Systems analysis of apoptosis protein expression allows the case-specific prediction of cell death responsiveness of melanoma cells.

Many cancer entities and their associated cell line models are highly heterogeneous in their responsiveness to apoptosis inducers and, despite a detailed understanding of the underlying signaling networks, cell death susceptibility currently cannot be predicted reliably from protein expression profiles. Here, we demonstrate that an integration of quantitative apoptosis protein expression data with pathway knowledge can predict the cell death responsiveness of melanoma cell lines. By a total of 612 measurements, we determined the absolute expression (nM) of 17 core apoptosis regulators in a panel of 11 melanoma cell lines, and enriched these data with systems-level information on apoptosis pathway topology. By applying multivariate statistical analysis and multi-dimensional pattern recognition algorithms, the responsiveness of individual cell lines to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) or dacarbazine (DTIC) could be predicted with very high accuracy (91 and 82% correct predictions), and the most effective treatment option for individual cell lines could be pre-determined in silico. In contrast, cell death responsiveness was poorly predicted when not taking knowledge on protein-protein interactions into account (55 and 36% correct predictions). We also generated mathematical predictions on whether anti-apoptotic Bcl-2 family members or x-linked inhibitor of apoptosis protein (XIAP) can be targeted to enhance TRAIL responsiveness in individual cell lines. Subsequent experiments, making use of pharmacological Bcl-2/Bcl-xL inhibition or siRNA-based XIAP depletion, confirmed the accuracy of these predictions. We therefore demonstrate that cell death responsiveness to TRAIL or DTIC can be predicted reliably in a large number of melanoma cell lines when investigating expression patterns of apoptosis regulators in the context of their network-level interplay. The capacity to predict responsiveness at the cellular level may contribute to personalizing anti-cancer treatments in the future

Experiences in applying a general modelling methodology

During a four month consultancy project for a company with a complex multi-functional decision making problem different methodologies were used. The systems audit methodology and a five stage mathematical modelling methodology were considered. The company problem was initially tackled using the systems audit approach but difficulties arose because of the compiled nature of the knowledge. The five stages of the modelling approach were then found helpful in developing a computer model for the quantitative aspects of the problem, but an approach based on expert systems ideas was found more useful for the qualitative aspects. Reviews with the company confirmed that the approaches had been successful in structuring the problem and developing a consistent approach to the decisions, indicating that the methodologies may be beneficial in other practical situations, as well as in teaching.mathematical modelling decision making expert systems modelling methodologies

Synthesis of novel spin-labeled podophyllotoxin derivatives as potential antineoplastic agents: Part XXV

A series of novel spin-labeled 4β-[(4-substituted)-1,2,3-triazol-1-yl]podophyllotoxin derivatives (17a–h) were firstly designed and synthesized with significant regioselectivity by employing Cu(I) catalyzed click approach, and evaluated for cytotoxicity against four human tumor cell lines (A-549, DU145, KB, and KBvin). Among them, compound 17h displayed the highest cytotoxic activity against the tumor cell lines tested. Significantly, compound 17h showed superior cytotoxic activity compared with etoposide (IC(50) 6.30 to>10 μM), a clinically available anticancer drug. Significant activity toward the drug resistant KBvin cell line revealed promising future for compound 17h as a new generation of epipodophyllotoxin-derived antitumor clinical trial candidate