Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses

Area of Science:

• Computational biology
• Genomics
• Cancer research

Background:

• The Infinite Sites Assumption is central to tumor phylogeny reconstruction.
• Single-cell sequencing reveals widespread mutation recurrence and loss in tumors.
• Existing methods for inferring phylogenies with mutation loss require improvement.

Purpose of the Study:

• To develop a robust computational method for inferring cancer progression from single-cell sequencing data.
• To introduce a novel evolutionary model that accounts for mutation loss.
• To enhance the accuracy of tumor phylogeny reconstruction.

Main Methods:

• Development of Simulated Annealing Single-Cell inference (SASC), a new computational approach.
• Implementation of the Dollo-k model, extending evolutionary models to include limited mutation loss.
• Testing SASC's accuracy on simulated and real single-cell sequencing datasets.

Main Results:

• SASC demonstrates high accuracy in inferring cancer progression.
• The method effectively handles mutation loss in tumor evolutionary histories.
• SASC shows competitive performance compared to existing computational tools.

Conclusions:

• SASC provides a robust and accurate method for tumor phylogeny reconstruction from single-cell sequencing data.
• The incorporation of mutation loss in the Dollo-k model advances cancer progression inference.
• SASC is an open-source tool available for broader research application.