CALDER: Inferring Phylogenetic Trees from Longitudinal Tumor Samples

Abstract

Longitudinal DNA sequencing of cancer patients yields insight into how tumors evolve over time or in response to treatment. However, sequencing data from bulk tumor samples often have considerable ambiguity in clonal composition, complicating the inference of ancestral relationships between clones. We introduce Cancer Analysis of Longitudinal Data through Evolutionary Reconstruction (CALDER), an algorithm to infer phylogenetic trees from longitudinal bulk DNA sequencing data. CALDER explicitly models a longitudinally observed phylogeny incorporating constraints that longitudinal sampling imposes on phylogeny reconstruction. We show on simulated bulk tumor data that longitudinal constraints substantially reduce ambiguity in phylogeny reconstruction and that CALDER outperforms existing methods that do not leverage this longitudinal information. On real data from two chronic lymphocytic leukemia patients, we find that CALDER reconstructs more plausible and parsimonious phylogenies than existing methods, with CALDER phylogenies containing fewer tumor clones per sample. CALDER's use of longitudinal information will be advantageous in further studies of tumor heterogeneity and evolution.

Keywords: algorithm; cancer; longitudinal sampling; phylogenetics; tumor heterogeneity; tumor phylogeny.