Huang Kaishun, Liu Hui
College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211800, Jiangsu, China.
Commun Biol. 2025 Mar 31;8(1):530. doi: 10.1038/s42003-025-07959-3.
The presence of pre-existing or acquired drug-resistant cells within the tumor often leads to tumor relapse and metastasis. Single-cell RNA sequencing (scRNA-seq) enables elucidation of the subtle differences in drug responsiveness among distinct cell subpopulations within tumors. A few methods have employed scRNA-seq data to predict the drug response of individual cells to date, but their performance is far from satisfactory. In this study, we propose SSDA4Drug, a semi-supervised few-shot transfer learning method for inferring drug-resistant cancer cells. SSDA4Drug extracts pharmacogenomic features from both bulk and single-cell transcriptomic data using semi-supervised adversarial domain adaptation. This allows us to transfer knowledge of drug sensitivity from bulk-level cell lines to single cells. We conduct extensive performance evaluation experiments across multiple independent scRNA-seq datasets, demonstrating SSDA4Drug's superior performance over current state-of-the-art methods. Remarkably, with only one or two labeled target-domain samples, SSDA4Drug significantly boosts the predictive performance of single-cell drug responses. Moreover, SSDA4Drug accurately recapitulates the temporally dynamic changes of drug responses during continuous drug exposure of tumor cells, and successfully identifies reversible drug-responsive states in lung cancer cells, which initially acquire resistance through drug exposure but later restore sensitivity during drug holidays. Also, our predicted drug responses consistently align with the developmental patterns of drug sensitivity observed along the evolutionary trajectory of oral squamous cell carcinoma cells. In addition, our derived SHAP values and integrated gradients effectively pinpoint the key genes involved in drug resistance in prostate cancer cells. These findings highlight the exceptional performance of our method in determining single-cell drug responses. This powerful tool holds the potential for identifying drug-resistant tumor cell subpopulations, paving the way for advancements in precision medicine and novel drug development.
肿瘤内预先存在或获得性耐药细胞的存在通常会导致肿瘤复发和转移。单细胞RNA测序（scRNA-seq）能够阐明肿瘤内不同细胞亚群之间药物反应性的细微差异。到目前为止，已有一些方法利用scRNA-seq数据来预测单个细胞的药物反应，但其性能远不能令人满意。在本研究中，我们提出了SSDA4Drug，一种用于推断耐药癌细胞的半监督少样本迁移学习方法。SSDA4Drug使用半监督对抗域自适应从批量和单细胞转录组数据中提取药物基因组特征。这使我们能够将药物敏感性知识从批量水平的细胞系转移到单个细胞。我们在多个独立的scRNA-seq数据集上进行了广泛的性能评估实验，证明了SSDA4Drug相对于当前最先进方法的优越性能。值得注意的是，仅使用一两个标记的目标域样本，SSDA4Drug就能显著提高单细胞药物反应的预测性能。此外，SSDA4Drug准确地概括了肿瘤细胞持续药物暴露期间药物反应的时间动态变化，并成功地识别了肺癌细胞中可逆的药物反应状态，这些细胞最初通过药物暴露获得耐药性，但在药物假期期间恢复敏感性。此外，我们预测得到的药物反应与在口腔鳞状细胞癌细胞进化轨迹上观察到的药物敏感性发育模式一致。此外，我们推导得出的SHAP值和积分梯度有效地确定了前列腺癌细胞中与耐药性相关的关键基因。这些发现突出了我们的方法在确定单细胞药物反应方面的卓越性能。这个强大的工具具有识别耐药肿瘤细胞亚群的潜力，为精准医学和新型药物开发的进步铺平了道路。
