Mutation Analysis (MutSig v1.5)
Cholangiocarcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1SQ8ZC0
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/C1SQ8ZC0
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: CHOL-TP

  • Number of patients in set: 35

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:CHOL-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 14

  • Mutations seen in COSMIC: 42

  • Significantly mutated genes in COSMIC territory: 7

  • Significantly mutated genesets: 13

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

Mutation Preprocessing

  • Read 35 MAFs of type "Baylor-Illumina"

  • Total number of mutations in input MAFs: 6755

  • After removing 48 mutations outside chr1-24: 6707

  • After removing 211 blacklisted mutations: 6496

  • After removing 769 noncoding mutations: 5727

  • After collapsing adjacent/redundant mutations: 5726

Mutation Filtering

  • Number of mutations before filtering: 5726

  • After removing 297 mutations outside gene set: 5429

  • After removing 12 mutations outside category set: 5417

Results
Breakdown of Mutations by Type

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

type count
Frame_Shift_Del 96
Frame_Shift_Ins 23
In_Frame_Del 51
In_Frame_Ins 5
Missense_Mutation 3596
Nonsense_Mutation 295
Nonstop_Mutation 2
Silent 1222
Splice_Site 118
Translation_Start_Site 9
Total 5417
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->(A/T) 729 55014621 0.000013 13 3.2 2.1
*Cp(A/C/T)->(A/T) 1824 462273375 3.9e-06 3.9 0.96 2.7
A->(C/G) 677 504890172 1.3e-06 1.3 0.33 3.3
flip 374 1022178168 3.7e-07 0.37 0.089 5.3
indel+null 580 1022178168 5.7e-07 0.57 0.14 NaN
double_null 11 1022178168 1.1e-08 0.011 0.0026 NaN
Total 4195 1022178168 4.1e-06 4.1 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: CHOL-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->(A/T)

  • n2 = number of nonsilent mutations of type: *Cp(A/C/T)->(A/T)

