Reductionist biology has yielded tremendous insight into the basis of biochemistry and genetic disease. However, the remarkable failure of reductionist biology to explain complex problems, especially cancer, has led to the development of systems biology. The vast complexity of biological systems remains the most difficult problem in biology today. In order to understand this complexity, we need tools to massively multiplex measurements of a signaling network. Therefore, we developed lysate microarray technology to fill this need. In this work, we discuss three ways in which lysate microarrays were applied to human disease. In the first work, we discuss a key stage in malaria development. The liver-stage malaria parasite represents a promising target for intervention, and we present the first use of lysate microarray technology as a screening tool for host-parasite interactions in an infectious disease. We identified three cancer-related pathways that are modified in malaria infection, and studied the p53 pathway in depth. Our finding that the parasite downregulates p53 and that treatment with Nutlin-3 strongly decreases parasite load may lead to the development of a prophylactic malaria vaccine. In the second work, we began by screening drug combinations and varying dosing schedule in triple-negative breast cancers (TNBCs). We systematically explored stimulation space and collected a large lysate microarray dataset, which was used for statistical analysis. We identified a sensitization effect when a growth factor signaling inhibitor was presented before a genotoxic agent. This sensitization was generalizable among a subset of TNBCs and may generally be important for cancers driven by growth factor signaling, as we found the effect extends to nonTNBC cancers. We hope this data will be useful in guiding cancer treatment strategies in patients. In the third work, we study the changing role of the DNA Damage Response (DDR) as a cell line evolves towards cancer. We used the MCF10A progression series and studied how these cell lines respond to genotoxic agents. We identified differences in cell fates after treatment, and collected a large lysate microarray dataset for statistical analysis. Early analysis of the data indicates gross rewiring within the DDR between the MCF10A cell lines.

Background: The biological mechanisms underlying early-and advanced-stage epithelial ovarian cancers (EOCs) are still poorly understood. This study explored kinase-driven metabolic signalling in early and advanced EOCs, and its role in tumour progression and response to carboplatin-paclitaxel treatment. Methods: Tumour epithelia were isolated from two independent sets of primary EOC (n-72 and 30 for the discovery and the validation sets, respectively) via laser capture microdissection. Reverse phase protein microarrays were used to broadly profile the kinase-driven metabolic signalling of EOC with particular emphasis on the LBK1-AMPK and AKT-mTOR axes. Signalling activation was compared between early and advanced lesions, and carboplatin-paclitaxel-sensitive and -resistant tumours. Results: Advanced EOCs were characterised by a heterogeneous kinase-driven metabolic signature and decreased phosphorylation of the AMPK-AKT-mTOR axis compared to early EOC (P<0.05 for AMPK alpha T172, AMPK alpha 1 S485, AMPK beta 1 S108, AKT S473 and T308, mTOR S2448, p70S6 S371, 4EBP1 S65, GSK-3 alpha/beta S21/9, FOXO1 T24/FOXO3 T32, and FOXO1 S256). Advanced tumours with low relative activation of the metabolic signature and increased FOXO1 T24/FOXO3 T32 phosphorylation (P=0.041) were associated with carboplatin-paclitaxel resistance. Conclusions: If validated in a larger cohort of patients, the decreased AMPK-AKT-mTOR activation and phosphorylation of FOXO1 T24/FOXO3 T32 may help identify carboplatin-paclitaxel-resistant EOC patients.