FiMO

FiMO infers the temporal order of somatic mutations within clonal phylogenies to elucidate how mutations accumulate during cancer progression.

Key Features:

• Bayesian Framework: Employs a Bayesian approach to analyze noisy single-cell DNA sequencing (SCS) mutational profiles and infer mutation timelines while accounting for technical artifacts.
• Finite-Sites Model Compatibility: Implements tumor evolutionary models that accommodate finite-sites phenomena including mutation recurrence and losses caused by deletion, loss of heterozygosity, and parallel mutations.
• Quantification of Uncertainty: Computes posterior probabilities for mutation placements on the clonal phylogeny to quantify uncertainty in inferred temporal orders.
• Performance on Synthetic Data: Demonstrated superior performance relative to existing methods on synthetic datasets generated under various settings.
• Application to Experimental Data: Applied to experimental colon cancer datasets to infer mutation timelines and report posterior probabilities.

Scientific Applications:

• Cancer progression inference: Infers chronological sequences of somatic mutations to study tumor evolutionary trajectories.
• Intra-tumor heterogeneity (ITH) analysis: Resolves the temporal origins of mutations to characterize ITH within tumors.
• Support for personalized therapy research: Provides temporal mutation information that can inform studies aiming to design personalized cancer therapies and precision medicine strategies.

Methodology:

Applies a Bayesian inference framework integrated with tumor evolutionary finite-sites models to process single-cell DNA sequencing mutational profiles, accommodate mutation recurrence and losses, and compute posterior probabilities for mutation placements.