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

  • n4 = number of nonsilent mutations of type: flip

  • 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: 14. 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 BAP1 BRCA1 associated protein-1 (ubiquitin carboxy-terminal hydrolase) 50481 9 8 9 1 0 0 3 0 6 0 0.53 8.9e-13 1.6e-08
2 PBRM1 polybromo 1 164472 8 8 8 0 0 0 0 0 8 0 0.26 9.3e-09 0.000085
3 TP53 tumor protein p53 38052 5 5 5 0 1 2 1 0 0 1 0.29 1.4e-08 0.000085
4 HLA-B major histocompatibility complex, class I, B 34635 5 5 3 0 0 5 0 0 0 0 0.16 6.8e-07 0.0031
5 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 44126 4 4 1 0 4 0 0 0 0 0 0.3 1.2e-06 0.0042
6 CDC27 cell division cycle 27 homolog (S. cerevisiae) 88485 5 5 5 0 2 2 1 0 0 0 0.22 1.4e-06 0.0042
7 OTOP1 otopetrin 1 48900 4 4 3 0 2 1 1 0 0 0 0.25 4.9e-06 0.013
8 EPHA2 EPH receptor A2 70989 4 4 4 0 0 0 0 2 2 0 0.56 0.000013 0.03
9 DDHD1 DDHD domain containing 1 83718 4 4 1 0 0 0 4 0 0 0 0.3 0.000017 0.035
10 CLIP4 CAP-GLY domain containing linker protein family, member 4 75868 4 4 4 0 0 1 1 0 2 0 0.47 0.000019 0.035
11 LCE4A late cornified envelope 4A 10574 2 2 2 0 0 0 1 1 0 0 0.64 0.000028 0.048
12 FTH1 ferritin, heavy polypeptide 1 19740 3 3 1 0 0 3 0 0 0 0 0.33 4e-05 0.057
13 ARID1A AT rich interactive domain 1A (SWI-like) 167004 6 5 6 1 0 1 0 0 5 0 0.94 4e-05 0.057
14 RNPC3 RNA-binding region (RNP1, RRM) containing 3 15649 2 2 2 0 0 0 1 1 0 0 0.57 6e-05 0.078
15 TRAF4 TNF receptor-associated factor 4 39328 3 3 3 0 0 1 2 0 0 0 0.45 0.000082 0.1
16 GXYLT1 glucoside xylosyltransferase 1 39085 3 3 3 0 0 1 0 2 0 0 0.64 0.000096 0.11
17 ZNF676 zinc finger protein 676 62249 3 3 2 0 0 0 0 3 0 0 0.68 0.00011 0.12
18 MLL3 myeloid/lymphoid or mixed-lineage leukemia 3 500153 7 7 4 0 0 1 0 1 5 0 0.28 0.00012 0.12
19 NAP1L1 nucleosome assembly protein 1-like 1 42885 3 3 3 0 0 1 1 1 0 0 0.55 0.00019 0.18
20 TCHH trichohyalin 182773 5 5 4 0 1 1 1 2 0 0 0.36 0.0002 0.18
21 DBNDD2 dysbindin (dystrobrevin binding protein 1) domain containing 2 14960 2 2 2 0 1 0 1 0 0 0 0.49 0.00022 0.19
22 ZNF100 zinc finger protein 100 57713 3 3 3 0 0 1 1 1 0 0 0.52 0.00022 0.19
23 RAD54L2 RAD54-like 2 (S. cerevisiae) 129151 4 4 4 0 1 1 0 1 1 0 0.31 0.00026 0.2
24 KRTAP5-5 keratin associated protein 5-5 24938 2 2 2 1 1 0 1 0 0 0 0.85 0.00026 0.2
25 OLFM3 olfactomedin 3 47475 3 3 3 0 0 2 1 0 0 0 0.42 0.00027 0.2
26 NF2 neurofibromin 2 (merlin) 60982 3 3 3 0 1 0 0 0 2 0 0.39 0.0003 0.21
27 FRG1 FSHD region gene 1 28280 2 2 1 0 2 0 0 0 0 0 0.35 0.00036 0.24
28 ZNF345 zinc finger protein 345 51389 3 3 3 1 0 0 1 2 0 0 0.83 0.00038 0.25
29 OR2L8 olfactory receptor, family 2, subfamily L, member 8 32997 2 2 1 0 0 0 0 2 0 0 0.69 0.00049 0.3
30 NLRP8 NLR family, pyrin domain containing 8 109159 4 4 3 0 1 2 0 0 1 0 0.23 0.0005 0.3
31 PAXIP1 PAX interacting (with transcription-activation domain) protein 1 109239 4 4 4 0 0 3 0 0 1 0 0.31 0.00055 0.32
32 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 20395 2 2 2 0 1 0 0 1 0 0 0.53 0.00059 0.32
33 PLXNA4 plexin A4 198712 5 5 5 0 2 2 0 1 0 0 0.16 0.0006 0.32
34 PSMA5 proteasome (prosome, macropain) subunit, alpha type, 5 26589 2 2 2 0 0 0 0 0 2 0 0.6 0.0006 0.32
35 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 24411 2 2 2 0 0 1 0 1 0 0 0.72 0.00064 0.32
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: 7. 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 4 5 4 175 5968 1.1e-15 5e-12
2 TP53 tumor protein p53 5 356 5 12460 1178 2.8e-09 6.3e-06
3 IDH2 isocitrate dehydrogenase 2 (NADP+), mitochondrial 2 6 2 210 166 3.7e-07 0.00056
4 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 2 52 2 1820 14472 0.000028 0.031
5 PDGFD platelet derived growth factor D 2 1 1 35 1 0.00014 0.093
6 TBX20 T-box 20 2 1 1 35 1 0.00014 0.093
7 ZDHHC4 zinc finger, DHHC-type containing 4 1 1 1 35 1 0.00014 0.093
8 APC adenomatous polyposis coli 4 839 3 29365 34 0.00027 0.11
9 CHD1L chromodomain helicase DNA binding protein 1-like 1 2 1 70 1 0.00029 0.11
10 IMP4 IMP4, U3 small nucleolar ribonucleoprotein, homolog (yeast) 1 2 1 70 1 0.00029 0.11

