%0 Journal Article %1 bennett2018mutation %A Bennett, Laura %A Howell, Matthew %A Memon, Danish %A Smowton, Chris %A Zhou, Cong %A Miller, Crispin J. %D 2018 %J Scientific Reports %K MUSTREAD cancer-research fulltext methods mutations network-analysis rna-seq snp %N 1 %P 14830-- %R 10.1038/s41598-018-33276-3 %T Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma %U https://doi.org/10.1038/s41598-018-33276-3 %V 8 %X The genomic lesions found in malignant tumours exhibit a striking degree of heterogeneity. Many tumours lack a known driver mutation, and their genetic basis is unclear. By mapping the somatic mutations identified in primary lung adenocarcinomas onto an independent coexpression network derived from normal tissue, we identify a critical gene network enriched for metastasis-associated genes. While individual genes within this module were rarely mutated, a significant accumulation of mutations within this geneset was predictive of relapse in lung cancer patients that have undergone surgery. Since it is the density of mutations within this module that is informative, rather than the status of any individual gene, these data are in keeping with a ‘mini-driver’ model of tumorigenesis in which multiple mutations, each with a weak effect, combine to form a polygenic driver with sufficient power to significantly alter cell behaviour and ultimately patient outcome. These polygenic mini-drivers therefore provide a means by which heterogeneous mutation patterns can generate the consistent hallmark changes in phenotype observed across tumours.

@article{bennett2018mutation, abstract = {The genomic lesions found in malignant tumours exhibit a striking degree of heterogeneity. Many tumours lack a known driver mutation, and their genetic basis is unclear. By mapping the somatic mutations identified in primary lung adenocarcinomas onto an independent coexpression network derived from normal tissue, we identify a critical gene network enriched for metastasis-associated genes. While individual genes within this module were rarely mutated, a significant accumulation of mutations within this geneset was predictive of relapse in lung cancer patients that have undergone surgery. Since it is the density of mutations within this module that is informative, rather than the status of any individual gene, these data are in keeping with a ‘mini-driver’ model of tumorigenesis in which multiple mutations, each with a weak effect, combine to form a polygenic driver with sufficient power to significantly alter cell behaviour and ultimately patient outcome. These polygenic mini-drivers therefore provide a means by which heterogeneous mutation patterns can generate the consistent hallmark changes in phenotype observed across tumours.}, added-at = {2018-11-18T14:45:27.000+0100}, author = {Bennett, Laura and Howell, Matthew and Memon, Danish and Smowton, Chris and Zhou, Cong and Miller, Crispin J.}, biburl = {https://www.bibsonomy.org/bibtex/2241be80bfcd1af4ce042329d70c24eac/marcsaric}, description = {Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma | Scientific Reports}, doi = {10.1038/s41598-018-33276-3}, interhash = {994bb3442675bf7d9e9f73c05401eb37}, intrahash = {241be80bfcd1af4ce042329d70c24eac}, issn = {20452322}, journal = {Scientific Reports}, keywords = {MUSTREAD cancer-research fulltext methods mutations network-analysis rna-seq snp}, number = 1, pages = {14830--}, refid = {Bennett2018}, timestamp = {2018-11-18T14:45:27.000+0100}, title = {Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma}, url = {https://doi.org/10.1038/s41598-018-33276-3}, volume = 8, year = 2018 }