Mutation Analysis (MutSigCV v0.9) - Ovarian Serous Cystadenocarcinoma (Primary solid tumor)

Mutation Analysis (MutSigCV v0.9)

Ovarian Serous Cystadenocarcinoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

Maintainer Information

Citation Information

Maintained by David Heiman (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1833QXH

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: OV-TP
Number of patients in set: 316

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:OV-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: OV-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	nnon	npat	nsite	nsil	nnei	fMLE	p	score	time	q
TP53	298620	87216	279	276	143	3	4	3.7	3.4e-15	990	0.21	6.3e-11
OR4F21	60988	18644	3	3	1	0	20	0.47	0.000014	21	0.16	0.1
PECAM1	6952	2528	2	2	2	0	20	0	0.000021	13	0.14	0.1
RB1	899968	237316	9	9	9	0	20	0.64	0.000022	41	0.2	0.1
BRCA1	1441592	364348	12	12	12	0	14	0.82	0.00021	61	0.18	0.75
POTED	101752	23384	4	4	4	0	16	0.6	0.00062	18	0.16	1
TBP	249640	70468	4	4	2	0	20	2	0.0016	25	0.17	1
C1orf95	75208	22752	3	3	3	0	20	0.46	0.0017	14	0.15	1
DAD1	82476	26544	2	2	2	0	20	0.94	0.0018	14	0.15	1
DUSP19	164952	44872	4	4	4	0	20	0.38	0.0019	14	0.15	1
ZNF706	60040	14536	2	2	2	0	20	1.5	0.0024	12	0.14	1
GPX6	169692	45188	3	3	3	0	20	0.18	0.0032	14	0.17	1
CDK12	1078824	325796	9	9	9	0	20	1.2	0.0032	39	0.17	1
FAM200A	47716	12640	2	2	2	0	20	0	0.0046	8.6	0.12	1
C10orf140	406060	119448	5	5	3	1	20	1.2	0.0055	24	0.16	1
UTP14A	582704	159264	4	4	4	0	20	0.58	0.0057	21	0.16	1
C9orf171	204452	62568	5	5	5	0	20	1.4	0.0067	18	0.16	1
FRG2	21172	4424	2	2	2	0	20	2	0.0068	7.8	0.11	1
BDKRB1	253748	81212	4	4	4	0	20	0.18	0.0072	14	0.15	1
PAK3	417120	113760	5	5	5	0	11	0.24	0.0075	19	0.16	1
GABRA6	342228	97012	6	6	6	1	20	0.97	0.0083	19	0.19	1
SLC10A2	258804	77104	2	2	2	1	18	0.81	0.0092	13	0.15	1
GPHB5	59092	20540	2	2	2	0	20	0.9	0.0099	8.1	0.13	1
NXPH2	184228	50560	3	2	3	0	20	0.39	0.011	13	0.16	1
OR6A2	236052	74260	4	4	4	0	20	0.74	0.011	13	0.15	1
PAOX	338120	83740	4	4	4	0	20	0.46	0.011	19	0.16	1
LCOR	321372	91640	4	4	4	0	3	0.3	0.012	19	0.18	1
C10orf113	116604	29388	3	3	3	0	20	0.97	0.013	11	0.14	1
FAM167B	72048	23068	2	2	2	0	20	1.2	0.015	10	0.13	1
CHST2	243636	81212	5	5	5	1	20	1.3	0.016	16	0.15	1
CBLN4	135564	42344	3	3	3	0	20	1	0.016	11	0.14	1
UIMC1	551104	146308	4	4	4	0	20	0.29	0.017	16	0.15	1
ACBD4	253748	36656	3	3	3	0	20	2	0.018	18	0.16	1
CACNG6	120712	42028	2	2	2	0	20	0.62	0.018	13	0.15	1
FAM24A	80896	21488	2	2	2	0	20	0.89	0.018	7.7	0.11	1

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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