ProTargetMiner one step further : Deep comparative proteomics of Dying vs. Surviving cancer cells treated with anticancer compounds

Abstract: Cancer is a leading cause of mortality worldwide, responsible for nearly one in six deaths. Thus, there is a need for a greater understanding of cancer for the development of novel therapeutics. This master thesis project aims to compare the proteome signatures between dying and surviving cancer cells treated with diverse anticancer drugs. The first aim is to investigate if drug targets behave similarly and have the same sign (up- or down-regulation) in dying versus surviving cells. The second aim is to validate that combining the dying cancer cell’s proteome with the surviving cell’s can help improve drug target rankings for anticancer treatments. The third aim is to identify proteins and pathways involved in life and death decisions by comparing dying and surviving states in response to the anticancer drugs in different cell lines. First, we demonstrate that drug target behaviour in dying versus surviving cells is almost identical for nine diverse anticancer compounds with a correlation of 0.93. To identify drug targets, orthogonal partial least squares-discriminant analysis (OPLS-DA) modelling was performed to contrast the proteome signature of one anticancer drug against all other drugs and rank the proteins based on the magnitude of the model’s predictive component. There were occasions when the dying cells gave better rankings than the surviving ones. In some cases, the best target rankings were obtained when combining the data from both surviving and dying cells. To identify proteins and pathways involved in life and death decisions, OPLS-DA modelling contrasting the two states was performed, and heatmaps and scatterplots of dying and surviving log2 fold changes were made. As a result, several pathways involved in cell survival and cell death were identified. In addition, at least six proteins consistently differentially regulated between the surviving and dying cells were identified. Such proteins can be considered as putative survival (resistance) or sensitivity biomarkers and serve as potential drug targets for the development of novel anticancer agents.