Mutation Analysis (MutSigCV v0.9)
Breast Invasive Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1DJ5DBN
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/C1DJ5DBN
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: BRCA-TP

  • Number of patients in set: 987

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:BRCA-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 69

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: BRCA-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: 69. 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
CBFB 403011 101781 82185 24 23 22 1 0 20 0.47 0 130 0.73 0
MAP3K1 3150669 895254 328157 98 71 86 3 0 9 1.5 0 380 0.76 0
PIK3CA 2565408 656385 349734 356 320 53 6 0 20 1.3 0 1000 1.1 0
RUNX1 759018 237867 172500 32 31 23 2 0 13 1.1 0 170 0.78 0
MAP2K4 874542 226053 258306 32 32 28 0 0 10 1.4 2.2e-16 170 0.74 8.1e-13
CDH1 1928598 573447 305009 109 106 89 3 1 20 0.91 2.8e-15 610 0.77 8.4e-12
MLL3 11326425 3217785 1119110 83 69 82 6 0 13 1.3 4.9e-15 310 0.76 1.3e-11
GATA3 837858 267447 69790 101 97 56 2 0 20 1.2 8e-15 620 0.77 1.8e-11
TP53 932715 272412 174529 304 301 160 5 0 4 2.1 8.8e-15 1300 1 1.8e-11
PTEN 960783 230823 153666 38 36 34 0 0 20 0.77 9.9e-15 190 0.84 1.8e-11
RB1 2811396 741342 456369 22 19 21 3 0 20 0.73 1.6e-14 110 0.77 2.6e-11
TBX3 992382 282087 113965 27 27 26 1 0 1 0.51 2.2e-14 140 0.85 3.3e-11
NCOR1 5626110 1641441 779726 43 41 41 1 0 1 0.19 3.2e-14 170 0.77 4.4e-11
FOXA1 804777 235698 36452 24 23 16 0 0 20 1.5 6.4e-13 88 0.8 8.4e-10
GPS2 875484 253659 174576 11 11 11 0 0 20 0.84 5.7e-12 70 0.72 7e-09
THEM5 584319 166803 114192 11 11 8 1 0 20 0.91 3.5e-11 62 0.73 4e-08
CTCF 1741083 446124 182814 17 17 15 3 0 20 0.85 9.4e-11 81 0.73 1e-07
CDKN1B 459045 127338 140631 12 10 11 1 0 20 2.2 2.3e-09 64 0.71 2.3e-06
DNAH12 1067082 277377 176849 20 17 20 3 1 20 1 2.9e-09 64 0.72 2.8e-06
RPGR 2292174 593247 281554 21 18 19 1 0 11 0.59 4e-09 75 0.76 3.7e-06
FAM86B1 250548 79902 24115 9 8 8 0 4 20 1.1 4.7e-09 43 0.72 4.1e-06
ZFP36L1 754098 241830 40726 9 9 9 0 0 20 0.76 3e-08 53 0.75 0.000024
ASB10 752889 266760 41366 8 8 2 0 0 20 0.66 3e-08 52 0.74 0.000024
CASP8 1437117 336567 188217 12 12 12 1 0 20 0.5 8.9e-08 53 0.74 0.000067
HIST1H3B 303024 101661 21981 11 11 11 2 0 20 1.3 1.4e-07 45 0.73 0.000099
TBL1XR1 1052829 287142 157248 12 10 10 0 0 20 1.1 1.5e-07 56 0.74 0.0001
COL6A5 1339530 352320 74946 30 25 30 8 0 7 2.1 1.7e-07 81 0.73 0.00011
ARID1A 4408989 1298907 335369 29 27 27 2 0 2 0.82 1.8e-07 120 0.72 0.00012
MYH9 4577784 1229085 723684 22 19 21 5 0 20 0.51 5.2e-07 79 0.75 0.00033
TMEM151B 159894 49350 19448 6 6 6 0 0 20 1 2.1e-06 28 0.66 0.0012
AKD1 2469195 620166 447192 22 18 21 0 0 3 0.62 7.3e-06 64 0.76 0.0043
HLA-C 818118 251655 129929 9 9 9 0 0 20 0.53 9.2e-06 41 0.76 0.0053
PIK3R1 1839933 472803 310185 17 15 16 2 0 20 1.4 1e-05 63 0.75 0.0055
ANKRD12 4835703 1205202 210706 19 18 19 1 0 17 0.69 0.000023 74 0.73 0.013
PTHLH 344388 105594 329893 7 7 4 0 0 10 1.5 0.000025 37 0.74 0.013
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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