Resolving clonal evolution and selection of extrachromosomal DNA at single-cell resolution

Area of Science:

• Genomics
• Cancer Biology
• Computational Biology

Background:

• Extrachromosomal DNA (ecDNA) is crucial for cancer progression, influencing oncogene amplification, tumor growth, and drug resistance.
• Intratumor heterogeneity and clonal selection driven by ecDNA remain poorly understood.
• Understanding ecDNA dynamics is vital for developing effective cancer therapies.

Purpose of the Study:

• To develop a novel computational approach for analyzing ecDNA heterogeneity at the single-cell level.
• To investigate the clonal evolution and transcriptional cell states associated with ecDNA in cancer.
• To provide deeper insights into the role of ecDNA in intratumor heterogeneity and cellular plasticity.

Main Methods:

• Developed ecSingle, a computational method utilizing allelic imbalance and outlier expression from single-cell RNA sequencing (scRNA-seq).
• Validated findings using genome sequencing and single-molecule long-read sequencing for superior ecDNA resolution.
• Analyzed tumor samples to identify and characterize oncogene-carrying ecDNAs at the single-cell level.

Main Results:

• Identified oncogene-carrying ecDNAs in tumor samples using the ecSingle approach.
• Demonstrated extensive intratumor heterogeneity of ecDNA, with subclonal ecDNAs linked to distinct cell states.
• Showcased how rare ecDNA-positive clones in primary tumors can expand to become dominant in relapsed tumors.

Conclusions:

• Introduced a novel single-cell approach (ecSingle) for studying ecDNA, enabling clonal evolution and cell state analysis.
• Gained significant insights into the contribution of ecDNA to intratumor heterogeneity and cancer cell plasticity.
• Highlighted the potential of targeting ecDNA dynamics for cancer treatment strategies.