Extrachromosomal DNA driven oncogene spatial heterogeneity and evolution in glioblastoma

Area of Science:

• Oncology
• Genetics
• Computational Biology

Background:

• Extrachromosomal DNA (ecDNA) amplification is a key driver of cancer, particularly glioblastoma, contributing to treatment resistance and poor survival.
• The spatial dynamics and evolutionary impact of ecDNA remain poorly understood, hindering effective therapeutic strategies.

Purpose of the Study:

• To investigate the spatial-temporal evolution of ecDNA in glioblastoma and its clinical implications.
• To develop and apply a computational model for analyzing ecDNA dynamics in patient tumor samples.

Main Methods:

• Analysis of tumor samples from 94 treatment-naive IDH-wildtype glioblastoma patients.
• Development of the SPECIES computational model integrating whole-genome sequencing, DNA FISH, and RNAscope.
• Modeling of ecDNA spatial dynamics, copy number variation, and selection pressures.

Main Results:

• Identified distinct evolutionary trajectories for EGFR and PDGFRA ecDNAs, with EGFR ecDNAs showing higher copy numbers and stronger positive selection.
• Demonstrated that EGFR ecDNAs, including variants like EGFRvIII, often form early in tumor development, preceding clonal expansion.
• Showcased the SPECIES model's ability to infer spatial evolutionary dynamics and identify potential therapeutic windows based on ecDNA accumulation timing.

Conclusions:

• Spatial patterns of ecDNA copy number variation are predictable and influenced by the specific oncogene amplified.
• EGFR ecDNAs, particularly variants, arise early and contribute significantly to glioblastoma tumorigenesis.
• Early detection of ecDNA may provide a critical window for therapeutic intervention in glioblastoma.