Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result)
Rectum Adenocarcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1Z31XBT
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/C1Z31XBT
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: READ-TP

  • Number of patients in set: 69

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:READ-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 3

  • Mutations seen in COSMIC: 222

  • Significantly mutated genes in COSMIC territory: 10

  • Significantly mutated genesets: 33

Mutation Preprocessing

  • Read 38 MAFs of type "Broad"

  • Read 35 MAFs of type "Baylor-SOLiD"

  • Total number of mutations in input MAFs: 29413

  • After removing 257 invalidated mutations: 29156

  • After removing 200 noncoding mutations: 28956

  • After collapsing adjacent/redundant mutations: 21679

Mutation Filtering

  • Number of mutations before filtering: 21679

  • After removing 199 mutations outside gene set: 21480

  • After removing 172 mutations outside category set: 21308

  • After removing 2 "impossible" mutations in

  • gene-patient-category bins of zero coverage: 20935

Results
Breakdown of Mutations by Type

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

type count
De_novo_Start_InFrame 4
De_novo_Start_OutOfFrame 30
Frame_Shift_Del 151
Frame_Shift_Ins 155
In_Frame_Del 27
In_Frame_Ins 7
Missense_Mutation 14473
Nonsense_Mutation 1779
Nonstop_Mutation 6
Read-through 10
Silent 4628
Splice_Site 38
Total 21308
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 4918 99631386 0.000049 49 5.4 2.1
*Cp(A/C/T)->mut 6676 831878375 8e-06 8 0.89 3.3
A->mut 2743 908606480 3e-06 3 0.33 3.9
*CpG->(G/A) 135 99631386 1.4e-06 1.4 0.15 2.7
indel+null 2050 1840116241 1.1e-06 1.1 0.12 NaN
double_null 157 1840116241 8.5e-08 0.085 0.0094 NaN
Total 16679 1840116241 9.1e-06 9.1 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: READ-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)->mut

  • n3 = number of nonsilent mutations of type: A->mut

  • n4 = number of nonsilent mutations of type: *CpG->(G/A)

