 | transient receptor potential cation channel, subfamily M, member 3 | 1053359 | 2 | 2 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0.022 | 0.44 | 0.068 | 1 | 0.25 | 1
22 | CBL | Cas-Br-M (murine) ecotropic retroviral transforming sequence | 509442 | 2 | 2 | 2 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0.014 | 0.39 | 0.084 | 1 | 0.29 | 1
23 | COL12A1 | collagen, type XII, alpha 1 | 1846875 | 3 | 3 | 3 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.95 | 0.01 | 0.093 | 1 | 0.31 | 1
24 | NR2E1 | nuclear receptor subfamily 2, group E, member 1 | 182225 | 2 | 2 | 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0.3 | 0.091 | 0.1 | 1 | 0.33 | 1
25 | PDCD2L | programmed cell death 2-like | 161934 | 2 | 2 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0.35 | 0.075 | 0.1 | 1 | 0.34 | 1
26 | HNRNPK | heterogeneous nuclear ribonucleoprotein K | 293530 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0.025 | 0.79 | 0.11 | 1 | 0.34 | 1
27 | CSMD3 | CUB and Sushi multiple domains 3 | 2259590 | 2 | 2 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0.12 | 0.13 | 0.11 | 1 | 0.36 | 1
28 | SMC1A | structural maintenance of chromosomes 1A | 744857 | 7 | 7 | 7 | 0 | 3 | 0 | 1 | 2 | 1 | 0 | 0.47 | 0.032 | 0.11 | 1 | 0.36 | 1
29 | KIAA1683 | KIAA1683 | 543523 | 2 | 2 | 2 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0.48 | 0.072 | 0.12 | 1 | 0.37 | 1
30 | PHACTR1 | phosphatase and actin regulator 1 | 323868 | 3 | 3 | 2 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0.04 | 0.7 | 0.12 | 1 | 0.38 | 1
31 | PLCE1 | phospholipase C, epsilon 1 | 1441843 | 4 | 4 | 4 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0.068 | 0.92 | 0.12 | 1 | 0.38 | 1
32 | SI | sucrase-isomaltase (alpha-glucosidase) | 1117384 | 2 | 2 | 2 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0.064 | 0.24 | 0.13 | 1 | 0.39 | 1
33 | THRAP3 | thyroid hormone receptor associated protein 3 | 572876 | 2 | 2 | 2 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0.24 | 0.12 | 0.13 | 1 | 0.39 | 1
34 | NF1 | neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) | 1721977 | 2 | 2 | 2 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0.031 | 0.52 | 0.14 | 1 | 0.42 | 1
35 | ZBTB33 | zinc finger and BTB domain containing 33 | 398531 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0.2 | 0.13 | 0.15 | 1 | 0.43 | 1

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: 19. Number of genes displayed: 10

rank | gene | description | n | cos | n_cos | N_cos | cos_ev | p | q
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
1 | IDH1 | isocitrate dehydrogenase 1 (NADP+), soluble | 19 | 5 | 19 | 985 | 28348 | 0 | 0
2 | IDH2 | isocitrate dehydrogenase 2 (NADP+), mitochondrial | 20 | 6 | 20 | 1182 | 2000 | 0 | 0
3 | NPM1 | nucleophosmin (nucleolar phosphoprotein B23, numatrin) | 54 | 41 | 53 | 8077 | 112668 | 0 | 0
