Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result)
Kidney Chromophobe (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1PG1Q79
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result). Broad Institute of MIT and Harvard. doi:10.7908/C1PG1Q79
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: KICH-TP

  • Number of patients in set: 66

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:KICH-TP.final_analysis_set.maf

  • Significantly mutated genes (q ≤ 0.1): 6

  • Mutations seen in COSMIC: 43

  • Significantly mutated genes in COSMIC territory: 2

  • Significantly mutated genesets: 31

Mutation Preprocessing

  • Read 66 MAFs of type "Baylor-Illumina"

  • Total number of mutations in input MAFs: 7559

  • After removing 34 mutations outside chr1-24: 7525

  • After removing 520 blacklisted mutations: 7005

  • After removing 225 noncoding mutations: 6780

  • After collapsing adjacent/redundant mutations: 6767

Mutation Filtering

  • Number of mutations before filtering: 6767

  • After removing 282 mutations outside gene set: 6485

  • After removing 92 mutations outside category set: 6393

  • After removing 1 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 6018

Results
Breakdown of Mutations by Type

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

type count
Frame_Shift_Del 181
Frame_Shift_Ins 218
In_Frame_Del 53
In_Frame_Ins 18
Missense_Mutation 3904
Nonsense_Mutation 172
Silent 1741
Splice_Site 106
Total 6393
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 1158 113882076 1e-05 10 4.6 2.1
A->G 1170 1020035596 1.1e-06 1.1 0.51 2.3
*Cp(A/C/T)->T 587 947255610 6.2e-07 0.62 0.28 1.7
transver 987 2081173282 4.7e-07 0.47 0.21 5
indel+null 666 2081173282 3.2e-07 0.32 0.14 NaN
double_null 83 2081173282 4e-08 0.04 0.018 NaN
Total 4651 2081173282 2.2e-06 2.2 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: KICH-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: A->G

