Mutation Analysis (MutSig 2CV v3.1)
Cholangiocarcinoma (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/C1K936V8
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: CHOL-TP

  • Number of patients in set: 35

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:CHOL-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 12

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: 12. 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 PBRM1 polybromo 1 5417 23 0 0 0 6 0 2 8 8 8 2.6e-13 1 0.75 7.7e-12 1.4e-07
2 HLA-B major histocompatibility complex, class I, B 1119 555 0 0 5 0 0 0 5 5 3 4e-07 0.0018 0.97 2e-08 0.00018
3 MLL3 myeloid/lymphoid or mixed-lineage leukemia 3 14968 3 0 0 2 4 1 0 7 7 4 0.000037 4e-05 0.99 5.3e-08 0.00032
4 FTH1 ferritin, heavy polypeptide 1 564 108 0 0 3 0 0 0 3 3 1 0.0002 0.001 0.078 3.2e-06 0.015
5 TP53 tumor protein p53 1889 60 0 0 4 0 0 2 6 5 6 4e-07 1 0.36 6.3e-06 0.023
6 ARID1A AT rich interactive domain 1A (SWI-like) 6934 29 0 1 1 0 0 5 6 5 6 0.000025 1 0.025 0.000018 0.047
7 DDHD1 DDHD domain containing 1 2776 22 0 0 4 0 0 0 4 4 1 0.012 0.0001 1 0.000018 0.047
8 MUC2 mucin 2, oligomeric mucus/gel-forming 8640 122 0 2 8 0 0 0 8 7 7 0.000077 0.034 0.62 0.000038 0.076
9 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 1277 1000 0 0 4 0 0 0 4 4 1 0.00043 0.0066 0.94 0.000039 0.076
10 MUC21 mucin 21, cell surface associated 1709 429 0 0 3 0 0 0 3 3 2 0.00034 0.0071 0.82 0.000042 0.076
11 CLIP4 CAP-GLY domain containing linker protein family, member 4 2178 128 0 0 2 1 0 1 4 4 4 4.1e-06 1 0.8 0.000054 0.088
12 RUFY1 RUN and FYVE domain containing 1 2195 14 0 0 0 0 0 2 2 2 2 0.000058 NaN NaN 0.000058 0.088
13 BAP1 BRCA1 associated protein-1 (ubiquitin carboxy-terminal hydrolase) 2254 8 2 1 1 2 1 3 7 6 7 7.5e-06 1 0.65 0.000096 0.14
14 CDC27 cell division cycle 27 homolog (S. cerevisiae) 2565 189 0 0 5 0 0 0 5 5 5 8.8e-06 1 0.56 0.00011 0.15
15 FAM35A family with sequence similarity 35, member A 2781 52 0 0 3 0 0 0 3 3 2 0.0039 0.01 0.13 0.00012 0.15
16 PAXIP1 PAX interacting (with transcription-activation domain) protein 1 3292 27 0 0 3 0 1 0 4 4 4 0.00046 1 0.008 0.00015 0.18
17 PTTG1IP pituitary tumor-transforming 1 interacting protein 563 164 0 0 0 0 2 0 2 2 1 0.00044 0.037 1 0.00022 0.23
18 OTOP1 otopetrin 1 1861 50 0 0 4 0 0 0 4 4 3 0.00043 0.04 0.56 0.00027 0.28
19 IL12RB2 interleukin 12 receptor, beta 2 2649 20 0 0 2 0 0 0 2 2 1 0.0016 0.035 0.44 0.00039 0.37
20 EPHA2 EPH receptor A2 2995 72 0 0 2 1 0 1 4 4 4 0.00029 1 0.16 0.00059 0.54
21 SMG1 smg-1 homolog, phosphatidylinositol 3-kinase-related kinase (C. elegans) 11234 42 0 0 4 0 0 0 4 4 3 0.057 0.0035 0.28 0.00088 0.76
22 ARAF v-raf murine sarcoma 3611 viral oncogene homolog 1881 36 0 0 2 0 0 0 2 2 1 0.0088 0.01 0.062 0.00091 0.76
23 NBN nibrin 2325 129 0 0 2 0 0 0 2 2 1 0.01 0.01 0.011 0.0011 0.83
24 DNAH6 dynein, axonemal, heavy chain 6 12781 489 0 0 2 0 0 1 3 3 3 0.0027 1 0.032 0.0011 0.83
25 PRG4 proteoglycan 4 4319 240 0 1 4 0 0 0 4 4 3 0.018 0.0053 0.96 0.0011 0.83
26 MGA MAX gene associated 9290 14 0 0 2 0 1 0 3 3 3 0.0015 1 0.042 0.0014 0.96
27 ZNF676 zinc finger protein 676 1775 33 0 0 3 0 0 0 3 3 2 0.16 0.001 0.99 0.0015 1
28 ELF4 E74-like factor 4 (ets domain transcription factor) 2020 31 0 0 0 1 1 0 2 2 2 0.00018 1 0.99 0.0018 1
29 BCOR BCL6 co-repressor 5324 53 0 0 3 0 0 0 3 3 2 0.055 0.0031 0.93 0.0018 1
30 BCL6B B-cell CLL/lymphoma 6, member B (zinc finger protein) 1472 203 0 0 2 0 0 0 2 2 1 0.022 0.01 0.73 0.0021 1
31 AGAP6 ArfGAP with GTPase domain, ankyrin repeat and PH domain 6 2089 20 0 0 2 0 0 0 2 2 1 0.024 0.01 0.16 0.0022 1
32 SLC19A3 solute carrier family 19, member 3 1511 1000 0 0 2 0 0 0 2 2 1 0.027 0.01 0.027 0.0025 1
33 SMARCA5 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 5 3251 90 0 0 1 1 1 0 3 3 3 0.0008 1 0.27 0.0026 1
34 HTT huntingtin 9693 18 0 1 3 0 0 2 5 4 4 0.064 0.005 1 0.0031 1
35 EIF4ENIF1 eukaryotic translation initiation factor 4E nuclear import factor 1 3033 35 0 0 1 1 0 0 2 2 2 0.0019 1 0.098 0.0031 1
PBRM1

Figure S1.  This figure depicts the distribution of mutations and mutation types across the PBRM1 significant gene.

HLA-B

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

FTH1

Figure S3.  This figure depicts the distribution of mutations and mutation types across the FTH1 significant gene.

TP53

Figure S4.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

ARID1A

Figure S5.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

DDHD1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the DDHD1 significant gene.

MUC2

Figure S7.  This figure depicts the distribution of mutations and mutation types across the MUC2 significant gene.

IDH1

Figure S8.  This figure depicts the distribution of mutations and mutation types across the IDH1 significant gene.

MUC21

Figure S9.  This figure depicts the distribution of mutations and mutation types across the MUC21 significant gene.

CLIP4

Figure S10.  This figure depicts the distribution of mutations and mutation types across the CLIP4 significant gene.

RUFY1

Figure S11.  This figure depicts the distribution of mutations and mutation types across the RUFY1 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)