  Tumor cells 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 distinct genotypes survive. These subgroups of
cells are often referred to as subclones. In recent years, many statistical and
computational methods have been developed to identify tumor subclones, leading
to biologically significant discoveries and shedding light on tumor
progression, metastasis, drug resistance and other processes. However, most
existing methods are either not able to infer the phylogenetic structure among
subclones, or not able to incorporate copy number variations (CNV). In this
article, 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 compare the performance of SIFA with two
other commonly used methods using simulation studies with varying sequencing
depth, evolutionary tree size, and tree complexity. SIFA consistently yields
better results in terms of Rand Index and cellularity estimation accuracy. The
usefulness of SIFA is also demonstrated through its application to whole genome
sequencing (WGS) samples from four patients in a breast cancer study.
