Identification of a predictive efficacy signature from microarray analysis of human tumor xenografts treated with anticancer compounds.

2017 How therapeutic compounds affect tumors is a complex, dynamic process. Ultimately, all of the effects, whether on the tumor cell, the vasculature, or other stromal cells, are reflected in changes in gene expression in the tumor. Using this basic premise, we have devised a system where we rapidly assess the effects of compounds in an in vivo setting to determine multiple pharmacodynamic parameters simultaneously. By treating tumor-bearing mice with known and experimental anticancer compounds, we are able to see how compounds impact the tumor in the animal, accounting for protein binding, pharmacokinetics, tissue penetration, toxicity, and on-target effects. Using multiple compounds that work by a variety of mechanisms, we have been able to develop a consensus ‘signature’ that correlates the gene expression signature from a single effective dose with antitumor efficacy following a multiple dose regimen. Furthermore, we have built a predictive model using gene expression changes to evaluate multiple compounds in a lead optimization series to predict which compounds will be active in a given model. Finally, by analyzing multiple xenograft models, we have been able to identify pathways that are affected when a model is responsive compared to models that are not responsive to the same set of compounds. It should be clear from this presentation that this type of work during the lead optimization phase of drug discovery establishes a genetic basis for identifying sensitive patient populations, and the biologic markers to detect a response.