Mutation Analysis (MutSig 2CV v3.1)
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: BRCA-TP

  • Number of patients in set: 978

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:BRCA-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 62

Results
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: 62. 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 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 13 0 6 338 0 6 7 351 316 53 1e-16 1e-05 1e-05 1e-16 4.6e-13
2 TP53 tumor protein p53 1890 0 0 4 173 45 23 59 300 297 161 1e-16 1e-05 1e-05 1e-16 4.6e-13
3 GATA3 GATA binding protein 3 1351 20 0 2 9 2 1 89 101 97 56 1.6e-15 1e-05 0.36 1e-16 4.6e-13
4 MAP3K1 mitogen-activated protein kinase kinase kinase 1 4615 7 0 1 16 14 4 64 98 71 88 1e-16 2e-05 0.93 1e-16 4.6e-13
5 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 2709 6 0 1 16 29 12 52 109 107 90 1.2e-15 0.00053 0.68 2.2e-16 8.1e-13
6 CBFB core-binding factor, beta subunit 615 15 0 1 10 3 2 7 22 22 20 1e-16 0.52 0.07 6.7e-16 2e-12
7 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 145 0 0 13 7 1 15 36 35 33 3.9e-15 0.023 0.72 9.3e-15 2.4e-11
8 RUNX1 runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) 1520 4 0 2 11 2 1 16 30 29 23 4.4e-11 0.0043 0.00031 1.6e-14 3.7e-11
9 MLL3 myeloid/lymphoid or mixed-lineage leukemia 3 14968 1 0 6 28 23 2 30 83 70 82 2.5e-15 0.64 0.16 2.4e-14 4.8e-11
10 CDKN1B cyclin-dependent kinase inhibitor 1B (p27, Kip1) 605 3 0 1 0 5 0 7 12 10 11 5.4e-14 0.51 0.59 1.1e-12 2e-09
11 MAP2K4 mitogen-activated protein kinase kinase 4 1242 2 0 0 12 5 3 12 32 32 28 2.9e-12 0.016 0.46 3.9e-12 6.5e-09
12 ARID1A AT rich interactive domain 1A (SWI-like) 6934 37 0 3 10 9 2 7 28 27 26 2e-11 0.044 0.62 3.5e-11 5.3e-08
13 FOXA1 forkhead box A1 1423 13 0 0 19 0 0 5 24 23 16 1.4e-08 6e-05 0.17 1e-10 1.4e-07
14 GPS2 G protein pathway suppressor 2 1788 2 0 1 0 1 2 7 10 10 10 3.4e-11 1 0.09 2.6e-10 3.3e-07
15 TBX3 T-box 3 (ulnar mammary syndrome) 2260 50 0 1 8 1 1 17 27 27 26 4.8e-11 0.7 0.97 1.2e-09 1.5e-06
16 RB1 retinoblastoma 1 (including osteosarcoma) 3716 70 0 3 5 10 1 6 22 19 21 1.3e-10 0.45 0.46 1.8e-09 2e-06
17 CTCF CCCTC-binding factor (zinc finger protein) 2224 27 0 3 9 4 1 2 16 16 14 0.000016 0.00077 0.00011 3.7e-09 4e-06
18 RBMX RNA binding motif protein, X-linked 1265 450 0 1 3 0 0 10 13 13 5 0.000028 1e-05 0.43 6.5e-09 6.6e-06
19 HRNR hornerin 8561 20 0 9 27 2 0 5 34 31 28 0.0016 1e-05 0.98 3.1e-07 0.0003
20 ZMYM3 zinc finger, MYM-type 3 4227 3 0 1 6 1 1 7 15 15 13 2e-06 0.0078 0.94 3.7e-07 0.00033
21 NCOR1 nuclear receptor co-repressor 1 7666 0 0 2 19 11 3 8 41 40 39 1.1e-07 0.14 0.23 4e-07 0.00035
22 GPRIN2 G protein regulated inducer of neurite outgrowth 2 1381 128 0 2 9 0 0 2 11 11 7 0.000054 0.00024 0.58 5.6e-07 0.00047
23 SPEN spen homolog, transcriptional regulator (Drosophila) 11051 24 0 12 18 7 1 15 41 32 39 8.9e-07 0.067 0.59 1.5e-06 0.0012
24 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 12120 4 0 2 10 8 3 9 30 28 29 4.9e-07 0.26 0.68 2.6e-06 0.002
25 TCP11 t-complex 11 homolog (mouse) 1639 2 0 0 2 0 0 4 6 6 3 0.0004 0.00017 0.99 2.7e-06 0.002
26 SF3B1 splicing factor 3b, subunit 1, 155kDa 4035 20 0 2 15 1 0 0 16 16 9 0.025 1e-05 0.047 4.1e-06 0.0029
27 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 3896 22 0 2 21 0 0 0 21 20 14 0.027 1e-05 0.00076 4.3e-06 0.0029
28 RAB42 RAB42, member RAS oncogene family 318 195 0 0 1 0 0 3 4 4 2 0.0072 0.00076 0.047 0.000011 0.0071
29 CDC42EP1 CDC42 effector protein (Rho GTPase binding) 1 1180 12 0 2 2 0 0 3 5 5 3 0.00053 0.0012 1 0.000013 0.0082
30 KDM6A lysine (K)-specific demethylase 6A 4318 9 0 1 7 0 2 7 16 15 15 5.7e-06 0.13 0.29 0.000014 0.0085
31 AQP12A aquaporin 12A 902 49 0 0 6 0 0 0 6 6 4 0.0018 0.015 0.0019 0.000016 0.0093
32 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 709 275 0 0 6 0 0 0 6 6 3 0.014 9e-05 0.099 0.000019 0.011
33 NBPF9 neuroblastoma breakpoint family, member 9 2909 5 1 0 2 0 0 3 5 5 3 0.042 4e-05 0.26 3e-05 0.017
34 MYB v-myb myeloblastosis viral oncogene homolog (avian) 2346 15 0 0 4 1 1 6 12 12 12 0.000017 1 0.037 0.000032 0.017
35 ZNF362 zinc finger protein 362 1295 57 0 0 2 0 0 3 5 5 3 0.029 0.00038 0.053 0.000036 0.019
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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