Effect of different UCOE-promoter combinations in creation of engineered cell lines for the production of Factor VIII

<p>Abstract</p> <p>Background</p> <p>The most common approach used in generating cell lines for the production of therapetic proteins relies on gene amplification induced by a drug resistance gene e. g., DHFR and glutamine synthetase. Practically, this results in screening large number of clones for the one that expresses high levels of the biologic in a stable manner. The inefficiency of mammalian vector systems to express proteins in a stable manner typically involves silencing of the exogenous gene resulting from modifications such as methylation of CpG DNA sequences, histone deacetylation and chromatin condensation. The use of un-methylated CpG island fragments from housekeeping genes referred to as UCOE (ubiquitous chromatin opening elements) in plasmid vectors is now well established for increased stability of transgene expression. However, few UCOE-promoter combinations have been studied to date and in this report we have tested 14 different combinations.</p> <p>Findings</p> <p>In this report we describe studies with two different UCOEs (the 1.5 Kb human RNP fragment and the 3.2 Kb mouse RPS3 fragment) in combination with various promoters to express a large protein (B domain deleted factor VIII; BDD-FVIII) in a production cell line, BHK21. We show here that there are differences in expression of BDD-FVIII by the different UCOE-promoter combinations in both attached and serum free suspension adapted cells. In all cases, the 1.5 Kb human RNP UCOE performed better in expressing BDD-FVIII than their corresponding 3.2 Kb mouse RPS3 UCOE. Surprisingly, in certain scenarios described here, expression from a number of promoters was equivalent or higher than the commonly used and industry standard human CMV promoter.</p> <p>Conclusion</p> <p>This study indicates that certain UCOE-promoter combinations are better than others in expressing the BDD-FVIII protein in a stable manner in BHK21 cells. An empirical study such as this is required to determine the best combination of UCOE-promoter in a vector for a particular production cell line.</p

Protein expression based multimarker analysis of breast cancer samples

<p>Abstract</p> <p>Background</p> <p>Tissue microarray (TMA) data are commonly used to validate the prognostic accuracy of tumor markers. For example, breast cancer TMA data have led to the identification of several promising prognostic markers of survival time. Several studies have shown that TMA data can also be used to cluster patients into clinically distinct groups. Here we use breast cancer TMA data to cluster patients into distinct prognostic groups.</p> <p>Methods</p> <p>We apply weighted correlation network analysis (WGCNA) to TMA data consisting of 26 putative tumor biomarkers measured on 82 breast cancer patients. Based on this analysis we identify three groups of patients with low (5.4%), moderate (22%) and high (50%) mortality rates, respectively. We then develop a simple threshold rule using a subset of three markers (p53, Na-KATPase-β1, and TGF β receptor II) that can approximately define these mortality groups. We compare the results of this correlation network analysis with results from a standard Cox regression analysis.</p> <p>Results</p> <p>We find that the rule-based grouping variable (referred to as WGCNA*) is an independent predictor of survival time. While WGCNA* is based on protein measurements (TMA data), it validated in two independent Affymetrix microarray gene expression data (which measure mRNA abundance). We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.</p> <p>Conclusions</p> <p>We show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data. We identify a rule based on three tumor markers for predicting breast cancer survival outcomes.</p