Bootstrap confidence for molecular evolutionary estimates from tumor bulk sequencing data - Huzar, Jared; Shenoy, Madelyn; Sanderford, Maxwell D.; Kumar, Sudhir; Miura, Sayaka (Frontiers in Bioinformatics, 2023)
Bulk sequencing is commonly used to characterize the genetic diversity of cancer cell populations and the evolutionary relationships of tumor clones. However, bulk sequencing produces information on nucleotide variants and their sample frequencies, necessitating computational methods to predict clone sequences. Interestingly, no methods are available to measure the statistical confidence in the base assignments of the inferred clones. We introduce a bootstrap resampling approach that combines clone prediction and statistical confidence calculations. Analysis of computer-simulated datasets shows it works well in assessing the reliability of predicted clones and downstream inferences, i.e., metastatic cell migration paths, obtained using these clones. An application of the bootstrap approach in the analysis of empirical datasets from metastatic cancers reveals that the numbers of driver mutations involved in metastatic cell migration events sourced from primary tumors are not significantly different from those where metastatic tumors are the source of new metastases. The bootstrap approach developed in this study is available at https://github.com/SayakaMiura/CloneFinderPlus.