Towards a Framework for Predictive Mathematical Modeling of the Biomechanical Forces Causing Brain Tumor Mass-Effect

GBMs present with different growth phenotypes, ranging from invasive lesions without notable mass-effect to strongly displacing lesions that induce mechanical stresses and result in healthy-tissue deformation, midline shift or herniation. Biomechanical forces, such as those resulting from displacive tumor growth, are recognized to shape the tumor environment and to contribute to tumor progression. We therefore expect that biomechanical forces exerted by lesions on the brain parenchyma have implications on the biophysical level, and that they may affect treatment response and outcome. To better understand the role of biomechanics in the formation of different GBM phenotypes we started developing a framework for the predictive mathematical modeling of mechanical tumor-healthy tissue interaction on the macroscopic level. The tumor’s mass-effect is represented by a solid-mechanics model of brain tissue that computes tumor-induced strain based on local tumor cell concentration. The framework allows to seed tumors at multiple locations in a human brain atlas. It simulates tumor evolution over time and across different brain regions using literature-based parameter estimates for tumor cell proliferation, as well as isotropic motility, and mechanical tissue properties. Despite its simplicity, the mathematical model yielded realistic estimates of the mechanical impact of a growing tumor on intra-cranial pressure. However, comparison to publicly available GBM imaging data showed that asymmetric shapes could not be reproduced by isotropic growth assumptions. Here we present and evaluate an extended version of this mechanically-coupled reaction-diffusion model that takes into account tissue anisotropies based on MRI diffusion tensor imaging (MR-DTI). Structural anisotropies in brain tissue have been found to affect the directionality of tumor cell migration and are critical to mechanical behavior. This makes them likely to play a role also in the development of GBM phenotypes

CHIC – A Multi-scale Modelling Platform for in-silico Oncology

Models of normal physiology and disease are necessary in cancer research and clinical practice to optimally exploit the available (pre)clinical multi-scale and multi-modality data. Relevant models often cover multiple spatio-temporal scales and require automated access to heterogeneous and confidential data, making their development, validation and deployment challenging. The CHIC (Computational Horizons in Cancer) [1] project develops computational models for the cancer domain, as well as tools, services and a secure infrastructure for model and data access, and reuse. The architecture is designed to support the creation of complex disease models (hyper-models) by composition of reusable component models (hypo-models). It aims to provide individualized answers to concrete clinical questions by patient-specific parametrization of disease-specific hyper-models. We introduce the CHIC project and illustrate its approach to multi-scale cancer modelling by coupled execution of two component models operating on distinct spatial scales: - OncoSimulator (OS): a spatially discrete model of cancer cell proliferation and treatment effect in function of tumour, treatment and patient-specific parameters [2], implemented as cellular automaton model, - Bio-mechanical Simulator (BMS): a macroscopic continuum model of mechanical effects caused by tumour expansion in patient-specific anatomy, implemented as finite element model, based on [3]. Both component models exchange information about the spatial distribution of cancer cells and mechanical pressure in order to simulate the evolution of tumour volume and shape. Latter is achieved by correcting simple spherical growth (OS) by mechanically induced growth anisotropy (BMS). CHIC is working towards an extensible platform for in-silico oncology with a set of reusable component models at its core, covering sub-cellular, cellular and super-cellular scales. Viability of infrastructure and composite hyper-models is being evaluated against clinical questions in the treatment of Nephroblastoma, Glioblastoma and Non-small Cell Lung Cancer. [1] http://www.chic-vph.eu/ [2] Stamatakos, G., 2011. In silico oncology: PART I Clinically oriented cancer multilevel modeling based on discrete event simulation. In: Deisboeck, T., Stamatakos, G. (Eds.), Multiscale Cancer Modeling. Chapman & Hall/CRC, Boca Raton, Florida,USA. [3] C. P. May, E. Kolokotroni, G. S. Stamatakos, and P. Büchler, ‘Coupling biomechanics to a cellular level model: An approach to patient-specific image driven multi-scale and multi-physics tumor simulation’, Progress in Biophysics and Molecular Biology, vol. 107, no. 1, pp. 193–199, Oct. 2011

Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data.

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. 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, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parametrize 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 when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour 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 tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit

Towards a better understanding of the posttreatment hemodynamic behaviors in femoropopliteal arteries through personalized computational models based on OCT images.

