Jared A. Weis

Regenerative effects of transplanted mesenchymal stem cells in fracture healing.

Mesenchymal stem cells (MSC) have a therapeutic potential in patients with fractures to reduce the time of healing and treat nonunions. The use of MSC to treat fractures is attractive for several reasons. First, MSCs would be implementing conventional reparative process that seems to be defective or protracted. Secondly, the effects of MSCs treatment would be needed only for relatively brief duration of reparation. 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‐Lac Z‐MSC, we found that MSCs engrafted within the callus endosteal niche. Using MSCs from BMP‐2‐Lac Z mice genetically modified using a bacterial artificial chromosome system to be β‐gal reporters for bone morphogenic protein 2 (BMP‐2) expression, we found that MSCs contributed to the callus initiation by expressing BMP‐2. The knowledge of the multiple MSC regenerative abilities in fracture healing will allow design of novel MSC‐based therapies to treat fractures. STEM CELLS 2009;27:1887–1898

Role of mesenchymal stem cells in regenerative medicine: application to bone and cartilage repair

Background: Mesenchymal stem cells (MSC) are multipotent cells with the ability to differentiate into mesenchyme-derived cells including osteoblasts and chondrocytes. Objective: To provide an overview and expert opinion on the in vivo ability of MSC to home into tissues, their regenerative properties and potential applications for cell-based therapies to treat bone and cartilage disorders. Methods: Data sources including the PubMed database, abstract booklets and conference proceedings were searched for publications pertinent to MSC and their properties with emphasis on the in vivo studies and clinical use in cartilage and bone regeneration and repair. The search included the most current information possible. Conclusion: MSC can migrate to injured tissues and some of their reparative properties are mediated by paracrine mechanisms including their immunomodulatory actions. MSC possess a critical potential in regenerative medicine for the treatment of skeletal diseases, such as osteoarthritis or fracture healing failure, where treatments are partially effective or palliative.

TGF-β signaling is essential for joint morphogenesis

Despite its clinical significance, joint morphogenesis is still an obscure process. In this study, we determine the role of transforming growth factor β (TGF-β) signaling in mice lacking the TGF-β type II receptor gene (Tgfbr2) in their limbs (Tgfbr2PRX-1KO). In Tgfbr2PRX-1KO mice, the loss of TGF-β responsiveness resulted in the absence of interphalangeal joints. The Tgfbr2Prx1KO joint phenotype is similar to that in patients with symphalangism (SYM1-OMIM185800). By generating a Tgfbr2–green fluorescent protein–β–GEO–bacterial artificial chromosome β-galactosidase reporter transgenic mouse and by in situ hybridization and immunofluorescence, we determined that Tgfbr2 is highly and specifically expressed in developing joints. We demonstrated that in Tgfbr2PRX-1KO mice, the failure of joint interzone development resulted from an aberrant persistence of differentiated chondrocytes and failure of Jagged-1 expression. We found that TGF-β receptor II signaling regulates Noggin, Wnt9a, and growth and differentiation factor-5 joint morphogenic gene expressions. In Tgfbr2PRX-1KO growth plates adjacent to interphalangeal joints, Indian hedgehog expression is increased, whereas Collagen 10 expression decreased. We propose a model for joint development in which TGF-β signaling represents a means of entry to initiate the process.

Mesenchymal Stem Cells Expressing Insulin‐like Growth Factor‐I (MSCIGF) Promote Fracture Healing and Restore New Bone Formation in Irs1 Knockout Mice: Analyses of MSCIGF Autocrine and Paracrine Regenerative Effects

Failures of fracture repair (nonunions) occur in 10% of all fractures. The use of mesenchymal stem cells (MSC) in tissue regeneration appears to be rationale, safe, and feasible. The contributions of MSC to the reparative process can occur through autocrine and paracrine effects. The primary objective of this study is to find a novel mean, by transplanting primary cultures of bone marrow‐derived MSCs expressing insulin‐like growth factor‐I (MSCIGF), to promote these seed‐and‐soil actions of MSC to fully implement their regenerative abilities in fracture repair and nonunions. MSCIGF or traceable MSCIGF‐Lac‐Z were transplanted into wild‐type or insulin‐receptor‐substrate knockout (Irs1−/−) mice with a stabilized tibia fracture. Healing was assessed using biomechanical testing, microcomputed tomography (μCT), and histological analyses. We found that systemically transplanted MSCIGF through autocrine and paracrine actions improved the fracture mechanical strength and increased new bone content while accelerating mineralization. We determined that IGF‐I adapted the response of transplanted MSCIGF to promote their differentiation into osteoblasts. In vitro and in vivo studies showed that IGF‐I‐induced osteoglastogenesis in MSCs was dependent of an intact IRS1‐PI3K signaling. Furthermore, using Irs1−/− mice as a nonunion fracture model through altered IGF signaling, we demonstrated that the autocrine effect of IGF‐I on MSC restored the fracture new bone formation and promoted the occurrence of a well‐organized callus that bridged the gap. A callus that was basically absent in Irs1−/− left untransplanted or transplanted with MSCs. We provided evidence of effects and mechanisms for transplanted MSCIGF in fracture repair and potentially to treat nonunions. STEM CELLS 2011;29:1537–1548

Clinically relevant modeling of tumor growth and treatment response.

