Anti-AKT1 monoclonal antibody, clone 3F22

Background. EZH2 acts as an oncogene through canonical pathway EZH2/H3K27Me3 and uncanonical pathway pAkt1/pS21EZH2 in many solid tumors including ovarian cancer. However, the clinical value of EZH2/H3K27Me3 and pAkt1/pS21EZH2 remain unclear. In the current study, we aim to investigate the correlation between these two pathways to clinical-pathological parameters and prognosis. Methods. EZH2, H3K27Me3, pAkt1 and pS21EZH2 expression were evaluated by tissue micro-array and immunohistochemistry in a cohort of ovarian cancer patients. The results were analyzed based on clinical characteristics and survival outcomes. Results. EZH2, H3K27Me3, pAktl and pS21EZH2 were universally expressed in ovarian cancer specimens with a positive expression rate of 81.54% (53/65), 88.89% (48/54), 63.07% (41/65) and 75.38% (49/65). EZH2-pS21EZH2 (Spearman r = 0.580, P < 0.0001) and pS21EZH2-pAkt1 (Spearman r = 0.546, P < 0.0001) were closely correlated while EZH2- H3K27Me3 were less closely correlated (Spearman r = 0.307, P = 0.002). Low pS21EZH2 associated with better chemotherapy response (OR = 0.184; 95% CI [0.052-0.647], P = 0.008) according to logistic regression with an area under the curve of 0.789 (specificity 89.36%, sensitivity 68.42%) by ROC analysis and predicted improved progression-free survival (HR = 0.453; 95% CI [0.229-0.895], P = 0.023) as indicated by multivariate cox regression. A combination of EZH2(low)/H3K27Me3(low )status predicted better chemotherapy response (OR = 0.110; 95% CI [0.013-0.906], P = 0.040) and better progression-free survival (HR = 0.388; 95% CI [0.164-0.917], P = 0.031). The results suggested that EZH2/H3K27Me3 and pEZH2 predicted chemotherapy response and progression-free survival in ovarian cancer.

Background Identification of key regulator/s in ovarian cancer (OC) network is important for potential drug target and prevention from this cancer. This study proposes a method to identify the key regulators of this network and their importance. Methods The protein-protein interaction (PPI) network of ovarian cancer (OC) is constructed from curated 6 hundred genes from standard six important ovarian cancer databases (some of the genes are experimentally verified). We proposed a method to identify key regulators (KRs) from the complex ovarian cancer network based on the tracing of backbone hubs, which participate at all levels of organization, characterized by Newmann-Grivan community finding method. Knockout experiment, constant Potts model and survival analysis are done to characterize the importance of the key regulators in regulating the network. Results The PPI network of ovarian cancer is found to obey hierarchical scale free features organized by topology of heterogeneous modules coordinated by diverse leading hubs. The network and modular structures are devised by fractal rules with the absence of centrality-lethality rule, to enhance the efficiency of signal processing in the network and constituting loosely connected modules. Within the framework of network theory, we device a method to identify few key regulators (KRs) from a huge number of leading hubs, that are deeply rooted in the network, serve as backbones of it and key regulators from grassroots level to complete network structure. Using this method we could able to identify five key regulators, namely, AKT1, KRAS, EPCAM, CD44 and MCAM, out of which AKT1 plays central role in two ways, first it serves as main regulator of ovarian cancer network and second serves as key cross-talk agent of other key regulators, but exhibits disassortive property. The regulating capability of AKT1 is found to be highest and that of MCAM is lowest. Conclusions The popularities of these key hubs change in an unpredictable way at different levels of organization and absence of these hubs cause massive amount of wiring energy/rewiring energy that propagate over all the network. The network compactness is found to increase as one goes from top level to bottom level of the network organization.