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

  • 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 MUC2 mucin 2, oligomeric mucus/gel-forming 541641 10 8 9 10 4 0 3 2 1 0 0 1 0 0.72 0 0
2 MUC6 mucin 6, oligomeric mucus/gel-forming 475534 37 21 35 15 2 5 15 14 1 0 0 1 0 0.0059 0 0
3 TP53 tumor protein p53 85573 28 22 26 1 3 3 4 7 10 1 0.000078 0.0011 0.000017 9.1e-12 5.9e-15 3.6e-11
4 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 79261 7 6 7 0 0 1 0 1 3 2 0.0068 0.54 0.014 3.3e-06 8.1e-07 0.0037
5 PRSS3 protease, serine, 3 57102 8 7 6 4 1 1 1 2 3 0 0.046 0.81 0.11 2.9e-06 5.1e-06 0.018
6 RIMBP3 RIMS binding protein 3 246329 2 2 2 0 0 0 0 0 2 0 0.016 0.00014 0.00039 0.0038 0.000021 0.065
7 HLA-C major histocompatibility complex, class I, C 68086 7 7 7 5 1 1 0 5 0 0 0.0003 0.97 0.00065 0.0047 0.000042 0.11
8 TAS2R43 taste receptor, type 2, member 43 51426 5 5 5 0 1 2 0 2 0 0 0.021 0.44 0.044 0.00028 0.00015 0.34
9 TAS2R30 taste receptor, type 2, member 30 63624 6 5 5 2 0 2 1 1 2 0 0.29 0.85 0.4 0.000045 0.00022 0.44
10 CDKN1A cyclin-dependent kinase inhibitor 1A (p21, Cip1) 29948 2 2 2 0 0 0 0 0 1 1 0.49 0.043 0.037 0.001 0.00043 0.77
11 CBWD6 COBW domain containing 6 55998 2 2 1 0 0 0 0 0 2 0 0.016 0.1 0.049 0.0015 0.00076 1
12 FAM86B1 family with sequence similarity 86, member B1 44150 3 3 3 0 2 0 0 0 1 0 0.17 0.68 0.22 0.0006 0.0013 1
13 HLA-DRB5 major histocompatibility complex, class II, DR beta 5 28025 8 5 8 4 0 2 2 3 1 0 0.31 1 0.46 0.00033 0.0015 1
14 ADAM21 ADAM metallopeptidase domain 21 143418 3 3 3 0 1 0 0 1 1 0 0.044 0.11 0.012 0.015 0.0017 1
15 GFM1 G elongation factor, mitochondrial 1 153603 3 3 2 0 0 0 1 0 2 0 0.0021 0.98 0.0046 0.045 0.002 1
16 OR5H1 olfactory receptor, family 5, subfamily H, member 1 62304 3 2 2 1 0 0 0 3 0 0 0.00014 0.75 0.0022 0.11 0.0022 1
17 MUC16 mucin 16, cell surface associated 2894549 20 15 19 14 1 6 3 7 3 0 0.00022 0.28 0.00034 1 0.0031 1
18 RAB40A RAB40A, member RAS oncogene family 55240 2 2 2 0 0 0 0 0 2 0 0.013 0.14 0.09 0.0039 0.0031 1
19 NASP nuclear autoantigenic sperm protein (histone-binding) 165161 3 2 3 0 0 0 2 1 0 0 0.00031 0.24 0.00077 0.51 0.0035 1
20 NOM1 nucleolar protein with MIF4G domain 1 129243 3 1 3 1 0 0 0 0 2 1 0.00022 0.51 0.0018 0.23 0.0037 1
21 OR13C2 olfactory receptor, family 13, subfamily C, member 2 63360 3 3 3 1 0 0 2 0 1 0 0.13 0.12 0.073 0.0059 0.0038 1
22 RHBDD3 rhomboid domain containing 3 74948 2 2 1 0 0 0 0 0 2 0 0.0097 0.78 0.44 0.0014 0.0053 1
23 MRPS12 mitochondrial ribosomal protein S12 21911 2 2 2 0 0 0 0 0 2 0 NaN NaN NaN 0.0054 0.0054 1
24 RAP1GAP2 RAP1 GTPase activating protein 2 150540 2 2 2 0 1 1 0 0 0 0 0.017 0.0064 0.0023 0.3 0.0057 1
25 STAM signal transducing adaptor molecule (SH3 domain and ITAM motif) 1 106307 2 2 1 0 0 0 0 0 2 0 0.01 0.96 0.046 0.016 0.0059 1
26 AHNAK2 AHNAK nucleoprotein 2 1115113 17 10 17 13 2 7 3 5 0 0 0.00025 0.95 0.00079 1 0.0064 1
27 DTWD1 DTW domain containing 1 61284 2 2 2 0 0 0 0 0 2 0 0.033 0.3 0.082 0.012 0.0076 1
28 MAGEC1 melanoma antigen family C, 1 224484 6 5 6 1 0 0 2 4 0 0 0.00031 1 0.0011 0.95 0.0084 1
29 CCDC144NL coiled-coil domain containing 144 family, N-terminal like 44952 3 3 3 2 0 1 1 0 1 0 0.27 0.84 0.48 0.0025 0.0093 1
30 KLF15 Kruppel-like factor 15 82962 2 2 2 0 0 0 0 0 2 0 NaN NaN NaN 0.012 0.012 1
31 PABPC3 poly(A) binding protein, cytoplasmic 3 125375 9 7 9 1 3 3 2 1 0 0 0.044 1 0.084 0.023 0.014 1
32 C16orf3 chromosome 16 open reading frame 3 23342 2 2 2 0 0 1 0 0 1 0 0.048 0.98 1 0.002 0.014 1
33 SDHA succinate dehydrogenase complex, subunit A, flavoprotein (Fp) 131182 6 5 6 0 1 0 3 2 0 0 0.037 0.2 0.054 0.038 0.015 1
34 MUC17 mucin 17, cell surface associated 893222 9 5 9 3 1 2 1 5 0 0 0.00085 1 0.0021 1 0.015 1
35 UBXN11 UBX domain protein 11 99875 3 3 3 0 0 0 2 1 0 0 0.0012 0.95 0.0094 0.23 0.015 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: 2. Number of genes displayed: 10

