SASC

SASC infers cancer progression from single-cell sequencing (SCS) data using simulated annealing to fit a Dollo-k parsimony model that permits accumulation and limited loss of mutations, thereby relaxing the Infinite Sites Assumption (ISA).

Key Features:

• Simulated annealing optimization: Uses simulated annealing to explore complex solution spaces and identify optimal or near-optimal cancer progression models from SCS data.
• Dollo-k parsimony model: Implements a Dollo-k parsimony extension that allows accumulation and limited loss of mutations during tumor evolution.
• Relaxation of ISA: Explicitly relaxes the Infinite Sites Assumption (ISA) to accommodate recurrent mutation loss or reacquisition observed in tumor evolution.
• Single-cell sequencing input: Operates on single-cell sequencing (SCS) data for inferring tumor phylogenies and mutation histories.
• Empirical validation: Evaluated on simulated datasets and real-world samples, demonstrating higher accuracy than several other methods.
• Alternative to ISA-constrained methods: Provides a phylogenetic inference approach that is not constrained by the ISA, supporting more realistic models of cancer progression.

Scientific Applications:

• Cancer progression inference: Reconstruction of tumor evolutionary histories and mutation trajectories from single-cell sequencing data.
• Phylogenetic reconstruction with mutation loss: Building tumor phylogenies under a Dollo-k framework that models limited mutation loss and recurrent events.
• Study of tumor evolution: Analysis of cancer evolution and progression where mutation loss or reacquisition is relevant.

Methodology:

SASC applies simulated annealing optimization to fit a Dollo-k parsimony model that permits accumulation and limited loss of mutations from single-cell sequencing data.