Mutation Analysis (MutSig v1.5)
Skin Cutaneous Melanoma (Metastatic)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C16W98XQ
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSig v1.5). Broad Institute of MIT and Harvard. doi:10.7908/C16W98XQ
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: SKCM-TM

  • Number of patients in set: 278

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:SKCM-TM.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 88

  • Mutations seen in COSMIC: 754

  • Significantly mutated genes in COSMIC territory: 54

  • Significantly mutated genesets: 2

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

Mutation Preprocessing

  • Read 278 MAFs of type "Broad"

  • Total number of mutations in input MAFs: 258634

  • After removing 391 mutations outside chr1-24: 258243

  • After removing 1760 blacklisted mutations: 256483

  • After removing 3579 noncoding mutations: 252904

  • After collapsing adjacent/redundant mutations: 228614

Mutation Filtering

  • Number of mutations before filtering: 228614

  • After removing 8250 mutations outside gene set: 220364

  • After removing 345 mutations outside category set: 220019

  • After removing 9 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 211415

Results
Breakdown of Mutations by Type

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

type count
Frame_Shift_Del 955
Frame_Shift_Ins 246
In_Frame_Del 250
In_Frame_Ins 28
Missense_Mutation 132329
Nonsense_Mutation 8213
Nonstop_Mutation 53
Silent 72280
Splice_Site 5664
Translation_Start_Site 1
Total 220019
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
(C/T)p*C->T 101233 2214670000 0.000046 46 2.5 1.6
(A/G)p*C->T 11192 1859082941 6e-06 6 0.33 1.9
A->G 5621 3929510266 1.4e-06 1.4 0.077 2.3
transver 14279 8003263207 1.8e-06 1.8 0.097 5
indel+null 15113 8003263207 1.9e-06 1.9 0.1 NaN
double_null 296 8003263207 3.7e-08 0.037 0.002 NaN
Total 147734 8003263207 0.000018 18 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: SKCM-TM.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: (C/T)p*C->T

