Figure 3 from Extrachromosomal DNA–Driven Oncogene Spatial Heterogeneity and Evolution in Glioblastoma - Imran Noorani (9184605) (2025)
SPECIES spatial modeling of ecDNA evolution. A and B, DNA FISH staining of two representative GBM samples from the GB-UK cohort, revealing (A) EGFR- and (B) PDGFRA-ecDNA. EGFR-ecDNA seem to congregate into “hubs” (white arrows). Single-cell ecDNA copy-number distributions are derived from DNA FISH images using unbiased image analysis. C, (i) SPECIES is initiated with a single tumor cell containing k copies of ecDNA. (ii) Model of ecDNA selection. Cells carrying one or more copies of ecDNA divide at a rate s faster than cells lacking ecDNA. (iii) Cells push neighbors within a radius q on the lattice to make space required for cell division. D, Spatial computational model of ecDNA-driven tumors. ABC was used to infer optimal model parameters for the initial number of ecDNA, k; ecDNA-conferred selection, s; and cell pushing strength, q, for a given set of patient tumor measurements. E and F, Example output of the ABC model fitting algorithm using (E) 2 and (F) 3 spatial regions. (Left to right) Inferred k, s, and q and model best-fit distributions for each tumor region. The sum of the Wasserstein distance between patient and simulated distributions for each tumor region, representing closeness of fit, is denoted by σ. G, Comparison of inferred k, s, and q when analyzing 2 (core and margin) vs. 3 (core, margin, and leading edge) spatial regions. H, Summary of inferred model parameters for all sampled human GBM tumors. The highlighted color represents ecDNA-amplified oncogenes (EGFR, PDGFRA, or CDK4) in each tumor. I, Inferred model parameters for all patient tumors (excluding the tumor of patient A5, which contained separate EGFR- and PDGFRA-ecDNA species), stratified by ecDNA-amplified oncogene (EGFR or PDGFRA). (D, created using BioRender assets. https://BioRender.com/7f2pume)