Mutation Analysis (MutSig 2CV v3.1)
Pancreatic Adenocarcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1Z89BGB
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C1Z89BGB
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 2CV v3.1 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): 155

Results
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 1.  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 2.  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 3.  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:

  • nnon = 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

  • p = p-value (overall)

  • q = q-value, False Discovery Rate (Benjamini-Hochberg procedure)

Table 1.  Get Full Table A Ranked List of Significantly Mutated Genes. Number of significant genes found: 155. 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 longname codelen nnei nncd nsil nmis nstp nspl nind nnon npat nsite pCV pCL pFN p q
1 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 709 10 0 1 127 0 0 0 127 123 5 1e-16 1e-05 1e-05 1e-16 1.5e-13
2 TP53 tumor protein p53 1890 122 0 0 67 16 5 12 100 100 72 1.2e-15 0.003 1e-05 1e-16 1.5e-13
3 JMY junction mediating and regulatory protein, p53 cofactor 3007 2 0 0 0 2 0 54 56 55 3 1e-16 1e-05 0.97 1e-16 1.5e-13
4 RIOK1 RIO kinase 1 (yeast) 1771 22 0 0 1 0 0 54 55 55 2 1e-16 1e-05 1 1e-16 1.5e-13
5 LCE2A late cornified envelope 2A 325 155 0 0 0 0 0 46 46 46 1 1e-16 1e-05 1 1e-16 1.5e-13
6 C1QB complement component 1, q subcomponent, B chain 770 74 0 0 0 0 1 34 35 35 2 1e-16 1e-05 1e-05 1e-16 1.5e-13
7 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 1002 1 0 1 9 12 0 10 31 30 19 1e-16 0.00074 1e-05 1e-16 1.5e-13
8 AEBP1 AE binding protein 1 3557 25 0 1 2 2 0 26 30 26 5 1e-16 1e-05 1 1e-16 1.5e-13
9 RBM47 RNA binding motif protein 47 1798 8 0 2 2 0 0 25 27 25 3 1e-16 1e-05 0.97 1e-16 1.5e-13
10 ANKRD36 ankyrin repeat domain 36 6126 112 0 0 0 1 0 23 24 24 3 1e-16 1e-05 0.0042 1e-16 1.5e-13
11 RFX1 regulatory factor X, 1 (influences HLA class II expression) 3020 61 0 2 1 0 0 23 24 24 3 1e-16 1e-05 1 1e-16 1.5e-13
12 TYRO3 TYRO3 protein tyrosine kinase 2745 3 0 0 0 0 0 14 14 14 5 1e-16 1e-05 0.034 1e-16 1.5e-13
13 SMAD4 SMAD family member 4 1699 35 0 0 14 9 0 9 32 32 29 1.4e-16 0.0073 0.11 1.1e-16 1.6e-13
14 GPR6 G protein-coupled receptor 6 1091 93 0 0 1 0 0 11 12 12 3 3.8e-12 1e-05 0.88 1.4e-15 1.8e-12
15 LZTS1 leucine zipper, putative tumor suppressor 1 1799 2 0 2 2 0 0 8 10 10 3 3.7e-12 1e-05 0.94 1.4e-15 1.8e-12
16 NCOA3 nuclear receptor coactivator 3 4389 65 0 1 2 0 2 13 17 14 7 7e-12 1e-05 1 2.7e-15 3.1e-12
17 SIK3 SIK family kinase 3 3886 19 0 1 1 0 0 12 13 13 3 2.3e-10 1e-05 1 8.1e-14 8.8e-11
18 ZMIZ2 zinc finger, MIZ-type containing 2 2835 66 0 1 3 0 0 9 12 12 4 3.8e-10 1e-05 0.51 1.3e-13 1.3e-10
19 IRS4 insulin receptor substrate 4 3778 7 0 3 2 2 0 12 16 16 5 3.6e-09 1e-05 1 1.2e-12 1.1e-09
20 ESPN espin 2613 0 0 0 0 0 0 6 6 6 1 2.5e-08 1e-05 0.84 7.4e-12 6.8e-09
21 ATP2C1 ATPase, Ca++ transporting, type 2C, member 1 3395 7 0 0 3 0 1 7 11 8 8 6.7e-08 1e-05 0.97 1.9e-11 1.7e-08
22 FNDC1 fibronectin type III domain containing 1 5773 21 0 3 6 0 0 13 19 14 7 1.3e-07 1e-05 0.98 3.6e-11 3e-08
23 TMEM91 transmembrane protein 91 699 21 0 0 1 0 0 5 6 6 2 1.5e-10 NaN NaN 1.5e-10 1.2e-07
24 SHROOM4 shroom family member 4 4516 19 0 3 4 0 0 9 13 11 5 1.1e-06 1e-05 0.18 3e-10 2.3e-07
25 PABPC1 poly(A) binding protein, cytoplasmic 1 1966 23 0 0 1 0 0 8 9 9 4 1.6e-06 1e-05 0.8 4.1e-10 3e-07
26 OGFOD1 2-oxoglutarate and iron-dependent oxygenase domain containing 1 1793 231 0 0 0 0 0 5 5 5 1 0.000011 1e-05 4e-05 2.6e-09 1.9e-06
27 DCP1B DCP1 decapping enzyme homolog B (S. cerevisiae) 1888 61 0 1 4 0 0 7 11 9 5 3e-05 1e-05 1 6.8e-09 4.5e-06
28 RBMX RNA binding motif protein, X-linked 1265 540 0 0 0 0 0 5 5 5 1 3e-05 1e-05 0.27 6.9e-09 4.5e-06
29 ZNF880 zinc finger protein 880 1748 15 0 1 0 0 0 6 6 4 2 0.000033 1e-05 0.0001 7.6e-09 4.8e-06
30 UBAC1 UBA domain containing 1 1254 78 0 0 0 0 0 5 5 5 1 0.000056 1e-05 0.99 1.2e-08 7.6e-06
31 CASQ2 calsequestrin 2 (cardiac muscle) 1240 79 0 0 1 0 0 7 8 8 2 0.000062 1e-05 0.01 1.4e-08 8.1e-06
32 ZNF678 zinc finger protein 678 1755 6 0 0 1 0 0 4 5 5 3 3.3e-06 0.00015 0.92 1.6e-08 9.1e-06
33 RBM12 RNA binding motif protein 12 2803 102 0 1 5 0 0 0 5 5 1 0.000074 1e-05 0.082 1.6e-08 9.1e-06
34 VWA3A von Willebrand factor A domain containing 3A 3689 15 0 0 2 0 0 4 6 5 3 0.000035 9e-05 0.082 2.3e-08 0.000012
35 NBPF16 neuroblastoma breakpoint family, member 16 4146 54 0 0 0 0 0 7 7 7 1 0.00017 1e-05 0.15 3.6e-08 0.000019
KRAS

