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

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.

Quantitative Methods (q-bio.QM); Machine Learning (cs.LG)

arXiv:2608.21349 [q-bio.QM]

(or arXiv:2608.21349v1 [q-bio.QM] for this version)

https://doi.org/10.48550/arXiv.2608.21349 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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