CliP

CliP reconstructs the subclonal architecture of tumors from DNA sequencing data to characterize tumor heterogeneity.

Key Features:

• Penalized Likelihood Framework: Implements a penalized likelihood model with pair-wise penalization to cluster subclonal mutations without specifying the number of subclones.
• Integration of Genetic Variations: Integrates single nucleotide variants (SNVs) and copy number aberrations (CNAs) for joint analysis of mutation profiles across genomic regions.
• Efficiency and Scalability: Processes whole-genome sequencing (WGS) data from 2,778 tumor samples in 16 hours and whole-exome sequencing (WES) data from 9,564 tumor samples in 38 hours, supporting large-scale studies.
• High Accuracy: Demonstrated high accuracy in subclonal reconstruction across extensive simulation studies.
• Alternative to Bayesian Methods: Mitigates the need for prior knowledge of subclone numbers and addresses computational demands commonly associated with Bayesian approaches.
• Minimal Post-processing: Operates without requiring extensive post-processing steps.

Scientific Applications:

• Tumor Evolution Insight: Reconstructs subclonal architectures to provide biological insights into tumor evolution and heterogeneity.
• Precision Cancer Treatment: Generates detailed subclonal mutation profiles that can inform precision oncology and treatment stratification.

Methodology:

Uses a model-based penalized likelihood approach with pair-wise penalization to cluster subclonal mutations and operates without prior specification of the number of subclones or extensive post-processing.