Thyroid Adenocarcinoma: Mutation Analysis (MutSigCV v0.9)
(classical cohort)
Maintained by Dan DiCara (Broad Institute)
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: THCA-classical

  • Number of patients in set: 128

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
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).

Figure 1. 

Distribution of Mutation Counts, Coverage, and Mutation Rates Across Samples

Figure 2.  Patients counts and rates file used to generate this plot: THCA-classical.patients.counts_and_rates.txt

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

  • 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: 2. 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
BRAF 221501 63282 348751 98 98 1 0 0 19 0 0 340 0.073 0
PPM1D 154652 44238 109224 4 4 4 0 0 20 1.3 6.4e-07 25 0.066 0.0059
GPR44 40432 13507 23988 2 2 1 0 0 20 3.9 0.00019 14 0.076 1
TYSND1 59873 19006 36744 2 2 1 0 0 20 0 0.00026 14 0.06 1
EMG1 86506 26318 99493 2 2 1 0 0 20 0 0.00037 14 0.058 1
ARMCX3 113191 32384 23542 2 2 2 0 0 20 0 0.00038 13 0.06 1
ZNF185 131936 38328 152002 3 3 3 0 0 20 0 0.00043 14 0.06 1
TCEAL3 60572 15438 16689 2 2 2 0 0 20 0 0.0011 8.3 0.047 1
FAM22F 118145 39569 83008 2 2 2 0 0 20 0 0.0012 11 0.086 1
ZFAND5 66599 17536 95264 2 2 2 0 0 20 0 0.0012 10 0.053 1
C20orf144 10518 4146 4940 1 1 1 0 0 20 1.1 0.0014 7.8 0.043 1
PANX2 97692 29839 6876 2 2 2 1 0 20 1 0.0015 11 0.057 1
CDC27 249878 67545 282469 2 2 1 0 0 20 0 0.0024 13 0.059 1
EFCAB4A 47609 15059 38614 1 1 1 0 0 20 0.48 0.0024 7.5 0.042 1
ACD 201804 68958 171327 2 2 2 0 0 20 0 0.0031 10 0.055 1
COMTD1 19835 6851 15955 1 1 1 0 0 20 1.2 0.0033 7.5 0.043 1
PLAC4 18744 6773 4445 1 1 1 0 0 20 0 0.0037 7.5 0.052 1
SCUBE2 297010 79232 466286 2 2 1 0 0 20 0.98 0.0041 13 0.059 1
IFNGR1 144084 39413 90176 2 2 2 0 0 20 0 0.0042 10 0.053 1
DDX24 259267 73511 180313 2 2 1 0 0 20 0 0.0042 13 0.06 1
CCDC39 211463 50655 172495 3 3 3 1 0 12 3.9 0.0049 13 0.06 1
CST6 26062 7296 41160 1 1 1 0 0 20 1.7 0.005 7.3 0.15 1
KCNK18 114955 32985 63628 2 2 2 0 0 20 0 0.0051 7.7 0.047 1
HAUS8 125234 36441 207011 2 2 1 0 0 9 0 0.0051 13 0.081 1
FAM43B 27726 8743 4395 1 1 1 0 0 20 0 0.0055 7.4 0.053 1
TMEM90B 78002 22400 88559 2 2 1 0 0 18 3.9 0.0056 12 0.062 1
IL11 14230 5643 8423 1 1 1 0 0 20 0 0.006 7.2 0.041 1
UBE2W 33898 8615 67694 1 1 1 0 0 20 0 0.0062 7.2 0.048 1
PMEPA1 54934 16985 23821 2 2 2 0 0 13 0 0.0067 9.8 0.068 1
SOX21 33547 9650 2359 1 1 1 0 0 20 1.3 0.0073 7.3 0.055 1
GCSH 38639 9778 64363 1 1 1 0 0 20 2.8 0.008 7 0.049 1
LDHA 112431 29517 129013 2 2 2 0 0 20 1.2 0.0083 7.4 0.045 1
TWIST1 27041 8075 1352 1 1 1 0 0 20 2.2 0.0093 6.9 0.042 1
NAP1L5 54283 13913 21951 1 1 1 0 0 20 0 0.0097 7.2 0.051 1
TMCO2 55129 15488 42923 1 1 1 0 0 20 0 0.0099 7.1 0.044 1
BRAF

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] TCGA, Integrated genomic analyses of ovarian carcinoma, Nature 474:609 - 615 (2011)
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