  • 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: 3. 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 APC adenomatous polyposis coli 576224 66 57 56 0 1 4 4 0 39 18 0 0.93 0 6.6e-15 0 0
2 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 48604 38 38 8 0 0 36 1 0 1 0 0 0.002 0 8.1e-11 0 0
3 TP53 tumor protein p53 79667 45 45 30 1 19 6 6 2 12 0 6e-07 0 0 5e-15 0 0
4 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 40433 5 5 4 0 0 3 2 0 0 0 0.013 0.44 0.026 0.00034 0.00011 0.5
5 FBXW7 F-box and WD repeat domain containing 7 177744 12 9 10 0 6 2 2 0 2 0 0.077 0.063 0.057 0.00036 0.00024 0.87
6 SMAD2 SMAD family member 2 98964 5 5 5 0 0 3 1 0 1 0 0.012 0.34 0.02 0.0056 0.0011 1
7 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 227547 4 4 2 1 4 0 0 0 0 0 0.00017 0.026 0.00013 1 0.0013 1
8 SMAD4 SMAD family member 4 115264 8 8 6 0 2 3 3 0 0 0 0.17 0.18 0.26 0.00054 0.0014 1
9 PPPDE1 38842 2 1 2 0 0 1 0 0 1 0 0.14 0.36 0.29 0.00051 0.0014 1
10 ZNF706 zinc finger protein 706 15822 1 1 1 0 0 0 0 0 1 0 NaN NaN NaN 0.0018 0.0018 1
11 PFDN1 prefoldin subunit 1 22914 1 1 1 0 0 0 0 0 1 0 NaN NaN NaN 0.0021 0.0021 1
12 TCF7L2 transcription factor 7-like 2 (T-cell specific, HMG-box) 119026 7 7 7 1 2 3 0 0 2 0 0.11 0.072 0.077 0.003 0.0022 1
13 ELF3 E74-like factor 3 (ets domain transcription factor, epithelial-specific ) 71547 3 3 3 0 0 0 0 0 3 0 0.048 0.33 0.1 0.0027 0.0025 1
14 MARCKSL1 MARCKS-like 1 28945 2 2 1 0 0 0 0 0 2 0 0.0083 0.18 0.091 0.0034 0.0028 1
15 IQCK IQ motif containing K 56949 2 2 2 0 1 0 0 0 1 0 0.14 0.26 0.067 0.005 0.003 1
16 KIAA1804 162266 11 9 9 0 7 3 1 0 0 0 0.05 0.042 0.047 0.0094 0.0039 1
17 TLL2 tolloid-like 2 209091 3 3 2 2 2 0 1 0 0 0 0.00051 0.21 0.00062 0.82 0.0044 1
18 PALMD palmdelphin 114488 3 2 3 0 1 1 0 0 1 0 0.029 0.25 0.039 0.013 0.0044 1
19 DYNLRB2 dynein, light chain, roadblock-type 2 20963 1 1 1 0 0 0 0 0 1 0 NaN NaN NaN 0.005 0.005 1
20 NIT2 nitrilase family, member 2 58349 2 2 1 0 0 1 0 0 1 0 0.0098 0.15 0.069 0.0087 0.0051 1
21 GLRX glutaredoxin (thioltransferase) 22470 1 1 1 0 0 0 0 0 1 0 NaN NaN NaN 0.0056 0.0056 1
22 BOLA3 bolA homolog 3 (E. coli) 24127 1 1 1 0 0 0 0 0 1 0 NaN NaN NaN 0.0077 0.0077 1
23 KRTAP5-5 keratin associated protein 5-5 34487 2 2 1 0 0 0 0 2 0 0 0.1 0.0062 0.013 0.078 0.0081 1
24 HEBP2 heme binding protein 2 34871 1 1 1 0 0 0 0 0 1 0 NaN NaN NaN 0.0082 0.0082 1
25 PECAM1 platelet/endothelial cell adhesion molecule (CD31 antigen) 1292 1 1 1 0 0 1 0 0 0 0 NaN NaN NaN 0.0082 0.0082 1
26 SIP1 survival of motor neuron protein interacting protein 1 54290 2 2 2 0 0 0 0 0 2 0 0.4 0.19 0.74 0.0015 0.0087 1
27 FAM123B family with sequence similarity 123B 190503 8 6 8 1 0 2 1 0 5 0 0.07 0.64 0.14 0.0085 0.0091 1
28 GLT8D2 glycosyltransferase 8 domain containing 2 74554 2 2 2 1 0 1 0 0 1 0 0.13 0.62 0.23 0.0053 0.0093 1
29 BAG5 BCL2-associated athanogene 5 80849 4 2 3 0 2 0 0 0 2 0 0.072 0.12 0.037 0.034 0.0098 1
30 C6orf125 chromosome 6 open reading frame 125 25522 1 1 1 0 0 0 0 0 1 0 NaN NaN NaN 0.01 0.01 1
31 MAPK10 mitogen-activated protein kinase 10 99738 4 4 4 0 0 1 1 0 2 0 0.53 0.61 0.67 0.002 0.01 1
32 C6orf162 chromosome 6 open reading frame 162 20829 1 1 1 0 0 0 0 0 1 0 NaN NaN NaN 0.011 0.011 1
33 HSPB3 heat shock 27kDa protein 3 30908 1 1 1 0 0 0 0 0 1 0 NaN NaN NaN 0.011 0.011 1
34 TRIM69 tripartite motif-containing 69 99933 2 2 2 1 0 1 0 0 1 0 0.18 0.0063 0.014 0.12 0.012 1
35 FAM133A family with sequence similarity 133, member A 38160 2 2 2 1 0 1 0 0 1 0 0.54 0.034 0.041 0.042 0.013 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: 10. 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 38 52 37 3588 363199 1.8e-13 8e-10
2 TP53 tumor protein p53 45 824 45 56856 17987 1.7e-12 2.6e-09
3 APC adenomatous polyposis coli 66 839 50 57891 1048 1.7e-12 2.6e-09
4 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 5 33 5 2277 5755 3.1e-11 3.5e-08
5 FBXW7 F-box and WD repeat domain containing 7 12 91 6 6279 329 4.5e-11 4.1e-08
6 SMAD4 SMAD family member 4 8 159 6 10971 39 1.2e-09 9.3e-07
7 KRTAP5-5 keratin associated protein 5-5 2 1 2 69 2 1.9e-07 0.00012
8 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 4 42 3 2898 6 3e-06 0.0017
9 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 7 220 4 15180 1382 0.000013 0.0067
10 LRP1B low density lipoprotein-related protein 1B (deleted in tumors) 20 18 2 1242 2 0.000063 0.028

