Mutation Analysis (MutSig 2CV v3.1 hg38 beta)
Overview
Introduction

This report serves to describe the mutational landscape and properties of a given cohort, as well as rank genes and genesets according to mutational significance. MutSig 2CV v3.1 hg38 beta was used to generate the results found in this report.

  • Working with cohort: TCGA-UVM-TP

  • Number of patients in cohort: 80

**Please note that in this beta version for hg38 MAFs only the burden test (MutSigCV) is utilized. Beta version does not support positional or functional clustering tests (MutSigCL/MutSigFN, respectively). These tests currently only work in the hg19 compatible release.**

Input

The input for this pipeline is an annotated .maf file describing the mutations called for each individual in the given cancer cohort, and their properties.

Summary
Results
Breakdown of Mutation Rates by Category Type

Table 1.  Get Full Table A breakdown of mutation rates per category discovered for this cohort.

left from change right n N rate ci_low ci_high relrate autoname name type
A C t G 239 87689664 2.7e-06 2.4e-06 3.1e-06 40 A[C->t]G Ap*CpG->T point
CGT C t G 486 328072192 1.5e-06 1.4e-06 1.6e-06 21 CGT[C->t]G (C/G/T)p*CpG->T point
ACGT C t ACT 260 3449787450 7.5e-08 6.6e-08 8.5e-08 1.1 ACGT[C->t]ACT *Cp(A/C/T)->T point
ACGT A t ACGT 157 3738943361 4.2e-08 3.6e-08 4.9e-08 0.61 ACGT[A->t]ACGT A->G point
ACGT AC fs ACGT 431 15208985334 2.8e-08 2.6e-08 3.1e-08 0.41 ACGT[AC->fs]ACGT transver point
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 cohort. 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:

  • codelen = the gene's coding length

  • nncd = number of noncoding mutations in this gene across the cohort

  • nsil = number of silent mutations in this gene across the cohort

  • nmis = number of missense mutations in this gene across the cohort

  • nstp = number of readthrough mutations in this gene across the cohort

  • nspl = number of splice site mutations in this gene across the cohort

  • nind = number of indels in this gene across the cohort

  • nnon = number of (nonsilent) mutations in this gene across the cohort

  • npat = number of patients (individuals) with at least one nonsilent mutation

  • nsite = number of unique sites having a non-silent mutation

  • Abundance (pCV) = Probability that the gene's overall nonsilent mutation rate exceeds its inferred background mutation rate (BMR), which is computed based on the gene's own silent mutation rate plus silent mutation rates of genes with similar covariates. BMR calculations are normalized with respect to patient-specific and sequence context-specific mutation rates.

  • Clustering (pCL) = Probability that recurrently mutated loci in this gene have more mutations than expected by chance. While pCV assesses the gene's overall mutation burden, pCL assesses the burden of specific sites within the gene. This allows MutSig to differentiate between genes with uniformly distributed mutations and genes with localized hotspots.

  • Conservation (pFN) = Probability that mutations within this gene occur disproportionately at evolutionarily conserved sites. Sites highly conserved across vertebrates are assumed to have greater functional impact than weakly conserved sites.

  • p = p-value (overall)

  • q = q-value, False Discovery Rate (Benjamini-Hochberg procedure)

