Monday, March 23, 2015

Spatial model predicts dispersal and cell turnover cause reduced intra-tumor heterogeneity

, , , , ,

Tuesday, March 17, 2015

The overshoot and phenotypic equilibrium in characterizing cancer dynamics of reversible phenotypic plasticity

The overshoot and phenotypic equilibrium in characterizing cancer dynamics of reversible phenotypic plasticity

The paradigm of phenotypic plasticity indicates reversible relations of different cancer cell phenotypes, which challenges the cellular hierarchy proposed by the conventional cancer stem cell (CSC) theory. Since the validity of the reversible model versus the hierarchical model of cancer cells is still experimentally debated, it is worthwhile to theoretically explore the dynamic behavior characterizing the reversible model in comparison of the hierarchical model. By comparing the two models in predicting the cell-state dynamics observed in biological experiments, our results imply that the reversible model has advantages over the hierarchical model in predicting both long-term stable and short-term transient dynamics of cancer cells. In particular, it is found that i) the reversible model can predict the phenotypic equilibrium better than the hierarchical model, namely, the stability of the phenotypic mixture of cancer cells is more rooted in the reversible model; ii) the reversible model can perform various types of overshoot behavior, whereas the hierarchical model can never predict the overshoot of CSCs proportion. These also indicate that the phenotypic equilibrium and overshoot can be good candidates to characterize the models with the reversible phenotypic plasticity.
http://arxiv.org/abs/1503.04558

Tuesday, March 3, 2015

Phase i trials in melanoma: A framework to translate preclinical findings to the clinic

Phase i trials in melanoma: A framework to translate preclinical findings to the clinic

The combination of chemotherapy and an AKT inhibitor in patients with metastatic solid tumors including melanomas was tested in a recently completed phase 1 clinical trial. Our experiments showed that such regimens differentially induce autophagy in melanoma cells and autophagy modulates the response to treatment. Motivated by these observations, we formulated a mathematical model comprised of a system of ordinary differential equations that explains the dynamics of the response of melanoma cells to different mono and combination therapies. Model parameters were estimated using an optimization algorithm that minimizes differences between predicted cell populations and experimentally measured cell numbers. The model predicts that the combination therapy treatment protocol used in the trial is effective in short term tumor control but that the treatment will eventually fail, although smarter schedules can be applied to extend response. To move this model forward into a more clinically relevant setting, we implemented a phase i trial (a virtual/imaginary yet informed clinical trial), where a genetic algorithm was used to generate a cohort of virtual patients that captured the diversity of disease response observed in a comparable clinical trial. Simulated clinical trials with the cohort and sensitivity analysis defined parameters that discriminated virtual patients having more favorable versus less favorable outcomes. These analyses established the relevance of selecting patients based on rates of tumor growth and autophagic flux. Finally, the model predicts optimal therapeutic approaches across all virtual patients, laying the foundation for phase i-informed clinical melanoma trials. The specific melanoma model developed here is just one example of the much broader potential of the phase i framework, which can be applied to almost any parameterized cancer model.