Inferring Phylogenetic Trees of Cancer Evolution from Longitudinal Single-Cell Copy Number Profiles

Abstract

Understanding evolutionary dynamics is critical for unraveling the complex progression of diseases such as cancer. Cancer evolution is inherently a temporal process driven by the accumulation of mutations and clonal expansions over time. Traditional phylogenetic methods often rely solely on static, cross-sectional data, limiting their ability to infer the timing of key evolutionary events. To address this challenge, we developed NestedBD-Long, a novel method that integrates temporal data from longitudinal sampling into phylogenetic analyses using the birthdeath evolutionary model on copy numbers. This approach allows for the direct mapping of real-world time onto inferred evolutionary trees, providing a clearer and more accurate representation of cancers evolutionary trajectory. Evaluations demonstrate that NestedBD-Long outperforms traditional approaches, with accuracy improving as the number of temporal sampling points increases. This advancement provides a powerful framework for studying tumor progression, treatment resistance, and metastatic spread by capturing the dynamics between evolutionary events and real-world timelines. NestedBD-Long is available at https://github.com/Androstane/NestedBD.