Institute for Applied Mathematics, University of Bonn, Bonn 53115, Germany; Institute of Pathology, University Hospital Cologne, Cologne 50937, Germany.
Institute of Pathology, University Hospital Cologne, Cologne 50937, Germany.
Cell Syst. 2024 Nov 20;15(11):1061-1074.e7. doi: 10.1016/j.cels.2024.10.005. Epub 2024 Nov 13.
Prostate cancer (PCA) exhibits high levels of intratumoral heterogeneity. In this study, we developed a mathematical model to study the growth and genetic evolution of PCA. We explored the possible evolutionary patterns and demonstrated that tumor architecture represents a major bottleneck for divergent clonal evolution. Early consecutive acquisition of strong genetic alterations serves as a proxy for the formation of aggressive tumors. A limited number of clonal hierarchy patterns were identified. A biopsy study of synthetic tumors shows complex spatial intermixing of clones and delineates the importance of biopsy extent. Deep whole-exome multiregional next-generation DNA sequencing of the primary tumors from five patients was performed to validate the results, supporting our main findings from mathematical modeling. In conclusion, our model provides qualitatively realistic predictions of PCA genomic evolution, closely aligned with the evidence available from patient samples. We share the code of the model for further studies. A record of this paper's transparent peer review process is included in the supplemental information.
前列腺癌 (PCA) 表现出高度的肿瘤内异质性。在这项研究中，我们开发了一个数学模型来研究 PCA 的生长和遗传进化。我们探索了可能的进化模式，并表明肿瘤结构代表了发散克隆进化的主要瓶颈。早期连续获得强的遗传改变可作为形成侵袭性肿瘤的替代指标。确定了有限数量的克隆层次结构模式。对合成肿瘤的活检研究显示克隆的复杂空间混合，并阐明了活检范围的重要性。对来自五名患者的原发性肿瘤进行了深度全外显子多区域下一代 DNA 测序，以验证结果，支持我们从数学建模中得出的主要发现。总之，我们的模型对 PCA 基因组进化提供了定性上现实的预测，与来自患者样本的证据非常吻合。我们为进一步的研究共享模型的代码。补充材料中包含了该论文透明同行评审过程的记录。
