Clinical drug response prediction (DRP) is fundamentally constrained by small patient cohorts, characterised by high biological heterogeneity and pronounced response imbalance. While substantial progress has been made through model-centric innovations, data-centric strategies for augmenting transcriptomic and molecular representations remain underexplored.

We introduce GExMix, a model-agnostic data augmentation framework that generates high-utility patient representations via controlled, label-aware interpolation in molecular feature space. GExMix is designed to preserve biological structure while enriching underrepresented response patterns. We systematically evaluate GExMix across three clinical cohorts, five anticancer drugs, and six predictive models, including state-of-the-art DRP architectures.

GExMix consistently outperforms existing augmentation approaches, achieving average relative improvements of 5.7% in ROC-AUC and 6.3% in PR-AUC. Notably, it reduces the performance gap between simple linear models and complex deep learning approaches, highlighting its broad applicability. Mechanistically, GExMix preserves the underlying biological manifold and enhances signal detection, recovering clinically relevant oncogenic and resistance-associated features such as HMGA2 and CD40, which are often obscured in sparse datasets.

Furthermore, GExMix enables seamless integration of auxiliary preclinical data, including cell-line models, yielding an additional 4-6% performance gain by mitigating domain shift between in vitro and in vivo systems.

Together, these results position principled, stratified data augmentation as a critical and previously underutilised dimension in precision oncology. GExMix provides a robust and generalisable framework to improve clinical DRP, particularly in data-limited settings.

Co-authors: Diyuan Lu, Ginte Kutkaite, Alexander Ohnmacht, Francesco Paolo Casale, Michael Menden