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Abstract:
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Subpopulations of tumor cells characterized by mutation profiles may confer differential fitness and consequently influence the prognosis of cancers. Recent methods integrate single nucleotide variants and copy number aberrations to reconstruct subclonal architecture using whole-genome or whole-exome sequencing (WGS, WES) data from bulk tumor samples. Current Bayesian methods require heavy computational resources, prior knowledge of the number of subclones, and extensive manual post-processing. Regularized likelihood models may provide an alternative strategy without these drawbacks. We therefore propose Clonal structure identification through pairwise Penalization, CliP, for clustering subclonal mutations without prior knowledge. CliP demonstrates high accuracy in subclonal reconstruction in simulation studies. With real data, CliP takes 16 hours to process WGS data from 2,778 tumor samples in the ICGC-PCAWG study, and 1 hour to process WES data from 9,564 tumor samples in the TCGA study. In summary, a penalized likelihood framework for subclonal reconstruction has the potential to expand the scope of computational analysis for cancer evolution in large cancer genomic studies.
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