ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model

Area of Science:

• Genomics
• Computational Biology
• Cancer Research

Background:

• Tumors exhibit diverse somatic mutations, including single nucleotide variants (SNVs) and copy-number aberrations (CNAs), reflecting their evolutionary history.
• Current single-cell DNA sequencing (scDNA-seq) technologies struggle to accurately measure both SNVs and CNAs simultaneously, hindering accurate tumor phylogeny inference.

Purpose of the Study:

• To develop a novel evolutionary model and algorithm for robust tumor phylogeny inference from scDNA-seq data.
• To address the limitations of existing methods in simultaneously analyzing SNVs and CNAs for phylogenetic reconstruction.

Main Methods:

• Introduction of the constrained k-Dollo model, which utilizes SNVs as phylogenetic markers and constrains SNV losses based on cellular clusters.
• Development of the ConDoR algorithm to infer phylogenies from targeted scDNA-seq data using the constrained k-Dollo model.

Main Results:

• ConDoR demonstrates superior performance in inferring tumor phylogenies compared to existing methods.
• The algorithm accurately reconstructs evolutionary histories by effectively integrating SNV and CNA data.

Conclusions:

• The constrained k-Dollo model and ConDoR algorithm provide a significant advancement in analyzing tumor evolutionary dynamics from scDNA-seq data.
• This approach enables more accurate reconstruction of tumor phylogenies, facilitating a deeper understanding of cancer evolution.