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: 33. 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_ADRENERGIC Adrenergic receptors respond to epinephrine and norepinephrine signaling. AKT1, APC, AR, ASAH1, BF, BRAF, CAMP, CCL13, CCL15, CCL16, DAG1, EGFR, GAS, GNA11, GNA15, GNAI1, GNAQ, ITPKA, ITPKB, ITPR1, ITPR2, ITPR3, KCNJ3, KCNJ5, KCNJ9, MAPK1, MAPK10, MAPK14, PHKA2, PIK3CA, PIK3CD, PIK3R1, PITX2, PTX1, PTX3, RAF1, SRC 34 APC(66), AR(2), BRAF(2), DAG1(1), EGFR(1), GNAI1(1), GNAQ(1), ITPR1(8), ITPR2(6), ITPR3(1), KCNJ5(1), KCNJ9(1), MAPK10(4), PHKA2(1), PIK3CA(7), PIK3CD(1), PIK3R1(5), RAF1(2) 5018319 111 60 101 7 18 15 11 0 46 21 4.9e-08 <1.00e-15 <1.07e-13
2 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 18 ABCB1(5), ATM(10), CPB2(1), CSNK1D(1), FHL2(1), HIF1A(2), IGFBP3(2), MAPK8(5), TP53(45) 2001688 72 50 57 4 26 18 9 3 15 1 0.000018 <1.00e-15 <1.07e-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(4), CDC25C(1), TP53(45), YWHAH(1) 1621418 61 49 46 6 22 16 7 2 13 1 0.015 <1.00e-15 <1.07e-13
4 TELPATHWAY Telomerase is a ribonucleotide protein that adds telomeric repeats to the 3' ends of chromosomes. AKT1, BCL2, EGFR, G22P1, HSPCA, IGF1R, KRAS2, MYC, POLR2A, PPP2CA, PRKCA, RB1, TEP1, TERF1, TERT, TNKS, TP53, XRCC5 15 EGFR(1), IGF1R(1), PRKCA(2), RB1(3), TERF1(1), TP53(45), XRCC5(2) 2767314 55 48 40 6 22 9 9 2 13 0 0.0014 <1.00e-15 <1.07e-13
5 TGFBPATHWAY The TGF-beta receptor responds to ligand binding by activating the SMAD family of transcriptional regulations, commonly blocking cell growth. APC, CDH1, CREBBP, EP300, MADH2, MADH3, MADH4, MADH7, MADHIP, MAP2K1, MAP3K7, MAP3K7IP1, MAPK3, SKIL, TGFB1, TGFB2, TGFB3, TGFBR1, TGFBR2 13 APC(66), CDH1(1), CREBBP(4), EP300(2), MAP2K1(1), MAP3K7(2), MAPK3(1), SKIL(1), TGFB2(4), TGFBR1(3) 2554178 85 57 75 4 5 10 8 0 44 18 5.2e-06 1.11e-15 1.07e-13
6 PITX2PATHWAY The bicoid-related transcription factor Pitx2 is activated by Wnt binding to the Frizzled receptor and induces tissue-specific cell proliferation. APC, AXIN1, CREBBP, CTNNB1, DVL1, EP300, FZD1, GSK3B, HDAC1, HTATIP, LDB1, LEF1, PITX2, PPARBP, TRRAP, WNT1 13 APC(66), AXIN1(1), CREBBP(4), CTNNB1(4), EP300(2), FZD1(1), GSK3B(1), LDB1(2), TRRAP(4) 3136901 85 58 75 4 8 12 6 0 41 18 1e-06 1.22e-15 1.07e-13
