Accurate tumor clonal structures require single-cell analysis.

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Tumor clonal structure influences cancer progression and is typically studied using bulk samples.
• Limited research exists on single-cell resolution for analyzing tumor clonal architecture.
• A systematic comparison between bulk and single-cell approaches is needed.

Purpose of the Study:

• To systematically compare bulk and single-cell mutational data analysis in cancer.
• To investigate if co-mutations identified at the single-cell level correspond to bulk variant allele frequency (VAF) peaks.
• To assess the ability of each method to capture tumor heterogeneity and clonal evolution.

Main Methods:

• Analysis of bulk and single-cell mutational data from liver and colorectal cancers.
• Comparison of co-mutation patterns derived from single-cell data with bulk VAF distributions.
• Evaluation of subclonal peak detection in bulk versus single-cell analyses.

Main Results:

• Single-cell analysis identified coexisting subclones, contradicting bulk analysis findings of absent subclonal peaks in some cases.
• Overlapping VAF ranges in bulk data obscured distinct subclones identified by single-cell analysis.
• Complex tumor evolution and heterogeneity can be masked by bulk analysis, appearing as neutral evolution.

Conclusions:

• Bulk analysis can underestimate tumor heterogeneity and mask complex clonal structures.
• Single-cell resolution is crucial for accurate characterization of tumor clonal architecture and evolution.
• The study underscores the importance of single-cell sequencing for a comprehensive understanding of cancer biology.