MS05 - ONCO-1
Ohio Staters Traditions Room (#2120) in The Ohio Union

Digital twins for clinical oncology and cancer research

Wednesday, July 19 at 10:30am

SMB2023 SMB2023 Follow Wednesday during the "MS05" time block.
Room assignment: Ohio Staters Traditions Room (#2120) in The Ohio Union.
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Guillermo Lorenzo, Chengyue Wu, David A Hormuth II, Ernesto A. B. F. Lima, Lois C. Okereke, Thomas E. Yankeelov


The overall goal of this minisymposium is to present and discuss recent developments of digital twin technologies to (i) address the personalization and optimization of the clinical management of cancers, and (ii) advance the research of the biophysical mechanisms underlying these pathologies from the micro to the macroscale. A digital twin can be defined as a virtual representation of a physical object by means of a computational model (or a collection of models) that can continuously assimilate object-specific data to enable-decision making about the physical object based on its current and future states. Digital twins have undergone a widespread development in multiple areas of engineering (e.g., design, fabrication, and health monitoring of industrial products; energy and industrial plant operation; automatization of industrial components). Additionally, digital twins have been proven useful in several areas of medicine, such as surgical planning, cardiovascular disease interventions, convection-enhanced delivery of drugs to the brain, or glucose monitoring in diabetic patients. Given the increasing success of computational models to predict the development of cancer and its response to treatments, these models could be employed to construct digital twins to support the optimal diagnosis, monitoring, and treatment of individual patients as well as to assist the research of this disease in vitro, in vivo, and in silico. The talks in this minisymposium will present recent efforts in building digital twins for the clinical management of cancers and the (pre)clinical research of these diseases. The presentations will include a description of the specific area of application of the digital twins, the underlying models and computational techniques, the types of data required for their operation, performance metrics, and future developments.

Stéphane Bordas

University of Luxembourg (Department of Engineering Sciences)
"Digital twinning physiological processes: brain metabolism and cancer growth"
The Legato Team from the Department of Engineering at the University of Luxembourg is pleased to present three cutting-edge applications of bio-engineering that utilize advanced digital twin methods. These applications are focused on in vitro and in vivostudies at various scales, includ- ing cellular aggregate, cellular, and organ levels. The first digital twin application involves the reproduction of an experiment involving multi-cellular tumor spheroids encapsulated within alginate capsules [1]. This study aims to investigate the impact of mechanical forces on tumor growth by analyzing the deformation of the capsules, which provides insights into internal tumor pressure. The poromechanical model used in this study is rigorously calibrated and validated against various capsule geometries, and the results not only faithfully reproduce the experimental findings but also provide a refined interpretation of the ex- perimental results.The second example of digital twin application focuses on reproducing the metabolism of human astrocytes while considering their real 3D geometry [2]. By examining the influence of geometry on internal reaction-diffusion processes, this study provides a deep understanding of astrocyte functionalities in both normal physiological processes and neurodegenerative diseases. This ap- plication sheds light on the complex interactions within astrocytes and their role in neurodegener- ative conditions.The third example of digital twin application is performed in real-time computation under operation room conditions. Using patient 3D scan Lidar and clinical reference maps, the model generates patient-specific pre-operative drawings for breast conservative surgery [3]. This personalized approach has shown promising clinical applications and has the potential to improve surgical outcomes. The Legato Team is excited about the recent advancements in these digital twin applications, which have led to promising clinical applications [4, 5, 6, 7]. These studies demonstrate the po- tential of bio-engineering and digital twin methods to revolutionize medical research and clinical practice. References: 1]Ste ́phaneUrcun,Pierre-YvesRohan,WafaSkalli,PierreNassoy,Ste ́phaneP.A.Bordas,and Giuseppe Sciume`. Digital twinning of cellular capsule technology: Emerging outcomes from the perspective of porous media mechanics. PLOS ONE, 16(7):1–30, 07 2021. [2]SofiaFarina,SusanneClaus,JackSHale,AlexanderSkupin,andSte ́phanePABordas.Acut finite element method for spatially resolved energy metabolism models in complex neuro-cell morphologies with minimal remeshing. Advanced Modeling and Simulation in Engineering Sciences, 8:1–32, 2021.[3] Arnaud Mazier, Sophie Ribes, Benjamin Gilles, and Ste ́phane PA Bordas. A rigged model of the breast for preoperative surgical planning. Journal of Biomechanics, 128:110645, 2021. [4] Ste ́phane Urcun, Pierre-Yves Rohan, Giuseppe Sciume`, and Ste ́phane P.A. Bordas. Cor- tex tissue relaxation and slow to medium load rates dependency can be captured by a two- phase flow poroelastic model. Journal of the Mechanical Behavior of Biomedical Materials, 126:104952, 2022. [5] Stephane Urcun, Davide Baroli, Pierre-Yves Rohan, Wafa Skalli, Vincent Lubrano, Ste ́phane PA Bordas, and Giuseppe Sciume. Non-operable glioblastoma: proposition of patient-specific forecasting by image-informed poromechanical model. Brain Multiphysics, page 100067, 2023. [6] Sofia Farina, Vale ́rie Voorsluijs, Sonja Fixemer, David Bouvier, Susanne Claus, Ste ́phane PA Bordas, and Alexander Skupin. Mechanistic multiscale modelling of energy metabolism in hu- man astrocytes indicates morphological effects in alzheimer’s disease. bioRxiv, pages 2022– 07, 2022.[7] Thomas Lavigne, Arnaud Mazier, Antoine Perney, Ste ́phane Pierre Alain Bordas, Franc ̧ois Hild, and Jakub Lengiewicz. Digital volume correlation for large deformations of soft tissues: Pipeline and proof of concept for the application to breast ex vivo deformations. Journal of the mechanical behavior of biomedical materials, 136:105490, 2022.
Additional authors: S. Farina, University of Luxembourg; S. Urcun, University of Luxembourg; A. Mazier, University of Luxembourg; G. Sciume`, Univdersity of Bordeaux

