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Background: Glioblastoma is the deadliest, yet most common, brain tumor in adults, with poor survival and response to aggressive therapy. Therapeutic failure results from a number of causes inherent to these tumors. Imaging, computational, and drug delivery approaches can aid in the quest to access and kill each tumor cell in patients. One factor, interstitial fluid flow, is a driving force therapeutic delivery. However, convective and diffusive transport mechanisms are un-der-studied. In this study, we examine the application of a novel image analysis method to meas-ure fluid flow and diffusion in glioblastoma patients with MRI and compare to patient outcomes. Methods: Building on a prior imaging methodology tested and validated in vitro, in silico and in preclinical models of disease, here we apply our analysis method to archival patient data from the Ivy GAP dataset. Results: We characterize interstitial fluid flow and diffusion patterns in patients. We find strong correlations between flow rates measured within tumors and in the surrounding parenchymal space, where we hypothesized that velocities would be higher. Looking at overall magnitudes, there is significant correlation with both age and survival in this patient cohort. Additionally, we find that tumor size nor resection significantly alter the velocity magnitude. Last, we map the flow pathways in patient tumors and find variability in degree of directionality that we hypothesize in future studies may lead to information concerning treatment, invasive spread, and progression. Conclusions: Analysis of standard DCE-MRI in patients with glioblastoma offers more infor-mation regarding transport within and around tumor, can be measured post-resection and mag-nitudes correlate with patient prognosis.

Background: Glioblastoma (GBM) is the deadliest and most common brain tumor in adults, with poor survival and response to aggressive therapy. Limited access of drugs to tumor cells is one reason for such grim clinical outcomes. A driving force for therapeutic delivery is interstitial fluid flow (IFF), both within the tumor and in the surrounding brain parenchyma. However, convective and diffusive transport mechanisms are understudied. In this study, we examined the application of a novel image analysis method to measure fluid flow and diffusion in GBM patients. Methods: Here, we applied an imaging methodology that had been previously tested and validated in vitro, in silico, and in preclinical models of disease to archival patient data from the Ivy Glioblastoma Atlas Project (GAP) dataset. The analysis required the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is readily available in the database. The analysis results, which consisted of IFF flow velocity and diffusion coefficients, were then compared to patient outcomes such as survival. Results: We characterized IFF and diffusion patterns in patients. We found strong correlations between flow rates measured within tumors and in the surrounding parenchymal space, where we hypothesized that velocities would be higher. Analyzing overall magnitudes indicated a significant correlation with both age and survival in this patient cohort. Additionally, we found that neither tumor size nor resection significantly altered the velocity magnitude. Lastly, we mapped the flow pathways in patient tumors and found a variability in the degree of directionality that we hypothesize may lead to information concerning treatment, invasive spread, and progression in future studies. Conclusions: An analysis of standard DCE-MRI in patients with GBM offers more information regarding IFF and transport within and around the tumor, shows that IFF is still detected post-resection, and indicates that velocity magnitudes correlate with patient prognosis. Published version

While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2+) breast cancer, HER2+ patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2+ breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-Trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by >40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2+ treatment response.

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of hematological cancers and is currently being investigated for solid tumors including high-grade glioma brain tumors. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumors. In this work we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor t-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, CAR T-cell proliferation and exhaustion, and CAR T-cell persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyzer to parameterize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted as compared to higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumor growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumor characteristics including tumor growth rate and antigen level to maximize therapeutic benefit.Statement of SignificanceWe utilize a mathematical model to deconvolute the nonlinear contributions of CAR T-cell proliferation and exhaustion to predict therapeutic efficacy and dependence on CAR T-cell dose and target antigen levels.

While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response.