4 | NRAS | neuroblastoma RAS viral (v-ras) oncogene homolog | 15 | 33 | 15 | 6501 | 11840 | 0 | 0
5 | FLT3 | fms-related tyrosine kinase 3 | 56 | 124 | 52 | 24428 | 6301 | 0 | 0
6 | PTPN11 | protein tyrosine phosphatase, non-receptor type 11 (Noonan syndrome 1) | 9 | 32 | 8 | 6304 | 216 | 0 | 0
7 | KRAS | v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog | 8 | 52 | 7 | 10244 | 46308 | 0 | 0
8 | RUNX1 | runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) | 20 | 178 | 18 | 35066 | 93 | 0 | 0
9 | WT1 | Wilms tumor 1 | 12 | 185 | 9 | 36445 | 444 | 0 | 0
10 | KIT | v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog | 9 | 240 | 8 | 47280 | 4044 | 0 | 0

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: 64. 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 | HSA00271_METHIONINE_METABOLISM | Genes involved in methionine metabolism | AHCY, AMD1, BHMT, CBS, CTH, DNMT1, DNMT3A, DNMT3B, KIAA0828, MARS, MARS2, MAT1A, MAT2B, MTAP, MTFMT, MTR, SRM, TAT | 17 | DNMT1(1), DNMT3A(57), DNMT3B(1), MAT1A(1) | 5703150 | 60 | 53 | 32 | 0 | 36 | 2 | 3 | 5 | 14 | 0 | 1.2e-09 | 1.1e-15 | 2.7e-13
2 | ERYTHPATHWAY | Erythropoietin selectively stimulates erythrocyte differentiation from CFU-GEMM cells in bone marrow. | CCL3, CSF2, CSF3, EPO, FLT3, IGF1, IL11, IL1A, IL3, IL6, IL9, KITLG, TGFB1, TGFB2, TGFB3 | 14 | FLT3(56) | 2377790 | 56 | 56 | 30 | 0 | 0 | 0 | 1 | 16 | 39 | 0 | 0.048 | 2.4e-15 | 2.7e-13
3 | HSA00720_REDUCTIVE_CARBOXYLATE_CYCLE | Genes involved in reductive carboxylate cycle (CO2 fixation) | ACLY, ACO1, ACO2, ACSS1, ACSS2, FH, IDH1, IDH2, LOC441996, MDH1, MDH2, SUCLA2 | 11 | IDH1(19), IDH2(20) | 3866716 | 39 | 38 | 4 | 0 | 33 | 3 | 0 | 3 | 0 | 0 | 2e-07 | 2.8e-15 | 2.7e-13
4 | GLUTATHIONE_METABOLISM |  | ANPEP, G6PD, GCLC, GCLM, GGT1, GPX1, GPX2, GPX3, GPX4, GPX5, GSS, GSTA1, GSTA2, GSTA3, GSTA4, GSTM1, GSTM2, GSTM3, GSTM4, GSTM5, GSTO2, GSTP1, GSTT1, GSTT2, GSTZ1, IDH1, IDH2, MGST1, MGST2, MGST3, PGD | 29 | GCLM(1), GPX2(1), GSTM3(1), IDH1(19), IDH2(20) | 5170462 | 42 | 40 | 7 | 0 | 33 | 4 | 0 | 5 | 0 | 0 | 1.7e-07 | 2.9e-15 | 2.7e-13
5 | KREBPATHWAY | The Krebs (citric acid) cycle takes place in mitochondria, where it extracts energy in the form of electron carriers NADH and FADH2, which drive the electron transport chain. | ACO2, CS, FH, IDH2, MDH1, OGDH, SDHA, SUCLA2 | 8 | IDH2(20) | 2741649 | 20 | 20 | 2 | 0 | 16 | 3 | 0 | 1 | 0 | 0 | 0.00031 | 3e-15 | 2.7e-13
6 | NUCLEAR_RECEPTORS |  | ALK, AR, ESR1, ESR2, ESRRA, HNF4A, NPM1, NR0B1, NR1D2, NR1H2, NR1H3, NR1I2, NR1I3, NR2C2, NR2E1, NR2F1, NR2F2, NR2F6, NR3C1, NR4A1, NR4A2, NR5A1, NR5A2, PGR, PPARA, PPARD, PPARG, RARA, RARB, RARG, ROR1, RORA, RORC, RXRA, RXRB, RXRG, THRA, THRA, NR1D1, THRB, VDR | 38 | NPM1(54), NR2E1(2), THRB(1) | 11589707 | 57 | 56 | 10 | 0 | 1 | 1 | 1 | 1 | 52 | 1 | 0.25 | 3.8e-15 | 2.7e-13
