基于生成对抗网络的胶质母细胞瘤Waddington景观模式检测
Pattern Detection on Glioblastoma's Waddington landscape via Generative Adversarial Networks
摘要：
胶质母细胞瘤（GBM）是一种高致病性、高致死性、预后差的疾病。它们的新兴特性，如细胞异质性，治疗抗性和自我更新，在很大程度上归因于其称为胶质母细胞瘤衍生干细胞（GSC）的群体子集与其微环境之间的相互作用。确定GSC和成熟的、分化良好的GBM表型之间的发育轨迹中的因果模式仍然是肿瘤学中具有挑战性的问题。本文提出了一个复杂系统的方法来推断吸引动力学的儿科GBM和成人GSC的单细胞基因表达数据集的蓝图。这些算法包括Waddington景观重建，生成对抗网络和分形维数分析。在这里，我展示了一个类似罗斯勒的奇怪吸引子，其分形维数约为1.7，出现在所有12名患者的GAN重建模式中。研究结果表明，一个奇怪的吸引子可能驱动GBM在信号状态空间中的复杂动力学和自适应行为。
英文摘要：
Glioblastoma (GBM) is a highly morbid and lethal disease with poor prognosis. Their emergent properties such as cellular heterogeneity, therapy resistance, and self-renewal are largely attributed to the interactions between a subset of their population known as glioblastoma-derived stem cells (GSCs) and their microenvironment. Identifying causal patterns in the developmental trajectories between GSCs and the mature, well-differentiated GBM phenotypes remains a challenging problem in oncology. The paper presents a blueprint of complex systems approaches to infer attractor dynamics from the single-cell gene expression datasets of pediatric GBM and adult GSCs. These algorithms include Waddington landscape reconstruction, Generative Adversarial Networks, and fractal dimension analysis. Here I show, a Rossler-like strange attractor with a fractal dimension of roughly 1.7 emerged in the GAN-reconstructed patterns of all twelve patients. The findings suggest a strange attractor may be driving the complex dynamics and adaptive behaviors of GBM in signaling state-space.
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