Biological network topology features predict gene dependencies in cancer cell lines

Using recently published experimental cancer cell-line gene essentiality data, human protein-protein interaction (PPI) network data and individual cell-line genomic alteration data we have trained a range of machine learning classifiers to predict cell line specific acquired essential genes. Genetic alterations found in each individual cell line were modelled by removing protein nodes, to reflect loss of function mutations, and changing the weights of edges in each PPI to reflect gain of function mutations and gene expression changes. We find that PPI networks can be used to successfully classify human cell line specific acquired essential genes within individual cell lines and between cell lines, even across tissue types with AUC ROC scores of between 0.75 and 0.85. Our novel perturbed PPI network models further improved prediction power compared to the base PPI model and are shown to be more sensitive to genes on which the cell becomes dependent as a result of other changes. These improvements offer opportunities for personalised therapy with each individual9s cancer cell dependencies presenting a potential tailored drug target.