Sample-Efficient Adaptation of Drug-Response Models to Patient Tumors under Strong Biological Domain Shift

2026-03-17

AI Summary

This work addresses the challenge of poor sample efficiency in predicting patient tumor drug responses from in vitro cell line data, primarily caused by substantial biological domain shift. To mitigate this, the authors propose a staged transfer learning framework: first, disentangled representations of cells and drugs are learned separately via autoencoders on unlabeled pharmacogenomic data; second, these representations are aligned using labeled cell line drug response data; and finally, clinical adaptation is achieved with only a small amount of patient data. By decoupling representation learning from task-specific supervision, the method substantially reduces reliance on annotated patient samples under strong domain shift. Experiments show that the model matches the accuracy of end-to-end approaches on standard cell line benchmarks while achieving faster performance gains in few-shot patient-level settings, demonstrating the efficacy of unsupervised pretraining for cross-domain pharmacological response prediction.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Search and Optimization: Learning to Search
• Computer Vision: Representation Learning for Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Predicting drug response in patients from preclinical data remains a major challenge in precision oncology due to the substantial biological gap between in vitro cell lines and patient tumors. Rather than aiming to improve absolute in vitro prediction accuracy, this work examines whether explicitly separating representation learning from task supervision enables more sample-efficient adaptation of drug-response models to patient data under strong biological domain shift. We propose a staged transfer-learning framework in which cellular and drug representations are first learned independently from large collections of unlabeled pharmacogenomic data using autoencoder-based representation learning. These representations are then aligned with drug-response labels on cell-line data and subsequently adapted to patient tumors using few-shot supervision. Through a systematic evaluation spanning in-domain, cross-dataset, and patient-level settings, we show that unsupervised pretraining provides limited benefit when source and target domains overlap substantially, but yields clear gains when adapting to patient tumors with very limited labeled data. In particular, the proposed framework achieves faster performance improvements during few-shot patient-level adaptation while maintaining comparable accuracy to single-phase baselines on standard cell-line benchmarks. Overall, these results demonstrate that learning structured and transferable representations from unlabeled molecular profiles can substantially reduce the amount of clinical supervision required for effective drug-response prediction, offering a practical pathway toward data-efficient preclinical-to-clinical translation.

Problem

• drug-response prediction
• domain shift
• sample efficiency
• precision oncology
• transfer learning

Innovation

• domain shift
• representation learning
• few-shot adaptation
• transfer learning
• drug response prediction