Zhang Peijing, Wang Xueyi, Cen Xufeng, Zhang Qi, Fu Yuting, Mei Yuqing, Wang Xinru, Wang Renying, Wang Jingjing, Ouyang Hongwei, Liang Tingbo, Xia Hongguang, Han Xiaoping, Guo Guoji
Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China.
Liangzhu Laboratory, Zhejiang University, Hangzhou 311121, China.
Natl Sci Rev. 2024 Dec 10;12(2):nwae451. doi: 10.1093/nsr/nwae451. eCollection 2025 Feb.
Tumor heterogeneity plays a pivotal role in tumor progression and resistance to clinical treatment. Single-cell RNA sequencing (scRNA-seq) enables us to explore heterogeneity within a cell population and identify rare cell types, thereby improving our design of targeted therapeutic strategies. Here, we use a pan-cancer and pan-tissue single-cell transcriptional landscape to reveal heterogeneous expression patterns within malignant cells, precancerous cells, as well as cancer-associated stromal and endothelial cells. We introduce a deep learning framework named Shennong for screening of anticancer drugs for targeting each of the landscape cell clusters. Utilizing Shennong, we could predict individual cell responses to pharmacologic compounds, evaluate drug candidates' tissue damaging effects, and investigate their corresponding action mechanisms. Prioritized compounds in Shennong's prediction results include FDA-approved drugs currently undergoing clinical trials for new indications, as well as drug candidates reporting anti-tumor activity. Furthermore, the tissue damaging effect prediction aligns with documented injuries and terminated discovery events. This robust and explainable framework has the potential to accelerate the drug discovery process and enhance the accuracy and efficiency of drug screening.
肿瘤异质性在肿瘤进展和临床治疗耐药性中起着关键作用。单细胞RNA测序（scRNA-seq）使我们能够探索细胞群体中的异质性并识别稀有细胞类型，从而改进我们的靶向治疗策略设计。在此，我们使用泛癌和泛组织单细胞转录图谱来揭示恶性细胞、癌前细胞以及癌症相关基质细胞和内皮细胞中的异质性表达模式。我们引入了一个名为神农的深度学习框架，用于筛选针对每个图谱细胞簇的抗癌药物。利用神农，我们可以预测单个细胞对药理化合物的反应，评估候选药物的组织损伤效应，并研究其相应的作用机制。神农预测结果中的优先化合物包括目前正在进行新适应症临床试验的FDA批准药物，以及报告具有抗肿瘤活性的候选药物。此外，组织损伤效应预测与已记录的损伤和终止的发现事件相符。这个强大且可解释的框架有潜力加速药物发现过程，并提高药物筛选的准确性和效率。
