Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result)
Lung Adenocarcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C14T6H1H
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/C14T6H1H
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: LUAD-TP

  • Number of patients in set: 229

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:LUAD-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 93

  • Mutations seen in COSMIC: 515

  • Significantly mutated genes in COSMIC territory: 24

  • Significantly mutated genesets: 16

Mutation Preprocessing

  • Read 229 MAFs of type "Broad"

  • Total number of mutations in input MAFs: 251233

  • After removing 79 mutations outside chr1-24: 251154

  • After removing 1775 blacklisted mutations: 249379

  • After removing 144370 noncoding mutations: 105009

  • After collapsing adjacent/redundant mutations: 93596

Mutation Filtering

  • Number of mutations before filtering: 93596

  • After removing 1282 mutations outside gene set: 92314

  • After removing 181 mutations outside category set: 92133

Results
Breakdown of Mutations by Type

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

type count
Frame_Shift_Del 1482
Frame_Shift_Ins 469
In_Frame_Del 154
In_Frame_Ins 20
Missense_Mutation 60363
Nonsense_Mutation 4801
Nonstop_Mutation 56
Silent 22794
Splice_Site 1860
Translation_Start_Site 134
Total 92133
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 8867 379287204 0.000023 23 2.3 2.1
*Cp(A/C/T)->A 22038 3108508517 7.1e-06 7.1 0.7 5
C->(T/G) 18886 3487795721 5.4e-06 5.4 0.53 2.8
A->mut 10703 3354933814 3.2e-06 3.2 0.31 3.9
indel+null 8698 6842729535 1.3e-06 1.3 0.13 NaN
double_null 147 6842729535 2.1e-08 0.021 0.0021 NaN
Total 69339 6842729535 1e-05 10 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: LUAD-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

