Exploring background mutational processes to decipher cancer genetic heterogeneity

Goncearenco, Alexander; Rager, Stephanie L.; Li, Minghui; Sang, Qing‐Xiang Amy; Rogozin, Igor B.; Panchenko, Anna R.

doi:10.1093/nar/gkx367

Cited by 74 publications

(80 citation statements)

References 52 publications

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“…Computational methods developed to uncover such signatures from catalogs of somatic mutations (Alexandrov et al, 2013a,b;Helleday et al, 2014;Alexandrov and Stratton, 2014;Fischer et al, 2013;Goncearenco et al, 2017;Nik-Zainal et al, 2012), including the classical nonnegative matrix factorization (NMF) approach, build on the assumptions that the mutations observed in a cancer genome are a result of several mutational processes and various genomes might experience different exposure to each of the contributing mutagens. Previous analyses of cancer genomes from the perspective of mutational signatures have been very informative.…”

Section: Introductionmentioning

confidence: 99%

Detecting presence of mutational signatures in cancer with confidence

2017

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Motivation: Cancers arise as the result of somatically acquired changes in the DNA of cancer cells. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of somatic mutations resulting from normal DNA damage and repair processes as well as carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. The decomposition of a cancer genome's mutation catalog into mutations consistent with such signatures can provide valuable information about cancer etiology. However, the results from different decomposition methods are not always consistent. Hence, one needs to be able to not only decompose a patient's mutational profile into signatures but also establish the accuracy of such decomposition. Results: We proposed two complementary ways of measuring confidence and stability of decomposition results and applied them to analyze mutational signatures in breast cancer genomes. We identified both very stable and highly unstable signatures, as well as signatures that previously have not been associated with breast cancer. We also provided additional support for the novel signatures. Our results emphasize the importance of assessing the confidence and stability of inferred signature contributions. Availability and implementation: All tools developed in this paper have been implemented in an R package, called SignatureEstimation, which is available from https://www.ncbi.nlm.nih.gov/ CBBresearch/Przytycka/index.cgi\#signatureestimation.

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Section: Introductionmentioning

confidence: 99%

Detecting presence of mutational signatures in cancer with confidence

2017

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“…Although there are numerous studies of cancer variations, the functional verification of the relevance of those variants for the disease is usually missing. VariBench contains three datasets for variants in cancer, which have been experimentally tested [122][123][124], and links to three other sources, namely dbCPM [128], DoCM [130], and OncoKB [129]. In addition, there is the FASMIC dataset for variants which are largely cancer related [127].…”

Section: Disease-specific Datasetsmentioning

confidence: 99%

Variation Benchmark Datasets: Update, Criteria, Quality and Applications

Sarkar

Yang

Vihinen

2019

Preprint

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Development of new computational methods and testing their performance has to be done on experimental data. Only in comparison to existing knowledge can method performance be assessed. For that purpose, benchmark datasets with known and verified outcome are needed.High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. VariBench and VariSNP are the two existing databases for sharing

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“…To assure this, we removed recurrent mutations (observed twice or more times in the same site) as these mutations might be under selection in cancer. In the current study we used pan-cancer and cancer-specific mutational profiles for breast, lung adenocarcinoma, and skin adenocarcinoma cancer derived from MutaGene [51].…”

Section: Calculation Of Context-dependent Dna Background Mutabilitymentioning

confidence: 99%

Background mutability shapes observed mutational spectrum in cancer and improves driver mutation prediction

Brown

Goncearenco

et al. 2018

Preprint

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Identifying driver mutations in cancer is notoriously difficult. To date, recurrence of a mutation in patients remains one of the most reliable markers of mutation driver status. However, some mutations are more likely to occur than others due to differences in background mutation rates arising from various forms of infidelity of DNA replication and repair machinery, endogenous, and exogenous mutagens.We used cancer-type and mutagen-specific mutability to study the contribution of background processes of mutagenesis and DNA repair in shaping the observed mutational spectrum in cancer. We developed and tested probabilistic model that adjusts the number of mutation recurrences in patients by background mutability in order to find mutations which may be under selection in cancer.We showed that observed recurrence frequency of cancer mutations scaled with the background mutability, especially for tumor suppressor genes. In oncogenes, however, highly recurring mutations were characterized by relatively low mutability, resulting in a U-shaped trend.Mutations not yet observed in any tumor had relatively low mutability values, indicating that background mutability might limit the mutation occurrence.We compiled a dataset of missense mutations from 58 genes with experimentally validated functional and transforming impacts from different studies. We found that mutability of driver mutations was lower than the mutability of passengers and consequently adjusting mutation recurrence frequency by mutability significantly improved ranking of mutations and driver prediction. Even though no training on existing data was involved, our approach performed similar or better to the existing state-of-the-art methods.

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Exploring background mutational processes to decipher cancer genetic heterogeneity

Cited by 74 publications

References 52 publications

Detecting presence of mutational signatures in cancer with confidence

Detecting presence of mutational signatures in cancer with confidence

Variation Benchmark Datasets: Update, Criteria, Quality and Applications

Background mutability shapes observed mutational spectrum in cancer and improves driver mutation prediction

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