A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin.
Hugo J M MiniereErnesto A B F LimaGuillermo LorenzoDavid A HormuthSophia TyAmy BrockThomas E YankeelovPublished in: Cancer biology & therapy (2024)
Tumor heterogeneity contributes significantly to chemoresistance, a leading cause of treatment failure. To better personalize therapies, it is essential to develop tools capable of identifying and predicting intra- and inter-tumor heterogeneities. Biology-inspired mathematical models are capable of attacking this problem, but tumor heterogeneity is often overlooked in in-vivo modeling studies, while phenotypic considerations capturing spatial dynamics are not typically included in in-vitro modeling studies. We present a data assimilation-prediction pipeline with a two-phenotype model that includes a spatiotemporal component to characterize and predict the evolution of in-vitro breast cancer cells and their heterogeneous response to chemotherapy. Our model assumes that the cells can be divided into two subpopulations: surviving cells unaffected by the treatment, and irreversibly damaged cells undergoing treatment-induced death. MCF7 breast cancer cells were previously cultivated in wells for up to 1000 hours, treated with various concentrations of doxorubicin and imaged with time-resolved microscopy to record spatiotemporally-resolved cell count data. Images were used to generate cell density maps. Treatment response predictions were initialized by a training set and updated by weekly measurements. Our mathematical model successfully calibrated the spatiotemporal cell growth dynamics, achieving median [range] concordance correlation coefficients of > .99 [.88, >.99] and .73 [.58, .85] across the whole well and individual pixels, respectively. Our proposed data assimilation-prediction approach achieved values of .97 [.44, >.99] and .69 [.35, .79] for the whole well and individual pixels, respectively. Thus, our model can capture and predict the spatiotemporal dynamics of MCF7 cells treated with doxorubicin in an in-vitro setting.
Keyphrases
- breast cancer cells
- induced apoptosis
- cell cycle arrest
- single cell
- electronic health record
- drug delivery
- oxidative stress
- stem cells
- signaling pathway
- high resolution
- cancer therapy
- optical coherence tomography
- deep learning
- single molecule
- mesenchymal stem cells
- radiation therapy
- newly diagnosed
- machine learning
- replacement therapy
- high glucose
- pi k akt
- diabetic rats
- drug induced
- case control