Deconvolution and phylogeny inference of diverse variant types integrating bulk DNA-seq with single-cell RNA-seq.

Area of Science:

• Cancer Genomics
• Computational Biology
• Molecular Biology

Background:

• Reconstructing clonal lineage trees is crucial for cancer genomics, but current methods face limitations.
• Single-cell DNA sequencing (scDNA-seq) offers high resolution but is costly and technically challenging.
• Single-cell RNA sequencing (scRNA-seq) is more accessible but has limited coverage for detecting structural and copy number variations.

Purpose of the Study:

• To develop a computational framework that integrates bulk DNA sequencing and scRNA-seq for improved tumor phylogenetic inference.
• To accommodate single nucleotide variants (SNVs), copy number alterations (CNAs), and structural variants (SVs) within a unified model.
• To enhance the resolution of clonal substructure and mutational histories in cancer.

Main Methods:

• Developed TUSV-int, a deconvolution and phylogenetic inference framework.
• Utilized integer linear programming (ILP) to deconvolve heterogeneous variant types.
• Integrated bulk DNA-seq and scRNA-seq data for comprehensive analysis.

Main Results:

• Demonstrated improved deconvolution performance compared to methods using limited data or variant types.
• Showcased enhanced ability to resolve clonal structure and mutational histories.
• Successfully applied the method to a breast cancer dataset with both DNA-seq and scRNA-seq.

Conclusions:

• TUSV-int provides a robust approach for tumor phylogenetics by integrating diverse sequencing data.
• The framework overcomes limitations of existing methods, offering higher resolution for clonal substructure.
• This integration facilitates a more comprehensive understanding of cancer progression and evolution.