Mutation Analysis (MutSig v1.5)
Pancreatic Adenocarcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C16T0KP6
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/C16T0KP6
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: PAAD-TP

  • Number of patients in set: 146

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:PAAD-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 79

  • Mutations seen in COSMIC: 338

  • Significantly mutated genes in COSMIC territory: 6

  • Significantly mutated genesets: 36

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

Mutation Preprocessing

  • Read 146 MAFs of type "Broad"

  • Total number of mutations in input MAFs: 37705

  • After removing 5 mutations outside chr1-24: 37700

  • After removing 495 blacklisted mutations: 37205

  • After removing 993 noncoding mutations: 36212

Mutation Filtering

  • Number of mutations before filtering: 36212

  • After removing 1807 mutations outside gene set: 34405

  • After removing 284 mutations outside category set: 34121

Results
Breakdown of Mutations by Type

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

type count
Frame_Shift_Del 269
Frame_Shift_Ins 869
In_Frame_Del 559
In_Frame_Ins 400
Missense_Mutation 21226
Nonsense_Mutation 1382
Nonstop_Mutation 18
Silent 8107
Splice_Site 1150
Translation_Start_Site 141
Total 34121
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 7348 254419294 0.000029 29 4.9 2.1
*Cp(A/C/T)->T 5033 2025719249 2.5e-06 2.5 0.42 1.7
C->(G/A) 5104 2280138543 2.2e-06 2.2 0.38 4.7
A->mut 3845 2165008762 1.8e-06 1.8 0.3 3.9
indel+null 4424 4445147305 1e-06 1 0.17 NaN
double_null 260 4445147305 5.8e-08 0.058 0.01 NaN
Total 26014 4445147305 5.9e-06 5.9 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: PAAD-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)->T

