%0 Journal Article %1 shiraishi2015simple %A Shiraishi, Yuichi %A Tremmel, Georg %A Miyano, Satoru %A Stephens, Matthew %D 2015 %I Public Library of Science %J PLOS Genetics %K cancer context-dependent-mutation methods mixture_models mutation_motif somatic_mutation %N 12 %P 1-21 %R 10.1371/journal.pgen.1005657 %T A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures %U https://doi.org/10.1371/journal.pgen.1005657 %V 11 %X Author Summary Somatic (non-inherited) mutations are acquired throughout our lives in cells throughout our body. These mutations can be caused, for example, by DNA replication errors or exposure to environmental mutagens such as tobacco smoke. Some of these mutations can lead to cancer. Different cancers, and even different instances of the same cancer, can show different distinctive patterns of somatic mutations. These distinctive patterns have become known as “mutation signatures”. For example, C > A mutations are frequent in lung caners whereas C > T and CC > TT mutations are frequent in skin cancers. Each mutation signature may be associated with a specific kind of carcinogen, such as tobacco smoke or ultraviolet light. Identifying mutation signatures therefore has the potential to identify new carcinogens, and yield new insights into the mechanisms and causes of cancer, In this paper, we introduce new statistical tools for tackling this important problem. These tools provide more robust and interpretable mutation signatures compared to previous approaches, as we demonstrate by applying them to large-scale cancer genomic data.

@article{shiraishi2015simple, abstract = {Author Summary Somatic (non-inherited) mutations are acquired throughout our lives in cells throughout our body. These mutations can be caused, for example, by DNA replication errors or exposure to environmental mutagens such as tobacco smoke. Some of these mutations can lead to cancer. Different cancers, and even different instances of the same cancer, can show different distinctive patterns of somatic mutations. These distinctive patterns have become known as “mutation signatures”. For example, C > A mutations are frequent in lung caners whereas C > T and CC > TT mutations are frequent in skin cancers. Each mutation signature may be associated with a specific kind of carcinogen, such as tobacco smoke or ultraviolet light. Identifying mutation signatures therefore has the potential to identify new carcinogens, and yield new insights into the mechanisms and causes of cancer, In this paper, we introduce new statistical tools for tackling this important problem. These tools provide more robust and interpretable mutation signatures compared to previous approaches, as we demonstrate by applying them to large-scale cancer genomic data.}, added-at = {2017-05-31T08:31:29.000+0200}, author = {Shiraishi, Yuichi and Tremmel, Georg and Miyano, Satoru and Stephens, Matthew}, biburl = {https://www.bibsonomy.org/bibtex/222695d5eb1ab7c10eb57ccc556353665/peter.ralph}, doi = {10.1371/journal.pgen.1005657}, interhash = {4e577ec094274ff28bb8a07f54b7a050}, intrahash = {22695d5eb1ab7c10eb57ccc556353665}, journal = {PLOS Genetics}, keywords = {cancer context-dependent-mutation methods mixture_models mutation_motif somatic_mutation}, month = {12}, number = 12, pages = {1-21}, publisher = {Public Library of Science}, timestamp = {2017-05-31T08:31:29.000+0200}, title = {A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures}, url = {https://doi.org/10.1371/journal.pgen.1005657}, volume = 11, year = 2015 }