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

TP53

Figure S2.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

JMY

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

RIOK1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the RIOK1 significant gene.

LCE2A

Figure S5.  This figure depicts the distribution of mutations and mutation types across the LCE2A significant gene.

C1QB

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

CDKN2A

Figure S7.  This figure depicts the distribution of mutations and mutation types across the CDKN2A significant gene.

AEBP1

Figure S8.  This figure depicts the distribution of mutations and mutation types across the AEBP1 significant gene.

RBM47

Figure S9.  This figure depicts the distribution of mutations and mutation types across the RBM47 significant gene.

ANKRD36

Figure S10.  This figure depicts the distribution of mutations and mutation types across the ANKRD36 significant gene.

RFX1

Figure S11.  This figure depicts the distribution of mutations and mutation types across the RFX1 significant gene.

TYRO3

Figure S12.  This figure depicts the distribution of mutations and mutation types across the TYRO3 significant gene.

SMAD4

Figure S13.  This figure depicts the distribution of mutations and mutation types across the SMAD4 significant gene.

GPR6

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

LZTS1

Figure S15.  This figure depicts the distribution of mutations and mutation types across the LZTS1 significant gene.

NCOA3

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

SIK3

Figure S17.  This figure depicts the distribution of mutations and mutation types across the SIK3 significant gene.

ZMIZ2

Figure S18.  This figure depicts the distribution of mutations and mutation types across the ZMIZ2 significant gene.

IRS4

Figure S19.  This figure depicts the distribution of mutations and mutation types across the IRS4 significant gene.

ESPN

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

ATP2C1

Figure S21.  This figure depicts the distribution of mutations and mutation types across the ATP2C1 significant gene.

FNDC1

Figure S22.  This figure depicts the distribution of mutations and mutation types across the FNDC1 significant gene.

SHROOM4

Figure S23.  This figure depicts the distribution of mutations and mutation types across the SHROOM4 significant gene.

PABPC1

Figure S24.  This figure depicts the distribution of mutations and mutation types across the PABPC1 significant gene.

OGFOD1

Figure S25.  This figure depicts the distribution of mutations and mutation types across the OGFOD1 significant gene.

DCP1B

Figure S26.  This figure depicts the distribution of mutations and mutation types across the DCP1B significant gene.

ZNF880

Figure S27.  This figure depicts the distribution of mutations and mutation types across the ZNF880 significant gene.

UBAC1

Figure S28.  This figure depicts the distribution of mutations and mutation types across the UBAC1 significant gene.

CASQ2

Figure S29.  This figure depicts the distribution of mutations and mutation types across the CASQ2 significant gene.

RBM12

Figure S30.  This figure depicts the distribution of mutations and mutation types across the RBM12 significant gene.

VWA3A

Figure S31.  This figure depicts the distribution of mutations and mutation types across the VWA3A significant gene.

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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