  • n3 = number of nonsilent mutations of type: C->(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: 79. 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 RIOK1 RIO kinase 1 (yeast) 241633 55 55 2 0 1 0 0 0 54 0 0.7 <1.00e-15 <5.61e-12
2 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 144856 31 30 19 1 0 5 2 2 22 0 0.016 <1.00e-15 <5.61e-12
3 TP53 tumor protein p53 183985 100 100 72 0 24 12 12 19 33 0 4e-12 1.22e-15 5.61e-12
4 RFX1 regulatory factor X, 1 (influences HLA class II expression) 355291 24 24 3 2 0 0 1 0 23 0 0.96 1.44e-15 5.61e-12
5 JMY junction mediating and regulatory protein, p53 cofactor 342688 56 55 3 0 0 0 0 0 56 0 0.62 2.00e-15 5.61e-12
6 C1QB complement component 1, q subcomponent, B chain 112306 35 35 2 0 0 0 0 0 35 0 0.58 2.11e-15 5.61e-12
7 RBM47 RNA binding motif protein 47 251572 27 25 3 2 1 0 1 0 25 0 0.87 2.66e-15 5.61e-12
8 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 102559 127 123 5 1 0 56 62 9 0 0 1.9e-10 2.78e-15 5.61e-12
9 LCE2A late cornified envelope 2A 47450 46 46 1 0 0 0 0 0 46 0 1 2.78e-15 5.61e-12
10 SMAD4 SMAD family member 4 248546 32 32 29 0 5 1 2 6 18 0 0.0024 3.77e-15 6.87e-12
11 AEBP1 AE binding protein 1 492650 29 26 4 1 1 1 0 0 26 1 0.56 6.55e-15 1.08e-11
12 ANKRD36 ankyrin repeat domain 36 369016 24 24 3 0 0 0 0 0 24 0 0.72 1.08e-14 1.63e-11
13 TYRO3 TYRO3 protein tyrosine kinase 377486 14 14 5 0 0 0 0 0 14 0 1 2.97e-13 4.15e-10
14 GPR6 G protein-coupled receptor 6 158877 12 12 3 0 0 0 1 0 11 0 0.71 3.90e-13 5.07e-10
15 IRS4 insulin receptor substrate 4 550602 16 16 5 3 0 0 1 1 14 0 0.88 8.42e-11 1.02e-07
16 NCOA3 nuclear receptor coactivator 3 640731 16 14 6 1 0 0 1 1 13 1 0.78 1.70e-10 1.93e-07
17 ZMIZ2 zinc finger, MIZ-type containing 2 413036 12 12 4 1 1 0 1 1 9 0 0.66 1.48e-09 1.59e-06
18 SIK3 SIK family kinase 3 564609 13 13 3 1 1 0 0 0 12 0 0.9 3.30e-09 3.33e-06
19 FNDC1 fibronectin type III domain containing 1 705510 19 14 7 3 2 2 1 1 13 0 0.73 1.90e-08 1.82e-05
20 CASQ2 calsequestrin 2 (cardiac muscle) 181343 8 8 2 0 1 0 0 0 7 0 0.67 2.05e-08 1.87e-05
21 PABPC1 poly(A) binding protein, cytoplasmic 1 286758 9 9 4 0 0 0 0 1 8 0 0.73 2.49e-08 2.15e-05
22 TGFBR1 transforming growth factor, beta receptor I (activin A receptor type II-like kinase, 53kDa) 211078 8 8 8 0 2 3 1 0 2 0 0.19 3.06e-08 2.53e-05
23 DCP1B DCP1 decapping enzyme homolog B (S. cerevisiae) 275809 11 9 5 1 1 0 1 2 7 0 0.61 3.81e-08 3.01e-05
24 NBPF16 neuroblastoma breakpoint family, member 16 184594 7 7 1 0 0 0 0 0 7 0 1 5.45e-08 4.14e-05
25 RPL22 ribosomal protein L22 56771 5 5 1 0 0 0 0 0 4 1 1 8.87e-08 6.46e-05
26 AQP7 aquaporin 7 147194 7 7 6 0 0 4 0 1 2 0 0.11 2.36e-07 0.000165
27 LZTS1 leucine zipper, putative tumor suppressor 1 256143 10 10 3 2 1 1 0 0 8 0 0.79 2.45e-07 0.000165
28 IYD iodotyrosine deiodinase 129938 6 6 3 1 4 0 0 1 1 0 0.47 3.36e-07 0.000219
29 TGFBR2 transforming growth factor, beta receptor II (70/80kDa) 259260 10 8 10 0 2 0 1 3 4 0 0.1 4.48e-07 0.000281
30 RNF43 ring finger protein 43 336072 10 9 10 1 1 1 2 1 5 0 0.27 4.70e-07 0.000281
31 TMEM91 transmembrane protein 91 91396 6 6 2 0 0 1 0 0 5 0 0.64 4.79e-07 0.000281
32 DEFB119 defensin, beta 119 75472 5 5 5 0 4 0 0 1 0 0 0.52 1.05e-06 0.000598
33 HSPE1 heat shock 10kDa protein 1 (chaperonin 10) 46779 4 4 2 0 2 0 0 0 2 0 0.34 1.13e-06 0.000622
34 SDCBP syndecan binding protein (syntenin) 135554 6 6 5 0 0 0 1 1 4 0 0.65 1.33e-06 0.000711
35 CD86 CD86 molecule 148562 6 6 4 0 3 0 0 0 3 0 0.18 2.84e-06 0.00148
RIOK1

Figure S1.  This figure depicts the distribution of mutations and mutation types across the RIOK1 significant gene.

CDKN2A

Figure S2.  This figure depicts the distribution of mutations and mutation types across the CDKN2A significant gene.

TP53

Figure S3.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

RFX1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the RFX1 significant gene.

JMY

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

C1QB

Figure S6.  This figure depicts the distribution of mutations and mutation types across the C1QB significant gene.

RBM47

Figure S7.  This figure depicts the distribution of mutations and mutation types across the RBM47 significant gene.

