Abstract 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. While some phylogenetic inference methods can jointly reconstruct SNV and CNA evolution, none are designed for longitudinal single-cell data. Here, we present scaOrchard, a new sequential tree building algorithm to reconstruct longitudinal phylogenies of SNVs and CNAs from single-cell sequencing. scaOrchard maximizes a new factorized longitudinal tree reconstruction objective by incrementally growing the tree, adding SNVs and CNAs in the order they are observed across samples. Applied to a cohort of 15 acute myeloid leukemias (AMLs) and 4 TP53-mutated AMLs, scaOrchard produced phylogenies that are biologically and temporally consistent with clinical observations, with many inferred CNAs validated by orthogonal bulk sequencing from the same patient. These reconstructions further reveal that AML clones selected after therapy are often defined by large-scale CNAs as well as SNVs, highlighting the role of CNAs in disease progression and resistance. More broadly, scaOrchard can help uncover how SNVs and CNAs jointly shape the evolutionary trajectories of individual cancers.
Kulman et al. (Wed,) studied this question.