Jesús J. Bosque

University of Castilla-La Mancha (Spain) (Mathematical Oncology Laboratory (MOLAB))
"Less is more in glioma treatment: In silico and in vivo evidence towards a clinical trial"
Low-grade gliomas (LGG) are primary brain tumours that arise from glial cells. Patients typically have a prolonged survival (median 7 years), but LGG usually transform into a malignant state, eventually resulting in the patient's death. The alkylating agent temozolomide (TMZ) is the most important weapon used against LGG, but very often the patients end up developing drug resistance. Therefore, the treatment of LGG presents an important medical challenge. To investigate the optimum schedule for the administration of TMZ to LGG patients, we developed mathematical models based on ordinary differential equations and agent-based models. To model the acquisition of drug resistance, we considered an intermediate reversible phenotype of persister cells which evade therapy and turn to fully resistant under repeated TMZ exposure. We parametrised our models using data from mice experiments and magnetic resonance images from patients, and used them to generate cohorts of digital patients in which we tested different protocols of TMZ administration. The results from the in silico clinical trials showed that protocols using individual doses with intermediate rest weeks are more effective than the standard protocol to delay the appearance of resistance and increase the survival of the patients. Moreover, these results were further validated through animal experiments, confirming the efficacy of administration schedules with increased time between doses. Thus, our research lays the foundation for a prospective clinical trial that could improve the standard of care of LGG patients.
Additional authors: Juan Jiménez-Sánchez (University of Castilla-La Mancha); Thibault Delobel (Sorbonne Université); Luis E. Ayala-Hernández (University of Castilla-La Mancha); Berta Segura-Collar (Instituto de Salud Carlos III); Julián Pérez-Beteta (University of Castilla-La Mancha); Pilar Sánchez-Gómez (Instituto de Salud Carlos III); Víctor M. Pérez-García (University of Castilla-La Mancha)

Renee Brady-Nicholls

H. Lee Moffitt Cancer Center & Research Institute (Integrated Mathematical Oncology)
"An In Silico Study of Hormone Therapy in Metastatic Prostate Cancer"
African American (AA) men have the highest incidence and mortality rates of prostate cancer (PCa) compared to any other racial group. The increased incidence as well as mortality are likely due to socioeconomic factors, environmental exposure, access to care, and biologic variations. Deciphering the specific drivers of increased incidence and mortality is difficult due to a scarcity in available data from AA patients. In silico modeling can be used to generate pseudo patient data that can be used to compare response dynamics between groups. Here, we use propensity score matching to conduct a in silico study of hormone treatment in AA and European American (EA) PCa patients. Using longitudinal prostate-specific antigen (PSA) data from 57 metastatic PCa patients (N = 47 EA, N = 10 AA), we used propensity score matching to identify 15 EA patients that most closely matched the 10 AA patients. A simple mathematical model describing stem cell, differentiated cell, and PSA dynamics was calibrated to the data. Model parameters were compared between the matched patients and identified a significantly higher stem cell self-renewal rate in AA patients. Using this, an in silico study was performed by sampling from the race-specific parameter sets to create 100 in silico patients (N = 50 EA, N = 50 AA). Response dynamics during both continuous and adaptive therapy were compared between AA and EA groups and found that patients with higher stem cell self-renewal rates received the most benefit from adaptive treatment. This is an important step in identifying race-specific, patient-specific treatment options that can be used to maximally delay time to progression.
Additional authors: Alexandria Johnson, Integrated Mathematical Oncology Dept, H. Lee Moffitt Cancer Center & Research Institute

