Mutation Analysis (MutSig 2CV v3.1)
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1C828T2
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/C1C828T2
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: UCEC-TP

  • Number of patients in set: 248

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:UCEC-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 119

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: 119. 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 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 2 0 5 98 51 16 89 254 161 149 1e-16 1e-05 0.83 1e-16 3.4e-13
2 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 2361 26 2 1 21 17 2 77 117 82 88 2.2e-16 1e-05 0.46 1e-16 3.4e-13
3 TP53 tumor protein p53 1889 15 0 2 57 7 1 9 74 69 50 7.6e-16 1e-05 1e-05 1e-16 3.4e-13
4 CTCF CCCTC-binding factor (zinc finger protein) 2224 5 0 1 19 19 2 10 50 44 39 1e-16 0.0053 0.3 1e-16 3.4e-13
5 FBXW7 F-box and WD repeat domain containing 7 2580 69 2 1 35 8 0 2 45 38 29 1e-15 0.0005 0.069 1e-16 3.4e-13
6 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 8 0 3 156 0 6 8 170 131 74 2.6e-13 1e-05 1e-05 1.1e-16 3.4e-13
7 ARID1A AT rich interactive domain 1A (SWI-like) 6934 3 0 5 15 51 6 35 107 83 87 1e-16 0.77 0.74 3.8e-15 9.9e-12
8 ARHGAP35 glucocorticoid receptor DNA binding factor 1 4520 16 0 8 25 18 0 9 52 36 43 5.1e-16 0.62 0.26 8e-15 1.8e-11
9 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 709 2 1 1 52 0 0 0 52 52 11 9.3e-11 1e-05 0.0042 3.3e-14 6.8e-11
10 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 2406 7 0 7 80 0 0 0 80 74 25 5.2e-09 1e-05 1e-05 1.7e-12 3e-09
11 ZFHX3 zinc finger homeobox 3 11148 2 0 14 57 10 0 12 79 44 71 2.7e-09 0.0003 0.47 5.9e-11 9.9e-08
12 SPOP speckle-type POZ protein 1161 3 0 0 19 3 0 1 23 21 18 1.3e-07 0.0012 0.087 7.9e-10 1.2e-06
13 FGFR2 fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome, Pfeiffer syndrome, Jackson-Weiss syndrome) 2782 15 0 3 29 2 0 3 34 31 19 4.1e-06 1e-05 0.24 1e-09 1.4e-06
14 TCP11L2 t-complex 11 (mouse)-like 2 1596 5 0 0 11 4 3 0 18 14 18 1.2e-10 1 0.28 1.4e-09 1.8e-06
15 VPS11 vacuolar protein sorting 11 homolog (S. cerevisiae) 2887 4 0 0 6 5 1 1 13 11 12 5.3e-08 0.86 0.032 8.4e-08 0.0001
16 PPP2R1A protein phosphatase 2 (formerly 2A), regulatory subunit A , alpha isoform 1826 22 0 2 29 0 2 0 31 28 19 0.00071 1e-05 0.1 1.4e-07 0.00016
17 MAX MYC associated factor X 1967 33 1 0 8 0 1 0 9 9 5 0.0008 0.00011 0.0086 1.6e-07 0.00017
18 SOX17 SRY (sex determining region Y)-box 17 1249 6 0 1 6 0 0 1 7 7 3 0.0025 2e-05 0.096 4.6e-07 0.00046
19 CCND1 cyclin D1 904 20 0 1 11 1 0 2 14 14 12 1.5e-06 0.097 0.18 1.3e-06 0.0013
20 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 941 5 0 2 8 1 0 0 9 9 6 0.011 0.00013 0.014 1.8e-06 0.0017
21 EP300 E1A binding protein p300 7365 9 0 5 21 7 2 2 32 21 31 8.9e-07 0.48 0.089 3.1e-06 0.0026
22 KLHL8 kelch-like 8 (Drosophila) 1895 3 0 1 11 8 0 0 19 12 16 0.0001 0.003 0.19 3.1e-06 0.0026
23 ALG8 asparagine-linked glycosylation 8 homolog (S. cerevisiae, alpha-1,3-glucosyltransferase) 1694 11 0 1 5 4 2 0 11 11 11 2.2e-07 1 0.95 3.6e-06 0.0029
24 GNPTAB N-acetylglucosamine-1-phosphate transferase, alpha and beta subunits 3851 35 0 4 21 6 0 0 27 20 22 0.0003 0.00027 0.81 4.2e-06 0.0032
25 SIN3A SIN3 homolog A, transcription regulator (yeast) 3902 8 0 3 17 6 2 4 29 21 27 1.3e-06 0.35 0.13 4.6e-06 0.0034
26 ARID5B AT rich interactive domain 5B (MRF1-like) 3603 16 0 8 19 5 1 9 34 29 33 1.4e-06 0.15 0.97 5.1e-06 0.0035
27 NFE2L2 nuclear factor (erythroid-derived 2)-like 2 1834 9 0 0 14 0 0 0 14 14 11 0.0026 0.00039 0.033 5.3e-06 0.0036
28 ZNF471 zinc finger protein 471 1897 7 0 1 15 4 1 1 21 15 16 0.038 1e-05 0.56 5.9e-06 0.0039
29 MORC4 MORC family CW-type zinc finger 4 2878 4 0 3 16 8 1 2 27 20 25 3e-06 0.18 0.21 6.4e-06 0.004
30 SELP selectin P (granule membrane protein 140kDa, antigen CD62) 2558 0 0 0 9 2 2 0 13 10 11 0.00031 0.0008 0.89 7.1e-06 0.0043
31 RBMX RNA binding motif protein, X-linked 1265 1 0 0 6 1 0 6 13 12 8 0.00053 0.00057 0.29 8.6e-06 0.0051
32 FAT1 FAT tumor suppressor homolog 1 (Drosophila) 13871 0 0 27 67 19 3 2 91 40 87 2.2e-06 0.29 0.87 0.000012 0.0066
33 MARK3 MAP/microtubule affinity-regulating kinase 3 2330 27 0 4 7 4 0 3 14 11 11 0.014 8e-05 0.67 0.000014 0.008
34 SOS1 son of sevenless homolog 1 (Drosophila) 4092 5 0 1 12 1 0 0 13 12 10 0.11 1e-05 0.038 0.000017 0.0089
35 RBBP6 retinoblastoma binding protein 6 5499 0 0 5 20 11 3 4 38 22 36 3.3e-06 0.29 0.98 0.000018 0.0096
PTEN

