Mutation Analysis (MutSigCV v0.9)
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1S75FPV
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1S75FPV
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. MutSigCV v0.9 was used to generate the results found in this report.

  • Working with individual set: DLBC-TP

  • Number of patients in set: 48

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:DLBC-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 9

Results
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: DLBC-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:

  • 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

  • nflank = number of noncoding mutations from this gene's flanking region, across the individual set

  • 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: 9. 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).

gene Nnon Nsil Nflank nnon npat nsite nsil nflank nnei fMLE p score time q
IGLL5 13536 4176 115 40 17 33 25 0 20 6.8 7.2e-15 69 0.039 8.7e-11
B2M 13776 3840 320 15 13 13 0 0 20 0.28 9.5e-15 52 0.039 8.7e-11
TMSB4X 5424 1248 220 7 6 7 0 0 20 0.58 5.6e-10 27 0.04 3.4e-06
MYD88 33504 8832 390 7 7 4 0 0 20 0.29 2.8e-09 34 0.067 0.000013
BTG2 13008 3792 130 20 13 17 5 0 8 3.9 2.9e-08 39 0.044 0.00011
KLF2 6816 1872 85 5 4 5 1 0 20 1.1 2.8e-07 22 0.038 0.00082
SOCS1 6528 2016 40 9 5 9 4 0 20 1.1 3.1e-07 22 0.038 0.00082
BTG1 19248 5520 205 9 7 8 6 0 20 2.1 5.2e-07 27 0.038 0.0012
PIM1 34992 10272 595 41 10 32 19 6 20 5.1 6.9e-06 33 0.14 0.014
HIST1H1E 21456 7104 120 13 9 11 3 0 20 4.3 0.00025 23 0.038 0.46
KRTAP4-5 19680 4992 100 7 4 5 0 0 20 1.1 0.0003 19 0.045 0.5
TMEM30A 41472 11520 755 4 4 3 0 0 20 0.71 0.00056 19 0.038 0.83
P2RY8 33648 11328 105 9 8 9 0 0 13 0.64 0.00059 20 0.038 0.83
NFKBIE 26448 9264 495 5 4 5 0 0 20 1.7 0.0012 16 0.11 1
PAX5 39168 12000 835 4 4 4 0 0 20 0.34 0.0016 16 0.041 1
FAS 39696 10080 1210 5 5 5 0 0 7 0.62 0.002 18 0.038 1
TNFRSF14 25296 7536 610 4 4 4 0 0 20 0.78 0.0035 14 0.039 1
HLA-C 39792 12240 755 7 7 5 1 0 20 1.4 0.0039 18 0.038 1
CD79B 26208 7200 590 6 5 3 1 0 7 1.2 0.004 16 0.038 1
HRCT1 9072 3312 65 4 4 3 0 0 20 0.33 0.0042 11 0.033 1
KLHL6 70512 19872 705 7 7 6 0 0 20 0.3 0.0046 17 0.042 1
ID3 13008 4320 215 2 2 2 0 0 20 0.61 0.0046 9.3 0.032 1
IRF8 47952 13584 765 6 5 5 1 0 20 0.75 0.0047 15 0.037 1
SGK1 70512 18816 1530 26 4 21 4 0 20 0.84 0.0049 18 0.039 1
DNAJC9 24624 6096 420 3 3 3 0 0 20 0.27 0.0052 11 0.033 1
TET2 132192 35232 120 9 6 9 2 0 15 1 0.0056 26 0.038 1
SLC16A8 15408 5472 125 3 3 2 0 0 20 0.25 0.0057 11 0.037 1
TPRX1 19872 7248 65 2 2 1 1 0 20 1.7 0.0067 12 0.034 1
CD83 21552 6336 385 3 3 3 0 0 20 0.9 0.011 10 0.032 1
CSNK2A1 45888 12096 1185 4 3 4 0 0 13 0 0.013 11 0.037 1
HLA-A 39360 11904 770 7 5 7 1 0 20 3.9 0.014 18 0.039 1
HLA-DRB5 20736 6336 305 4 3 4 0 0 20 1.1 0.015 10 0.044 1
HIST4H4 10992 3936 120 3 3 3 1 0 20 0.89 0.016 8.8 0.031 1
C1orf210 12288 4224 220 2 2 2 0 0 20 0.93 0.016 8.3 0.031 1
OR9G1 33552 10272 135 4 4 3 0 0 20 0.57 0.016 10 0.033 1
B2M

Figure S1.  This figure depicts the distribution of mutations and mutation types across the B2M significant gene.

MYD88

Figure S2.  This figure depicts the distribution of mutations and mutation types across the MYD88 significant gene.

BTG2

Figure S3.  This figure depicts the distribution of mutations and mutation types across the BTG2 significant gene.

KLF2

Figure S4.  This figure depicts the distribution of mutations and mutation types across the KLF2 significant gene.

SOCS1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the SOCS1 significant gene.

BTG1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the BTG1 significant gene.

PIM1

Figure S7.  This figure depicts the distribution of mutations and mutation types across the PIM1 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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