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

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

  • 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_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: 93. 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 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 226023 14 14 14 1 0 1 4 4 5 0 0.000012 4e-07 0 1.3e-11 0 0
2 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 161903 60 60 6 0 0 47 10 3 0 0 0 0 0 8.8e-15 0 0
3 TP53 tumor protein p53 288082 128 119 106 2 8 22 31 18 49 0 0 1e-06 0 5.6e-16 0 0
4 EGFR epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian) 918061 33 27 19 7 2 5 4 13 9 0 0 0.0014 0 3.6e-15 0 0
5 STK11 serine/threonine kinase 11 199230 21 20 20 0 0 4 2 4 11 0 0.0021 0.024 0.0015 7.3e-15 4.4e-16 1.6e-12
6 KEAP1 kelch-like ECH-associated protein 1 418841 39 39 37 0 4 8 13 10 4 0 0.049 0.012 0.01 2.8e-15 1.1e-15 3.3e-12
7 RBM10 RNA binding motif protein 10 445634 12 12 12 1 1 0 1 0 10 0 0.072 0.23 0.09 3.8e-12 1e-11 2.6e-08
8 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 2003521 30 27 29 4 0 4 6 4 13 3 0.085 0.28 0.11 1.4e-11 4.2e-11 9.4e-08
9 GPR112 G protein-coupled receptor 112 2137715 57 47 56 18 2 25 16 10 4 0 0.044 0.25 0.055 4.5e-11 7e-11 1.4e-07
10 SMARCA4 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4 1051339 18 17 18 2 0 0 3 4 11 0 0.3 0.67 0.43 1.5e-11 1.7e-10 3.1e-07
11 FLG filaggrin 2748000 125 58 123 25 18 65 22 10 9 1 0.13 0.27 0.21 1e-10 5.4e-10 8.9e-07
12 HRNR hornerin 1502240 49 32 49 11 6 17 13 4 8 1 0.0018 0.88 0.0042 2.8e-07 2.6e-08 0.000039
13 MUC7 mucin 7, secreted 261518 18 16 18 1 0 8 3 4 3 0 0.07 0.55 0.13 2.2e-08 6.1e-08 0.000084
14 BRAF v-raf murine sarcoma viral oncogene homolog B1 510899 17 17 12 1 1 7 2 5 2 0 0.026 0.4 0.053 8.9e-08 9.5e-08 0.00012
15 COL11A1 collagen, type XI, alpha 1 1338734 66 48 65 14 3 27 13 8 15 0 0.83 0.14 0.32 1.6e-08 1e-07 0.00012
16 U2AF1 U2 small nuclear RNA auxiliary factor 1 188925 6 6 2 0 0 0 6 0 0 0 0.00027 0.003 0.000085 0.00015 2.4e-07 0.00027
17 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 134194 4 4 2 1 0 1 0 3 0 0 2e-07 0.2 1.2e-06 0.029 6.2e-07 0.00066
18 PCK1 phosphoenolpyruvate carboxykinase 1 (soluble) 436245 12 12 12 2 3 2 3 1 3 0 0.0029 0.04 0.003 0.000014 7.5e-07 0.00075
19 RIMS2 regulating synaptic membrane exocytosis 2 994318 45 39 45 4 4 16 10 8 7 0 0.14 0.18 0.15 4e-07 1e-06 0.00098
20 CSMD3 CUB and Sushi multiple domains 3 2626630 161 98 159 33 4 76 36 24 20 1 0.56 0.78 1 5.9e-08 1e-06 0.00098
21 RB1 retinoblastoma 1 (including osteosarcoma) 626086 13 13 13 1 0 2 1 1 9 0 0.45 0.6 0.57 1.5e-07 1.5e-06 0.0013
22 FTSJD1 FtsJ methyltransferase domain containing 1 530593 11 11 11 0 1 1 2 1 6 0 0.13 0.029 0.039 2.2e-06 1.5e-06 0.0013
23 RIT1 Ras-like without CAAX 1 155720 10 9 9 1 2 4 0 2 2 0 0.08 0.37 0.14 8.1e-07 1.9e-06 0.0015
24 LRP1B low density lipoprotein-related protein 1B (deleted in tumors) 3243556 147 85 145 26 7 57 36 24 22 1 0.73 0.022 0.11 1.2e-06 2.2e-06 0.0017
25 OR10J3 olfactory receptor, family 10, subfamily J, member 3 227168 11 11 11 3 1 4 4 2 0 0 0.016 0.01 0.0033 0.000083 4.4e-06 0.0032
26 LTBP1 latent transforming growth factor beta binding protein 1 1142710 28 28 28 7 1 11 9 3 4 0 0.68 0.22 0.56 1.2e-06 0.000011 0.0073
27 SETD2 SET domain containing 2 1461707 21 18 21 1 1 4 2 4 9 1 0.044 0.12 0.064 0.000012 0.000011 0.0075
28 ZCCHC5 zinc finger, CCHC domain containing 5 293120 16 15 15 4 1 10 3 0 2 0 0.51 0.83 0.68 1.1e-06 0.000012 0.0075
29 ADAMTS5 ADAM metallopeptidase with thrombospondin type 1 motif, 5 (aggrecanase-2) 589217 22 21 22 7 1 11 5 2 3 0 0.041 0.13 0.044 0.000019 0.000012 0.0076
30 GBA3 glucosidase, beta, acid 3 (cytosolic) 297929 12 12 12 2 1 4 3 1 3 0 0.0037 0.22 0.0064 0.00015 0.000014 0.0084
31 MYL10 myosin, light chain 10, regulatory 120225 7 7 7 1 1 3 0 0 3 0 0.47 0.36 0.52 2e-06 0.000015 0.0088
32 SMAD4 SMAD family member 4 389987 8 8 8 1 1 1 2 0 4 0 0.63 0.24 0.5 3.5e-06 0.000025 0.014
33 ZNF268 zinc finger protein 268 98241 7 7 7 1 0 2 0 1 4 0 0.55 0.028 0.1 2e-05 0.000029 0.016
34 OR4Q3 olfactory receptor, family 4, subfamily Q, member 3 216176 14 14 14 5 1 5 4 3 1 0 0.67 0.26 0.56 3.7e-06 0.000029 0.016
35 ARID1A AT rich interactive domain 1A (SWI-like) 1327513 16 14 16 1 0 0 3 2 11 0 0.78 0.52 1 2.5e-06 0.000035 0.018
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: 24. 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 60 52 60 11908 797017 0 0
2 LRP1B low density lipoprotein-related protein 1B (deleted in tumors) 147 18 8 4122 8 0 0
3 TP53 tumor protein p53 128 356 122 81524 18207 0 0
4 BRAF v-raf murine sarcoma viral oncogene homolog B1 17 89 12 20381 28944 0 0
5 STK11 serine/threonine kinase 11 21 130 14 29770 27 0 0
6 EGFR epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian) 33 293 29 67097 10294 0 0
7 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 14 332 14 76028 259 0 0
8 MET met proto-oncogene (hepatocyte growth factor receptor) 12 34 6 7786 64 3.1e-10 1.8e-07
9 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 11 220 9 50380 3946 4.1e-09 2.1e-06
10 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 30 285 9 65265 13 3.7e-08 0.000017