Noninvasive imaging technologies can help create patient-specific mathematical models to predict tumor growth. 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.

Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction–Diffusion Model

Although there are 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 oncologic 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 pathologic 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 (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.

Near Real-Time Computer Assisted Surgery for Brain Shift Correction Using Biomechanical Models

Conventional image-guided neurosurgery relies on preoperative images to provide surgical navigational information and visualization. However, these images are no longer accurate once the skull has been opened and brain shift occurs. To account for changes in the shape of the brain caused by mechanical (e.g., gravity-induced deformations) and physiological effects (e.g., hyperosmotic drug-induced shrinking, or edema-induced swelling), updated images of the brain must be provided to the neuronavigation system in a timely manner for practical use in the operating room. In this paper, a novel preoperative and intraoperative computational processing pipeline for near real-time brain shift correction in the operating room was developed to automate and simplify the processing steps. Preoperatively, a computer model of the patients brain with a subsequent atlas of potential deformations due to surgery is generated from diagnostic image volumes. In the case of interim gross changes between diagnosis, and surgery when reimaging is necessary, our preoperative pipeline can be generated within one day of surgery. Intraoperatively, sparse data measuring the cortical brain surface is collected using an optically tracked portable laser range scanner. These data are then used to guide an inverse modeling framework whereby full volumetric brain deformations are reconstructed from precomputed atlas solutions to rapidly match intraoperative cortical surface shift measurements. Once complete, the volumetric displacement field is used to update, i.e., deform, preoperative brain images to their intraoperative shifted state. In this paper, five surgical cases were analyzed with respect to the computational pipeline and workflow timing. With respect to postcortical surface data acquisition, the approximate execution time was 4.5 min. The total update process which included positioning the scanner, data acquisition, inverse model processing, and image deforming was ~ 11-13 min. In addition, easily implemented hardware, software, and workflow processes were identified for improved performance in the near future.

Current and emerging quantitative magnetic resonance imaging methods for assessing and predicting the response of breast cancer to neoadjuvant therapy

Reliable early assessment of breast cancer response to neoadjuvant therapy (NAT) would provide considerable benefit to patient care and ongoing research efforts, and demand for accurate and noninvasive early-response biomarkers is likely to increase. Response assessment techniques derived from quantitative magnetic resonance imaging (MRI) hold great potential for integration into treatment algorithms and clinical trials. Quantitative MRI techniques already available for assessing breast cancer response to neoadjuvant therapy include lesion size measurement, dynamic contrast-enhanced MRI, diffusion-weighted MRI, and proton magnetic resonance spectroscopy. Emerging yet promising techniques include magnetization transfer MRI, chemical exchange saturation transfer MRI, magnetic resonance elastography, and hyperpolarized MR. Translating and incorporating these techniques into the clinical setting will require close attention to statistical validation methods, standardization and reproducibility of technique, and scanning protocol design.

A Finite Element Inverse Analysis to Assess Functional Improvement during the Fracture Healing Process

Assessment of the restoration of load-bearing function is the central goal in the study of fracture healing process. During the fracture healing, two critical aspects affect its analysis: (1) material properties of the callus components, and (2) the spatio-temporal architecture of the callus with respect to cartilage and new bone formation. In this study, an inverse problem methodology is used which takes into account both features and yields material property estimates that can analyze the healing changes. Six stabilized fractured mouse tibias are obtained at two time points during the most active phase of the healing process, respectively 10 days (n=3), and 14 days (n=3) after fracture. Under the same displacement conditions, the inverse procedure estimations of the callus material properties are generated and compared to other fracture healing metrics. The FEA estimated property is the only metric shown to be statistically significant (p=0.0194) in detecting the changes in the stiffness that occur during the healing time points. In addition, simulation studies regarding sensitivity to initial guess and noise are presented; as well as the influence of callus architecture on the FEA estimated material property metric. The finite element model inverse analysis developed can be used to determine the effects of genetics or therapeutic manipulations on fracture healing in rodents.

Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy.

The use of quantitative medical imaging data to initialize and constrain mechanistic mathematical models of tumor growth has demonstrated a compelling strategy for predicting therapeutic response. More specifically, we have demonstrated a data-driven framework for prediction of residual tumor burden following neoadjuvant therapy in breast cancer that uses a biophysical mathematical model combining reaction-diffusion growth/therapy dynamics and biomechanical effects driven by early time point imaging data. Whereas early work had been based on a limited dimensionality reduction (two-dimensional planar modeling analysis) to simplify the numerical implementation, in this work, we extend our framework to a fully volumetric, three-dimensional biophysical mathematical modeling approach in which parameter estimates are generated by an inverse problem based on the adjoint state method for numerical efficiency. In an in silico performance study, we show accurate parameter estimation with error less than 3% as compared to ground truth. We apply the approach to patient data from a patient with pathological complete response and a patient with residual tumor burden and demonstrate technical feasibility and predictive potential with direct comparisons between imaging data observation and model predictions of tumor cellularity and volume. Comparisons to our previous two-dimensional modeling framework reflect enhanced model prediction of residual tumor burden through the inclusion of additional imaging slices of patient-specific data.