  • n2 = number of nonsilent mutations of type: (A/G)p*C->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_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: 88. 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 336707 51 47 41 0 21 1 4 4 20 1 9.9e-09 <1.00e-15 <1.00e-11
2 BRAF v-raf murine sarcoma viral oncogene homolog B1 617006 146 140 18 2 15 1 5 122 3 0 1.5e-11 1.11e-15 1.00e-11
3 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 162855 86 86 11 0 2 2 33 48 1 0 1.4e-10 3.00e-15 1.60e-11
4 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 251438 42 41 19 1 12 1 0 1 28 0 8e-05 3.55e-15 1.60e-11
5 RPS27 ribosomal protein S27 (metallopanstimulin 1) 56962 25 24 3 0 1 0 0 0 24 0 7.8e-06 6.22e-15 2.25e-11
6 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 311413 23 23 21 0 2 0 3 5 13 0 0.018 8.22e-15 2.47e-11
7 ACSM2B acyl-CoA synthetase medium-chain family member 2B 483642 56 44 44 8 39 4 1 3 9 0 2e-05 4.24e-11 1.09e-07
8 SLC38A4 solute carrier family 38, member 4 433466 38 34 32 4 28 2 1 4 3 0 0.0004 3.35e-10 7.55e-07
9 RAC1 ras-related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1) 171147 20 20 9 1 17 0 0 3 0 0 0.00085 4.36e-10 8.76e-07
10 BAGE B melanoma antigen 34978 8 8 6 1 0 0 0 0 8 0 0.083 1.37e-09 2.47e-06
11 LUZP2 leucine zipper protein 2 277052 28 27 25 4 19 1 1 2 5 0 0.012 1.56e-09 2.55e-06
12 LCE1B late cornified envelope 1B 100218 14 14 14 0 10 0 0 2 2 0 0.028 3.80e-09 5.71e-06
13 MRPS31 mitochondrial ribosomal protein S31 322993 20 19 5 0 1 0 0 1 18 0 0.002 4.39e-09 6.09e-06
14 PRB2 proline-rich protein BstNI subfamily 2 346958 50 38 46 2 45 1 1 2 1 0 0.018 1.77e-08 2.28e-05
15 GRXCR1 glutaredoxin, cysteine rich 1 243070 26 23 22 4 16 2 2 1 5 0 0.026 2.66e-08 3.20e-05
16 NRK Nik related kinase 688021 48 44 45 6 36 2 0 3 7 0 0.0012 7.67e-08 8.66e-05
17 NOTCH2NL Notch homolog 2 (Drosophila) N-terminal like 199533 15 15 14 1 5 0 1 7 2 0 0.092 1.68e-07 0.000178
18 HIST1H2AA histone cluster 1, H2aa 111198 12 12 10 1 7 1 0 3 1 0 0.067 1.81e-07 0.000182
19 TMEM216 transmembrane protein 216 87785 8 8 1 0 0 0 0 0 8 0 0.18 3.89e-07 0.000370
20 NAP1L2 nucleosome assembly protein 1-like 2 379788 28 24 26 2 20 1 3 2 2 0 0.0026 6.39e-07 0.000577
21 RUNX1T1 runt-related transcription factor 1; translocated to, 1 (cyclin D-related) 510988 44 34 38 5 30 3 1 3 7 0 0.00037 8.16e-07 0.000701
22 MUM1L1 melanoma associated antigen (mutated) 1-like 1 351485 39 33 38 6 29 1 1 7 1 0 0.016 8.91e-07 0.000712
23 C8A complement component 8, alpha polypeptide 479441 43 33 34 4 36 1 0 1 5 0 0.000069 9.07e-07 0.000712
24 FUT9 fucosyltransferase 9 (alpha (1,3) fucosyltransferase) 280587 30 29 26 7 19 2 2 5 2 0 0.069 9.86e-07 0.000742
25 RBM11 RNA binding motif protein 11 179464 14 14 12 0 9 0 0 2 3 0 0.051 1.57e-06 0.00114
26 NMS neuromedin S 137258 14 13 13 0 9 1 0 0 4 0 0.0024 1.73e-06 0.00120
27 PRB1 proline-rich protein BstNI subfamily 1 269470 37 26 29 2 32 0 2 2 1 0 0.018 1.83e-06 0.00123
28 VEGFC vascular endothelial growth factor C 330654 23 20 20 2 16 1 0 0 6 0 0.0031 3.46e-06 0.00223
29 ZNF98 zinc finger protein 98 (F7175) 264626 22 21 20 4 16 1 2 1 2 0 0.052 4.33e-06 0.00270
30 HBD hemoglobin, delta 125515 12 12 10 1 10 1 0 1 0 0 0.0041 5.51e-06 0.00332
31 USP17L2 ubiquitin specific peptidase 17-like 2 374799 25 22 23 2 17 0 2 2 4 0 0.00045 7.59e-06 0.00442
32 PPP6C protein phosphatase 6, catalytic subunit 279243 21 20 15 3 14 0 0 2 5 0 0.11 8.22e-06 0.00464
33 SNAP91 synaptosomal-associated protein, 91kDa homolog (mouse) 426264 39 30 37 5 27 3 2 2 5 0 0.0079 8.57e-06 0.00469
34 CDH9 cadherin 9, type 2 (T1-cadherin) 652026 48 37 41 6 36 2 1 4 5 0 0.0031 9.16e-06 0.00487
35 KLHL4 kelch-like 4 (Drosophila) 586674 28 25 27 2 18 4 1 2 3 0 0.0063 1.13e-05 0.00581
TP53

Figure S1.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

BRAF

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

NRAS

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

CDKN2A

Figure S4.  This figure depicts the distribution of mutations and mutation types across the CDKN2A significant gene.

RPS27

Figure S5.  This figure depicts the distribution of mutations and mutation types across the RPS27 significant gene.

PTEN

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

ACSM2B

Figure S7.  This figure depicts the distribution of mutations and mutation types across the ACSM2B significant gene.

SLC38A4

Figure S8.  This figure depicts the distribution of mutations and mutation types across the SLC38A4 significant gene.

RAC1

Figure S9.  This figure depicts the distribution of mutations and mutation types across the RAC1 significant gene.

BAGE

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

LUZP2

Figure S11.  This figure depicts the distribution of mutations and mutation types across the LUZP2 significant gene.

LCE1B

Figure S12.  This figure depicts the distribution of mutations and mutation types across the LCE1B significant gene.

MRPS31

Figure S13.  This figure depicts the distribution of mutations and mutation types across the MRPS31 significant gene.

GRXCR1

Figure S14.  This figure depicts the distribution of mutations and mutation types across the GRXCR1 significant gene.

NRK

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

NOTCH2NL

Figure S16.  This figure depicts the distribution of mutations and mutation types across the NOTCH2NL significant gene.

HIST1H2AA

Figure S17.  This figure depicts the distribution of mutations and mutation types across the HIST1H2AA significant gene.

