发表机构
University of Minnesota; National Library of Medicine; Inha University; University of Michigan（明尼苏达大学; 美国国家医学图书馆; 仁荷大学; 密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对患者癌症治疗反应预测的稀缺数据和肿瘤异质性问题，提出PerturbRx框架，通过学习治疗条件潜在迁移实现精准预测，在TCGA等基准中表现最优。
AI 中文摘要
稀缺数据和肿瘤异质性限制了患者层面的癌症治疗反应预测。现有方法从预处理分子谱和药物表示预测反应，未明确建模治疗下预期的分子变化。我们提出PerturbRx，一种治疗条件表示学习框架，学习干预诱导的潜在迁移并将其用作患者-药物反应特征。PerturbRx从上下文匹配但细胞未配对的对照和处理过的单细胞群体训练药物和剂量条件迁移预测器，随后冻结并将该预测器迁移到预处理患者谱，无需治疗后测量。将该迁移与患者和药物表示结合以预测反应。在TCGA和患者来源异种移植基准测试中，PerturbRx在评估方法中实现了最强的综合预测性能。这些结果支持扰动预训练的潜在迁移作为患者层面药物反应预测的有用表示。
英文摘要
Scarce data and tumor heterogeneity limit patient-level cancer treatment-response prediction. Existing approaches predict response from pretreatment molecular profiles and drug representations, without explicitly modeling the molecular changes expected under treatment. We propose PerturbRx, a treatment-conditioned representation learning framework that learns intervention-induced latent transitions and uses them as patient-drug response features. PerturbRx trains a drug- and dose-conditioned transition predictor from context-matched but cell-unpaired control and treated single-cell populations, then freezes and transfers the predictor to pretreatment patient profiles without requiring post-treatment measurements. The transition is combined with patient and drug representations to predict response. Across TCGA and patient-derived xenograft benchmarks, PerturbRx achieves the strongest aggregate predictive performance among the evaluated methods. These results support perturbation-pretrained latent transitions as useful representations for patient-level drug-response prediction.
