Purpose: Metastasis describes the process of cancer cells spreading from the primary tumor to other tissues and organs of the body where they form new tumors; this accounts for over 90% of cancer deaths. A crucial step in the metastatic cascade is migration. We have recently quantified the impact of current cancer treatment approaches on cancer cell migration, using bioimpedance as a readout. Here, we present computational fits for experimental data which provides mechanistic insights into the role of various chemotherapeutic and radiotherapeutic approaches on cancer metastasis.
Methods: Having recently used a commercially available Electric Cell Impedance Sensor (ECIS) to quantify the migration of various cancer cell lines following chemotherapy and following radiotherapy (using a cell irradiator, Faxitron CellRad), we applied equivalent circuits and power-law equations to model impedance data using MATLAB codes. Additionally, R codes were used to model with three different curve fit algorithms: smoothing spline, logistic model, and segmented regression.
Results: Even without data fitting, we find that the irradiated T98G cells and U87 cells (Glioblastoma, brain cancer cells) attach and migrate significantly more than non-irradiated cells in the first 20 hours post irradiation. Fits of equivalent circuit models and power-law models quantify and characterize the raw impedance data for brain cancer cells. MATLAB codes capture the increased migration of irradiated cells prior to cell death.
Conclusion: In MATLAB, the model parameters such as the power-law exponent capture the increased migration of irradiated cells prior to cell death. The R codes we now apply seem promising in providing more robust and biophysically relevant insights into metastasis which, in turn, can potentially inform urgently needed anti-metastasis strategies in cancer treatments, especially chemoradiotherapy against brain cancers.
Radiation Therapy, Radiobiology, Brain