Background: The therapeutic control of cancer progression critically depends on the responses of the individual cells that constitute the entire tumor mass. A key reason for poor therapeutic responses is the emergence of drug resistance due to intracellular and extracellular cellular heterogeneity that seriously impair the efficacy of chemotherapy and radiation therapy.

Method: We have developed a hybrid multiscale computational model of breast cancer growth and treatment that incorporates individual cell-cycle dynamics and the effects of a changing microenvironment through the oxygen dynamics to consider optimum treatment protocols for breast cancer. In this model, the internal cell-cycle dynamics determine the growth strategy of the cancer cells, making the model more biologically relevant. This also helps classify the cancer cells according to their cell-cycle state and to analyse the effect of various cell-cycle-dependent chemotherapeutic drugs. The incorporation of oxygen dynamics within this hybrid model allows study of the effects of the microenvironment in cell-cycle regulation and drug resistance due to a hypoxia-induced quiescence (G0/G1 arrest) of the cancer cells. Furthermore, the effects of radiation therapy are included in the model using a modified linear quadratic model for the radiation damage, incorporating the effects of hypoxia and cell-cycle in determining the cell based radiosensitivity. The model is then used to study various combinations of multiple doses of cell-cycle-dependent chemotherapies and radiation therapy given that radiation may work better by the partial synchronisation of cells in a radiosensitive phase of the cell-cycle.

Results: Using this computational model, we have shown that the cytotoxic effect of combination therapy is very much dependent on the timing of drug delivery, the time delay between the doses of chemotherapeutic drug and cell-cycle heterogeneity. The effectiveness of the drugs is also dependent on the spatial distribution of the tumor cell mass as this greatly influences the surrounding tumor microenvironment and drug distribution. After each dose of the chemotherapeutic drug, the changing spatial dynamics of the tumor cells redistributes the tissue oxygen and subsequently delivered drug doses within the tumor. Moreover, the cell-cycle phase-based chemotherapy also helps in favor of radiation therapy through the partial synchronisation of cells in the most radiosensitive phase of the cell-cycle.

Conclusions: The incorporation of the spatial distribution of the breast cancer cells along with the internal and external cellular heterogeneity is critical to optimising scheduling strategies for chemotherapy and combined chemotherapy and radiation therapy. Overall, the computational simulation results highlight the potential usefulness of the model in developing patient-specific optimal treatment strategies.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P5-05-02.