癌细胞表现出显着的可塑性，可以根据肿瘤微环境改变谱系。细胞可塑性驱动侵袭和转移，并帮助癌细胞通过对放射和细胞毒性化疗产生抵抗力来逃避治疗。通过表观遗传重编程加深对细胞命运决定的理解对于发现癌细胞如何实现转录组和表型可塑性至关重要。胶质母细胞瘤是癌症进化的一个完美例子，其中细胞通过激活或维持祖细胞发育程序来保持固有的可塑性水平。然而，控制胶质母细胞瘤细胞可塑性的表观遗传驱动因素的原理仍然知之甚少。在这里，我们使用机器学习 (ML)，结合胶质母细胞瘤的表观遗传特征（ATAC-seq、CTCF ChIP-seq、RNAPII ChIP-seq、H3K27Ac ChIP-seq 和 RNA-seq）对转录本表达进行跨患者预测干细胞（GSC）。我们针对此任务研究了不同的 ML 和深度学习 (DL) 模型，并使用 XGBoost 构建最终的管道。在一名患者身上训练的模型可以推广到另一名患者，这表明即使 GSC 可能非常不同，控制基因转录的表观遗传信号在患者之间是一致的。我们证明 H3K27Ac 是表观遗传特征，为跨患者基因表达预测提供了最重要的贡献。此外，利用来自患者来源的 GSC 的 H3K27Ac 信号，我们可以预测人类神经嵴干细胞的基因表达，表明这些恶性和良性干细胞亚群之间存在共同的表观遗传发育轨迹。我们的跨患者 ML/DL 模型确定了表观遗传标记对胶质母细胞瘤患者以及 GSC 和神经嵴干细胞之间基因表达的影响的加权模式。我们建议更广泛地应用这种分析可以重塑我们对胶质母细胞瘤肿瘤进化的看法，并为新的表观遗传靶向疗法的设计提供信息。

Cancer cells show remarkable plasticity and can switch lineages in response to the tumor microenvironment. Cellular plasticity drives invasiveness and metastasis and helps cancer cells to evade therapy by developing resistance to radiation and cytotoxic chemotherapy. Increased understanding of cell fate determination through epigenetic reprogramming is critical to discover how cancer cells achieve transcriptomic and phenotypic plasticity. Glioblastoma is a perfect example of cancer evolution where cells retain an inherent level of plasticity through activation or maintenance of progenitor developmental programs. However, the principles governing epigenetic drivers of cellular plasticity in glioblastoma remain poorly understood. Here, using machine learning (ML) we employ cross-patient prediction of transcript expression using a combination of epigenetic features (ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq) of glioblastoma stem cells (GSCs). We investigate different ML and deep learning (DL) models for this task and build our final pipeline using XGBoost. The model trained on one patient generalizes to another one suggesting that the epigenetic signals governing gene transcription are consistent across patients even if GSCs can be very different. We demonstrate that H3K27Ac is the epigenetic feature providing the most significant contribution to cross-patient prediction of gene expression. In addition, using H3K27Ac signals from patients-derived GSCs, we can predict gene expression of human neural crest stem cells suggesting a shared developmental epigenetic trajectory between subpopulations of these malignant and benign stem cells. Our cross-patient ML/DL models determine weighted patterns of influence of epigenetic marks on gene expression across patients with glioblastoma and between GSCs and neural crest stem cells. We propose that broader application of this analysis could reshape our view of glioblastoma tumor evolution and inform the design of new epigenetic targeting therapies.