TMEM216

Figure S18.  This figure depicts the distribution of mutations and mutation types across the TMEM216 significant gene.

NAP1L2

Figure S19.  This figure depicts the distribution of mutations and mutation types across the NAP1L2 significant gene.

RUNX1T1

Figure S20.  This figure depicts the distribution of mutations and mutation types across the RUNX1T1 significant gene.

MUM1L1

Figure S21.  This figure depicts the distribution of mutations and mutation types across the MUM1L1 significant gene.

C8A

Figure S22.  This figure depicts the distribution of mutations and mutation types across the C8A significant gene.

FUT9

Figure S23.  This figure depicts the distribution of mutations and mutation types across the FUT9 significant gene.

RBM11

Figure S24.  This figure depicts the distribution of mutations and mutation types across the RBM11 significant gene.

PRB1

Figure S25.  This figure depicts the distribution of mutations and mutation types across the PRB1 significant gene.

ZNF98

Figure S26.  This figure depicts the distribution of mutations and mutation types across the ZNF98 significant gene.

HBD

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

USP17L2

Figure S28.  This figure depicts the distribution of mutations and mutation types across the USP17L2 significant gene.

PPP6C

Figure S29.  This figure depicts the distribution of mutations and mutation types across the PPP6C significant gene.

SNAP91

Figure S30.  This figure depicts the distribution of mutations and mutation types across the SNAP91 significant gene.

CDH9

Figure S31.  This figure depicts the distribution of mutations and mutation types across the CDH9 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: 54. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 NDUFB9 NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 9, 22kDa 8 1 8 278 8 5e-15 2.2e-11
2 STK19 serine/threonine kinase 19 18 2 8 556 16 9.7e-15 2.2e-11
3 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 16 5 12 1390 17904 2.4e-14 3.6e-11
4 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 86 33 83 9174 102748 1.4e-13 1.5e-10
5 BRAF v-raf murine sarcoma viral oncogene homolog B1 146 89 139 24742 1795885 2.7e-13 1.9e-10
6 TP53 tumor protein p53 51 356 48 98968 5013 2.8e-13 1.9e-10
7 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 42 332 42 92296 1570 2.9e-13 1.9e-10
8 EPHA6 EPH receptor A6 67 8 6 2224 6 6.4e-12 3.6e-09
9 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 23 767 22 213226 410 2.6e-10 1.3e-07
10 EPHA7 EPH receptor A7 52 13 5 3614 5 1e-08 4.7e-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: 2. 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 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 BFAR(1), BRAF(146), CAMP(2), CREB3(1), CREB5(9), MAPK1(4), RAF1(10), SNX13(5), SRC(2), TERF2IP(1) 4563015 181 157 52 33 38 4 6 127 6 0 0.0023 5.5e-11 3.4e-08
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 CCND1(2), CDK4(7), CDKN1A(3), CDKN1B(1), CDKN2A(42), CFL1(2), E2F1(5), E2F2(5), MDM2(1), NXT1(2), PRB1(37), TP53(51) 3459942 158 99 114 17 80 5 6 12 54 1 8.4e-13 2.9e-09 9.1e-07
3 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(2), MYC(5), SP1(5), SP3(1), TP53(51), WT1(8) 2903335 72 58 61 5 31 4 7 6 23 1 1.5e-07 0.00052 0.11
4 SA_REG_CASCADE_OF_CYCLIN_EXPR Expression of cyclins regulates progression through the cell cycle by activating cyclin-dependent kinases. CCNA1, CCNA2, CCND1, CCNE1, CCNE2, CDK2, CDK4, CDKN1B, CDKN2A, E2F1, E2F2, E2F4, PRB1 13 CCNA1(14), CCND1(2), CCNE1(4), CCNE2(8), CDK4(7), CDKN1B(1), CDKN2A(42), E2F1(5), E2F2(5), E2F4(3), PRB1(37) 3685602 128 88 93 20 74 2 4 15 33 0 2.9e-07 0.019 1
5 HSA00472_D_ARGININE_AND_D_ORNITHINE_METABOLISM Genes involved in D-arginine and D-ornithine metabolism DAO 1 DAO(11) 293722 11 11 11 2 8 1 1 1 0 0 0.088 0.086 1
6 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(13), CDKN2A(42), E2F1(5), MDM2(1), MYC(5), PIK3CA(10), PIK3R1(2), POLR1A(13), POLR1B(9), POLR1C(1), RAC1(20), RB1(10), TBX2(6), TP53(51), TWIST1(1) 8282634 189 115 143 28 96 9 7 15 58 4 4.7e-11 0.34 1
7 HSA00627_1,4_DICHLOROBENZENE_DEGRADATION Genes involved in 1,4-dichlorobenzene degradation CMBL 1 CMBL(5) 210423 5 5 5 1 5 0 0 0 0 0 0.37 0.36 1
8 HSA00401_NOVOBIOCIN_BIOSYNTHESIS Genes involved in novobiocin biosynthesis GOT1, GOT2, TAT 3 GOT1(6), GOT2(6), TAT(19) 1077313 31 20 30 7 23 3 2 1 2 0 0.0061 0.84 1
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(2), DNAJC3(3), EIF2S1(1), MAP3K14(6), NFKB1(6), NFKBIA(2), RELA(4), TP53(51) 3955534 75 62 65 13 35 5 5 7 22 1 0.00032 0.95 1
10 BOTULINPATHWAY Blockade of Neurotransmitter Relase by Botulinum Toxin CHRM1, CHRNA1, SNAP25, STX1A, VAMP2 5 CHRM1(5), CHRNA1(7), SNAP25(8) 1337968 20 18 18 3 15 1 0 1 3 0 0.0017 0.95 1

