Mutation Analysis (MutSigCV v0.9)
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1ZS2TJJ
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1ZS2TJJ
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: KIRP-TP

  • Number of patients in set: 114

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: KIRP-TP.patients.counts_and_rates.txt

Needs description.

Figure 3.  Needs description.

Figure 4.  Needs description.

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: 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
NF2 146034 36822 18760 7 7 7 0 0 20 0.61 1.1e-08 41 0.1 0.0002
IL32 46512 12198 7303 4 4 2 0 0 20 0.62 1.8e-06 25 0.11 0.016
ELF3 101346 27816 10586 4 4 3 0 0 20 0 0.000097 21 0.12 0.59
STAG2 356250 88464 43684 6 6 5 1 0 20 0.67 0.00025 32 0.22 1
PIPOX 105678 30894 10921 3 3 2 0 0 20 0 0.00053 18 0.11 1
CDC27 222072 60078 23517 4 4 1 0 0 20 0.52 0.00055 23 0.11 1
C6orf195 33516 10146 1608 2 2 2 0 0 20 0.88 0.00081 13 0.1 1
SFRS2IP 397518 106020 18425 5 5 2 1 0 20 0.45 0.0011 25 0.1 1
PARD6B 93822 26676 2814 4 4 4 0 0 20 0.69 0.0015 18 0.11 1
RAB27B 60420 15960 6901 2 2 1 0 0 20 0.39 0.0018 13 0.1 1
POMC 42864 12654 2345 3 3 3 0 0 20 0 0.002 13 0.1 1
LRFN4 82764 31008 1809 3 3 3 0 0 20 0.45 0.0022 15 0.11 1
BHMT 109212 30438 10117 4 4 4 0 0 20 0 0.0025 15 0.27 1
LGI4 55404 17784 3618 4 4 4 0 0 20 1.2 0.0029 13 0.11 1
SEPT7 82422 20748 10452 2 2 2 0 0 20 0.61 0.003 13 0.094 1
SAV1 102372 28500 6834 3 3 3 0 0 20 0.67 0.0049 15 0.1 1
SMARCB1 111492 30780 11926 3 3 3 0 0 16 0 0.0061 15 0.11 1
LHFPL4 62814 19608 3886 2 2 1 0 0 20 0.52 0.0063 12 0.1 1
DARS 138852 36366 20904 3 3 3 0 0 20 0 0.0065 15 0.11 1
POU4F2 88464 26448 2881 2 2 1 0 0 20 0.47 0.0074 12 0.1 1
PPARGC1B 242364 72504 15008 3 3 1 0 0 20 1.1 0.008 17 0.11 1
PTEN 111036 26676 11658 3 3 3 0 0 20 1.2 0.0095 14 0.11 1
CALML4 54150 14478 6834 3 3 2 0 0 8 0 0.011 9.7 0.096 1
VPS37D 16074 5700 804 1 1 1 0 0 20 0.43 0.011 6.9 0.075 1
LYPD3 87438 29412 6231 2 2 2 0 0 15 1 0.013 12 0.1 1
H2AFJ 32832 11514 1608 2 2 2 0 0 20 1.4 0.013 9.6 0.087 1
OGG1 230052 61674 23852 4 4 3 0 0 12 0.77 0.015 19 0.11 1
WFDC10A 21546 6042 2881 1 1 1 0 0 20 0.46 0.015 6.7 0.074 1
ARRB1 102372 29868 17889 3 3 2 0 0 20 0 0.015 9.5 0.091 1
SRCAP 808716 283290 51724 9 9 7 1 0 17 0.91 0.015 30 0.11 1
MRPL54 33630 10032 3819 2 2 2 0 0 20 0 0.016 7 0.079 1
JUNB 29184 9918 1072 2 2 2 0 0 20 1.9 0.017 7.1 0.082 1
TMEM37 47538 15390 1474 2 2 1 0 0 20 0 0.017 6.9 0.084 1
KDELR3 58596 16758 6097 2 2 2 0 0 20 0.58 0.018 9.4 0.096 1
KLRK1 61218 13908 9112 2 2 2 0 0 20 1.5 0.018 9.4 0.097 1
NF2

Figure S1.  This figure depicts the distribution of mutations and mutation types across the NF2 significant gene.

IL32

Figure S2.  This figure depicts the distribution of mutations and mutation types across the IL32 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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle