Improving cellular phylogenies through the integrated use of mutation order and optimality principles

Area of Science:

• Cancer Biology
• Computational Biology
• Genomics

Background:

• Single-cell sequencing revolutionizes tumor evolution studies by analyzing somatic variations.
• Accurate inference of single-cell evolutionary relationships is crucial but challenged by sequencing errors.

Purpose of the Study:

• To develop an advanced computational approach for robust cellular phylogenies from noisy single-cell sequence data.
• To enhance the accuracy of reconstructing tumor evolution and inferring cell migration.

Main Methods:

• Integrative application of phylogenetic optimality principles.
• Utilizing patterns of co-occurrence of sequence variations.
• Developed a novel computational framework for single-cell data analysis.

Main Results:

• The new approach produces more expansive and accurate cellular phylogenies.
• Demonstrated effectiveness on single-cell sequencing and CRISPR/Cas9 genome editing datasets.
• Successfully reconstructed recurrent mutations, mutational reversals, and inferred metastatic cell migrations.

Conclusions:

• The developed computational method significantly improves phylogenetic inference for single-cell genomic data.
• The approach is versatile, applicable to diverse datasets including genome editing experiments.
• Enables deeper insights into tumor evolution, mutation dynamics, and cancer phylodynamics.