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 HSA00472_D_ARGININE_AND_D_ORNITHINE_METABOLISM Genes involved in D-arginine and D-ornithine metabolism DAO 1 DAO(11) 293722 11 11 11 2 8 1 1 1 0 0 0.088 0.086 1
2 HSA00627_1,4_DICHLOROBENZENE_DEGRADATION Genes involved in 1,4-dichlorobenzene degradation CMBL 1 CMBL(5) 210423 5 5 5 1 5 0 0 0 0 0 0.37 0.36 1
3 HSA00401_NOVOBIOCIN_BIOSYNTHESIS Genes involved in novobiocin biosynthesis GOT1, GOT2, TAT 3 GOT1(6), GOT2(6), TAT(19) 1077313 31 20 30 7 23 3 2 1 2 0 0.0061 0.84 1
4 BOTULINPATHWAY Blockade of Neurotransmitter Relase by Botulinum Toxin CHRM1, CHRNA1, SNAP25, STX1A, VAMP2 5 CHRM1(5), CHRNA1(7), SNAP25(8) 1337968 20 18 18 3 15 1 0 1 3 0 0.0017 0.95 1
5 FOSBPATHWAY FOSB gene expression and drug abuse CDK5, FOSB, GRIA2, JUND, PPP1R1B 5 CDK5(6), FOSB(5), GRIA2(41), JUND(1), PPP1R1B(2) 1504102 55 47 52 19 33 3 2 8 9 0 0.07 0.96 1
6 HSA00031_INOSITOL_METABOLISM Genes involved in inositol metabolism ALDH6A1, TPI1 2 ALDH6A1(4), TPI1(1) 678404 5 5 4 1 3 0 0 2 0 0 0.34 0.97 1
7 SLRPPATHWAY Small leucine-rich proteoglycans (SLRPs) interact with and reorganize collagen fibers in the extracellular matrix. BGN, DCN, DSPG3, FMOD, KERA, LUM 5 BGN(7), DCN(18), FMOD(6), KERA(15), LUM(12) 1447420 58 44 54 22 45 4 3 3 3 0 0.01 0.98 1
8 1_AND_2_METHYLNAPHTHALENE_DEGRADATION ADH1A, ADH1A, ADH1B, ADH1C, ADH1B, ADH1C, ADH4, ADH6, ADH7, ADHFE1 7 ADH1A(17), ADH1B(30), ADH1C(1), ADH4(10), ADH6(14), ADH7(16), ADHFE1(6) 2308177 94 70 77 29 76 4 4 6 4 0 0.0026 0.98 1
9 HSA00785_LIPOIC_ACID_METABOLISM Genes involved in lipoic acid metabolism LIAS, LIPT1, LOC387787 2 LIAS(2), LIPT1(1) 632316 3 2 3 0 0 2 0 0 1 0 0.39 0.99 1
10 TERCPATHWAY hTERC, the RNA subunit of telomerase, and hTERT, the catalytic protein subunit, are required for telomerase activity and are overexpressed in many cancers. NFYA, NFYB, NFYC, RB1, SP1, SP3 6 NFYA(1), NFYB(2), NFYC(2), RB1(10), SP1(5), SP3(1) 2727826 21 19 21 4 4 2 2 5 6 2 0.14 0.99 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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