First-in-human evaluation of a biological regenerative vascular conduit for hemodialysis access.

INTRODUCTION: The long-term survival and low complication rate of autogenous fistulas for hemodialysis access is often offset by early thrombosis and slow or failed maturation leading to the use of central venous catheters. A regenerative material may have the potential to overcome these limitations. A completely biological acellular vascular conduit was investigated in this first-in-human clinical study. METHODS: With approval of the ethics board and patients' informed consent, five subjects were enrolled based on predetermined inclusion criteria. Five patients underwent implant of a novel acellular, biological tissue conduit (TRUE AVC™) in the upper arm in a curved configuration between brachial artery and axillary vein. After maturation, standard dialysis was commenced through the new access. Patients were followed up to 26 weeks with ultrasound and physical exam. Serum samples were evaluated for an immune response to the novel allogeneic human tissue implant. RESULTS: This new tissue conduit handled well surgically, with properties similar to that of native human vein. Post procedure conduit flow was excellent in all cases, averaging 1098 ± 388 ml/min at week 4 and remaining stable through 1248 ± 355 ml/min at 26 weeks. Surgical site healing was normal with no edema or erythema by week 4. Six-month primary assisted patency was 80% and secondary patency was 100%. Prescribed dialysis was successfully delivered without infection, and there was no significant change in conduit diameter. Serum testing showed no increase in PRA or IgG specific to the TRUE AVC. One implant required intervention at 5 months with thrombectomy and covered stent procedure. CONCLUSION: This first-in-human 6-month study with favorable patency and low complication rate establishes the initial safety and feasibility of this novel biological tissue conduit for dialysis access in patients with end-stage kidney disease. Its mechanical durability and lack of immune response establishes TRUE AVC as a potential regenerative material for clinical use.

Main Manuscript for Cell migration simulator-based biomarkers for glioblastoma.

Glioblastoma is the most aggressive malignant brain tumor with poor survival due to its invasive nature driven by cell migration, with unclear linkage to transcriptomic information. Here, we applied a physics-based motor-clutch model, a cell migration simulator (CMS), to parameterize the migration of glioblastoma cells and define physical biomarkers on a patient-by-patient basis. We reduced the 11-dimensional parameter space of the CMS into 3D to identify three principal physical parameters that govern cell migration: motor number - describing myosin II activity, clutch number - describing adhesion level, and F-actin polymerization rate. Experimentally, we found that glioblastoma patient-derived (xenograft) (PD(X)) cell lines across mesenchymal (MES), proneural (PN), classical (CL) subtypes and two institutions (N=13 patients) had optimal motility and traction force on stiffnesses around 9.3kPa, with otherwise heterogeneous and uncorrelated motility, traction, and F-actin flow. By contrast, with the CMS parameterization, we found glioblastoma cells consistently had balanced motor/clutch ratios to enable effective migration, and that MES cells had higher actin polymerization rates resulting in higher motility. The CMS also predicted differential sensitivity to cytoskeletal drugs between patients. Finally, we identified 11 genes that correlated with the physical parameters, suggesting that transcriptomic data alone could potentially predict the mechanics and speed of glioblastoma cell migration. Overall, we describe a general physics-based framework for parameterizing individual glioblastoma patients and connecting to clinical transcriptomic data, that can potentially be used to develop patient-specific anti-migratory therapeutic strategies generally.

Predicting Glioblastoma Cellular Motility from In Vivo MRI with a Radiomics Based Regression Model.

Characterizing the motile properties of glioblastoma tumor cells could provide a useful way to predict the spread of tumors and to tailor the therapeutic approach. Radiomics has emerged as a diagnostic tool in the classification of tumor grade, stage, and prognosis. The purpose of this work is to examine the potential of radiomics to predict the motility of glioblastoma cells. Tissue specimens were obtained from 31 patients undergoing surgical resection of glioblastoma. Mean tumor cell motility was calculated from time-lapse videos of specimen cells. Manual segmentation was used to define the border of the enhancing tumor T1-weighted MR images, and 107 radiomics features were extracted from the normalized image volumes. Model parameter coefficients were estimated using the adaptive lasso technique validated with leave-one-out cross validation (LOOCV) and permutation tests. The R-squared value for the predictive model was 0.60 with p-values for each individual parameter estimate less than 0.0001. Permutation test models trained with scrambled motility failed to produce a model that out-performed the model trained on the true data. The results of this work suggest that it is possible for a quantitative MRI feature-based regression model to non-invasively predict the cellular motility of glioblastomas.

Education and Outreach in Physical Sciences in Oncology.

Physical sciences are often overlooked in the field of cancer research. The Physical Sciences in Oncology Initiative was launched to integrate physics, mathematics, chemistry, and engineering with cancer research and clinical oncology through education, outreach, and collaboration. Here, we provide a framework for education and outreach in emerging transdisciplinary fields.

Modeling distributed forces within cell adhesions of varying size on continuous substrates.

Cell migration and traction are essential to many biological phenomena, and one of their key features is sensitivity to substrate stiffness, which biophysical models, such as the motor-clutch model and the cell migration simulator can predict and explain. However, these models have not accounted for the finite size of adhesions, the spatial distribution of forces within adhesions. Here, we derive an expression that relates varying adhesion radius ( R) and spatial distribution of force within an adhesion (described by s) to the effective substrate stiffness ( κsub ), as a function of the Young's modulus of the substrate ( E Y ), which yields the relation, κsub=RsEY , for two-dimensional cell cultures. Experimentally, we found that a cone-shaped force distribution ( s = 1.05) can describe the observed displacements of hydrogels deformed by adherent U251 glioma cells. Also, we found that the experimentally observed adhesion radius increases linearly with the cell protrusion force, consistent with the predictions of the motor-clutch model with spatially distributed clutches. We also found that, theoretically, the influence of one protrusion on another through a continuous elastic environment is negligible. Overall, we conclude cells can potentially control their own interpretation of the mechanics of the environment by controlling adhesion size and spatial distribution of forces within an adhesion.

Myosin IIA suppresses glioblastoma development in a mechanically sensitive manner.

The ability of glioblastoma to disperse through the brain contributes to its lethality, and blocking this behavior has been an appealing therapeutic approach. Although a number of proinvasive signaling pathways are active in glioblastoma, many are redundant, so targeting one can be overcome by activating another. However, these pathways converge on nonredundant components of the cytoskeleton, and we have shown that inhibiting one of these-the myosin II family of cytoskeletal motors-blocks glioblastoma invasion even with simultaneous activation of multiple upstream promigratory pathways. Myosin IIA and IIB are the most prevalent isoforms of myosin II in glioblastoma, and we now show that codeleting these myosins markedly impairs tumorigenesis and significantly prolongs survival in a rodent model of this disease. However, while targeting just myosin IIA also impairs tumor invasion, it surprisingly increases tumor proliferation in a manner that depends on environmental mechanics. On soft surfaces myosin IIA deletion enhances ERK1/2 activity, while on stiff surfaces it enhances the activity of NFκB, not only in glioblastoma but in triple-negative breast carcinoma and normal keratinocytes as well. We conclude myosin IIA suppresses tumorigenesis in at least two ways that are modulated by the mechanics of the tumor and its stroma. Our results also suggest that inhibiting tumor invasion can enhance tumor proliferation and that effective therapy requires targeting cellular components that drive both proliferation and invasion simultaneously.