bioRxivCancer BiologyPreprint
An Atlas of Extrachromosomal DNA Structures Illuminates Its Evolution and Biogenesis in Cancer
Chee‐Hong WongiD, Kuo‐Chen WeiiD, Kuan-Yu Kung, Li-Hao Yang, Yin-Cheng Huang, C. Shing Huang, I-Jiuan Chung, Huang Meng-fan, Pei‐Hua Peng, Meihong Li, Chew Yee NganiD, Kou-Juey WuiD, Chia‐Lin WeiiD
Abstract
Extrachromosomal circular DNA (ecDNA) is a prevalent driver of oncogene amplification across diverse types of cancers. Leveraging single-molecule long-read sequencing and a de novo circular genome assembler, ecLego3, we charted a comprehensive atlas of ecDNA structures from longitudinal pairs of primary and recurrent glioblastoma (GBM). Our approach resolved 23 ecDNA genomes representing major oncogenes associated with GBM pathogenesis and revealed a dynamic configuration characterized by pronounced structural variation and multi-species co-existence within individual tumors. Evolutionary trajectories were shaped by hierarchical accrual of structural variants, predominated by deletions and inversions, giving rise to novel oncogene isoforms and enhancer repositioning. Such structural plasticity promotes intratumor heterogeneity and tumor fitness. Multiomic analyses at single cell resolution linked ecDNA abundance and structure to transcriptional output, uncovering non-linear scaling and synergistic co-selection among multiple ecDNA species. Comparative profiling revealed locus-dependent configuration, a contraction of ecDNA diversity upon recurrence, and convergent structural architectures across patients, implicating repeat-mediated genomic rearrangements and replication stress in ecDNA biogenesis. These findings elucidate ecDNA-driven mechanisms of tumor adaptation and highlight new molecular vulnerabilities, informing potential biomarkers and therapeutic strategies for high-grade gliomas.
- organism: human
- assay: long-read sequencing
- assay: multiome
- research focus: clustering
- research focus: gene regulatory network inference
- research focus: cell type annotation
- research focus: trajectory inference
- approach: deep learning
- approach: statistical model
- Citations
- 0
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