Glioblastomas (GBMs) are the most aggressive primary brain tumours and have no known cure. Each individual tumour comprises multiple sub-populations of genetically-distinct cells that may respond differently to targeted therapies and may contribute to disappointing clinical trial results. Image-localized biopsy techniques allow multiple biopsies to be taken during surgery and provide information that identifies regions where particular sub-populations occur within an individual GBM, thus providing insight into their regional genetic variability. These sub-populations may also interact with one another in a competitive or cooperative manner; it is important to ascertain the nature of these interactions, as they may have implications for responses to targeted therapies.
We combine genetic information from biopsies with a mechanistic model of interacting GBM sub-populations to characterise the nature of interactions between two commonly occurring GBM sub-populations, those with EGFR and PDGFRA genes amplified. We study population levels found across image-localized biopsy data from a cohort of 25 patients and compare this to model outputs under competitive, cooperative and neutral interaction assumptions. We explore other factors affecting the observed simulated sub-populations, such as selection advantages and phylogenetic ordering of mutations, which may also contribute to the levels of EGFR and PDGFRA amplified populations observed in biopsy data.

中文翻译：

---

确定分子上不同的胶质母细胞瘤亚群的时空动态

胶质母细胞瘤 (GBM) 是最具侵袭性的原发性脑肿瘤，目前尚无治愈方法。每个单独的肿瘤都包含多个基因不同的细胞亚群，这些细胞可能对靶向治疗有不同的反应，并可能导致令人失望的临床试验结果。图像定位活检技术允许在手术过程中进行多次活检，并提供识别单个 GBM 中出现特定亚群的区域的信息，从而深入了解其区域遗传变异性。这些亚群也可能以一种竞争或合作的方式相互影响；确定这些相互作用的性质很重要，因为它们可能对靶向治疗的反应产生影响。
我们将活检的遗传信息与相互作用的 GBM 亚群的机械模型相结合，以表征两个常见的 GBM 亚群之间相互作用的性质，即那些具有 EGFR 和 PDGFRA 基因扩增的亚群。我们研究了从一组 25 名患者的图像定位活检数据中发现的人口水平，并将其与竞争、合作和中立交互假设下的模型输出进行比较。我们探索了影响观察到的模拟亚群的其他因素，例如选择优势和突变的系统发育排序，这也可能有助于活检数据中观察到的 EGFR 和 PDGFRA 扩增群体的水平。