ChromoPattern: characterizing chromosomal rearrangement patterns of copy number alteration in heterogeneous tumor cell populations at single-cell resolution.

Cancer is characterized by aneuploidy, often resulting from chromosomal instability (CIN) due to events such as whole-genome duplication (WGD), breakage-fusion-bridge (BFB) cycles, and chromothripsis. These catastrophic events drive de novo copy number alterations (CNAs) with different chromosomal rearrangement patterns, contributing to tumor heterogeneity and evolution. The lack of methods capable of dissecting complex patterns of CNAs at single-cell resolution limits the understanding of how chromosome aneuploidy influences tumor evolution. To address this, we developed ChromoPattern, a computational framework to quantify four distinct chromosomal rearrangement patterns associated with complex CNA mechanisms based on single-cell copy number profiles. ChromoPattern effectively differentiates these patterns through simulations and real-world data. We applied ChromoPattern to single-cell DNA sequencing data from a liver cancer cell line and breast cancer patients. Our analysis identified the oscillation pattern of CNAs as a significant contributor to tumor heterogeneity and evolution. This pattern was associated with a 20% increase in the de novo CNA rate during tumor evolution and was linked to increased intratumor heterogeneity and subclonality. Further analysis using single-cell RNA sequencing data demonstrated that tumor clones with an enriched oscillation pattern display increased cell cycle activity and a stem-like phenotype. These findings underscore the significant and differential roles of chromosomal rearrangement patterns in tumor heterogeneity and evolution. The identification of the oscillation pattern as a key driver of tumor heterogeneity and evolution opens new avenues for developing targeted therapies and diagnostic tools.