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

PIK3R1

Figure S2.  This figure depicts the distribution of mutations and mutation types across the PIK3R1 significant gene.

TP53

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

CTCF

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

FBXW7

Figure S5.  This figure depicts the distribution of mutations and mutation types across the FBXW7 significant gene.

PIK3CA

Figure S6.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

ARID1A

Figure S7.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

KRAS

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

CTNNB1

Figure S9.  This figure depicts the distribution of mutations and mutation types across the CTNNB1 significant gene.

ZFHX3

Figure S10.  This figure depicts the distribution of mutations and mutation types across the ZFHX3 significant gene.

SPOP

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

FGFR2

Figure S12.  This figure depicts the distribution of mutations and mutation types across the FGFR2 significant gene.

TCP11L2

Figure S13.  This figure depicts the distribution of mutations and mutation types across the TCP11L2 significant gene.

VPS11

Figure S14.  This figure depicts the distribution of mutations and mutation types across the VPS11 significant gene.

PPP2R1A

Figure S15.  This figure depicts the distribution of mutations and mutation types across the PPP2R1A significant gene.

MAX

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

SOX17

Figure S17.  This figure depicts the distribution of mutations and mutation types across the SOX17 significant gene.

CCND1

Figure S18.  This figure depicts the distribution of mutations and mutation types across the CCND1 significant gene.

NRAS

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

EP300

Figure S20.  This figure depicts the distribution of mutations and mutation types across the EP300 significant gene.

KLHL8

Figure S21.  This figure depicts the distribution of mutations and mutation types across the KLHL8 significant gene.

ALG8

Figure S22.  This figure depicts the distribution of mutations and mutation types across the ALG8 significant gene.

ARID5B

Figure S23.  This figure depicts the distribution of mutations and mutation types across the ARID5B significant gene.

NFE2L2

Figure S24.  This figure depicts the distribution of mutations and mutation types across the NFE2L2 significant gene.

ZNF471

Figure S25.  This figure depicts the distribution of mutations and mutation types across the ZNF471 significant gene.

MORC4

Figure S26.  This figure depicts the distribution of mutations and mutation types across the MORC4 significant gene.

SELP

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

FAT1

Figure S28.  This figure depicts the distribution of mutations and mutation types across the FAT1 significant gene.

MARK3

Figure S29.  This figure depicts the distribution of mutations and mutation types across the MARK3 significant gene.

SOS1

Figure S30.  This figure depicts the distribution of mutations and mutation types across the SOS1 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)
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