Abstract:
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Tumor tissues acquire different genetic alterations during the course of evolution in cancer patients. As a result of competition and selection, only a few subgroups of cells with unique genotypes survive. These subgroups of cells are often referred as subclones. Most existing subclone identification methods are either not able to infer the phylogenetic structure among subclones, or not able to incorporate copy number variations (CNV). In this presentation, we propose SIFA (Tumor Subclone Identification by Feature Allocation), a Bayesian model which takes into account both CNV and tumor phylogeny structure to infer tumor subclones. We evaluate the performance of SIFA by comparing it with two other popular methods using 11 simulation scenarios with different sequencing depth, evolution tree size, or tree complexity. SIFA yields consistently better results in terms of Rand Index and cellularity estimation accuracy. We also demonstrate the usefulness of our method through its application to whole genome sequencing data for four samples from a breast cancer patient.
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