In this paper we explore computational approaches that enable us to identify genes that have become essential in individual 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 built a range of machine learning classification models 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.nnWe found 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 individuals cancer cell dependencies presenting a potential tailored drug target.nnThe overriding motivation for predicting cancer cell line specific acquired essential genes is to provide a low-cost approach to identifying personalised cancer drug targets without the cost of exhaustive loss of function screening.