DENDRO

DENDRO infers clonal membership and reconstructs phylogenetic relationships from single-cell RNA sequencing (scRNA-seq) data to characterize tumor heterogeneity and clonal evolution.

Key Features:

• Somatic mutation detection: Leverages transcribed point mutations observed in scRNA-seq data to detect somatic variants.
• Clonal clustering: Clusters single cells into genetically distinct subclones based on detected transcribed mutations.
• Phylogenetic reconstruction: Reconstructs phylogenetic trees that relate inferred subclones.
• Noise and stochasticity handling: Accounts for technical noise and expression stochasticity inherent to scRNA-seq data.
• Cell-to-clone assignment: Assigns each single cell from scRNA-seq datasets to specific inferred subclones.
• Benchmarking: Validated using simulated data and real-world datasets across three different cancer types.
• Immunotherapy case study: Applied to a mouse melanoma model to delineate the role of neoantigens in response to immunotherapy.

Scientific Applications:

• Intratumor heterogeneity analysis: Characterizes clonal composition and heterogeneity within tumors using scRNA-seq.
• Clonal evolution inference: Reconstructs evolutionary relationships among tumor subclones.
• Neoantigen and immunotherapy studies: Investigates neoantigen distribution across clones and their association with immunotherapy response, as demonstrated in a mouse melanoma model.
• Method benchmarking: Provides benchmarking data for variant detection and clonality inference using simulated and real datasets across cancer types.

Methodology:

Leverages transcribed point mutations from scRNA-seq while accounting for technical noise and expression stochasticity to cluster cells into genetically distinct subclones and reconstruct phylogenetic trees; benchmarking was performed on simulated data and real-world datasets across three cancer types and applied to a mouse melanoma immunotherapy model.