DEVOLUTION-A method for phylogenetic reconstruction of aneuploid cancers based on multiregional genotyping data

Area of Science:

• Oncology
• Bioinformatics
• Computational Biology

Background:

• Phylogenetic reconstruction of cancer cell populations is difficult.
• Existing tools struggle with tumors characterized by aneuploidy and limited point mutations, common in pediatric and high-risk adult cancers.
• Analyzing tumors across space and time requires advanced deconvolution methods.

Purpose of the Study:

• To introduce DEVOLUTION, a novel algorithm for subclonal deconvolution and phylogenetic reconstruction.
• To enable analysis of cancer evolution using bulk genotyping data, integrating copy number and sequencing information.
• To address limitations in studying tumors with high aneuploidy and few point mutations.

Main Methods:

• DEVOLUTION algorithm integrates copy number and sequencing data across multiple tumor regions.
• Requires known mutated clone fractions for each mutation.
• Utilizes bulk genotyping data for inference.

Main Results:

• Validated on 56 pediatric tumors (253 biopsies), demonstrating robust performance.
• Successfully simulated bulk genotyping data.
• Benchmarked favorably against similar bioinformatics tools on an external dataset.

Conclusions:

• DEVOLUTION provides a powerful approach for phylogenetic reconstruction from bulk genotyping data.
• It is particularly valuable for tumors with a high burden of chromosomal copy number alterations.
• The algorithm can advance insights into cancer development, progression, and treatment response.