Chase Christenson

University of Texas at Austin (Biomedical Enginering)
"Fast digital twin construction for modeling the response of breast cancer to therapy using proper orthogonal decomposition."
Introduction: Digital twins provide an avenue to personalize and optimize therapy for cancer by simulating response in the digital space, prior to physical delivery of treatment. Mathematical models that accurately predict spatial response to various therapies have been developed but are limited in their practical application due to their heavy computational loads. Reduced order modeling (ROM) techniques, such as proper orthogonal decomposition, can be used to alleviate this burden and make the construction of digital twins more tractable for clinical application. Methods: Our lab has developed a reaction-diffusion model that describes the spatio-temporal response of breast tumors due to cell invasion, proliferation, and response to neoadjuvant therapy (1). The model is initialized and calibrated with sequential magnetic resonance imaging (MRI) data from 50 patients. The MRI data consists of diffusion-weighted MRI and dynamic contrast enhanced MRI to inform tumor cellularity and drug concentration, respectively. We use a data driven ROM formulation, where patient-specific cellularity estimates are used to determine a reduction basis appropriate for the mathematical model and individual patient. Model parameters (e.g., spatial proliferation rates, global diffusivity, and treatment efficacy) are then estimated by fitting the reduced model to the patient-specific scans. The resulting digital twin is evaluated by its ability to predict future response, and its similarity to the output from a full order model (FOM). Results: The correlation between FOM and ROM for global (i.e., whole tumor ROI) changes in total tumor volume and total tumor cellularity both achieve concordance correlation coefficients >0.99 for the calibrated and predicted time points. At the local level (i.e., individual voxels), the ROM achieves a median percent difference from FOM of 1.59% at calibrated time points, and 6.60% for predictions across the 50 patients. Critically, the ROM output requires only 1.33 minutes, nearly 100× faster than the FOM time of 128.43 minutes. Conclusions: We have developed a computational framework that can accurately calibrate a digital twin to individual patient data in a fraction of the time previously required. This reduced model can then be used to make accurate predictions of spatial response to therapy. References: (1) Wu, Chengyue, et al. 'MRI-based digital models forecast patient-specific treatment responses to neoadjuvant chemotherapy in triple-negative breast cancer.' Cancer Research 82.18 (2022): 3394-3404. Acknowledgements: The authors thank the NIH for funding through NCI U01CA142565, U01CA174706, and U24CA226110. They thank the Cancer Prevention and Research Institute of Texas for support through CPRIT RR160005. T.E. Yankeelov is a CPRIT Scholar in Cancer Research
Additional authors: Chengyue Wu, University of Texas Oden Institute for Computational Engineering and Sciences; David A. Hormuth II, University of Texas Oden Institute for Computational Engineering and Sciences, and Livestrong Cancer Institutes; Graham Pash, University of Texas Oden Institute for Computational Engineering and Sciences; Casey Stowers, University of Texas Oden Institute for Computational Engineering and Sciences; Megan LaMonica, University of Texas Department of Biomedical Engineering; Karen Wilcox, University of Texas Oden Institute for Computational Engineering and Sciences, and Department of Aerospace Engineering and Engineering Mechanics; Thomas E. Yankeelov, University of Texas at Austin Departments of Biomedical Engineering, Diagnostic Medicine, Oncology, Livestrong Cancer Institutes, Oden Institute for Computational Engineering and Sciences, and University of Texas M.D. Anderson Cancer Center; Jingfei Ma, University of Texas M.D. Anderson Cancer Center; Gaiane M. Rauch; University of Texas M.D. Anderson Cancer Center

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