Mutation Analysis (MutSig 2CV v3.1)
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 was used to generate the results found in this report.

  • Working with cohort: BRCA-noHyper

  • Number of patients in cohort: 127

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
ACGT C t G 2774 741501280 3.7e-06 3.6e-06 3.9e-06 9.1 ACGT[C->t]G *CpG->T point
T C tfs ACT 5101 4338041394 1.2e-06 1.1e-06 1.2e-06 2.9 T[C->tfs]ACT Tp*Cp(A/C/T)->mut point
ACGT C fs G 693 1483002560 4.7e-07 4.3e-07 5e-07 1.1 ACGT[C->fs]G *CpG->(G/A) point
ACG C tfs ACT 3945 12345740541 3.2e-07 3.1e-07 3.3e-07 0.78 ACG[C->tfs]ACT (A/C/G)p*Cp(A/C/T)->mut point
ACGT A tfs ACGT 2455 17637713148 1.4e-07 1.3e-07 1.4e-07 0.34 ACGT[A->tfs]ACGT A->mut 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: 7. 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 TP53 tumor protein p53 1448 69 0 1 26 16 2 12 56 54 46 4.2e-16 0.0042 0.013 1e-16 6.4e-13
2 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3293 14 0 0 41 0 1 6 48 42 21 8.4e-16 1e-05 0.0057 1e-16 6.4e-13
3 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1246 125 0 0 3 0 4 4 11 9 11 6.3e-13 0.17 0.37 1.7e-12 8e-09
4 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 2717 22 0 0 4 3 1 3 11 9 11 1.5e-08 0.067 0.55 1.7e-08 0.000064
5 MAP3K1 mitogen-activated protein kinase kinase kinase 1 4617 49 0 2 5 2 1 9 17 11 17 1e-08 1 0.58 2e-07 0.00063
6 AKT1 v-akt murine thymoma viral oncogene homolog 1 1570 93 0 0 5 0 0 0 5 5 2 0.00046 0.0014 0.0043 2.6e-07 0.00071
7 GATA3 GATA binding protein 3 1367 146 0 0 3 0 0 6 9 9 9 1e-07 1 0.074 4.3e-07 0.001
8 CBFB core-binding factor, beta subunit 630 94 0 0 1 0 0 3 4 4 3 0.0001 0.045 0.53 0.000064 0.14
9 EIF2AK3 eukaryotic translation initiation factor 2-alpha kinase 3 3427 8 0 0 2 2 0 0 4 3 4 0.00044 1 0.037 0.00021 0.4
10 C10orf137 chromosome 10 open reading frame 137 3821 5 0 0 3 1 0 0 4 2 4 0.014 0.0035 0.24 0.00031 0.45
11 ASH1L ash1 (absent, small, or homeotic)-like (Drosophila) 9160 2 0 1 6 0 1 1 8 7 8 0.0017 0.013 0.95 0.00031 0.45
12 KMT2C myeloid/lymphoid or mixed-lineage leukemia 3 15159 3 0 1 4 3 1 4 12 11 12 0.000028 1 0.46 0.00032 0.45
13 RUNX1 runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) 1644 3 0 0 2 0 0 3 5 5 5 0.000029 1 0.94 0.00033 0.45
14 GPS2 G protein pathway suppressor 2 1050 67 0 1 2 0 0 2 4 4 4 0.0014 1 0.0065 0.00042 0.54
15 SP3 Sp3 transcription factor 2374 21 0 0 2 1 1 0 4 4 4 0.000047 1 0.38 0.00051 0.61
16 SLC25A47 solute carrier family 25, member 47 959 581 0 0 2 0 1 1 4 4 3 0.0033 0.021 0.44 0.00064 0.7
17 MLLT4 myeloid/lymphoid or mixed-lineage leukemia (trithorax homolog, Drosophila); translocated to, 4 5704 2 0 0 2 0 1 3 6 6 6 0.000062 1 0.49 0.00066 0.7
18 ADAM23 ADAM metallopeptidase domain 23 2795 6 0 0 1 1 1 0 3 3 3 0.00039 1 0.16 0.00072 0.7
19 COG1 component of oligomeric golgi complex 1 3001 10 0 0 2 1 2 0 5 5 5 7e-05 1 0.78 0.00074 0.7
20 NCOR1 nuclear receptor co-repressor 1 7573 1 0 0 0 0 1 2 3 3 3 0.00035 1 0.2 0.00076 0.7
21 RYR1 ryanodine receptor 1 (skeletal) 15543 1 1 2 9 1 1 1 12 11 12 0.015 0.018 0.27 0.00081 0.71
22 PSD4 pleckstrin and Sec7 domain containing 4 3247 7 0 0 1 2 1 0 4 4 4 0.000097 1 0.78 0.001 0.83
23 PALB2 partner and localizer of BRCA2 3613 5 0 0 1 0 1 1 3 3 3 0.00034 1 0.33 0.0011 0.9
24 C6orf132 chromosome 6 open reading frame 132 3639 149 0 0 6 0 0 2 8 6 8 0.00012 1 0.93 0.0012 0.94
25 EPB41L1 erythrocyte membrane protein band 4.1-like 1 2858 8 0 1 1 0 1 1 3 3 3 0.0025 1 0.029 0.0013 0.94
26 BAI1 brain-specific angiogenesis inhibitor 1 4887 72 0 2 5 0 1 1 7 7 7 0.00038 1 0.25 0.0013 0.94
27 TRIM14 tripartite motif-containing 14 1402 3 0 0 1 1 0 0 2 2 2 0.014 1 0.01 0.0014 0.94
28 ANKRD33 ankyrin repeat domain 33 1450 18 0 0 2 0 1 0 3 3 3 0.00054 1 0.24 0.0014 0.94
29 GABRG3 gamma-aminobutyric acid (GABA) A receptor, gamma 3 1496 26 0 0 2 1 0 0 3 2 3 0.061 0.0027 0.74 0.0016 1
30 TTC28 tetratricopeptide repeat domain 28 7538 2 0 0 4 0 1 0 5 5 5 0.032 0.013 0.25 0.0017 1
31 TOR1AIP1 torsin A interacting protein 1 1854 68 0 0 2 1 1 0 4 4 4 0.00018 1 0.84 0.0017 1
32 MYL9 myosin, light chain 9, regulatory 541 336 0 0 2 0 0 0 2 2 1 0.018 0.013 0.25 0.0018 1
33 SBNO2 strawberry notch homolog 2 (Drosophila) 4350 0 0 0 3 1 0 1 5 5 5 0.0021 1 0.039 0.0022 1
34 ARSF arylsulfatase F 1836 18 0 0 1 1 1 0 3 3 3 0.0049 1 0.037 0.0022 1
35 DUSP11 dual specificity phosphatase 11 (RNA/RNP complex 1-interacting) 1363 50 0 0 1 0 0 1 2 2 2 0.015 1 0.01 0.0025 1
TP53

Figure S1.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

PIK3CA

Figure S2.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

PTEN

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

CDH1

Figure S4.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the CDH1 significant gene.

MAP3K1

Figure S5.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the MAP3K1 significant gene.

AKT1

Figure S6.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the AKT1 significant gene.

GATA3

Figure S7.  Get High-res Image This figure depicts the distribution of mutations and mutation types across the GATA3 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)
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