Explainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature Attributions

Fosca Giannotti, Riccardo Guidotti, Dino Pedreschi, Martino Ciaperoni, Margherita Lalli, Simone Piaggesi, Martina Varisco, Francesco Carli, Francesco Raimondi

arXiv · PDF · Open in the Atlas

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Abstract

Predicting cancer drug response from transcriptomic profiles is a cornerstone of precision oncology, yet the scientific value of machine learning models hinges not solely on predictive accuracy, but also on their capacity to generate reliable biological insights. Current explainability approaches in this setting are computationally costly, lack robustness, and reduce complex drug response to univariate gene importance scores, overlooking the coordinated gene activity that drives sensitivity and resistance. In this work, we present ILLUME+, a scalable post-hoc explainability framework that moves beyond single-gene assessments to capture multiple, complementary forms of explanation. Integrated into our end-to-end pipeline, ILLUME+ produces more stable gene importance scores than existing baselines, recovers established drug-gene associations and mechanisms of action, and enables AIassisted hypothesis generation to uncover novel interaction-driven molecular signals in cancer biology. • Computing methodologies → Supervised learning; Classification; • Applied computing → Bioinformatics.

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