The hemodynamic behavior following endovascular treatment of patients with peripheral arterial disease plays a significant role on the occurrence of restenosis in femoro-popliteal (FP) arteries. The atheroprone flow conditions that are generally accepted to promote restenosis can be calculated by computational fluid dynamics (CFD) analyses, and these results can be used to assess individualized treatment outcomes. However, the impact of endovascular therapy on the flow behaviors of FP arteries are still poorly understood, as the imaging modalities used in existing numerical works (X-ray angiography, computed tomography angiography) are unable to accurately represent the post-treatment arterial geometry due to their low resolutions. Therefore, this study proposes a new algorithm that combines intra-arterial lumen geometry obtained from high-resolution optical coherence tomography (OCT) images with centerlines generated from X-ray images to reconstruct the FP artery with an in-plane resolution of 10 µm. This superior accuracy allows modeling characteristic geometrical structures, such as angioplasty-induced arterial dissections, that are too small to be reconstructed with other imaging modalities. The framework is applied on the clinical data of patients treated either with only-percutaneous transluminal angioplasty (PTA) (n = 4) or PTA followed by stenting (n = 4). Based on the generated models, PTA was found to cause numerous arterial dissections, covering approximately 10% of the total surface area of the lumen, whereas no dissections were identified in the stented arteries. CFD simulations were performed to investigate the hemodynamic conditions before and after treatment. Regardless of the treatment method, the areas affected by low time-averaged wall shear stress (< 0.5 Pa) were significantly higher (p < 0.05) following endovascular therapy (pre-PTA: 0.95 ± 0.59 cm2; post-PTA: 2.10 ± 1.09cm2; post-stent: 3.10 ± 0.98 cm2). There were no statistical differences between the PTA and the stent groups. However, within the PTA group, adverse hemodynamics were mainly concentrated at regions created by arterial dissections, which may negatively impact the outcomes of a leave-nothing-behind strategy. These observations show that OCT-based numerical models have great potential to guide clinicians regarding the optimal treatment approach

Challenges and opportunities for improving the landscape for Lewy body dementia clinical trials.

Lewy body dementia (LBD), including dementia with Lewy bodies and Parkinson\u27s disease dementia, affects over a million people in the USA and has a substantial impact on patients, caregivers, and society. Symptomatic treatments for LBD, which can include cognitive, neuropsychiatric, autonomic, sleep, and motor features, are limited with only two drugs (cholinesterase inhibitors) currently approved by regulatory agencies for dementia in LBD. Clinical trials represent a top research priority, but there are many challenges in the development and implementation of trials in LBD. To address these issues and advance the field of clinical trials in the LBDs, the Lewy Body Dementia Association formed an Industry Advisory Council (LBDA IAC), in addition to its Research Center of Excellence program. The LBDA IAC comprises a diverse and collaborative group of experts from academic medical centers, pharmaceutical industries, and the patient advocacy foundation. The inaugural LBDA IAC meeting, held in June 2019, aimed to bring together this group, along with representatives from regulatory agencies, to address the topic of optimizing the landscape of LBD clinical trials. This review highlights the formation of the LBDA IAC, current state of LBD clinical trials, and challenges and opportunities in the field regarding trial design, study populations, diagnostic criteria, and biomarker utilization. Current gaps include a lack of standardized clinical assessment tools and evidence-based management strategies for LBD as well as difficulty and controversy in diagnosing LBD. Challenges in LBD clinical trials include the heterogeneity of LBD pathology and symptomatology, limited understanding of the trajectory of LBD cognitive and core features, absence of LBD-specific outcome measures, and lack of established standardized biologic, imaging, or genetic biomarkers that may inform study design. Demands of study participation (e.g., travel, duration, and frequency of study visits) may also pose challenges and impact trial enrollment, retention, and outcomes. There are opportunities to improve the landscape of LBD clinical trials by harmonizing clinical assessments and biomarkers across cohorts and research studies, developing and validating outcome measures in LBD, engaging the patient community to assess research needs and priorities, and incorporating biomarker and genotype profiling in study design

Evaluating the Effect of Tissue Anisotropy on Brain Tumor Growth using a Mechanically-coupled Reaction-Diffusion Model

Glioblastoma (GBM), the most frequent malignant brain tumor in adults, is char- acterized by rapid growth and healthy tissue invasion. Long-term prognosis for GBM remains poor with median overall survival between 1 y to 2 y [15]. GBM presents with different growth phenotypes, ranging from invasive tumors without notable mass-effect to strongly displacing lesions. Biomechanical forces, such as those resulting from displacive tumor growth, shape the tumor environment and contribute to tumor progression [9]. We present an extended version of a mechanically–coupled reaction-diffusion model of brain tu- mor growth [1] that simulates tumor evolution over time and across different brain regions using literature-based parameter estimates for tumor cell proliferation, as well as isotropic motility, and mechanical tissue properties. This model yielded realistic estimates of the mechanical impact of a growing tumor on intra-cranial pressure. However, comparison to imaging data showed that asymmetric shapes could not be reproduced by isotropic growth assumptions. We modified this model to account for structural tissue anisotropy which is known to affect the directionality of tumor cell migration and may influence the mechanical behavior of brain tissue. Tumors were seeded at multiple locations in a human MR-DTI brain atlas and their spatio-temporal 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. We found the impact on tumor shape to be strongly location dependent and highest for tumors located in brain regions that are characterized by a single dominant white matter direction, such as the corpus callosum. However, despite strongly anisotropic growth assumptions, all simulated tumors remained more spherical than real lesions at the corresponding location and similar volume. This finding is in agreement with previous studies [17, 6] suggesting that anisotropic cell migration along white matter 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

Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta-analysis.

