PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction

2026-08-21

AI Summary

针对癌症治疗反应预测中数据稀缺和肿瘤异质性问题，提出PerturbRx框架，通过学习药物干预下的潜在状态转换来提高预测准确性。

Abstract

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.

Problem

• cancer treatment-response prediction
• molecular changes
• tumor heterogeneity
• scarce data

Innovation

• treatment-conditioned representation learning
• latent transitions
• drug response prediction
• single-cell populations