Mutation Analysis (MutSig 2CV v3.1)
Rectum Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C1S46RDB
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. MutSig 2CV v3.1 was used to generate the results found in this report.

  • Working with individual set: READ-TP

  • Number of patients in set: 122

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:READ-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 33

Results
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 1.  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 2.  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 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

  • 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: 33. 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).

rank gene longname codelen nnei nncd nsil nmis nstp nspl nind nnon npat nsite pCV pCL pFN p q
1 APC adenomatous polyposis coli 8592 2 0 25 50 107 4 30 191 108 136 1.6e-15 1e-05 1 1e-16 1.8e-12
2 CRIPAK cysteine-rich PAK1 inhibitor 1341 42 0 1 6 0 0 7 13 11 8 1.1e-11 1e-05 0.34 4.1e-15 3.8e-11
3 TP53 tumor protein p53 1890 0 0 5 81 12 5 10 108 91 70 8.4e-11 5e-05 1e-05 3e-14 1.8e-10
4 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 941 10 0 1 11 0 0 0 11 11 6 9.4e-07 1e-05 0.063 2.5e-10 1.1e-06
5 FMN2 formin 2 5237 35 0 3 4 0 0 11 15 14 8 1.7e-06 1e-05 1 4.3e-10 1.6e-06
6 SHROOM4 shroom family member 4 4516 2 0 1 3 0 0 6 9 7 4 0.000015 1e-05 1 3.6e-09 0.000011
7 RPTN repetin 2363 182 0 1 0 0 0 5 5 5 1 0.000094 1e-05 0.32 2.1e-08 0.000054
8 ARID1A AT rich interactive domain 1A (SWI-like) 6934 80 0 2 5 5 1 2 13 11 13 2.6e-07 0.15 0.053 1.3e-07 0.00029
9 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 2709 126 0 2 10 1 1 0 12 12 12 3.6e-07 1 0.14 1.4e-06 0.0028
10 RBM10 RNA binding motif protein 10 2882 119 0 0 0 5 1 0 6 6 5 4.7e-07 0.28 0.88 2.9e-06 0.0051
11 HAUS6 HAUS augmin-like complex, subunit 6 2932 27 0 0 1 1 3 0 5 5 4 0.00014 0.001 0.14 3.1e-06 0.0051
12 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 2406 7 0 6 21 0 0 0 21 19 19 0.014 9e-05 0.37 6.4e-06 0.0097
13 GABRP gamma-aminobutyric acid (GABA) A receptor, pi 1359 7 0 0 2 2 0 0 4 4 3 8e-05 0.0089 0.63 0.000011 0.015
14 PON3 paraoxonase 3 1101 11 0 1 3 0 1 0 4 4 2 0.0079 0.0001 0.082 0.000012 0.016
15 RLIM ring finger protein, LIM domain interacting 1887 34 0 0 4 0 0 0 4 4 2 0.016 0.0001 0.82 0.000022 0.027
16 SMAD2 SMAD family member 2 1444 224 0 0 6 0 0 0 6 6 5 0.000058 0.092 0.061 3e-05 0.035
17 TCF7L2 transcription factor 7-like 2 (T-cell specific, HMG-box) 2138 2 1 1 6 2 2 2 12 12 11 0.000017 0.31 0.2 0.000037 0.038
18 OXSM 3-oxoacyl-ACP synthase, mitochondrial 1390 81 0 0 3 0 0 1 4 4 3 0.0036 0.011 0.058 0.000037 0.038
19 PCBP1 poly(rC) binding protein 1 1071 173 0 0 5 0 0 0 5 5 4 0.00046 0.043 0.092 0.000045 0.041
20 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 709 0 1 3 61 0 1 1 63 63 14 0.34 1e-05 0.028 0.000047 0.041
21 FLT1 fms-related tyrosine kinase 1 (vascular endothelial growth factor/vascular permeability factor receptor) 4390 14 0 2 9 0 0 0 9 8 7 0.043 0.0014 0.0092 0.000047 0.041
22 SLC12A6 solute carrier family 12 (potassium/chloride transporters), member 6 3669 15 0 1 3 2 1 0 6 6 5 0.0042 0.022 0.0091 0.000049 0.041
23 AKAP9 A kinase (PRKA) anchor protein (yotiao) 9 11920 43 0 4 8 1 0 0 9 7 7 0.48 3e-05 0.49 0.000063 0.05
24 VCX2 variable charge, X-linked 2 426 226 0 0 1 0 0 4 5 5 2 0.00089 0.0037 0.98 0.000078 0.059
25 ELF3 E74-like factor 3 (ets domain transcription factor, epithelial-specific ) 1148 88 0 0 0 1 0 3 4 4 4 0.000018 1 0.36 0.0001 0.076
26 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 0 0 16 53 2 1 0 56 37 47 0.88 1e-05 0.0046 0.00011 0.079
27 BRAF v-raf murine sarcoma viral oncogene homolog B1 2371 1 0 5 12 1 1 0 14 13 11 0.48 9e-05 0.016 0.00012 0.08
28 KIAA1804 3147 11 0 0 12 0 0 0 12 10 10 0.0012 0.062 0.012 0.00012 0.08
29 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 3889 32 0 1 9 0 0 0 9 9 7 0.012 0.0074 0.012 0.00014 0.089
30 CDKL5 cyclin-dependent kinase-like 5 3657 12 0 0 2 0 2 0 4 4 3 0.00066 0.018 0.88 0.00015 0.093
31 ZNF354C zinc finger protein 354C 1681 13 0 0 6 0 0 0 6 5 4 0.05 0.00019 0.89 0.00016 0.093
32 LIG1 ligase I, DNA, ATP-dependent 2868 10 0 1 4 0 0 0 4 4 1 0.14 0.0001 0.91 0.00017 0.093
33 IQCD IQ motif containing D 1052 342 0 0 3 0 0 0 3 3 2 0.014 0.0062 0.02 0.00017 0.093
34 ZBED4 zinc finger, BED-type containing 4 3516 13 0 1 4 1 0 0 5 4 5 0.038 0.0087 0.047 0.00025 0.13
35 GMCL1 germ cell-less homolog 1 (Drosophila) 1600 27 0 1 4 0 0 0 4 4 3 0.0044 0.017 0.29 0.00027 0.14
APC