KRAS

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

LCE2A

Figure S9.  This figure depicts the distribution of mutations and mutation types across the LCE2A significant gene.

SMAD4

Figure S10.  This figure depicts the distribution of mutations and mutation types across the SMAD4 significant gene.

AEBP1

Figure S11.  This figure depicts the distribution of mutations and mutation types across the AEBP1 significant gene.

ANKRD36

Figure S12.  This figure depicts the distribution of mutations and mutation types across the ANKRD36 significant gene.

TYRO3

Figure S13.  This figure depicts the distribution of mutations and mutation types across the TYRO3 significant gene.

GPR6

Figure S14.  This figure depicts the distribution of mutations and mutation types across the GPR6 significant gene.

IRS4

Figure S15.  This figure depicts the distribution of mutations and mutation types across the IRS4 significant gene.

NCOA3

Figure S16.  This figure depicts the distribution of mutations and mutation types across the NCOA3 significant gene.

ZMIZ2

Figure S17.  This figure depicts the distribution of mutations and mutation types across the ZMIZ2 significant gene.

SIK3

Figure S18.  This figure depicts the distribution of mutations and mutation types across the SIK3 significant gene.

FNDC1

Figure S19.  This figure depicts the distribution of mutations and mutation types across the FNDC1 significant gene.

CASQ2

Figure S20.  This figure depicts the distribution of mutations and mutation types across the CASQ2 significant gene.

PABPC1

Figure S21.  This figure depicts the distribution of mutations and mutation types across the PABPC1 significant gene.

TGFBR1

Figure S22.  This figure depicts the distribution of mutations and mutation types across the TGFBR1 significant gene.

DCP1B

Figure S23.  This figure depicts the distribution of mutations and mutation types across the DCP1B significant gene.

AQP7

Figure S24.  This figure depicts the distribution of mutations and mutation types across the AQP7 significant gene.

LZTS1

Figure S25.  This figure depicts the distribution of mutations and mutation types across the LZTS1 significant gene.

IYD

Figure S26.  This figure depicts the distribution of mutations and mutation types across the IYD significant gene.

TGFBR2

Figure S27.  This figure depicts the distribution of mutations and mutation types across the TGFBR2 significant gene.

RNF43

Figure S28.  This figure depicts the distribution of mutations and mutation types across the RNF43 significant gene.

SDCBP

Figure S29.  This figure depicts the distribution of mutations and mutation types across the SDCBP 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: 6. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 127 52 126 7592 1718370 0 0
2 GNAS GNAS complex locus 12 7 7 1022 1470 0 0
3 TP53 tumor protein p53 100 356 96 51976 25723 0 0
4 SMAD4 SMAD family member 4 32 159 17 23214 60 0 0
5 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 31 332 31 48472 1128 0 0
6 TGFBR2 transforming growth factor, beta receptor II (70/80kDa) 10 12 2 1752 2 0.000052 0.039
7 STK11 serine/threonine kinase 11 3 130 3 18980 7 0.00021 0.14
8 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 6 42 2 6132 4 0.00063 0.28
9 AVPR2 arginine vasopressin receptor 2 (nephrogenic diabetes insipidus) 2 1 1 146 0 0.00085 0.28
10 DRG1 developmentally regulated GTP binding protein 1 1 1 1 146 1 0.00085 0.28

