SCIFIL

SCIFIL infers fitness landscapes and clonal selection from single-cell sequencing data to quantify selective advantages and reconstruct evolutionary dynamics within heterogeneous tumor cell populations.

Key Features:

• Clonal Selection Inference: Analyzes single-cell sequencing data to infer which clones are being selected within a tumor population.
• Fitness Landscape Estimation: Estimates fitness landscapes by calculating maximum likelihood fitness values for cancer cell variants and determining their order of appearance within the tumor phylogeny.
• Evolutionary Model Fitting: Fits an evolutionary model to the inferred tumor phylogeny to study clonal competition and evolutionary trajectories.
• Application to Experimental Data: Applied to experimental tumor data to infer clonal selection dynamics and reconstruct evolutionary histories of cancer populations.

Scientific Applications:

• Cancer Evolution Studies: Dissects intra-tumor heterogeneity and clonal dynamics using single-cell sequencing-derived fitness estimates.
• Quantification of Selective Advantages: Measures selective advantages of specific cancer cell variants within a tumor population.
• Therapy Resistance Analysis: Supports analysis of how tumors adapt to therapeutic interventions and how resistance mechanisms emerge.
• Treatment Strategy Informing: Provides data to inform targeting of specific clonal populations or pathways involved in tumor progression.

Methodology:

Integrates single-cell sequencing data with computational algorithms to reconstruct tumor phylogeny, uses maximum likelihood estimation to derive fitness values, orders clone variants by appearance and selective advantage, and fits an evolutionary model to the inferred phylogeny.