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: 13. 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 CDK2(1), CDKN1A(1), CDKN2A(2), MDM2(1), TP53(5) 401952 10 9 10 0 2 2 2 1 2 1 0.087 1e-06 0.00037
2 PLK3PATHWAY Active Plk3 phosphorylates CDC25c, blocking the G2/M transition, and phosphorylates p53 to induce apoptosis. ATM, ATR, CDC25C, CHEK1, CHEK2, CNK, TP53, YWHAH 7 ATM(4), ATR(1), CDC25C(1), CHEK1(1), CHEK2(2), TP53(5) 825226 14 12 14 1 2 6 2 1 2 1 0.13 1.2e-06 0.00037
3 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 CDKN2A(2), MDM2(1), PIK3CA(2), PIK3R1(2), TP53(5) 1017333 12 11 12 0 2 4 2 2 1 1 0.042 0.000038 0.0079
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(1), ATM(4), CDKN1A(1), MDM2(1), TP53(5) 1064878 12 11 12 1 2 3 1 1 4 1 0.21 0.000069 0.011
5 G2PATHWAY Activated Cdc2-cyclin B kinase regulates the G2/M transition; DNA damage stimulates the DNA-PK/ATM/ATR kinases, which inactivate Cdc2. ATM, ATR, BRCA1, CCNB1, CDC2, CDC25A, CDC25B, CDC25C, CDC34, CDKN1A, CDKN2D, CHEK1, CHEK2, EP300, GADD45A, MDM2, MYT1, PLK, PRKDC, RPS6KA1, TP53, WEE1, YWHAH, YWHAQ 22 ATM(4), ATR(1), BRCA1(1), CDC25C(1), CDKN1A(1), CHEK1(1), CHEK2(2), EP300(3), MDM2(1), PRKDC(2), TP53(5) 2178745 22 15 22 1 2 9 3 2 5 1 0.026 0.00013 0.016
6 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(4), CDC25C(1), CDK2(1), CHEK1(1), TP53(5) 903263 12 10 12 1 1 5 2 1 2 1 0.18 0.0002 0.02
7 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 30 ANPEP(1), GPX2(1), IDH1(4), IDH2(2), MGST1(1), PGD(2) 910815 11 11 8 1 5 4 0 1 1 0 0.089 0.00031 0.027
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 ATM(4), BRCA1(1), CDKN1A(1), CHEK1(1), CHEK2(2), MDM2(1), RAD50(1), TP53(5) 1482189 16 14 16 2 2 5 3 1 4 1 0.23 0.00045 0.034
9 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 DAXX(2), PAX3(1), RARA(1), SP100(1), TP53(5) 906533 10 9 10 0 3 4 1 1 0 1 0.057 0.00051 0.034
10 P53PATHWAY p53 induces cell cycle arrest or apoptosis under conditions of DNA damage. APAF1, ATM, BAX, BCL2, CCND1, CCNE1, CDK2, CDK4, CDKN1A, E2F1, GADD45A, MDM2, PCNA, RB1, TIMP3, TP53 16 APAF1(1), ATM(4), CDK2(1), CDKN1A(1), MDM2(1), TIMP3(1), TP53(5) 939238 14 11 14 2 1 5 2 1 4 1 0.31 0.00056 0.034