Background: This study proposes a method for inferring the premorbid glenoid shape and orientation of scapulae affected by glenohumeral osteoarthritis (OA) to inform restorative surgery., Methods: A statistical shape model (SSM) built from 64 healthy scapulae was used to reconstruct the premorbid glenoid shape based on anatomic features that are considered unaffected by OA. First. the method was validated on healthy scapulae by quantifying the accuracy of the predicted shape in terms of surface distance, glenoid version, and inclination. The SSM-based reconstruction was then applied to 30 OA scapulae. Glenoid version and inclination were measured fully automatically and compared between the original OA glenoids, SSM-based glenoid reconstructions, and healthy scapulae., Results: Validation on healthy scapulae showed a root-mean-square surface distance between original and predicted glenoids of 1.0 +/- 0.2 mm. The prediction error was 2.3 degrees +/- 1.8 degrees for glenoid version and 2.1 degrees +/- 2.0 degrees for inclination. When applied to an OA dataset. SSM-based reconstruction restored average glenoid version and inclination to values similar to the healthy situation. No differences were observed between average orientation values measured on SSM-based reconstructed and healthy scapulae (P >= 10). However, the average orientation of the reconstructed premorbid glenoid differed from the average orientation of OA glenoids for Walch classes Al (version) and 132 (version, inclination, and medialization)., Conclusion: The proposed SSM can predict the premorbid glenoid cavity of healthy scapulae with millimeter accuracy. This technique has the potential to reconstruct the premorbid glenoid cavity shape, as it was prior to OA, and thus to guide the positioning of glenoid implants in total shoulder arthmplasty. (C) 2018 Journal of Shoulder and Elbow Surgery Board of Trustees. All rights reserved.

Chimeric antigen receptor (CAR) T-cell therapy is an emerging targeted immunotherapy which has shown success in liquid cancers such as leukemias. CAR T-cells are also being used for the treatment of solid tumors such as glioblastoma, which is a primary brain tumor. Ongoing phase I trials have been designed to evaluate CAR T-cell dosing, scheduling, and route of administration in order to understand and improve the efficacy of CAR T-cell therapy. A better understanding of factors leading to the success of CAR T-cell immunotherapy for solid tumors will be necessary to improve outcomes for patients with solid tumors and to advance the field of CAR T-cell immuno-oncology. Here we use mathematical model to explore factors in determining a successful response to CAR T-cell therapy: proliferation, persistence, and killing capacity of CAR T-cells. Using a novel in vitro experimental apparatus, we are able to measure the density of cancer cells over several days in 15 minute interval time resolution. This highly temporally resolved data provides a unique opportunity to confidently estimate parameters of the model and to provide insights into the dynamics of CAR T-cell proliferation, persistence, and killing capacity. Furthermore we explore the relationship between these factor with CAR T-cell dose level. We will show results from experiments using patient-derived cancer cell lines as well as cancer cells engineered to express specific levels of the target antigen (IL13Rα2) to quantitatively evaluate the roles of proliferation, persistence, and killing in cells with different levels of antigen expression. We will discuss the interpretation of the model parameters and demonstrate the clinical value of this analysis through an application of CAR T-cell treatment tailored to the dynamics of an individual patient’s cancer growth rate.

Interstitial fluid flow around brain tumors is increased above normal levels. This force has been shown to drive tumor cell invasion in brain tumors and alter the extracellular matrix, vasculature, immune and cellular niches in non-nervous tissues in ways that promote tumor progression [1]. Here, by applying our dynamic contrast enhanced MRI method and analysis [2], we measured interstitial fluid flow in multiple patient cell line-derived xenograft models of glioma in mice (n=6–8 per group, four different lines). The range of interstitial velocity magnitudes is similar across these models (range from 0.1–2.6 micron/s), though intratumoral variability is high. We examined tumors by immunohistochemistry including tumor cells (proliferating cells, ki67; stem-like cells, Nestin and Sox2; apoptotic cells, Caspase 3/7), the cellular microenvironment (astrocytes, GFAP and GLAST; microglia/macrophages, Iba-1 and CX3CR1; blood vessels, CD31; neurons, NeuN and Neurofilament; oligodendrocytes, OSP1), and of the extracellular space (hyaluronan, HABP; tenascin C; white matter tracts, fluoromyelin; cytokines). Fluid flow pathways and rates are dictated by anatomical features of the brain in these models (i.e. blood vessels and white matter tracts). Blood vessel density did not significantly correlate with regions of higher velocities. Based on our observations we performed in vitro experiments in a 3D tissue engineered model of the glioma microenvironment [3] to assess invasion within the range of measured flow rates. Velocities were controlled using either microfluidic pump driven flow or pressure driven flow and cellular outcomes were assessed using flow cytometry and immunocytochemistry of the 3D gel system. The results of these studies will be discussed. 1) Munson, Shieh, Cancer Management Research (2014) 6: 317; 2) Kingsmore, Vaccari, Abler, et al. APL Bioengineering (In press); 3) Harris, Yuan, Munson, Methods (2018) 134:20–31.

