An Ensemble Cascade Forest‐Based Framework for Multi‐Omics Drug Response and Synergy Prediction

Abstract

The obscure drug response continues to be a limiting factor for accurate cures for cancer. Next generation sequencing technologies have propelled the pharmacogenomic studies with characterized large panels of cancer cell line at multi‐omics level. However, the sufficient integration of the multi‐omics data and the efficient prediction for drug response and synergy still remain a challenge. To address these problems, ECFD is designed, an ensemble cascade forest‐based framework that predicts drug response and synergy using five types of omics data. Experimental results show the significant advantages of the ECFD model over existing models. The best integration of feature extraction is determined and the superiorities of robust stability in the face of new and small samples are highlighted. In addition, the methodological framework highlights the explainability of the model, the mechanisms of drug resistance and drug combination treatment strategies based on explainable analyses and biological networks. In sum, ECFD may facilitate the evaluation of drug response and speculation of potential synergy therapies in personalized and precision treatment.