rank gene description n cos n_cos N_cos cos_ev p q
1 TP53 tumor protein p53 28 356 24 23496 2567 5.3e-13 2.4e-09
2 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 7 767 7 50622 36 4.4e-11 9.9e-08
3 RCN1 reticulocalbin 1, EF-hand calcium binding domain 1 1 1 66 1 0.00015 0.22
4 CENPC1 centromere protein C 1 1 2 1 132 1 0.00029 0.33
5 ALPK2 alpha-kinase 2 1 3 1 198 1 0.00044 0.33
6 SMARCC2 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily c, member 2 2 3 1 198 1 0.00044 0.33
7 DDR2 discoidin domain receptor tyrosine kinase 2 1 4 1 264 1 0.00059 0.38
8 RB1 retinoblastoma 1 (including osteosarcoma) 2 267 2 17622 4 0.00076 0.4
9 DCLK3 doublecortin-like kinase 3 1 6 1 396 1 0.00088 0.4
10 FLT4 fms-related tyrosine kinase 4 4 6 1 396 1 0.00088 0.4

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: 31. 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 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 ATM(3), CDKN1A(2), NFKBIB(1), TP53(28) 2134520 34 24 32 1 6 3 4 8 11 2 0.00052 3.3e-15 4.7e-13
2 G1PATHWAY CDK4/6-cyclin D and CDK2-cyclin E phosphorylate Rb, which allows the transcription of genes needed for the G1/S cell cycle transition. ABL1, ATM, ATR, CCNA1, CCND1, CCNE1, CDC2, CDC25A, CDK2, CDK4, CDK6, CDKN1A, CDKN1B, CDKN2A, CDKN2B, DHFR, E2F1, GSK3B, HDAC1, MADH3, MADH4, RB1, SKP2, TFDP1, TGFB1, TGFB2, TGFB3, TP53 25 ABL1(1), ATM(3), ATR(2), CDK6(1), CDKN1A(2), CDKN1B(1), RB1(2), TGFB2(1), TP53(28) 2965387 41 27 39 2 6 6 4 8 13 4 0.00065 5.4e-15 4.7e-13
3 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(3), CDC25C(1), MYT1(1), RB1(2), TP53(28), WEE1(1) 1768224 36 25 34 1 5 4 5 9 10 3 0.00074 6.4e-15 4.7e-13
4 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 PAX3(1), PML(1), RB1(2), SIRT1(2), TP53(28) 1877723 34 25 32 4 4 3 4 7 13 3 0.033 6.6e-15 4.7e-13
5 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(1), MYC(1), PIK3R1(1), POLR1A(1), POLR1D(1), RB1(2), TP53(28) 1963704 35 24 33 1 4 4 5 8 11 3 0.00052 6.6e-15 4.7e-13
6 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 MYC(1), TP53(28) 697656 29 23 27 1 3 3 4 7 11 1 0.0013 6.8e-15 4.7e-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 IKBKB(1), JAK2(1), RB1(2), TP53(28) 1928715 32 23 30 1 4 4 4 7 10 3 0.00072 6.9e-15 4.7e-13
8 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 ATM(3), CDKN1A(2), PCNA(1), RB1(2), TP53(28) 1801915 36 24 34 1 5 3 4 9 11 4 0.001 7e-15 4.7e-13
9 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 ARF3(1), CDKN1A(2), CDKN1B(1), TP53(28) 805992 32 24 30 3 3 3 4 7 13 2 0.012 7.7e-15 4.7e-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 CHUK(1), TP53(28) 1018591 29 22 27 1 3 3 4 7 11 1 0.0017 7.7e-15 4.7e-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.

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