通过模拟用药前后细胞变化，提升癌症药物反应预测精度。
PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction
- 基于用药和剂量条件学习细胞状态的潜在转变路径。
- 在TCGA和患者来源异种移植数据上表现优于现有方法。
- 无需治疗后数据，适合缺乏动态监测的临床场景。
数据稀疏与肿瘤异质性限制了个体患者癌症治疗反应的预测。现有方法仅基于治疗前分子特征和药物表示预测反应，未显式建模治疗引起的分子变化。我们提出PerturbRx，一种治疗条件化的表征学习框架，通过学习干预诱导的潜在转移，并将其作为患者-药物反应特征。PerturbRx从匹配上下文但细胞不配对的对照组和处理组单细胞群体中训练药物与剂量条件化的转移预测器，随后冻结并迁移到治疗前患者特征，无需治疗后测量。该转移特征与患者及药物表示结合用于反应预测。在TCGA和患者来源异种移植（PDX）基准测试中，PerturbRx在所评估方法中取得了最强的综合预测性能。结果表明，扰动预训练的潜在转移是患者水平药物反应预测的有效表征。
原文摘要 · Abstract (English)
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.
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