Glioblastoma (GBM) is the most frequent malignant brain tumour in adults. Its growth is characterized by infiltration of surrounding healthy tissue, and the formation of a necrotic core. GBM presents with varying degree of mass-effect which results in healthy-tissue deformation, midline shift or herniation. Biomechanical forces, such as those resulting from displacive tumour growth, shape the tumour environment, contribute to tumour progression and may affect treatment response and outcome.To investigate the role of tumour mass-effect for tumour evolution, we have previously developed a mechanically-coupled reaction-diffusion model that captures three dominant aspects of macroscopic GBM growth: (a) tumour cell proliferation, (b) the diffuse invasion of the growing tumour into surrounding healthy tissue, and (c) the resulting mass effect.Here we present an implementation of this model in FENICS and first steps towards an image-based optimization approach, based on dolphin-adjoint, to estimate patient-specific parameters from clinical magnetic-resonance imaging (MRI).

Glioblastoma (GBM), a highly aggressive form of brain tumor, is a disease marked by extensive invasion into the surrounding brain. Interstitial fluid flow (IFF), or the movement of fluid within the spaces between cells, has been linked to increased invasion of GBM cells. Better characterization of IFF could elucidate underlying mechanisms driving this invasion in vivo. Here, we develop a technique to non-invasively measure interstitial flow velocities in the glioma microenvironment of mice using dynamic contrast-enhanced magnetic resonance imaging (MRI), a common clinical technique. Using our in vitro model as a phantom “tumor” system and in silico models of velocity vector fields, we show we can measure average velocities and accurately reconstruct velocity directions. With our combined MR and analysis method, we show that velocity magnitudes are similar across four human GBM cell line xenograft models and the direction of fluid flow is heterogeneous within and around the tumors, and not always in the outward direction. These values were not linked to the tumor size. Finally, we compare our flow velocity magnitudes and the direction of flow to a classical marker of vessel leakage and bulk fluid drainage, Evans blue. With these data, we validate its use as a marker of high and low IFF rates and IFF in the outward direction from the tumor border in implanted glioma models. These methods show, for the first time, the nature of interstitial fluid flow in models of glioma using a technique that is translatable to clinical and preclinical models currently using contrast-enhanced MRI.

BACKGROUND A large proportion of age-related fecal incontinence is attributed to weakness or degeneration of the muscles composing the anal continence organ. However, the individual role of these muscles and their functional interplay remain poorly understood. METHODS This study employs a novel technique based on the combination of MR imaging and FLIP measurements (MR-FLIP) to obtain anatomical and mechanical information simultaneously. Unlike previous methods used to assess the mechanics of the continence organ, MR-FLIP allows inter-individual comparisons and statistical analysis of the sphincter morpho-mechanical parameters. The anatomy as well as voluntary and involuntary mechanical properties of the anal continence organ were characterized in 20 healthy senior volunteers. RESULTS Results showed that the external anal sphincter (EAS) forms a funnel-like shape with wall thickness increasing by a factor of 2.5 from distal (6 ± 0 mm) to proximal (15 ± 3 mm). Both voluntary and involuntary mechanical properties in this region correlate strongly with the thickness of the muscle. The positions of least compliance and maximal orifice closing were both located toward the proximal EAS end. In addition, maximal contraction during squeeze maneuvers was reached after 2 s, but high muscle fatigue was measured during a 7 s holding phase, corresponding to about 60% loss of the energy produced by the muscles during the contraction phase. CONCLUSIONS This work reports baseline parameters describing the morpho-mechanical condition of the sphincter muscle of healthy elderly volunteers. New parameters were also proposed to quantify the active properties of the muscles based on the mechanical energy associated with muscle contraction and fatigue. This information could be used to assess patients suffering from AI or for the design of novel implants.

