A novel explainable AI for revealing determinants of cancer drug response through integrative multi-omics analysis

Cancer drug response rates differ across patients and cell lines; however, many current computational prediction models still function as uninterpretable black boxes, offering limited insight into why a given treatment works well for some patients or cell lines but poorly for others.

Here, we introduce an interpretable cancer drug response prediction framework that leverages multi-omics data from the Genomics of Drug Sensitivity in Cancer 2 (GDSC2) resource, including genomics, transcriptomics, and proteomics, where available, together with explicit chemical drug descriptors derived from SMILES and InChI representations. We use a Modified Neighbor-Joining Algorithm (MNJA) to generate topology-aware gene-sequence trees. Combined multi-omics and drug features are summarized into high-level deep descriptors via a decimal-scaled GoogLeNet (DS-GoogLeNet), together with lightweight handcrafted features. A Smoluchowski Kookaburra Optimization Algorithm (SKOA) then selects informative multimodal features, which are used to classify the sensitivity or resistance of each cell line-drug pair with an explainable Aranda Graph Attention Network (EA-GAT).

By analyzing model behavior using SHAP-based feature attributions and subsequently subjecting SHAP-ranked genes to pathway enrichment analysis, we highlight the recurrent involvement of the PI3K/AKT/mTOR pathway and related downstream signaling cascades in drug response. Under leakage-safe stratified 10-fold cross-validation on 2614 GDSC2 cell line-drug pairs, the framework attains an accuracy of 95.87% and an F1-score of 95.87%, with an area under the receiver operating characteristic curve (AUROC) of 0.957 and an area under the precision-recall curve (AUPRC) of 0.946.

Overall, the framework appears to predict drug response accurately while also supporting biologically meaningful interpretation, making it a useful computational tool for hypothesis generation and biomarker-focused investigation in oncology.