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

2026-07-01

AI Summary

Existing explainable AI methods for predicting cancer drug response suffer from high computational costs, limited robustness, and an exclusive focus on single-gene importance scores, which hinders the elucidation of synergistic gene network mechanisms. To address these limitations, this work proposes ILLUME+, a novel framework that, for the first time, integrates multiple complementary forms of explanation into an end-to-end pipeline. ILLUME+ combines transcriptome data–driven machine learning models with post-hoc interpretability algorithms and incorporates pathway analysis alongside mechanistic validation. The resulting approach yields more stable, multi-gene interaction–based explanations that not only recapitulate known drug–gene associations and underlying mechanisms but also uncover novel cooperative driver signals, thereby enhancing biological interpretability while maintaining scalability.

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 AI-assisted hypothesis generation to uncover novel interaction-driven molecular signals in cancer biology.

Problem

• Explainable AI
• Cancer Drug Response
• Transcriptomic Profiles
• Gene Interaction
• Precision Oncology

Innovation

• Explainable AI
• Drug Response Prediction
• Transcriptomic Profiles
• Gene Interaction
• ILLUME+