PerturbRx: Learning Treatment-Conditioned Latent Transitions from Single-Cell Perturbations for Patient Drug Response

Scarce data and tumor heterogeneity limit patient-level prediction of cancer treatment response. Many existing approaches predict response from patient profiles and drug representations without explicitly modeling treatment-induced molecular changes. We propose PerturbRx, a treatment-conditioned representation learning framework that learns drug- and dose-conditioned latent transitions from context-matched control and treated single-cell populations. The learned transition operator generalizes to compounds excluded from source-stage training, predicting held-out treatment effects more accurately than baselines (mean cosine similarity 0.229 vs. 0.198). We freeze and transfer the operator to pretreatment patient profiles, using the learned treatment-induced transformation as an additional representation of how each patient state may respond to therapy, without requiring matched post-treatment measurements. The transferred representation achieves the strongest pooled out-of-fold performance among evaluated methods on TCGA-485 (AUROC/AUPRC: 0.709/0.794) and an independent PDX cohort (0.685/0.612), with promising improvements beyond strong patient+drug controls in both domains.