The macroscopic growth of brain tumors has been studied by means of different computational modeling approaches. Glioblastoma multiforme (GBM) is the most common malignant type and is commonly modeled as a reaction–diffusion type system, accounting for its invasive growth pattern. Purely biomechanical models have been proposed to represent the mass effect caused by the growing tumor, but only a few models consider mass effect and tissue invasion effects in a single 3D model. We report first results of a comparative study that evaluates the ability of a simple computational model to reproduce the shape of pathologies found in patients. GBM invasion into brain tissue and the mechanical interaction between tumor and healthy tissue components are simulated using the finite element method (FEM). Cell proliferation and invasion are modeled as a reaction–diffusion process; simulation of the mechanic interaction relies on a linear elastic material model. Both are coupled by relating the local increase in tumor cell concentration to the generation of isotropic strain in the corresponding tissue element. The model accounts for multiple brain regions with values for proliferation, isotropic diffusion, and mechanical properties derived from literature. Tumors were seeded at multiple locations in FEM models derived from publicly available human brain atlases. Simulation results for a given tumor volume were compared to patient images. Simulated tumors showed a more symmetric growth pattern compared to their real counterparts. Resulting levels of tumor invasiveness were in agreement with simulation parameters and tumor-induced pressures of realistic magnitude were found.

Aim Continence results from a complex interplay between anal canal (AC) muscles and sensorimotor feedback mechanisms. The passive ability of the AC to withstand opening pressure - its compliance - has recently been shown to correlate with continence. A functional lumen imaging probe (FLIP) is used to assess AC compliance, although it provides no anatomical information. Therefore, assessment of the compliance specific anatomical structures has not been possible, and the anatomical position of critical functional zones remains unknown. In addition, the FLIP technique assumes a circular orifice cross-section, which has not been shown for the AC. To address these shortcomings, a technique combining FLIP with a medical imaging modality is needed. Method We implemented a new research method (MR-FLIP) that combines FLIP with MR imaging. Twenty healthy volunteers underwent MR-FLIP and conventional FLIP assessment. MR-FLIP was validated by comparison with FLIP results. Anatomical markers were identified, and the cross-sectional shape of the orifice was investigated. Results MR-FLIP provides compliance measurements identical to those obtained by conventional FLIP. Anatomical analysis revealed that the least compliant AC zone was located at the proximal end of the external anal sphincter (EAS). The cross-sectional shape of the AC was found to deviate only slightly from circularity in healthy volunteers. Conclusion The proposed method is equivalent to classical FLIP. It establishes for the first time direct mapping between local tissue compliance and anatomical structure, which is key to gaining novel insights into (in)continence. In addition, MR-FLIP provides a tool for better understanding conventional FLIP measurements in the AC by quantifying its limitations and assumptions.

Biomechanical forces are known to affect tumor growth and evolution [1]. Likewise, tumor growth drives physical changes in the micro-environment that affect tissue solid and fluid mechanics. Tumor mass effect, resulting from rapid tumor cell proliferation, has been shown to be prognostic for poor outcome in glioblastoma (GBM) patients and to be associated with the expression of gene signatures consistent with proliferative growth phenotype [2]. Similarly, elevated interstitial fluid flow (IFF) has been shown to drive GBM invasion [3]. This study investigates the relationship between tumor mass effect, diffusion, perfusion and IFF in GBM using anatomical (pre- and post-contrast T1 weighted, T2/FLAIR) and quantitative MR imaging (Dynamic Contrast Enhanced (DCE) MRI, and Diffusion Weighted Imaging (DWI)). We use data from 39 patients from the Ivy Glioblastoma Atlas Project (Ivy GAP)[4] which provides matched imaging, ISH, RNA, gene expression and clinical data over the course of treatment. We analyze pre-operative anatomic imaging data to determine the tumor-induced mass effect in each patient using quantitative measures such as ‘Lateral ventricle displacement’ [2]. Perfusion and diffusion measures are derived from pre-operative DCE and DWI imaging. Additionally, we estimate IFF velocities in the tumor region using DCE imaging data in combination with a computational model of fluid flow [5]. We will report the results of quantitative imaging analysis in relation to tumor mass effect and examine correlations with biological tumor characteristics and treatment outcome. We further investigate our findings in patient-derived xenograft models of GBM. References: [1] R.K. Jain et al. Annu. Rev. Biomed. Eng., 2014, 16, 321–346. [2] T.C. Steed et al. Scientific Reports, 2018, 8, 2827. [3] K.M. Kingsmore et al. Integr. Biol., 2016, 8 1246-1260 [4] N. Shah et al. Data from Ivy GAP. The Cancer Imaging Archive 2016. [5] K.M. Kingsmore et al. APL Bioengineering (In press).