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: 36. 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 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(5), CDKN2A(31), MDM2(2), PIK3CA(4), PIK3R1(1), POLR1A(3), POLR1B(3), RB1(3), TBX2(1), TP53(100) 4538065 153 108 113 4 28 21 21 25 58 0 6.45e-14 <1.00e-15 <1.23e-13
2 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(1), ARF3(1), CDK4(2), CDKN2A(31), MDM2(2), NXT1(1), TP53(100) 1893625 138 106 98 2 28 17 17 21 55 0 1.99e-14 <1.00e-15 <1.23e-13
3 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(10), ATR(9), TP53(100), YWHAH(1) 3507307 120 105 92 5 27 14 16 23 40 0 4.56e-07 <1.00e-15 <1.23e-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(5), AKT1(1), ATM(10), CSNK1D(1), FHL2(1), HIC1(2), HIF1A(1), MAPK8(2), MDM2(2), NFKBIB(1), TP53(100) 4637114 126 103 98 7 27 15 19 27 38 0 1.02e-08 <1.00e-15 <1.23e-13
5 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), DNAJC3(2), NFKBIA(1), RELA(2), TP53(100) 2220923 106 101 78 2 25 13 13 21 34 0 3.12e-10 <1.00e-15 <1.23e-13
6 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(5), ATM(10), BRCA1(3), MAPK8(2), MDM2(2), MRE11A(2), NFKBIA(1), RAD50(1), RBBP8(3), RELA(2), TP53(100) 6591814 131 106 103 3 31 17 19 24 40 0 2.59e-11 1.67e-15 1.71e-13
7 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(1), IFNGR1(2), IKBKB(4), JAK2(6), LIN7A(1), NFKBIA(1), RB1(3), RELA(2), TNFRSF1A(2), TNFRSF1B(1), TP53(100), USH1C(2) 4102157 125 104 96 10 29 15 19 22 40 0 3.56e-07 2.22e-15 1.88e-13
8 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(10), ATR(9), BRCA1(3), CCNB1(1), CDC25A(1), CDC25B(1), CDC34(1), EP300(6), MDM2(2), MYT1(11), PRKDC(7), RPS6KA1(2), TP53(100), YWHAH(1) 9364724 155 109 127 11 37 20 21 32 45 0 2.20e-08 2.44e-15 1.88e-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(10), CDC25A(1), CDC25B(1), CDK4(2), MYT1(11), RB1(3), TP53(100), YWHAH(1) 3877709 129 104 101 5 32 16 19 25 37 0 1.90e-09 3.22e-15 1.98e-13
10 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 MAX(1), SP1(1), SP3(1), TP53(100) 1555114 103 102 75 0 25 12 13 19 34 0 7.99e-14 3.22e-15 1.98e-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 CFTRPATHWAY The cAMP-regulated chloride channel CFTR (deficient in cystic fibrosis) is regulated by the surface-localized beta-adrenergic receptor. ADCY1, ADRB2, CFTR, GNAS, PRKACB, PRKACG, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, SLC9A3R1, VIL2 11 ADCY1(4), ADRB2(1), CFTR(7), GNAS(12), PRKACB(1), PRKACG(3), PRKAR1A(1), PRKAR1B(2), PRKAR2B(1), SLC9A3R1(1) 2957408 33 18 25 3 15 6 4 4 4 0 0.01 0.027 1
2 CREMPATHWAY The transcription factor CREM activates a post-meiotic transcriptional cascade culminating in spermatogenesis. ADCY1, CREM, FHL5, FSHB, FSHR, GNAS, XPO1 7 ADCY1(4), FHL5(1), FSHB(1), FSHR(3), GNAS(12), XPO1(2) 2159135 23 14 18 2 12 2 4 3 2 0 0.071 0.064 1
