Mutation Analysis (MutSigCV v0.9) - Pheochromocytoma and Paraganglioma (Primary solid tumor)

Mutation Analysis (MutSigCV v0.9)

Pheochromocytoma and Paraganglioma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

Maintainer Information

Citation Information

Maintained by David Heiman (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1NP23HK

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: PCPG-TP
Number of patients in set: 178

Input

The input for this pipeline is a set of individuals with the following files associated for each:

An annotated .maf file describing the mutations called for the respective individual, and their properties.
A .wig file that contains information about the coverage of the sample.

Summary

MAF used for this analysis:PCPG-TP.final_analysis_set.maf
Blacklist used for this analysis: pancan_mutation_blacklist.v14.hg19.txt
Significantly mutated genes (q ≤ 0.1): 1

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: PCPG-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: 1. 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	nnei	fMLE	p	score	time	q
HRAS	82414	23852	15308	18	18	3	20	0	2.4e-15	63	0.2	4.5e-11
EPAS1	349948	97544	53044	8	8	4	20	0.96	0.000067	25	0.19	0.61
NF1	1675336	469564	210752	16	15	16	0	0	0.00045	70	0.18	1
NDUFS7	49484	15486	12282	2	2	2	20	0	0.0016	11	0.13	1
AMMECR1	95764	26878	35956	2	2	1	20	0	0.0018	13	0.082	1
CSDE1	359382	98078	67818	4	4	4	9	0	0.0023	23	0.16	1
GYPE	32218	9612	8544	2	2	1	20	1.6	0.0029	8.8	0.13	1
TRDN	155572	40406	43966	3	3	2	14	3.9	0.0029	18	0.13	1
SOX4	67996	21182	890	2	2	2	20	1.7	0.0043	13	0.12	1
VHL	53756	16910	7832	3	3	3	13	0	0.0053	13	0.15	1
RET	408866	124244	53756	7	6	4	20	0	0.0087	18	0.12	1
CLEC17A	105020	29370	29014	2	2	1	16	0	0.011	13	0.19	1
FAM83D	196156	60520	12816	3	3	3	20	1.1	0.012	13	0.15	1
NKX6-3	21004	6942	1958	1	1	1	20	0	0.015	7.2	0.16	1
PI3	49128	14952	7476	1	1	1	20	1.1	0.016	6.9	0.11	1
NKD1	156284	47526	22606	2	2	2	20	1.4	0.016	13	0.19	1
SLC46A1	166786	53578	15486	2	2	2	20	0	0.017	9.9	0.12	1
RPS27L	37380	9790	14774	1	1	1	20	1.4	0.018	6.9	0.088	1
P2RY1	152724	45924	4094	2	2	2	20	0	0.018	10	0.13	1
ATP6V1G3	61588	11748	14774	2	2	2	20	1.4	0.02	7.2	0.14	1
NDUFAF2	69954	18512	16732	2	2	1	20	2	0.022	7.3	0.14	1
ECHDC2	96832	32930	47170	2	2	2	12	0	0.023	9.9	0.21	1
ANKK1	189926	57138	13706	2	2	2	20	1.3	0.024	12	0.12	1
RNF149	148452	44144	23496	2	2	2	20	0	0.032	7	0.12	1
PRMT1	145782	37736	38982	2	2	2	20	0	0.033	9.7	0.14	1
FAM72B	53222	13528	10324	1	1	1	20	1.3	0.034	6.7	0.23	1
TCTEX1D4	58384	20114	9790	1	1	1	20	1.1	0.034	6.8	0.1	1
UBL4A	53222	16732	8900	1	1	1	20	1.3	0.035	6.8	0.09	1
FAM162B	45212	12994	11036	1	1	1	20	0	0.035	6.9	0.12	1
TCF23	61232	21360	7654	1	1	1	20	2	0.036	6.8	0.071	1
LIG1	344608	102528	81168	2	2	2	20	0	0.036	9.6	0.18	1
UBE2W	47882	12104	11214	1	1	1	20	0	0.036	6.8	0.096	1
AVP	22962	6942	3916	1	1	1	20	0	0.038	4.1	0.16	1
DHRS4	106800	34176	25098	1	1	1	20	0.81	0.039	6.7	0.15	1
PTMA	37202	8544	32040	1	1	1	7	2.3	0.04	6.7	0.088	1

HRAS

Figure S1. This figure depicts the distribution of mutations and mutation types across the HRAS 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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