Cancer subclone detection based on DNA copy number in single-cell and spatial omic sequencing data

Area of Science:

• Genomics
• Computational Biology
• Cancer Research

Background:

• Somatic mutations, including copy number alterations, drive cancer progression and intratumor heterogeneity.
• Understanding the genetic makeup and spatial arrangement of tumor subclones is crucial for effective cancer treatment and unraveling tumor evolution.

Purpose of the Study:

• To introduce Clonalscope, a novel computational method for detecting and characterizing tumor subclones.
• To apply Clonalscope to spatial transcriptomics and single-cell sequencing data for comprehensive subclone analysis.

Main Methods:

• Clonalscope utilizes copy number profiles and a nested Chinese Restaurant Process for de novo subclone identification.
• The method can integrate prior information from bulk DNA sequencing for enhanced subclone detection and malignant cell labeling.
• It is applicable to single-cell RNA sequencing (scRNA-seq), single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq), and spatial transcriptomics data.

Main Results:

• Clonalscope successfully labeled malignant cells and identified distinct subclones in gastrointestinal tumors using scRNA-seq and scATAC-seq data.
• In spatial transcriptomics data from various tumors, Clonalscope identified spatially segregated subclones with varying differentiation levels.
• The method revealed subclones expressing genes linked to drug resistance and survival in both primary and metastatic tumors.

Conclusions:

• Clonalscope is an effective tool for dissecting intratumor heterogeneity using copy number profiles.
• The method provides insights into tumor evolution, spatial architecture, and potential therapeutic vulnerabilities.
• Clonalscope's ability to analyze diverse single-cell and spatial data enhances its utility in cancer research and precision medicine.