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 PLK3PATHWAY Active Plk3 phosphorylates CDC25c, blocking the G2/M transition, and phosphorylates p53 to induce apoptosis. ATM, ATR, CDC25C, CHEK1, CHEK2, CNK, TP53, YWHAH 6 ATM(4), ATR(1), CDC25C(1), CHEK1(1), CHEK2(2) 787174 9 8 9 1 1 4 1 1 2 0 0.3 0.0014 0.42
2 HISTONE_METHYLTRANSFERASE Genes with HMT activity AOF2, KDM6A, ASH1L, ASH2L, C17orf79, CARM1, CTCFL, DOT1L, EED, EHMT1, EHMT2, EZH1, EZH2, FBXL10, FBXL11, FBXO11, HCFC1, HSF4, JMJD1A, JMJD1B, JMJD2A, JMJD2B, JMJD2C, JMJD2D, JMJD3, JMJD4, JMJD6, MEN1, MLL, MLL2, MLL3, MLL4, MLL5, NSD1, OGT, PAXIP1, PPP1CA, PPP1CB, PPP1CC, PRDM2, PRDM6, PRDM7, PRDM9, PRMT1, PRMT5, PRMT6, PRMT7, PRMT8, RBBP5, SATB1, SETD1A, SETD1B, SETD2, SETD7, SETD8, SETDB1, SETDB2, SETMAR, SMYD3, STK38, SUV39H1, SUV39H2, SUV420H1, SUV420H2, SUZ12, WHSC1, WHSC1L1 57 ASH1L(1), ASH2L(1), CTCFL(1), DOT1L(2), EHMT2(1), EZH2(1), HSF4(2), JMJD4(1), MLL(2), MLL2(5), MLL3(7), MLL4(1), NSD1(1), PAXIP1(4), PPP1CB(1), PRDM6(1), PRDM9(3), PRMT7(1), SATB1(1), SETD1B(2), SETD2(2), SETD7(1), SETD8(1), WHSC1(1), WHSC1L1(2) 6274985 46 24 43 3 5 18 5 2 15 1 0.004 0.004 0.42
3 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 GRB2(1), MAP2K1(1), PIK3CA(2), PIK3R1(2), PLCG1(1), PRKCA(1), RASA1(1), SOS1(1), STAT1(1), STAT5A(1), THPO(2) 1493290 14 12 14 1 1 7 2 2 2 0 0.082 0.004 0.42
4 SA_TRKA_RECEPTOR The TrkA receptor binds nerve growth factor to activate MAP kinase pathways and promote cell growth. AKT1, AKT2, AKT3, ARHA, CDKN1A, ELK1, GRB2, HRAS, MAP2K1, MAP2K2, NGFB, NGFR, NTRK1, PIK3CA, PIK3CD, SHC1, SOS1 15 CDKN1A(1), ELK1(1), GRB2(1), MAP2K1(1), NTRK1(1), PIK3CA(2), SOS1(1) 827926 8 8 8 0 1 3 0 3 1 0 0.15 0.0045 0.42
5 IGF1RPATHWAY Insulin-like growth factor receptor IGF-1R promotes cell growth and inhibits apoptosis on binding of ligands IGF-1 and 2 via Ras activation and the AKT pathway. AKT1, BAD, GRB2, HRAS, IGF1R, IRS1, MAP2K1, MAPK1, MAPK3, PIK3CA, PIK3R1, RAF1, SHC1, SOS1, YWHAH 15 BAD(1), GRB2(1), IGF1R(1), IRS1(2), MAP2K1(1), PIK3CA(2), PIK3R1(2), SOS1(1) 953052 11 9 11 1 1 7 1 2 0 0 0.15 0.0046 0.42
6 ACETAMINOPHENPATHWAY Acetaminophen selectively inhibits Cox-3, which is localized to the brain, and yields the toxic metabolite NAPQI when processed by CAR in the liver. CYP1A2, CYP2E1, CYP3A, NR1I3, PTGS1, PTGS2 5 CYP2E1(1), NR1I3(2), PTGS1(1) 265148 4 4 4 0 1 0 2 0 1 0 0.46 0.0048 0.42
7 TRKAPATHWAY Nerve growth factor (NGF) promotes neuronal survival and proliferation by binding its receptor TrkA, which activates PI3K/AKT, Ras, and the MAP kinase pathway. AKT1, DPM2, GRB2, HRAS, KLK2, NGFB, NTRK1, PIK3CA, PIK3R1, PLCG1, PRKCA, PRKCB1, SHC1, SOS1 12 GRB2(1), NTRK1(1), PIK3CA(2), PIK3R1(2), PLCG1(1), PRKCA(1), SOS1(1) 773480 9 8 9 0 1 5 1 1 1 0 0.071 0.0048 0.42
8 LAIRPATHWAY The local acute inflammatory response is mediated by activated macrophages and mast cells or by complement activation. BDK, C3, C5, C6, C7, ICAM1, IL1A, IL6, IL8, ITGA4, ITGAL, ITGB1, ITGB2, SELP, SELPLG, TNF, VCAM1 16 C3(1), C7(1), ICAM1(1), ITGA4(2), ITGAL(1), ITGB1(1), SELP(1), SELPLG(1), VCAM1(1) 1263806 10 9 10 0 2 5 2 0 1 0 0.041 0.0077 0.59
9 NGFPATHWAY Nerve growth factor (NGF) stimulates neural survival and proliferation via the TrkA and p75 receptors, which induce DAG and IP3 production and activate Ras. CSNK2A1, DPM2, ELK1, FOS, GRB2, HRAS, JUN, KLK2, MAP2K1, MAPK3, MAPK8, NGFB, NGFR, PIK3CA, PIK3R1, PLCG1, RAF1, SHC1, SOS1 18 ELK1(1), GRB2(1), MAP2K1(1), PIK3CA(2), PIK3R1(2), PLCG1(1), SOS1(1) 942043 9 8 9 0 1 4 1 3 0 0 0.094 0.0086 0.59
10 CTLA4PATHWAY T cell activation requires interaction with an antigen-MHC-I complex on an antigen-presenting cell (APC), as well as CD28 interaction with the APC's CD80 or 86. CD28, CD3D, CD3E, CD3G, CD3Z, CD80, CD86, CTLA4, GRB2, HLA-DRA, HLA-DRB1, ICOS, ICOSL, IL2, ITK, LCK, PIK3CA, PIK3R1, PTPN11, TRA@, TRB@ 17 GRB2(1), HLA-DRA(1), ITK(1), PIK3CA(2), PIK3R1(2) 652159 7 6 7 0 2 3 1 1 0 0 0.14 0.013 0.68
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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