A statistical framework to examine the combinations of clones that co-exist in tumors. More precisely, the algorithm finds pairs of genes that are mutated in the same tumor but in different clones, i.e. their subclonal mutation profiles are mutually exclusive. We refer to this as clonally exclusive. It means that the mutations occurred in different branches of the tumor phylogeny, indicating parallel evolution of the clones. Our statistical framework assesses whether a pattern of clonal exclusivity occurs more often than expected by chance alone across a cohort of patients. The required input data are the mutated gene-to-clone assignments from a cohort of cancer patients, which were obtained by running phylogenetic tree inference methods. Reconstructing the evolutionary history of a tumor and detecting the clones is challenging. For nondeterministic algorithms, repeated tree inference runs may lead to slightly different mutation-to-clone assignments. Therefore, our algorithm was designed to allow the input of multiple gene-to-clone assignments per patient. They may have been generated by repeatedly performing the tree inference, or by sampling from the posterior distribution of trees. The tree inference methods designate the mutations to individual clones. The mutations can then be mapped to genes or pathways. Hence our statistical framework can be applied on the gene level, or on the pathway level to detect clonally exclusive pairs of pathways. If a pair is significantly clonally exclusive, it points towards the fact that this specific clone configuration confers a selective advantage, possibly through synergies between the clones with these mutations.
|Author||Ariane L. Moore, Jack Kuipers and Niko Beerenwinkel|
|Bioconductor views||BiomedicalInformatics FeatureExtraction FunctionalGenomics GeneticVariability Genetics GenomicVariation MathematicalBiology Pathways PatternLogic SomaticMutation SystemsBiology|
|Maintainer||Ariane L. Moore <email@example.com>|
|Package repository||View on GitHub|
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