Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data

Area of Science:

• Genomics
• Computational Biology
• Cancer Research

Background:

• Single-cell DNA sequencing is crucial for understanding tumor evolution and mutation acquisition.
• Existing whole-genome amplification methods often favor either single-nucleotide variation (SNV) or copy number aberration (CNA) detection, not both.
• This limitation hinders comprehensive phylogenetic tree inference and the study of SNV-CNA interplay.

Purpose of the Study:

• To introduce SCsnvcna, a computational tool designed to construct phylogenetic trees by integrating SNV and CNA data.
• To address the technical challenges of detecting SNVs and CNAs from the same cell populations.
• To provide a more practical approach for analyzing tumor evolution using independent SNV and CNA datasets.

Main Methods:

• SCsnvcna employs a Bayesian probabilistic model to infer evolutionary trees.
• It utilizes genotype constraints and cellular prevalence to optimize SNV placement on a CNA-derived phylogeny.
• The method accommodates independent cell sets for SNV and CNA data acquisition.

Main Results:

• SCsnvcna demonstrated robustness and accuracy across comprehensive simulations and comparisons with seven state-of-the-art methods.
• The tool consistently achieved lower error rates and scaled effectively with varying tree leaf nodes, SNVs, and SNV cell counts.
• Application to colorectal cancer datasets confirmed SNV cell and SNV placement consistency and refined the placement of ATP7B.

Conclusions:

• SCsnvcna offers a practical and accurate solution for constructing phylogenetic trees from independent SNV and CNA data.
• The tool enhances the analysis of complex multitumor samples, providing refined insights into tumor evolution.
• SCsnvcna advances the investigation of the interplay between single-nucleotide variations and copy number aberrations in cancer.