Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

Bergstrom, Erik N.; Barnes, Mark; Alexandrov, Ludmil B.

doi:10.1186/s12859-020-03772-3

Cited by 34 publications

(37 citation statements)

References 21 publications

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“…The reported comparisons for SBS-96 scenarios rely on a cosine similarity ≥0.90 for determining TP signatures and <0.90 for determining FP signatures. Note that a cosine similarity ≥0.90 is highly unlikely to happen purely by chance (p-value = 5.90 x 10 -9 ) as two random nonnegative vectors are expected to have an average cosine similarity of 0.75 purely by chance 33 . Importantly, SigProfilerExtractor’s performance does not depend on the specific value of the cosine similarity threshold ( Figure 3 a ) as the tool consistently outperforms other bioinformatics approaches for almost any value of the threshold above 0.80 (p-value: 0.057).…”

Section: Resultsmentioning

confidence: 99%

“…Cosine similarity was used to compare the profiles of different mutational signatures. P-values can be attributed to cosine similarities based on a null hypothesis of uniform random distribution of nonnegative vectors 33 .…”

Section: Methodsmentioning

confidence: 99%

Section: Extended Benchmarking Of Sigprofilerextractor and The Other mentioning

confidence: 99%

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Uncovering novel mutational signatures byde novoextraction with SigProfilerExtractor

Islam

Díaz‐Gay

et al. 2020

Preprint

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Mutational signature analysis is commonly performed in genomic studies surveying cancer and normal somatic tissues. Here we present SigProfilerExtractor, an automated tool for accurate de novo extraction of mutational signatures for all types of somatic mutations. Benchmarking with a total of 33 distinct scenarios encompassing 1,106 simulated signatures operative in more than 200,000 synthetic genomes demonstrates that SigProfilerExtractor outperforms ten other tools across all datasets with and without noise. For simulations with 5% noise, reflecting high-quality genomic datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true positive signatures while yielding more than 5-fold less false positive signatures. Applying SigProfilerExtractor to 2,778 whole-genome sequenced cancers reveals three previously missed mutational signatures. Two of the signatures are confirmed in independent cohorts with one of these signatures associating with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting mutational signatures, and several novel mutational signatures including a signature putatively attributed to direct tobacco smoking mutagenesis in bladder cancer and in normal bladder epithelium.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Extended Benchmarking Of Sigprofilerextractor and The Other mentioning

confidence: 99%

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Uncovering novel mutational signatures byde novoextraction with SigProfilerExtractor

Islam

Díaz‐Gay

et al. 2020

Preprint

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“…The remaining set of somatic mutations were used in the subsequent analyses and evaluation for mutational signatures. Analysis of mutational signatures was performed using our previously derived set of reference mutational signatures 33 as well as our previously established methodology with the SigProfiler suite of tools used for summarization, simulation, visualization, and extraction of mutational signatures 54–56 .…”

Section: Methodsmentioning

confidence: 99%

DNA damage and somatic mutations in mammalian cells after irradiation with a nail polish dryer

Zhivagui¹,

Valenzuela²,

Yeh³

et al. 2021

Preprint

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Ultraviolet A light (UVA) is commonly emitted by nail polish dryers with recent reports suggesting that long-term use of UV-nail polish dryers may increase the risk for developing skin cancer. However, no experimental evaluation has been conducted to reveal the effect of radiation emitted by UV-nail polish dryers on mammalian cells. Here, we examine the pre-mutagenic and mutagenic changes imprinted on the genomes of human and murine primary cell models due to irradiation by a UV-nail dryer. Our findings demonstrate that radiation from UV-nail devices is cytotoxic and genotoxic. Importantly, high levels of reactive oxygen species were observed in all irradiated samples. Analysis of somatic mutations revealed a dose-dependent increase of C:G>A:T substitutions in irradiated samples with a pattern consistent to the one of COSMIC signature 18, a mutational signature attributed to reactive oxygen species. Examination of previously generated skin cancer genomics data revealed that signature 18 is ubiquitously present in melanoma and that it accounts for ~12% of the observed driver mutations. In summary, this study demonstrates that radiation emitted by UV-nail polish dryers can both damage DNA and can permanently imprint somatic mutations on the genomes of mammalian cells. These results have far reaching implications in regard to public health and to preventing skin cancer due to occupational- or consumer-based exposure to ultraviolet light from artificial sources.

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“…Mutation simulation was performed by SigProfilerSimulator 50 to preclude the bias of tri-nucleotide composition which could affect the mutation distribution in local regions. Briefly, the total number of simulated mutations for each sample is equal to the observed mutations, but the position of the mutation is relocated according to the frequency of tri-nucleotide context along the given region.…”

Section: Methodsmentioning

confidence: 99%

Deficiency in DNA mismatch repair of methylation damage is a major mutational process in cancer

Zhu

et al. 2020

Preprint

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DNA mismatch repair (MMR) is essential for maintaining genome integrity with its deficiency predisposing to cancer1. MMR is well known for its role in the post-replicative repair of mismatched base pairs that escape proofreading by DNA polymerases following cell division2. Yet, cancer genome sequencing has revealed that MMR deficient cancers not only have high mutation burden but also harbour multiple mutational signatures3, suggesting that MMR has pleotropic effects on DNA repair. The mechanisms underlying these mutational signatures have remained unclear despite studies using a range of in vitro4,5 and in vivo6 models of MMR deficiency. Here, using mutation data from cancer genomes, we identify a previously unknown function of MMR, showing that the loss of non-canonical replication-independent MMR activity is a major mutational process in human cancers. MMR is comprised of the MutSα (MSH2/MSH6) and MutLα (MLH1/PMS2) complexes7. Cancers with deficiency of MutSα exhibit mutational signature contributions distinct from those deficient of MutLα. This disparity is attributed to mutations arising from the unrepaired deamination of 5-methylcytosine (5mC), i.e. methylation damage, as opposed to replicative errors by DNA polymerases induced mismatches. Repair of methylation damage is strongly associated with H3K36me3 chromatin but independent of binding of MBD4, a DNA glycosylase that recognise 5mC and can repair methylation damage. As H3K36me3 recruits MutSα, our results suggest that MutSα is the essential factor in mediating the repair of methylation damage. Cell line models of MMR deficiency display little evidence of 5mC deamination-induced mutations as their rapid rate of proliferation limits for the opportunity for methylation damage. We thus uncover a non-canonical role of MMR in the protection against methylation damage in non-dividing cells.

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Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

Cited by 34 publications

References 21 publications

Uncovering novel mutational signatures byde novoextraction with SigProfilerExtractor

Uncovering novel mutational signatures byde novoextraction with SigProfilerExtractor

DNA damage and somatic mutations in mammalian cells after irradiation with a nail polish dryer

Deficiency in DNA mismatch repair of methylation damage is a major mutational process in cancer

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