Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session P23: Physics in Medicine: Computational Modeling |
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Sponsoring Units: GMED Chair: Robert Austin, Princeton University Room: BCEC 158 |
Wednesday, March 6, 2019 2:30PM - 2:42PM |
P23.00001: Synergistic Effect of Immunotherapy and Radiotherapy: a Computational Model Damijan Valentinuzzi, Katja Ursic, Urban Simončič, Matea Maruna, Simon Bucek, Maruša Turk, Martina Vrankar, Maja Cemazar, Gregor Sersa, Robert Jeraj The Nobel Prize in Medicine 2018 was awarded for discovery of cancer immunotherapy (IT). Treatment effects are impressive, however, only a minority of patients respond. Because of favourable effects of radiotherapy (RT) on the immune system, combinations of IT and RT have been widely studied to improve the response rates. To identify possible biomarkers of response, we built a physical model capable of simulating tumour response to IT + RT. Interplay between a tumour, the immune system and the therapeutic effects is described with an experimentally verifiable population model, because the underlying biology is described with a minimum number of parameters. The model was able to reproduce experimental results from literature. To analyse possible biomarkers of response, sensitivity study of key parameters was performed. The most sensitive parameter was major histocompatibility complex class I expression, i.e. a receptor responsible for presentation of foreign proteins. Model predictions will be benchmarked against experiments on 3 different murine tumours, which will receive a single dose of RT with different IT schedules. Such models show promise to support, guide and accelerate immunotherapy research. |
Wednesday, March 6, 2019 2:42PM - 2:54PM |
P23.00002: Computational model of treatment resistance heterogeneity Maruša Turk, Urban Simončič, Alison Roth, Damijan Valentinuzzi, Robert Jeraj In metastatic cancer patients, diverse levels of resistance, which are the result of genetic heterogeneity, lead to treatment response (TR) heterogeneity. To evaluate the role of resistance on TR, we constructed a population model, simulating cellular dynamics in individual metastasis. The model was benchmarked with imaging metrics extracted from the 18F-NaF PET/CT scans of 39 metastatic prostate cancer patients, received at baseline and after 3 cycles of therapy. Patients were treated with chemotherapy or hormonal therapy. Two model settings were evaluated: one considering only inter-patient and one considering both inter- and intra-patient heterogeneity in the proportion of intrinsically resistant cells (IR). Model performance, considering both settings, was evaluated using the Akaike information criterion (AIC). TR after 6, 9, and 12 months was predicted and compared using the Wilcoxon rank sum test. Considering both inter- and intra-patient heterogeneity in IR resulted in significantly better model performance (AIC=-250) than considering only inter-patient heterogeneity (AIC=6). Differences in predicted TR were not significant between treatment groups (p>0.15). The model has identified IR as an important factor influencing on inter- and intra-patient TR heterogeneity. |
Wednesday, March 6, 2019 2:54PM - 3:06PM |
P23.00003: Dynamics of Tumor Subpopulations in Response to Targeted Therapies David McClatchy, Changran Geng, Sophia Kamran, Henning Willers, Harald Paganetti, Aaron Hata, Clemens Grassberger Drugs targeting the specific genetic expression of a patient’s tumor have revolutionized the treatment of metastatic cancer. However, these tumors commonly recur due to the somatic evolution of drug persistent and resistant subpopulations during treatment. To better understand and combat acquired drug resistance, we developed a coupled, non-linear, differential system to model the dynamics of resistant and persistent tumor subpopulations. A Gompertz growth model is used to simulate bounded cell growth, while a general stochastic evolutionary pathway leading to drug resistance is implemented, based on in-vitro observations. Work will be presented on model development, and its parameterization based on measured tumor responses in lung cancer patients treated with targeted therapy. We further performed a comprehensive sensitivity analysis of model behavior in response to varying degrees of genomic instability, and derive estimates of initial pre-existing/persisting drug resistance that hold independent of parameter choice. |
Wednesday, March 6, 2019 3:06PM - 3:18PM |
