Thyroid Adenocarcinoma: Mutation Analysis (MutSigCV v0.9)
(follicular 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-follicular

  • Number of patients in set: 71

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-follicular.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: 3. 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
NRAS 32825 8662 44409 18 18 2 0 0 20 0 9.1e-15 66 0.039 1.7e-10
BRAF 122852 35097 185326 12 12 2 0 0 19 2.2 2.9e-11 38 0.039 2.6e-07
HRAS 34095 9843 29606 8 8 2 0 0 20 8 1.8e-08 28 0.037 0.00011
R3HDM2 104701 32085 115488 3 3 1 0 0 20 0 0.000067 18 0.036 0.31
PPTC7 39074 11218 55252 2 2 1 0 0 5 0 0.00093 13 0.042 1
EIF1AX 25275 5893 62335 2 2 2 0 0 20 0 0.001 7.9 0.027 1
CXCL5 14558 4214 30584 1 1 1 0 0 20 0 0.0033 7.3 0.031 1
PRB2 64596 23402 36663 2 2 2 0 0 12 2.5 0.0051 9.3 0.034 1
EDDM3B 25772 5798 7109 1 1 1 0 0 20 0 0.0066 7 0.023 1
JUNB 19492 6600 3533 1 1 1 0 0 20 0 0.0082 7 0.024 1
PXMP2 26341 7668 48216 1 1 1 0 0 20 0 0.0085 6.9 0.025 1
MIA 22411 6792 42602 1 1 1 0 0 20 0 0.0097 6.9 0.024 1
ADO 12681 4219 71 1 1 1 0 0 20 0 0.0098 4.4 0.015 1
CYB5R4 86884 22721 118254 2 2 2 0 0 10 0 0.01 6.9 0.035 1
PTGES 12142 3716 16845 1 1 1 0 0 20 0 0.011 4.3 0.016 1
TPRX1 30005 11003 6328 1 1 1 0 0 20 0 0.012 6.8 0.024 1
ALPP 72274 23168 64038 2 2 2 0 0 20 5.8 0.012 9.1 0.031 1
RSPO1 44327 12685 43596 1 1 1 0 0 20 0 0.013 6.8 0.024 1
RPTN 136200 31169 27564 3 3 3 0 0 5 0 0.013 14 0.035 1
KCNK12 20147 6605 2496 1 1 1 0 0 20 0 0.013 6.7 0.024 1
TG 506163 147672 579354 6 6 6 2 0 7 8.7 0.014 22 0.038 1
DBP 29365 9583 21620 1 1 1 0 0 20 0 0.014 6.7 0.031 1
FAU 25393 8472 41878 1 1 1 0 0 20 0 0.015 6.7 0.025 1
SOX2 39706 11531 24 1 1 1 0 0 20 0 0.015 6.7 0.025 1
FUNDC1 22273 5941 23230 1 1 1 0 0 20 0 0.016 6.5 0.023 1
IL32 30331 7832 61789 1 1 1 0 0 20 0 0.016 6.7 0.025 1
TMEM89 24611 8590 18092 1 1 1 0 0 20 0 0.018 4.1 0.015 1
CDKN2C 27855 8449 24649 1 1 1 0 0 20 0 0.018 4.1 0.014 1
FBXW4 55120 16330 83200 1 1 1 0 0 20 2.4 0.019 6.5 0.025 1
FKRP 22104 7124 4568 1 1 1 0 0 20 0 0.019 6.6 0.024 1
IQGAP1 279522 74785 330091 2 2 2 0 0 20 0 0.019 9 0.031 1
C9orf7 28889 8139 33664 1 1 1 0 0 20 2.9 0.021 6.4 0.024 1
FOXO1 79780 22010 113660 1 1 1 0 0 20 1.9 0.021 6.5 0.025 1
CCDC137 33454 8801 33505 1 1 1 0 0 20 0 0.021 6.6 0.025 1
LPPR3 34481 11801 12711 1 1 1 0 0 20 0 0.022 6.6 0.025 1
NRAS

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

BRAF

Figure S2.  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)