%0 Journal Article %1 10.1371/journal.pcbi.1004027 %A Sakoparnig, Thomas %A Fried, Patrick %A Beerenwinkel, Niko %D 2015 %I Public Library of Science %J PLOS Computational Biology %K cancer-research drivers fulltext %N 1 %P 1-11 %R 10.1371/journal.pcbi.1004027 %T Identification of Constrained Cancer Driver Genes Based on Mutation Timing %U https://doi.org/10.1371/journal.pcbi.1004027 %V 11 %X Author Summary Cancer genome sequencing projects result in vast amounts of cancer mutation data. However, our understanding of which mutations are driving tumor growth and which are selectively neutral is lagging behind. Functional interactions among mutations can result in mutational dependencies, and these mutations then display low marginal mutation frequencies across tumor samples complicating the identification of these drivers. Here, we present a simple method for calling candidate driver mutations by discriminating dependent mutations from independent ones based on their dynamical patterns of occurrence. The gene ranking procedure measures deviation from neutral mutation timing patterns. We demonstrate, for different types of cancers and genetic alterations, improvement over classical frequency-based approaches if drivers do not occur independently, and we show complementarity to other approaches.

@article{10.1371/journal.pcbi.1004027, abstract = {Author Summary Cancer genome sequencing projects result in vast amounts of cancer mutation data. However, our understanding of which mutations are driving tumor growth and which are selectively neutral is lagging behind. Functional interactions among mutations can result in mutational dependencies, and these mutations then display low marginal mutation frequencies across tumor samples complicating the identification of these drivers. Here, we present a simple method for calling candidate driver mutations by discriminating dependent mutations from independent ones based on their dynamical patterns of occurrence. The gene ranking procedure measures deviation from neutral mutation timing patterns. We demonstrate, for different types of cancers and genetic alterations, improvement over classical frequency-based approaches if drivers do not occur independently, and we show complementarity to other approaches.}, added-at = {2017-05-20T23:39:30.000+0200}, author = {Sakoparnig, Thomas and Fried, Patrick and Beerenwinkel, Niko}, biburl = {https://www.bibsonomy.org/bibtex/2edf1e9319e555574066a109118bd70f7/marcsaric}, description = {Identification of Constrained Cancer Driver Genes Based on Mutation Timing}, doi = {10.1371/journal.pcbi.1004027}, interhash = {974f5780db35dfdf68fb27592d105984}, intrahash = {edf1e9319e555574066a109118bd70f7}, journal = {PLOS Computational Biology}, keywords = {cancer-research drivers fulltext}, month = {01}, number = 1, pages = {1-11}, publisher = {Public Library of Science}, timestamp = {2017-05-20T23:39:30.000+0200}, title = {Identification of Constrained Cancer Driver Genes Based on Mutation Timing}, url = {https://doi.org/10.1371/journal.pcbi.1004027}, volume = 11, year = 2015 }