7 WNTPATHWAY The Wnt glycoprotein binds to membrane-bound receptors such as Frizzled to activate a number of signaling pathways, including that of beta-catenin. APC, AXIN1, BTRC, CCND1, CREBBP, CSNK1A1, CSNK1D, CSNK2A1, CTBP1, CTNNB1, DVL1, FRAT1, FZD1, GSK3B, HDAC1, MADH4, MAP3K7, MAP3K7IP1, MYC, NLK, PPARD, PPP2CA, TCF1, TLE1, WIF1, WNT1 21 APC(66), AXIN1(1), BTRC(3), CREBBP(4), CSNK1D(1), CTNNB1(4), FZD1(1), GSK3B(1), MAP3K7(2), PPARD(1) 2701685 84 58 74 4 8 9 8 0 41 18 1.8e-06 1.22e-15 1.07e-13
8 WNT_SIGNALING Wnt signaling genes APC, ARHA, AXIN1, C2orf31, CCND1, CCND2, CCND3, CSNK1E, CSNK1E, LOC400927, CTNNB1, DIPA, DVL1, DVL2, DVL3, FBXW2, FOSL1, FRAT1, FZD1, FZD10, FZD2, FZD3, FZD5, FZD6, FZD7, FZD8, FZD9, GSK3B, JUN, LDLR, MAPK10, MAPK9, MYC, PAFAH1B1, PLAU, PPP2R5C, PPP2R5E, PRKCA, PRKCB1, PRKCD, PRKCE, PRKCG, PRKCH, PRKCI, PRKCM, PRKCQ, PRKCZ, PRKD1, RAC1, RHOA, SFRP4, TCF7, WNT1, WNT10A, WNT10B, WNT11, WNT16, WNT2, WNT2B, WNT3, WNT4, WNT5A, WNT5B, WNT6, WNT7A, WNT7B 57 APC(66), AXIN1(1), CTNNB1(4), DVL2(1), DVL3(2), FBXW2(1), FZD1(1), FZD10(2), FZD3(3), FZD6(3), GSK3B(1), LDLR(1), MAPK10(4), MAPK9(2), PLAU(1), PPP2R5C(1), PRKCA(2), PRKCE(2), PRKCG(2), PRKCH(1), PRKCQ(1), PRKD1(5), RHOA(1), SFRP4(1), TCF7(3), WNT10B(1), WNT2(1), WNT2B(2), WNT6(1) 5514820 117 58 107 13 15 27 8 0 49 18 6.7e-06 1.44e-15 1.09e-13
9 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(10), CDK4(1), PCNA(1), RB1(3), TIMP3(1), TP53(45) 1820960 62 50 47 6 21 14 9 2 15 1 0.002 1.67e-15 1.09e-13
10 ALKPATHWAY Activin receptor-like kinase 3 (ALK3) is required during gestation for cardiac muscle development. ACVR1, APC, ATF2, AXIN1, BMP10, BMP2, BMP4, BMP5, BMP7, BMPR1A, BMPR2, CHRD, CTNNB1, DVL1, FZD1, GATA4, GSK3B, MADH1, MADH4, MADH5, MADH6, MAP3K7, MEF2C, MYL2, NKX2-5, NOG, NPPA, NPPB, RFC1, TCF1, TGFB1, TGFB2, TGFB3, TGFBR1, TGFBR2, TGFBR3, WNT1 31 ACVR1(1), APC(66), ATF2(1), AXIN1(1), BMP10(1), BMP2(1), BMP4(1), BMP5(1), BMP7(1), BMPR2(3), CTNNB1(4), FZD1(1), GSK3B(1), MAP3K7(2), MEF2C(2), NPPB(1), RFC1(4), TGFB2(4), TGFBR1(3), TGFBR3(3) 3414527 102 59 92 3 13 19 9 0 43 18 6.1e-09 1.78e-15 1.09e-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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