DCGPert-CDR: A Novel Computational Framework for Cancer Drug Response Prediction Integrating Drug, Cell Line, and Gene Perturbation Features

Introduction: The integration of cell line features and drug features in computational Cancer Drug Response (CDR) prediction methods enables a nuanced understanding of cellular responses and drug effects, which may lead to improvements in drug discovery and precision oncology. It helps identify promising drug candidates for experimental validation, avoid treatments that are unlikely to benefit a patient, and reduce unnecessary exposure to toxic drugs. Methods: In this paper, we propose DCGPert-CDR, which integrates drug structural features, cell line multi-omics data, and target gene perturbation profiles for predicting IC50 responses. The methodology involves the extraction of cell line multi-omics data, including genomics, transcriptomics, and epigenomics, together with the molecular structural features of the drug. The gene perturbation profiles are computed from transcriptional changes of the prioritized target genes before and after the drug treatment. A graph clustering approach, followed by network propagation, is applied to prioritize drug target genes. The resultant feature vectors are concatenated and fed into a prediction module, consisting of a ResNet, which predicts the IC50 values of drugs across various cancer cell lines. Results: DCGPert-CDR produces promising results when compared to similar methods, with Pearson’s correlation rp of 0.841 and Spearman’s correlation rs of 0.786 computed between predicted and actual IC50 values, while for other methods, rp was in the range of 0.768 to 0.8183 and rs was between 0.735 to 0.757. Drugs such as Foretinib, Crizotinib, Tivozanib, SNX-2112, and PHA- 665752 are found to be most sensitive after analyzing the predicted response values across various cancer cell lines. Discussion: The improved results indicate that the proposed method effectively predicts responses that closely match the known IC50 values. Case studies are conducted on 24 TCGA cancer types, also revealing sensitive drugs for each cancer type, which are corroborated with clinical evidence. Dependence on the availability of drug and cell line data, as well as the absence of real-time data validation, remains a key challenge. Conclusion: The method can reliably capture the relationship between drugs and cell lines, indicating its potential utility in predicting drug sensitivity. The method effectively identified the most sensitive drugs among individual cancer types.