Revealing active mutational processes in tumours using DigiPico/MutLX at unprecedented accuracy
|Study ID||Alternative Stable ID||Type|
Active mutational processes in a tumor result in genetic micro-heterogeneity that can determine the tumor’s evolutionary trajectory. Identification of these processes by studying micro-heterogeneity can unveil novel aspects of tumor evolution with potential therapeutic implications. Such studies, however, are inherently problematic because of the discovery of excessive false positive mutations. Here we report the optimization and validation of a robust whole genome sequencing and analysis pipeline (DigiPico/MutLX) that virtually eliminates false positive results. Using our method, we identified, for the first time, a sub-clonal local hyper-mutation (kataegis) event in a recurrent ovarian carcinoma, which was unidentifiable from the bulk WGS data of the same tumor. Overall, we propose DigiPico/MutLX method as a powerful framework for the reliable study of genetic micro-heterogeneity in tumor and normal tissues.
Study Datasets 1 dataset.
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The dataset contains Bam files from DigiPico runs as well as bulk sequencing data used in the "A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes" publication.
|HiSeq X Ten,Illumina HiSeq 4000,NextSeq 550||22|
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