Phylogeny-based tumor subclone identification using a Bayesian feature allocation model

TL;DR

This paper introduces SIFA, a Bayesian model that jointly infers tumor subclones, their phylogenetic relationships, and copy number variations, improving accuracy over existing methods in cancer genomics analysis.

Contribution

SIFA is the first Bayesian model to simultaneously incorporate phylogeny and CNV data for tumor subclone identification.

Findings

SIFA outperforms existing methods in simulation studies.

SIFA achieves higher Rand Index and cellularity accuracy.

Application to breast cancer data demonstrates practical utility.

Abstract

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.