What question did this study set out to answer?

To develop and validate a model for predicting cancer cell drug responses using biological knowledge and multi-omics data.

March 19, 2026Open Access

BKDRP: a biological knowledge-driven approach for drug response prediction using multi-omics data in cancer cell lines

Key Points

To develop and validate a model for predicting cancer cell drug responses using biological knowledge and multi-omics data.
Developed BKDRP incorporating biological information and molecular fingerprints.
Evaluated on the GDSC dataset using multi-omics data: gene expression, protein expression, mutation, copy number variation.
Conducted four experiments to assess generalizability to unknown drug-cell line combinations and unseen cancer types.
Assessed performance using AUC and AUPR metrics.
Achieved an AUC of 0.8845 in drug response prediction, outperforming traditional models.
Demonstrated robust performance across various cancer types and treatment scenarios.
Identified key biomarkers and proteins associated with lung adenocarcinoma, consistent with existing literature.

Abstract

Abstract Background: Cancer heterogeneity results in patients with the same diagnosis responding differently to drugs, making treatments extremely challenging. Advances in computational power enable personalized treatments that suppress tumors and extend patient survival. Therefore, accurate prediction of cancer cell response to a particular medication is of utmost importance. Current deep learning-based models have achieved impressive accuracy, but they often function as a “black box” and cannot explain the reason for the prediction. To address this limitation, we develop a deep learning-based model, BKDRP, which incorporates prior biological information into the architecture, along with molecular fingerprints of drugs, while embedding biological priors into its architecture. Specifically, it incorporates the fact that genes encode proteins that combine to form protein complexes, which in turn regulate biological pathways, ultimately targeted by drugs. Results: We evaluate BKDRP on the GDSC (Genomics of Drug Sensitivity and Cancer) cell line dataset using multi-omics gene expression, protein expression, mutation, and copy number variation. Four rigorous experiments have been conducted to test the model’s generalizability: prediction of unknown drug–cell line responses, responses to unseen drugs (LODO: Leave-One-Drug-Out), responses to unseen cell lines (LOCLO: Leave-On-Cell-Line-Out), and responses across unseen cancer types (LOCO: Leave-One-Cancer-Out). The performance of the proposed method and baseline algorithms is assessed using two metrics: Area Under the ROC Curve (AUC) and Area Under the Precision-Recall Curve (AUPR). The experimental results demonstrate that BKDRP performs well in different evaluation techniques. Notably, BKDRP has achieved an AUC of 0.8845, surpassing traditional machine learning and deep learning approaches and demonstrating robustness in handling biological variability across cancer types. A case study of lung adenocarcinoma (LUAD) highlights known biomarkers ( KRAS , EGFR , STK11 ), key proteins (SOCS1, HSPA8, SMC3), and drugs (Erlotinib, Palbociclib) that are consistent with the literature. Conclusions: In conclusion, BKDRP presents a novel biological knowledge-driven deep neural network model for cancer drug response prediction that shows strong predictive accuracy and interpretability. By integrating multi-omics data and incorporating domain knowledge, BKDRP has the strong potential for applications in biomarker discovery and the advancement of personalized oncology.

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