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

CRIPAK

Figure S2.  This figure depicts the distribution of mutations and mutation types across the CRIPAK significant gene.

TP53

Figure S3.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

NRAS

Figure S4.  This figure depicts the distribution of mutations and mutation types across the NRAS significant gene.

FMN2

Figure S5.  This figure depicts the distribution of mutations and mutation types across the FMN2 significant gene.

SHROOM4

Figure S6.  This figure depicts the distribution of mutations and mutation types across the SHROOM4 significant gene.

RPTN

Figure S7.  This figure depicts the distribution of mutations and mutation types across the RPTN significant gene.

ARID1A

Figure S8.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

CDH1

Figure S9.  This figure depicts the distribution of mutations and mutation types across the CDH1 significant gene.

RBM10

Figure S10.  This figure depicts the distribution of mutations and mutation types across the RBM10 significant gene.

HAUS6

Figure S11.  This figure depicts the distribution of mutations and mutation types across the HAUS6 significant gene.

CTNNB1

Figure S12.  This figure depicts the distribution of mutations and mutation types across the CTNNB1 significant gene.

GABRP

Figure S13.  This figure depicts the distribution of mutations and mutation types across the GABRP significant gene.

PON3

Figure S14.  This figure depicts the distribution of mutations and mutation types across the PON3 significant gene.

RLIM

Figure S15.  This figure depicts the distribution of mutations and mutation types across the RLIM significant gene.

SMAD2

Figure S16.  This figure depicts the distribution of mutations and mutation types across the SMAD2 significant gene.

TCF7L2

Figure S17.  This figure depicts the distribution of mutations and mutation types across the TCF7L2 significant gene.

OXSM

Figure S18.  This figure depicts the distribution of mutations and mutation types across the OXSM significant gene.