Chimeric antigen receptor (CAR) T-cell therapy is a promising emerging area of immunotherapy for the treatment of recurrent glioblastoma. In this therapy, T-cells are genetically modified to target tumor-specific antigens. Efficacy of CAR T-cell therapy depends on several factors, including CAR T-cell proliferation, persistence, and tumor cell killing capacity. Here we use a mathematical model to investigate the role of these three factors in determining response of recurrent GBM to this novel immunotherapy. Additionally, the impact of dosing and scheduling of CAR T-cell infusions on the therapeutic response is explored. We model the interaction between the two cell populations (cancer cells and CAR T-cells) using an ordinary differential equation based formalism. The growth and death of cancer cells are simulated as rates of proliferation and interaction between cancer cells and CAR T-cells, respectively. Biological data was used to parameterize the model. Analysis of the dynamics of interaction between cancer cells and CAR T-cells was performed to determine the maximum efficacy of a single and multiple doses of CAR T-cells. Our mathematical model and analysis shows that a critical parameter for the success of CAR T-cell therapy is the ratio of cancer cell proliferation to the killing capacity of the CAR T-cells. We quantify the dose level of CAR T-cells required to eliminate the cancer cell population. Furthermore, we use the mathematical model to predict the time to progression for specific dose levels, which may help in optimizing and scheduling multiple doses of CAR T-cell monotherapy. We compare the mathematical results with patient data from an ongoing dose-escalation study (NCT02208362) using central memory derived IL13Rα2-targeted CAR T-cell therapy for recurrent glioblastoma.

Glioblastoma (GBM) is the most frequent malignant brain tumor in adults and presents with different growth phenotypes. We use a mechanically coupled reaction–diffusion model to study the influence of structural brain tissue anisotropy on tumor growth. Tumors were seeded at multiple locations in a human MR-DTI brain atlas and their spatiotemporal evolution was simulated using the Finite Element Method. We evaluated the impact of tissue anisotropy on the model’s ability to reproduce the aspherical shapes of real pathologies by comparing predicted lesions to publicly available GBM imaging data. The impact of tissue anisotropy on tumor shape was strongly location dependent and highest for tumors in brain regions with a single dominating white matter fiber direction, such as the corpus callosum. Despite strongly anisotropic growth assumptions, all simulated tumors remained more spherical than real lesions at the corresponding anatomic location and similar volume. These findings confirm previous simulation studies, suggesting that cell migration along WM fiber tracks is not a major determinant of tumor shape in the setting of reaction–diffusion-based tumor growth models and for most locations across the brain.

Gliomas are the most common malignant brain tumors in adults, with Glioblastoma (GBM) being the most agressive subtype. GBM is clinically evaluated with magnetic resonance imaging (MRI) and presents with different growth phenotypes, involving varying degrees of healthy tissue invasion and tumor induced herniation, also known as mass effect. GBM growth in the brain is frequently modeled as a reaction-diffusion process in which varying ratios of diffusion and proliferation coefficients mimic the observed spectrum of growth phenotypes ranging from nodal to diffuse. However, reaction-diffusion models alone are insufficient to explain tumor-induced mass effect on normal peripheral tissues, which is a critical clinical issue. We propose an analysis method and framework for estimating GBM growth properties (proliferation, invasiveness, displacive potential) from MRI data routinely collected in the clinical management of GBM. This framework accounts for the mass-effect of the growing tumor by assuming a coupling between local tumor-cell density and volumetric expansion of the tissue. We evaluate the reconstruction workflow on synthetic data that represents a range of realistic growth situations and levels of uncertainty. For most parameter combinations (90%) that correspond to tumors detectable by T1-weighted MRI, target parameters are recovered with a relative error of less than 15%.