3 ACHPATHWAY Nicotinic acetylcholine receptors are ligand-gated ion channels that primarily mediate neuromuscular signaling and may inhibit neuronal apoptosis via the AKT pathway. AKT1, BAD, CHRNB1, CHRNG, FOXO3A, MUSK, PIK3CA, PIK3R1, PTK2, PTK2B, RAPSN, SRC, TERT, TNFSF6, YWHAH 13 AKT1(1), BAD(1), MUSK(6), PIK3CA(4), PIK3R1(1), PTK2(10), SRC(2), TERT(5), YWHAH(1) 3679200 31 16 27 4 5 7 3 6 10 0 0.046 0.097 1
4 AGPCRPATHWAY G-protein coupled receptors (GPCRs) transduce extracellular signals across the plasma membrane; attenuation occurs by signal molecule degradation or receptor-mediated endocytosis. ARRB1, GNAS, GNB1, GNGT1, GPRK2L, PRKACB, PRKACG, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, PRKCA, PRKCB1 11 ARRB1(1), GNAS(12), GNB1(3), PRKACB(1), PRKACG(3), PRKAR1A(1), PRKAR1B(2), PRKAR2B(1) 2195611 24 13 19 2 14 4 3 2 1 0 0.021 0.13 1
5 RABPATHWAY Rab family GTPases regulate vesicle transport, endocytosis and exocytosis, and vesicle docking via interactions with the rabphilins. ACTA1, MEL, RAB11A, RAB1A, RAB2, RAB27A, RAB3A, RAB4A, RAB5A, RAB6A, RAB7, RAB9A 9 ACTA1(3), RAB11A(1), RAB27A(2), RAB3A(1), RAB4A(2), RAB6A(1), RAB9A(1) 941380 11 5 11 1 5 3 1 1 1 0 0.082 0.14 1
6 ALTERNATIVEPATHWAY The alternative complement pathway is an antibody-independent mechanism of immune activation that results in cell lysis via the membrane attack complex. BF, C3, C5, C6, C7, C8A, C9, DF, PFC 6 C3(11), C5(2), C6(6), C7(4), C8A(4), C9(2) 2775102 29 12 29 6 7 7 5 4 6 0 0.13 0.19 1
7 ST_G_ALPHA_S_PATHWAY The G-alpha-s protein activates adenylyl cyclases, which catalyze cAMP formation. ASAH1, BF, BFAR, BRAF, CAMP, CREB1, CREB3, CREB5, EPAC, GAS, GRF2, MAPK1, RAF1, SNX13, SRC, TERF2IP 12 ASAH1(1), BFAR(2), BRAF(3), CAMP(2), CREB1(2), CREB5(1), SNX13(5), SRC(2), TERF2IP(1) 2499222 19 10 19 2 2 2 4 4 7 0 0.2 0.19 1
8 HSA00072_SYNTHESIS_AND_DEGRADATION_OF_KETONE_BODIES Genes involved in synthesis and degradation of ketone bodies ACAT1, ACAT2, BDH1, BDH2, HMGCL, HMGCS1, HMGCS2, OXCT1, OXCT2 9 ACAT1(2), ACAT2(1), BDH1(1), BDH2(1), HMGCS2(2), OXCT1(2), OXCT2(1) 1598389 10 6 10 1 1 4 0 1 4 0 0.2 0.2 1
9 GCRPATHWAY Corticosteroids activate the glucocorticoid receptor (GR), which inhibits NF-kB and activates Annexin-1, thus inhibiting the inflammatory response. ADRB2, AKT1, ANXA1, CALM1, CALM2, CALM3, CRN, GNAS, GNB1, GNGT1, HSPCA, NFKB1, NOS3, NPPA, NR3C1, PIK3CA, PIK3R1, RELA, SYT1 17 ADRB2(1), AKT1(1), ANXA1(3), GNAS(12), GNB1(3), NOS3(8), NR3C1(2), PIK3CA(4), PIK3R1(1), RELA(2), SYT1(2) 4041489 39 22 34 6 13 4 11 7 3 1 0.097 0.2 1
10 SHHPATHWAY Sonic hedgehog (Shh) signaling in the developing CNS induces neuronal proliferation via interaction with the patched (Ptc-1) and smoothened receptors. DYRK1A, DYRK1B, GLI, GLI2, GLI3, GSK3B, PRKACB, PRKACG, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, PTCH, SHH, SMO, SUFU 14 DYRK1A(4), DYRK1B(2), GLI2(4), GLI3(10), GSK3B(2), PRKACB(1), PRKACG(3), PRKAR1A(1), PRKAR1B(2), PRKAR2B(1), SMO(1), SUFU(1) 3738609 32 15 32 9 14 6 5 6 1 0 0.13 0.27 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)
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