Longitudinal Phylogenetic Inference of Copy Number Alterations and Single Nucleotide Variants from Single-Cell Sequencing

Longitudinal phylogenetic reconstruction reveals how cancers evolve over time and respond to treatments. Advances in targeted single-cell sequencing, combined with longitudinal sampling, now enable detailed longitudinal tracking of single nucleotide variants (SNVs) and copy number alterations (CNAs) at single-cell resolution. Here, we introduce LoPhy, the first method designed to reconstruct the evolution of SNVs and CNAs from these new longitudinal single-cell data. LoPhy is a sequential tree-building algorithm that reconstructs longitudinally-consistent phylogenies of SNVs and CNAs by maximizing a new factorized tree reconstruction objective. The algorithm incrementally grows a clone tree, adding SNVs and CNAs in the order they are observed across time points. Applied to a cohort of 15 acute myeloid leukemias (AMLs) and 4 TP53-mutated AMLs, LoPhy produced phylogenies that are biologically and temporally consistent with clinical observations, with many inferred CNAs validated by orthogonal bulk sequencing from the same cancer. These reconstructions highlight the role of CNAs in disease progression and resistance, revealing that AML clones selected after therapy are often defined by both large-scale CNAs and SNVs. More broadly, LoPhy can help uncover how SNVs and CNAs jointly shape the evolutionary trajectories of individual cancers at single-cell resolution. The LoPhy source code is available under a CC-BY-ND license at https://github.com/ethanumn/LoPhy. Author summaryLongitudinal single-cell DNA sequencing is increasingly used to track somatic mutations in cancer, including single nucleotide variants (SNVs) and copy number alterations (CNAs). Phylogenetic analysis of such data can reveal the mutations that characterize key subpopulations of cancerous cells and how they evolve over time. However, no existing methods are designed to reconstruct the joint evolution of SNVs and CNAs from longitudinal single-cell data. To address this gap, we developed LoPhy, an algorithm that infers a phylogenetic tree from longitudinal single-cell data to capture the joint evolutionary history of SNVs and CNAs in an individual cancer. We applied LoPhy to simulated datasets and 19 acute myeloid leukemias (AMLs). LoPhys reconstructions reveal that AML subpopulations selected after treatment are frequently defined by both SNVs and large-scale CNAs--highlighting that joint longitudinal modeling of SNVs and CNAs is crucial for understanding disease progression and therapeutic resistance.