P23.00004: Understand the role of chemotherapeutic gradient in the emergence of polyploid giant cancer cells using mean field model Ke-Chih Lin, Gonzalo Torga, Yusha Sun, Robert Axelrod, Kenneth J. Pienta, James Sturm, Robert Austin Polyploid giant cancer cells (PGCCs) have been shown to correlate with poor response to chemotherapy and contribute to tumor heterogeneity regulation through asymmetric cell division [1][2]. However, most in vitro cancer studies do not replicate the complexity of in vivo tumors, while animal models are difficult to study in a comprehensive manner. The dynamics of PGCC emergence remain unclear in the context of the complex heterogeneity of the tumor ecosystem. In a recent paper, we presented the Evolution Accelerator (EA) [3], which allowed the quantitative study of the interactions of multiple cell types on a chemotherapy gradient. Utilizing the EA technology, we discovered that a docetaxel gradient greatly elevated the emergence of PGCCs and increased survival of the cancer population. With population analysis and careful experimental control, we demonstrated with a simple mean field model that the coexistence of the emerging drug-resistance PGCCs and proliferative diploid cells may serve as a survival strategy for the cancer population. |
Wednesday, March 6, 2019 3:18PM - 3:30PM |
P23.00005: TOPAS-nBio: Modeling effects of radiation with nanometer-scale Monte Carlo simulations Jan Schuemann, Aimee L McNamara, Jose Ramos-Mendez, Joseph Perl, Kathryn D Held, Harald Paganetti, Sebastien Incerti, Bruce Faddegon The Monte Carlo (MC) method has been successfully employed to simulate radiotherapy down to the cellular scale. In order to understand how energy deposition within irradiated cells (physics) connects via molecular reactions (chemistry) to cell kill/repair (biology), one has to understand how damage and repair of cellular components is linked to frequencies of energy depositions in sub-cellular targets such as DNA. |
Wednesday, March 6, 2019 3:30PM - 3:42PM |
P23.00006: Computational Assessment of Radiation Dose Enhancement and Secondary Electron Production for Variable Sizes and Concentrations of Gold Nanospheres in a Tumor using MCNP6.2 Tara Gray, Kathryn Mayer, Neil Kirby The purpose of this study is to computationally quantify dose enhancement effects of using different concentrations and sizes of gold nanospheres in high dose rate (HDR) brachytherapy and external beam radiotherapy. A MicroSelectron HDR Ir-192 brachytherapy source and a Varian 600C gantry head with a 6MV photon energy were modeled using Monte Carlo N-Particle radiation transport software (MCNP 6.2, Los Alamos National Laboratory). The repeating structures capability of MCNP6.2 was utilized to simulate nanospheres of sizes 4.5 nm, 30 nm and 60 nm at varying nanosphere concentrations of 5 nM, 10 nM and 20 nM, inside a tumor, with a diameter of 1 x 1 x 1 cm3. Dose enhancement factors (DEFs) were computed as the ratio of dose to the tumor containing gold nanospheres relative to that without. The highest DEF of 1.7 was observed with the Ir-192 source for a total nanosphere concentration of 20 nM and diameters of 4.5 nm. It was observed that increasing concentration and decreasing the size of the nanospheres produced the greatest dose enhancement for both HDR brachytherapy and external beam radiotherapy cases. This work indicates the potential for significant dose enhancement and more effective tumor cell killing in radiation oncology practice. |
Wednesday, March 6, 2019 3:42PM - 3:54PM |
P23.00007: Physical dose enhancement of gold nanoparticles and their impact on water radiolysis in radiotherapy Benedikt Rudek, Aimee L McNamara, Hilary Byrne, Zdenka Kuncic, Jan Schuemann Gold nanoparticle (GNP) radio-sensitization is a promising technique to increase the dose deposition in the tumor while sparing neighboring healthy tissue. The sensitization is most pronounced for keV x-rays, where the mass energy-absorption coefficient of gold is up to 150 times larger than that of soft tissue. Measurements in vitro and in vivo also showed an effect on cell survival and tumor control for other modalities such as MV photons and proton beams, where the physical dose enhancement by GNPs is expected to be negligible. |
Wednesday, March 6, 2019 3:54PM - 4:06PM |
