MixClone

MixClone infers the cellular prevalences and allelic configurations of tumor subclonal populations from whole genome sequencing data of paired normal-tumor samples using a probabilistic mixture model.

Key Features:

• Probabilistic Mixture Model: Employs a probabilistic mixture model to infer cellular prevalences of subclonal populations directly from whole genome sequencing data of paired normal-tumor samples, distinguishing it from methods that rely primarily on somatic point mutations.
• Integration of Sequence Information: Integrates somatic copy number alterations and allele frequencies within a unified probabilistic framework, addressing limitations associated with variant calling algorithms and deep sequencing coverage requirements.
• Performance Superiority: Demonstrated to outperform existing methods for inferring tumor subclonal populations on simulated and real cancer sequencing datasets.
• Visualization Outputs: Provides visualization outputs to aid interpretation of complex subclonal structures and allelic configurations.

Scientific Applications:

• Tumor Evolution: Characterizes subclonal composition to study temporal and spatial dynamics of tumor evolution.
• Clonal Dynamics: Quantifies cellular prevalences to analyze clonal expansion, contraction, and heterogeneity within tumors.
• Targeted Therapy Development: Informs development and assessment of targeted therapies by resolving subclonal architectures that may drive treatment response or resistance.
• Cancer Progression and Resistance: Provides insights into tumor progression and mechanisms of therapeutic resistance through detailed allelic and subclonal resolution.

Methodology:

Uses a probabilistic framework that integrates somatic copy number alterations and allele frequencies from whole genome sequencing of paired normal-tumor samples to estimate subclonal cellular prevalences and allelic configurations.