Table 2.  Get Full Table A Ranked List of Significantly Mutated Genes. Number of significant genes found: 8. 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 codelen nnei nncd nsil nmis nstp nspl nind nnon npat nsite pCV pCL pFN p q
1 EIF1AX 531 45 0 0 9 0 0 1 10 10 6 1e-16 NaN NaN 1e-16 1.9e-12
2 BAP1 2895 48 0 0 5 5 0 11 21 20 21 5.2e-16 NaN NaN 5.2e-16 4.9e-12
3 GNAQ 1164 70 0 0 40 1 0 0 41 40 4 2e-15 NaN NaN 2e-15 9.8e-12
4 GNA11 1164 75 0 0 35 0 0 0 35 35 2 2.1e-15 NaN NaN 2.1e-15 9.8e-12
5 SF3B1 4297 21 0 0 18 0 0 0 18 18 5 4.5e-12 NaN NaN 4.5e-12 1.7e-08
6 SRSF2 756 525 0 0 0 0 0 3 3 3 3 2.2e-07 NaN NaN 2.2e-07 0.0007
7 CYSLTR2 1053 291 0 0 3 0 0 0 3 3 1 2.7e-06 NaN NaN 2.7e-06 0.0073
8 EIF1B 390 69 0 0 1 0 0 1 2 2 2 0.000042 NaN NaN 0.000042 0.098
9 MAOB 1743 205 0 0 1 0 1 0 2 2 2 0.000093 NaN NaN 0.000093 0.2
10 TMEM39A 1587 80 0 0 2 0 0 0 2 2 2 0.0011 NaN NaN 0.0011 1
11 ERP27 954 128 0 0 0 0 0 1 1 1 1 0.0018 NaN NaN 0.0018 1
12 DCPS 1086 51 0 0 0 0 0 1 1 1 1 0.002 NaN NaN 0.002 1
13 DUSP11 1447 166 0 0 0 1 0 0 1 1 1 0.0021 NaN NaN 0.0021 1
14 WISP1 1200 166 0 0 2 0 0 0 2 2 2 0.0021 NaN NaN 0.0021 1
15 CFP 1548 118 0 0 0 1 0 0 1 1 1 0.0022 NaN NaN 0.0022 1
16 SERTM1 360 59 0 0 1 0 0 0 1 1 1 0.0024 NaN NaN 0.0024 1
17 PAGR1 801 35 0 0 0 1 0 0 1 1 1 0.0024 NaN NaN 0.0024 1
18 RNF43 2822 107 0 0 2 0 0 0 2 2 2 0.0026 NaN NaN 0.0026 1
19 KCNK13 1251 234 0 0 3 0 0 0 3 2 3 0.0027 NaN NaN 0.0027 1
20 FAM3A 1005 23 0 0 0 0 0 1 1 1 1 0.0028 NaN NaN 0.0028 1
21 TKFC 1953 234 0 0 2 0 0 0 2 2 2 0.003 NaN NaN 0.003 1
22 OTUD4 3645 74 0 0 0 1 0 0 1 1 1 0.0032 NaN NaN 0.0032 1
23 CCDC155 1941 21 0 0 0 1 0 0 1 1 1 0.0032 NaN NaN 0.0032 1
24 PAXBP1 3096 8 0 0 0 0 0 1 1 1 1 0.0033 NaN NaN 0.0033 1
25 RBM10 3075 110 0 0 0 0 0 2 2 2 2 0.0033 NaN NaN 0.0033 1
26 MTMR10 2610 118 0 0 1 0 0 1 2 2 2 0.0036 NaN NaN 0.0036 1
27 ZC3H7A 3216 89 0 0 0 1 0 0 1 1 1 0.0036 NaN NaN 0.0036 1
28 PUS3 1507 60 0 0 0 1 0 0 1 1 1 0.0037 NaN NaN 0.0037 1
29 EIF2AK2 1896 175 0 0 1 0 0 0 1 1 1 0.0038 NaN NaN 0.0038 1
30 GMEB2 1707 20 0 0 1 1 0 0 2 2 2 0.0039 NaN NaN 0.0039 1
31 NCR3 756 75 0 0 0 1 0 0 1 1 1 0.004 NaN NaN 0.004 1
32 TLDC1 1479 158 0 0 0 1 0 0 1 1 1 0.0041 NaN NaN 0.0041 1
33 VPREB3 402 152 0 0 1 0 0 0 1 1 1 0.0043 NaN NaN 0.0043 1
34 RPL11 609 111 0 0 0 0 0 1 1 1 1 0.0043 NaN NaN 0.0043 1
35 REC114 878 63 0 0 0 1 0 0 1 1 1 0.0046 NaN NaN 0.0046 1
EIF1AX

Figure S1.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the EIF1AX significant gene.

BAP1

Figure S2.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the BAP1 significant gene.

GNAQ

Figure S3.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the GNAQ significant gene.

GNA11

Figure S4.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the GNA11 significant gene.

SF3B1

Figure S5.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the SF3B1 significant gene.

SRSF2

Figure S6.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the SRSF2 significant gene.

CYSLTR2

Figure S7.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the CYSLTR2 significant gene.

EIF1B

Figure S8.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the EIF1B 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]

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)