Learning occurs when an outcome differs from expectations, generating a reward prediction error signal (RPE). The RPE signal has been hypothesized to simultaneously embody the valence of an outcome (better or worse than expected) and its surprise (how far from expectations). Nonetheless, growing evidence suggests that separate representations of the two RPE components exist in the human brain. Meta-analyses provide an opportunity to test this hypothesis and directly probe the extent to which the valence and surprise of the error signal are encoded in separate or overlapping networks. We carried out several meta-analyses on a large set of fMRI studies investigating the neural basis of RPE, locked at decision outcome. We identified two valence learning systems by pooling studies searching for differential neural activity in response to categorical positive-versus-negative outcomes. The first valence network (negative > positive) involved areas regulating alertness and switching behaviours such as the midcingulate cortex, the thalamus and the dorsolateral prefrontal cortex whereas the second valence network (positive > negative) encompassed regions of the human reward circuitry such as the ventral striatum and the ventromedial prefrontal cortex. We also found evidence of a largely distinct surprise-encoding network including the anterior cingulate cortex, anterior insula and dorsal striatum. Together with recent animal and electrophysiological evidence this meta-analysis points to a sequential and distributed encoding of different components of the RPE signal, with potentially distinct functional roles

Cortical thickness and resting-state cardiac function across the lifespan: a cross-sectional pooled mega analysis

Understanding the association between autonomic nervous system [ANS] function and brain morphology across the lifespan provides important insights into neurovisceral mechanisms underlying health and disease. Resting state ANS activity, indexed by measures of heart rate [HR] and its variability [HRV] has been associated with brain morphology, particularly cortical thickness [CT]. While findings have been mixed regarding the anatomical distribution and direction of the associations, these inconsistencies may be due to sex and age differences in HR/HRV and CT. Previous studies have been limited by small sample sizes, which impede the assessment of sex differences and aging effects on the association between ANS function and CT. To overcome these limitations, 20 groups worldwide contributed data collected under similar protocols of CT assessment and HR/HRV recording to be pooled in a mega-analysis (N = 1,218 (50.5% female), mean age 36.7 years (range: 12-87)). Findings suggest a decline in HRV as well as CT with increasing age. CT, particularly in the orbitofrontal cortex, explained additional variance in HRV, beyond the effects of aging. This pattern of results may suggest that the decline in HRV with increasing age is related to a decline in orbitofrontal CT. These effects were independent of sex and specific to HRV; with no significant association between CT and HR. Greater CT across the adult lifespan may be vital for the maintenance of healthy cardiac regulation via the ANS – or greater cardiac vagal activity as indirectly reflected in HRV may slow brain atrophy. Findings reveal an important association between cortical thickness and cardiac parasympathetic activity with implications for healthy aging and longevity that should be studied further in longitudinal research

Modeling Tumor Growth Biomechanics — Approaches, Challenges & Opportunities

Physical forces are recognized to play a critical role in shaping the micro-environment of tumors. Compression of cancer and stromal cells, as well as blood and lymphatic vessels, are direct consequences of mechanical solid stress, the compressive and tensile mechanical forces exerted by the solid components of the tissue. By altering the mechanical micro-environment of tumors, elevated solid stress can affect their pathophysiology, driving tumors to more aggressive phenotypes and compromise therapeutic outcome [1]. Mechanical stress also affects healthy tissue: It causes neuronal loss in brain tissue [2], and is linked to neurological deficits and reduced survival in patients with glioblastoma (GBM) [3], the most common malignant primary brain tumor in adults. Given their far-reaching micro- and macroscopic consequences, tumor-induced mechanical forces may provide mechanistic insights into inter and intra-tumor heterogeneities, differential response to treatment and other phenotypical characteristics. In this contribution, we survey the literature of spatial tumor growth modeling from a perspective of macroscopic tissue mechanics to assess the current status of mechanically-coupled growth models and to identify opportunities for further research: We summarize the types of modeling approaches previously used for capturing tumor-induced mechanical effects and their biological or physiological consequences. Based on this review, we identify the scenarios in which accounting for tissue mechanics proved to improve calibration to and prediction of clinical data. Drawing from examples of our [4] and others’ research on mechanically-coupled growth modeling for GBM, we discuss challenges involved in the implementation and calibration of such models. In this context, we identify areas of mechanically-coupled growth modeling where further research is needed and explore application opportunities that such models may open