P23.00008: Compensatory enlargement of atherosclerotic vessels — An analysis through morphoelasticity Pak-Wing Fok In 1987 Seymour Glagov published a key result on how atherosclerotic arteries remodel. His post-mortem data on human coronary arteries suggested that over the course of atherosclerotic disease, the vessel wall expands, keeping the lumen area approximately constant before before luminal encroachment occurs. This “compensatory” enlargement has been confirmed in-vivo and in other organisms. However, this behavior has never been explained physically. |
Wednesday, March 6, 2019 4:06PM - 4:18PM |
P23.00009: Computational Modeling Helps Tissue Engineered Heart Repair Moritz Kalhöfer-Köchling, Martin Uecker, Wolfram Zimmermann, Eberhard Bodenschatz, Yong Wang Understanding the mechanical influence of scarred tissue is key to understand how ischemia affects the efficacy of the heart and consequently how supportive devices can help restoring the same. With medical images and nonlinear mechanics, we are developing a patient-specific heart model for tissue engineering. Employing the Holzapfel-Ogden rule we analyse how different types of infarcts impair the contractile function of the left ventricle using end diastolic and end systolic pressure volume relations as key markers. The patient specific tissue properties and hence model parameters play a crucial role on how much heart function will be compromised after an infarct, demanding an in-depth study of said parameter space, in order to develop patient-specific solutions. As such, we also investigate how engineered heart muscle tissue can restore healthy capacity targeting at clinical applications. |
Wednesday, March 6, 2019 4:18PM - 4:30PM |
P23.00010: Improving influenza vaccine development with the pEpitope model: application to the 2018-19 season Melia Bonomo, Rachel Kim, Michael Deem The annual influenza vaccine has been shown to reduce flu-related hospitalizations and severe illness outcomes. Minimizing the antigenic differences between the vaccine strain and circulating strains ensures the vaccine will adequately prime the immune system against infection. We developed a theory of antibody response to infection following vaccination that produced a novel measure of antigenic distance. The model, called pEpitope, considers the modularity and hierarchy of antibody binding to the epitope regions of the viral hemagglutinin protein. The pEpitope model is able to explain over 90% of the variance in human vaccine epidemiological data from recent studies conducted by the US Centers for Disease Control and Prevention. Analysis of A(H3N2) strains circulating during the 2017-18 season identified the emergence of a new quasispecies cluster that is sufficiently distant from the 2018-19 vaccine and is therefore predicted to dominate. The pEpitope model is a valuable tool that predicts vaccine effectiveness to enhance vaccine strain selection and development. |
Wednesday, March 6, 2019 4:30PM - 4:42PM |
P23.00011: Network physiology reveals relations between network topology and physiological function Xiyun Zhang, Fabrizio Lombardi, Ronny Bartsch, Plamen Ch Ivanov The human organism is an integrated network where complex physiological systems, each with its own regulatory mechanisms, continuously interact, and where failure of one system can trigger a breakdown of the entire network. Identifying and quantifying dynamical networks of diverse systems with different types of interactions is a challenge. Here we develop a framework to probe interactions among diverse systems, and we identify organ interaction networks. We find that each physiological state is characterized by a specific network structure, demonstrating a robust interplay between network topology and function. Across physiological states, the organ interaction network undergoes specific reorganization process that is preserved across subject with different ages, indicating the existence of a robust relation between organ interaction networks and physiological states. |
Wednesday, March 6, 2019 4:42PM - 4:54PM |
P23.00012: Mathematical Models for the Holistic Medicine Part 1 Christina Pospisil, Tong Shu In this first part we present a theoretical mathematical model for the information flow from teeth to organs, which is a part of a study modelling illness (-> f.e catching a cold, etc.) and health conditions from the physics point of view and leads to the question of interaction/ reciprocal action of information as a phenomenon (-> as we understood it, physics is the science of phenomena) in general (-> getting ill is a certain kind of interaction with the environment). Moreover, we will give an overview about how this model can be tested experimentally. |
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