7 | CITRATE_CYCLE_TCA_CYCLE |  | ACO1, ACO2, CS, DLD, DLST, DLSTP, FH, IDH1, IDH2, IDH3A, IDH3B, IDH3G, MDH1, MDH2, PC, PCK1, SDHA, SDHA, SDHAL2, SDHB, SUCLA2, SUCLG1, SUCLG2 | 20 | DLD(1), IDH1(19), IDH2(20) | 5929109 | 40 | 39 | 5 | 1 | 33 | 4 | 0 | 3 | 0 | 0 | 1.5e-06 | 4.1e-15 | 2.7e-13
8 | REDUCTIVE_CARBOXYLATE_CYCLE_CO2_FIXATION |  | ACO1, ACO2, FH, IDH1, IDH2, MDH1, MDH2, SDHB, SUCLA2 | 9 | IDH1(19), IDH2(20) | 2582079 | 39 | 38 | 4 | 0 | 33 | 3 | 0 | 3 | 0 | 0 | 2.8e-07 | 4.1e-15 | 2.7e-13
9 | HSA04060_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION | Genes involved in cytokine-cytokine receptor interaction | ACVR1, ACVR1B, ACVR2A, ACVR2B, AMH, AMHR2, BMP2, BMP7, BMPR1A, BMPR1B, BMPR2, CCL1, CCL11, CCL13, CCL14, CCL15, CCL16, CCL17, CCL18, CCL19, CCL2, CCL20, CCL21, CCL22, CCL23, CCL24, CCL25, CCL26, CCL27, CCL28, CCL3, CCL4, CCL5, CCL7, CCL8, CCR1, CCR2, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, CCR9, CD27, CD40, CD40LG, CD70, CLCF1, CNTF, CNTFR, CRLF2, CSF1, CSF1R, CSF2, CSF2RA, CSF2RB, CSF3, CSF3R, CTF1, CX3CL1, CX3CR1, CXCL1, CXCL10, CXCL11, CXCL12, CXCL13, CXCL14, CXCL16, CXCL2, CXCL3, CXCL5, CXCL6, CXCL9, CXCR3, CXCR4, CXCR6, EDA, EDA2R, EDAR, EGF, EGFR, EPO, EPOR, FAS, FASLG, FLJ78302, FLT1, FLT3, FLT3LG, FLT4, GDF5, GH1, GH2, GHR, HGF, IFNA1, IFNA10, IFNA13, IFNA14, IFNA16, IFNA17, IFNA2, IFNA21, IFNA4, IFNA5, IFNA6, IFNA7, IFNA8, IFNAR1, IFNAR2, IFNB1, IFNE1, IFNG, IFNGR1, IFNGR2, IFNK, IFNW1, IL10, IL10RA, IL10RB, IL11, IL11RA, IL12A, IL12B, IL12RB1, IL12RB2, IL13, IL13RA1, IL15, IL15RA, IL17A, IL17B, IL17RA, IL17RB, IL18, IL18R1, IL18RAP, IL19, IL1A, IL1B, IL1R1, IL1R2, IL1RAP, IL2, IL20, IL20RA, IL21, IL21R, IL22, IL22RA1, IL22RA2, IL23A, IL23R, IL24, IL25, IL26, IL28A, IL28B, IL28RA, IL29, IL2RA, IL2RB, IL2RG, IL3, IL3RA, IL4, IL4R, IL5, IL5RA, IL6, IL6R, IL6ST, IL7, IL7R, IL8, IL8RA, IL8RB, IL9, IL9R, INHBA, INHBB, INHBC, INHBE, KDR, KIT, KITLG, LEP, LEPR, LIF, LIFR, LOC728045, LTA, LTB, LTBR, MET, MPL, NGFR, OSM, OSMR, PDGFB, PDGFC, PDGFRA, PDGFRB, PF4, PF4V1, PLEKHO2, PPBP, PRL, PRLR, RELT, TGFB1, TGFB2, TGFB3, TGFBR1, TGFBR2, TNF, TNFRSF10A, TNFRSF10B, TNFRSF10C, TNFRSF10D, TNFRSF11A, TNFRSF11B, TNFRSF12A, TNFRSF13B, TNFRSF13C, TNFRSF14, TNFRSF17, TNFRSF18, TNFRSF19, TNFRSF1A, TNFRSF1B, TNFRSF21, TNFRSF25, TNFRSF4, TNFRSF6B, TNFRSF8, TNFRSF9, TNFSF10, TNFSF11, TNFSF12, TNFSF13, TNFSF13B, TNFSF14, TNFSF15, TNFSF18, TNFSF4, TNFSF8, TNFSF9, TPO, TSLP, VEGFA, VEGFB, VEGFC, XCL1, XCL2, XCR1 | 242 | ACVR2B(1), CCL11(1), CCL16(1), CCL20(1), CCL21(1), CD70(1), CSF3R(1), EGFR(2), FLT1(1), FLT3(56), IL1R1(1), KDR(2), KIT(9), MPL(1), PDGFRA(1), PDGFRB(1), TPO(1) | 49587264 | 82 | 78 | 52 | 4 | 7 | 5 | 2 | 27 | 40 | 1 | 0.014 | 4.3e-15 | 2.7e-13
10 | 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 | JAK2(1), TP53(19), WT1(12) | 5112938 | 32 | 28 | 30 | 2 | 4 | 3 | 4 | 3 | 17 | 1 | 0.02 | 4.3e-15 | 2.7e-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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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