Single-cell RNA-sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve treatment of heterogenous tumors.

中文翻译：

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泛癌细胞系和单细胞转录组谱的整合能够推断异质肿瘤的治疗脆弱性

单细胞 RNA 测序 (scRNA-seq) 通过识别不同的癌细胞亚群，极大地促进了对肿瘤内异质性的理解。然而，由于缺乏促进有效药物发现来应对异质性肿瘤的工具，将生物学差异转化为治疗策略具有挑战性。开发此类方法需要在单细胞水平上准确预测药物反应，以便为特定细胞亚群提供治疗选择。在这里，我们开发了一个透明的计算框架（昵称 scIDUC），通过将单细胞转录组谱与数据丰富的大型泛癌细胞系筛选数据集相结合来预测单个细胞的治疗效果。该方法在将细胞分离到正确的细胞药物反应状态方面实现了高精度。在涵盖不同疾病（横纹肌肉瘤、胰腺导管腺癌和去势抵抗性前列腺癌）的三项不同前瞻性测试中，使用 scIDUC 的预测结果准确且反映了生物学预期。在前两项测试中，该框架确定了针对因内在耐药性或肿瘤微环境影响而对标准护理（SOC）疗法产生耐药性的细胞亚群的药物，结果与原始研究的实验结果高度一致。在使用新生成的 SOC 治疗耐药细胞系的第三次测试中，scIDUC 确定了针对耐药细胞系的有效药物，并通过体外实验验证了预测。总之，这项研究证明了 scIDUC 快速将 scRNA-seq 数据转化为单个细胞药物反应的潜力，展示了作为改善异质肿瘤治疗工具的潜力。