PCBP1

Figure S19.  This figure depicts the distribution of mutations and mutation types across the PCBP1 significant gene.

KRAS

Figure S20.  This figure depicts the distribution of mutations and mutation types across the KRAS significant gene.

FLT1

Figure S21.  This figure depicts the distribution of mutations and mutation types across the FLT1 significant gene.

SLC12A6

Figure S22.  This figure depicts the distribution of mutations and mutation types across the SLC12A6 significant gene.

AKAP9

Figure S23.  This figure depicts the distribution of mutations and mutation types across the AKAP9 significant gene.

VCX2

Figure S24.  This figure depicts the distribution of mutations and mutation types across the VCX2 significant gene.

PIK3CA

Figure S25.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

BRAF

Figure S26.  This figure depicts the distribution of mutations and mutation types across the BRAF significant gene.

ERBB2

Figure S27.  This figure depicts the distribution of mutations and mutation types across the ERBB2 significant gene.

CDKL5

Figure S28.  This figure depicts the distribution of mutations and mutation types across the CDKL5 significant gene.

ZNF354C

Figure S29.  This figure depicts the distribution of mutations and mutation types across the ZNF354C significant gene.

LIG1

Figure S30.  This figure depicts the distribution of mutations and mutation types across the LIG1 significant gene.

IQCD

Figure S31.  This figure depicts the distribution of mutations and mutation types across the IQCD significant gene.

Methods & Data
Methods

MutSig and its evolving algorithms have existed since the youth of clinical sequencing, with early versions used in multiple publications. [1][2][3]

"Three significance metrics [are] calculated for each gene, using the […] methods MutSigCV [4], MutSigCL, and MutSigFN [5]. These measure the significance of mutation burden, clustering, and functional impact, respectively […]. MutSigCV determines the P value for observing the given quantity of non-silent mutations in the gene, given the background model determined by silent (and noncoding) mutations in the same gene and the neighbouring genes of covariate space that form its 'bagel'. […] MutSigCL and MutSigFN measure the significance of the positional clustering of the mutations observed, as well as the significance of the tendency for mutations to occur at positions that are highly evolutionarily conserved (using conservation as a proxy for probably functional impact). MutSigCL and MutSigFN are permutation-based methods and their P values are calculated as follows: The observed nonsilent coding mutations in the gene are permuted T times (to simulate the null hypothesis, T = 108 for the most significant genes), randomly reassigning their positions, but preserving their mutational 'category', as determined by local sequence context. We [use] the following context categories: transitions at CpG dinucleotides, transitions at other C-G base pairs, transversions at C-G base pairs, mutations at A-T base pairs, and indels. Indels are unconstrained in terms of where they can move to in the permutations. For each of the random permutations, two scores are calculated: SCL and SFN, measuring the amount of clustering and function impact (measured by conservation) respectively. SCL is defined to be the fraction of mutations occurring in hotspots. A hotspot is defined as a 3-base-pair region of the gene containing many mutations: at least 2, and at least 2% of the total mutations. SFN is defined to be the mean of the base-pair-level conservation values for the position of each non-silent mutation […]. To determine a PCL, the P value for the observed degree of positional clustering, the observed value of SCL (computed for the mutations actually observed), [is] compared to the distribution of SCL obtained from the random permutations, and the P value [is] defined to be the fraction of random permutations in which SCL [is] at least as large as the observed SCL. The P value for the conservation of the mutated positions, PFN, [is] computed analogously." [6]

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] Getz G, Höfling H, Mesirov JP, Golub TR, Meyerson M, Tibshirani R, Lander ES, Comment on "The Consensus Coding Sequences of Human Breast and Colorectal Cancers", Science 317(5844):1500b (2007)
[3] TCGA, Integrated genomic analyses of ovarian carcinoma, Nature 474(7353):609-615 (2011)
[4] Lawrence MS, et al., Mutational heterogeneity in cancer and the search for new cancer-associated genes, Nature 499(7457):214-218 (2013)
[6] Lawrence MS, et al., Discovery and saturation analysis of cancer genes across 21 tumour types, Nature 505(7484):495-501 (2014)