LICHeE

LICHeE reconstructs multi-sample cancer cell lineage trees from variant allele frequencies of somatic single nucleotide variants (SNVs) derived from deep sequencing to infer subclonal composition and tumor evolutionary trajectories.

Key Features:

• Multi-sample phylogenetic inference: Constructs cell lineage trees using data from multiple somatic samples to represent tumor evolution across sites or timepoints.
• Variant allele frequency (VAF) analysis: Leverages VAFs derived from somatic SNVs obtained by deep sequencing as the primary input for inference.
• Specialized tree-building for heterogeneity: Implements tree-building approaches that account for sample heterogeneity in reconstructing phylogenies.
• Subclonal composition inference: Infers subclonal structure and the distribution of clones across samples.
• Lineage marker identification: Identifies lineage-specific mutations useful for tracing evolutionary history across samples from the same patient.
• Automated phylogenetic reconstruction: Automates the process of deriving phylogenetic relationships from VAF and SNV data.

Scientific Applications:

• Tumor phylogenetics and clonal evolution: Reconstructs evolutionary trajectories of cancer cell populations.
• Subclone detection and composition analysis: Characterizes subclonal architecture within and across samples.
• Tracing intra-patient evolutionary history: Maps lineage markers and mutation orders across multiple samples from the same patient.
• Studying cancer heterogeneity and progression: Supports analyses of spatial and temporal heterogeneity during tumor development.
• Informing personalized medicine studies: Provides evolutionary context that can be used in precision oncology research.

Methodology:

Uses variant allele frequencies of somatic SNVs from deep sequencing to automatically construct multi-sample cell lineage trees via specialized tree-building and phylogenetic inference approaches.