Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be expressed as mathematical models. To explore this further, quantitative analysis of the most classical of these were performed. The models were assessed against data from two in vivo experimental systems: an ectopic syngeneic tumor (Lewis lung carcinoma) and an orthotopically xenografted human breast carcinoma. The goals were threefold: 1) to determine a statistical model for description of the measurement error, 2) to establish the descriptive power of each model, using several goodness-of-fit metrics and a study of parametric identifiability, and 3) to assess the models' ability to forecast future tumor growth. The models included in the study comprised the exponential, exponential-linear, power law, Gompertz, logistic, generalized logistic, von Bertalanffy and a model with dynamic carrying capacity. For the breast data, the dynamics were best captured by the Gompertz and exponential-linear models. The latter also exhibited the highest predictive power, with excellent prediction scores (≥80%) extending out as far as 12 days in the future. For the lung data, the Gompertz and power law models provided the most parsimonious and parametrically identifiable description. However, not one of the models was able to achieve a substantial prediction rate (≥70%) beyond the next day data point. In this context, adjunction of a priori information on the parameter distribution led to considerable improvement. For instance, forecast success rates went from 14.9% to 62.7% when using the power law model to predict the full future tumor growth curves, using just three data points. These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations, but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinic.

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It is here demonstrated that the set of gene expressions underlying the angiogenic balance in tissues can be molecularly reset en masse by a single protein. Using genome-wide expression profiling, coupled with RT-PCR and phosphorylation analysis, we show that the endogenous angiogenesis inhibitor endostatin downregulates many signaling pathways in human microvascular endothelium associated with proangiogenic activity. Simultaneously, endostatin is found to upregulate many antiangiogenic genes. The result is a unique alignment between the direction of gene regulation and angiogenic status. Profiling further reveals the regulation of genes not heretofore associated with angiogenesis. Our analysis of coregulated genes shows complex interpathway communications in an intricate signaling network that both recapitulates and extends on current understanding of the angiogenic process. More generally, insights into the nature of genetic networking from the cell biologic and therapeutic perspectives are revealed.

Her-2-neu expression appears to increase with progression to androgen independence. Thus, therapeutic targeting of this tyrosine kinase in prostate cancer may be warranted.