Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Nat Med. 2024 Nov;30(11):3196-3208. doi: 10.1038/s41591-024-03224-y. Epub 2024 Sep 20.
Glioblastoma, the most aggressive primary brain cancer, has a dismal prognosis, yet systemic treatment is limited to DNA-alkylating chemotherapies. New therapeutic strategies may emerge from exploring neurodevelopmental and neurophysiological vulnerabilities of glioblastoma. To this end, we systematically screened repurposable neuroactive drugs in glioblastoma patient surgery material using a clinically concordant and single-cell resolved platform. Profiling more than 2,500 ex vivo drug responses across 27 patients and 132 drugs identified class-diverse neuroactive drugs with potent anti-glioblastoma efficacy that were validated across model systems. Interpretable molecular machine learning of drug-target networks revealed neuroactive convergence on AP-1/BTG-driven glioblastoma suppression, enabling expanded in silico screening of more than 1 million compounds with high patient validation accuracy. Deep multimodal profiling confirmed Ca-driven AP-1/BTG-pathway induction as a neuro-oncological glioblastoma vulnerability, epitomized by the anti-depressant vortioxetine synergizing with current standard-of-care chemotherapies in vivo. These findings establish an actionable framework for glioblastoma treatment rooted in its neural etiology.
胶质母细胞瘤是最具侵袭性的原发性脑癌，预后极差，但系统治疗仅限于 DNA 烷化化疗药物。通过探索胶质母细胞瘤的神经发育和神经生理脆弱性，可能会出现新的治疗策略。为此，我们使用临床一致且单细胞解析的平台，在胶质母细胞瘤患者手术材料中系统筛选可重新利用的神经活性药物。对 27 名患者和 132 种药物的 2500 多种体外药物反应进行分析，确定了具有强大抗胶质母细胞瘤功效的类多样化神经活性药物，这些药物在模型系统中得到了验证。对药物-靶标网络的可解释分子机器学习揭示了神经活性药物在 AP-1/BTG 驱动的胶质母细胞瘤抑制上的趋同，使对超过 100 万种化合物的扩展计算机筛选具有高患者验证准确性。深度多模态分析证实，Ca 驱动的 AP-1/BTG 通路诱导是神经肿瘤学胶质母细胞瘤的脆弱性，抗抑郁药文拉法辛与体内现有标准治疗化疗药物协同作用就是一个例证。这些发现为基于神经病因学的胶质母细胞瘤治疗建立了一个可行的框架。
