Network-Based Molecular Features Selection to Predict the Drug Synergy in Cancer Cells

Abstract

Identifying synergistic drug combinations in cancer treatment is challenging due to the complex molecular circuitry of cancer and the exponentially increasing number of drugs. Therefore, computational approaches for predicting drug synergy are crucial in guiding experimental efforts toward finding rational combination therapies. This research selects the molecular features of cancer cells with a diffusion network-based approach. Additionally, a model is developed using non-linear regression algorithms, namely Random Forest, Extremely Randomized Tree, and XGBoost, to predict the synergy score of drug combinations against the selected cancer cell features. The data used are drug combination screening data and cancer cell molecules provided by AstraZeneca-Sanger DREAM Challenge. The feature selection results demonstrate the relevance of cancer cell molecular features selected by the diffusion network. The prediction results indicate that the Random Forest algorithm shows a good correlation value of 0.570 in the model with a small dataset. In contrast, for the model with an instance or row size larger than the number of features or columns, the XGBoost algorithm achieves a good correlation value of 0.932.
 INDEX TERMS cancer, drug combination, drug synergy, network diffusion kernel, non-linear regression.