Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors

Abstract

Background Cancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor’s evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor’s evolutionary history as either linear or branched. Results We introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach. Conclusion Phyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor’s single-cell DNA sequencing data.

CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data

“…In the simulations performed by the author, ScisTree’s accuracy is similar to SCIPhI and SCITE, but it is much faster. Related approaches for single-cell SNV data include methods focused on cell clustering and clonal phylogeny [ 34 – 41 ], phylogenetic model selection [ 42 , 43 ], mutation ordering [ 44 ], genotype correction [ 45 ], or longitudinal comparisons [ 46 ]. Methods also exist for inferring phylogenies from single-cell copy number variants [ 47 ], but these are out of the scope of this study.…”

Clonal Evolution at First Sight: A Combined Visualization of Diverse Diagnostic Methods Improves Understanding of Leukemic Progression

“…Different bioinformatic approaches already exist for the calculation of clonal evolution of bulk sequencing data. In addition, the single cell sequencing method has expanded the field of reconstructing clonal evolution (14,(17)(18)(19). A study of Morita et al has shown both genetic and phenotypic evolution in AML by single cell sequencing and cell surface protein analyses (14).…”

MoTERNN: Classifying the Mode of Cancer Evolution Using Recursive Neural Networks

“…Outside the above categories, Phyolin [40] and the method of [41] identify the mode of evolution given the binary genotype matrices obtained from single-cell SNVs. These methods, however, are aimed at distinguishing between linear and nonlinear modes (i.e., a binary classification), which is a simpler problem than the one we address here.…”