Mesenchymal stem cells (MSC) have a therapeutic potential in patients with fractures to reduce the time of healing and treat non-unions. The use of MSC to treat fractures is attractive as it would be implementing a reparative process that should be in place but occurs to be defective or protracted and MSC effects would be needed only for the repairing time that is relatively brief. However, an integrated approach to define the multiple regenerative contributions of MSC to the fracture repair process is necessary before clinical trials are initiated. In this study, using a stabilized tibia fracture mouse model, we determined the dynamic migration of transplanted MSC to the fracture site, their contributions to the repair process initiation and their role in modulating the injury-related inflammatory responses. Using MSC expressing luciferase, we determined by bioluminescence imaging that the MSC migration at the fracture site is time- and dose-dependent and, it is exclusively CXCR4-dependent. MSC improved the fracture healing affecting the callus biomechanical properties and such improvement correlated with an increase in cartilage and bone content, and changes in callus morphology as determined by micro-computed-tomography and histological studies. Transplanting CMV-Cre-R26R-LacZ-MSC, we found that MSC engrafted within the callus endosteal niche. Using MSC from BMP-2-Lac-Z mice genetically modified using a bacterial artificial chromosome system to be β-gal reporters for BMP-2 expression, we found that MSC contributed to the callus initiation by expressing BMP-2. The knowledge of the multiple MSC regenerative abilities in fracture healing will allow to design novel MSC-based therapies to treat fractures.

These data suggest that loss of spatial registration with preoperative images is gravity-dominated and of sufficient extent that attention to errors resulting from misregistration during the course of surgery is warranted.

Current mathematical models of tumor growth are limited in their clinical application because they require input data that are nearly impossible to obtain with sufficient spatial resolution in patients even at a single time point—for example, extent of vascularization, immune infiltrate, ratio of tumor-to-normal cells, or extracellular matrix status. Here we propose the use of emerging, quantitative tumor imaging methods to initialize a new generation of predictive models. In the near future, these models could be able to forecast clinical outputs, such as overall response to treatment and time to progression, which will provide opportunities for guided intervention and improved patient care.

While there is considerable data on the use of mathematical modeling to describe tumor growth and response to therapy, previous approaches are often not of the form that can be easily applied to clinical data to generate testable predictions in individual patients. Thus, there is a clear need to develop and apply clinically-relevant oncological models that are amenable to available patient data and yet retain the most salient features of response prediction. In this study we show how a biomechanical model of tumor growth can be initialized and constrained by serial patient-specific magnetic resonance imaging data, obtained at two time points early in the course of therapy (before initiation and following one cycle of therapy), to predict the response for individual patients with breast cancer undergoing neoadjuvant therapy. Using our mechanics coupled modeling approach, we are able to predict, after the first cycle of therapy, breast cancer patients that would eventually achieve a complete pathological response and those who would not, with receiver operating characteristic area under the curve (AUC) of 0.87, sensitivity of 92%, and specificity of 84%. Our approach significantly outperformed the AUCs achieved by standard (i.e., not mechanically coupled)reaction-diffusion predictive modeling (0.75), simple analysis of the tumor cellularity estimated from imaging data (0.73), and the Response Evaluation Criteria in Solid Tumors (RECIST; 0.71). Thus, we show the potential for mathematical model prediction for use as a prognostic indicator of response to therapy. The work indicates the considerable promise of image-driven biophysical modeling for predictive frameworks within therapeutic applications.