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: 16. 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 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(8), ATM(22), BAX(1), BCL2(1), CCND1(2), CCNE1(3), CDK2(4), CDK4(2), CDKN1A(1), E2F1(3), GADD45A(1), MDM2(2), PCNA(1), RB1(13), TP53(128) 6266585 192 143 169 16 16 37 42 29 68 0 1.9e-06 <1.00e-15 <1.54e-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 19 ABCB1(20), ATM(22), BAX(1), CDKN1A(1), CPB2(4), CSNK1A1(1), CSNK1D(2), GADD45A(1), HIF1A(1), IGFBP3(2), MAPK8(3), MDM2(2), NQO1(3), TP53(128) 7172280 191 138 167 18 17 40 45 27 62 0 0.000012 <1.00e-15 <1.54e-13
3 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 IFNG(4), IFNGR1(3), IFNGR2(3), IKBKB(3), JAK2(4), LIN7A(2), NFKB1(3), NFKBIA(1), RB1(13), TNFRSF1B(1), TP53(128), USH1C(4), WT1(8) 6332308 177 133 155 16 15 35 41 21 65 0 1.8e-06 <1.00e-15 <1.54e-13
4 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), CCND1(2), CDK2(4), CDK4(2), CDKN1A(1), CDKN1B(3), CDKN2A(14), E2F1(3), MDM2(2), NXT1(1), PRB1(2), TP53(128) 2958909 163 125 141 8 15 29 38 24 57 0 1.1e-09 <1.00e-15 <1.54e-13
5 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(2), ATM(22), BRCA1(8), CDKN1A(1), CHEK1(6), CHEK2(2), GADD45A(1), JUN(1), MAPK8(3), MDM2(2), MRE11A(6), NFKB1(3), NFKBIA(1), RAD50(3), RAD51(1), RBBP8(3), TP53(128), TP73(4) 10278207 197 139 174 16 15 41 53 28 60 0 1.7e-06 1.78e-15 2.19e-13
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(22), CDC25A(4), CDC25B(1), CDC25C(3), CDK2(4), CDK4(2), CHEK1(6), MYT1(6), RB1(13), TP53(128), WEE1(3) 6070332 192 140 169 13 15 37 43 29 68 0 1.6e-07 2.22e-15 2.28e-13
7 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(22), ATR(16), CDC25C(3), CHEK1(6), CHEK2(2), TP53(128) 5517526 177 136 154 7 10 35 48 28 56 0 1e-08 3.77e-15 3.16e-13
8 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(3), MAX(3), SP1(1), SP3(4), TP53(128), WT1(8) 2427858 147 122 125 10 9 29 35 21 53 0 1.2e-06 4.11e-15 3.16e-13
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(3), DNAJC3(3), MAP3K14(1), NFKB1(3), NFKBIA(1), TP53(128) 3372941 139 121 117 9 9 23 36 18 53 0 2.2e-06 5.77e-15 3.95e-13
10 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 CREBBP(4), DAXX(6), HRAS(1), PAX3(6), PML(7), RARA(3), RB1(13), SIRT1(1), SP100(9), TNFRSF1B(1), TP53(128) 6622222 179 131 157 15 14 30 47 25 63 0 3.8e-07 7.77e-15 4.60e-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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