Simulation Of Tumor Growth Research Articles

Tool for simulating tumor growth in various regions of the human body in three dimensions

Tool for simulating tumor growth in various regions of the human body in three dimensions

Diffusion tensor transformation for personalizing target volumes in radiation therapy

Diffusion tensor imaging (DTI) is used in tumor growth models to provide information on the infiltration pathways of tumor cells into the surrounding brain tissue. When a patient-specific DTI is not available, a template image such as a DTI atlas can be transformed to the patient anatomy using image registration. This study investigates a model, the invariance under coordinate transform (ICT), that transforms diffusion tensors from a template image to the patient image, based on the principle that the tumor growth process can be mapped, at any point in time, between the images using the same transformation function that we use to map the anatomy. The ICT model allows the mapping of tumor cell densities and tumor fronts (as iso-levels of tumor cell density) from the template image to the patient image for inclusion in radiotherapy treatment planning. The proposed approach transforms the diffusion tensors to simulate tumor growth in locally deformed anatomy and outputs the tumor cell density distribution over time. The ICT model is validated in a cohort of ten brain tumor patients. Comparative analysis with the tumor cell density in the original template image shows that the ICT model accurately simulates tumor cell densities in the deformed image space. By creating radiotherapy target volumes as tumor fronts, this study provides a framework for more personalized radiotherapy treatment planning, without the use of additional imaging.

Targeting tumor-associated macrophages with mannosylated nanotherapeutics delivering TLR7/8 agonist enhances cancer immunotherapy

Tumor-associated macrophages (TAMs) constitute 50–80% of stromal cells in most solid tumors with high mortality and poor prognosis. Tumor-infiltrating dendritic cells (TIDCs) and TAMs are key components mediating immune responses within the tumor microenvironment (TME). Considering their refractory properties, simultaneous remodeling of TAMs and TIDCs is a potential strategy of boosting tumor immunity and restoring immunosurveillance. In this study, mannose-decorated poly(lactic-co-glycolic acid) nanoparticles loading with R848 (Man-pD-PLGA-NP@R848) were prepared to dually target TAMs and TIDCs for efficient tumor immunotherapy. The three-dimensional (3D) cell culture model can simulate tumor growth as influenced by the TME and its 3D structural arrangement. Consequently, cancer spheroids enriched with tumor-associated macrophages (TAMs) were fabricated to assess the therapeutic effectiveness of Man-pD-PLGA-NP@R848. In the TME, Man-pD-PLGA-NP@R848 targeted both TAMs and TIDCs in a mannose receptor-mediated manner. Subsequently, Man-pD-PLGA-NP@R848 released R848 to activate Toll-like receptors 7 and 8, following dual-reprograming of TIDCs and TAMs. Man-pD-PLGA-NP@R848 could uniquely reprogram TAMs into antitumoral phenotypes, decrease angiogenesis, reprogram the immunosuppressive TME from “cold tumor” into “hot tumor”, with high CD4+ and CD8+ T cell infiltration, and consequently hinder tumor development in B16F10 tumor-bearing mice. Therefore, dual-reprograming of TIDCs and TAMs with the Man-pD-PLGA-NP@R848 is a promising cancer immunotherapy strategy.

Relating Macroscopic PET Radiomics Features to Microscopic Tumor Phenotypes Using a Stochastic Mathematical Model of Cellular Metabolism and Proliferation.

Cancers can manifest large variations in tumor phenotypes due to genetic and microenvironmental factors, which has motivated the development of quantitative radiomics-based image analysis with the aim to robustly classify tumor phenotypes in vivo. Positron emission tomography (PET) imaging can be particularly helpful in elucidating the metabolic profiles of tumors. However, the relatively low resolution, high noise, and limited PET data availability make it difficult to study the relationship between the microenvironment properties and metabolic tumor phenotype as seen on the images. Most of previously proposed digital PET phantoms of tumors are static, have an over-simplified morphology, and lack the link to cellular biology that ultimately governs the tumor evolution. In this work, we propose a novel method to investigate the relationship between microscopic tumor parameters and PET image characteristics based on the computational simulation of tumor growth. We use a hybrid, multiscale, stochastic mathematical model of cellular metabolism and proliferation to generate simulated cross-sections of tumors in vascularized normal tissue on a microscopic level. The generated longitudinal tumor growth sequences are converted to PET images with realistic resolution and noise. By changing the biological parameters of the model, such as the blood vessel density and conditions for necrosis, distinct tumor phenotypes can be obtained. The simulated cellular maps were compared to real histology slides of SiHa and WiDr xenografts imaged with Hoechst 33342 and pimonidazole. As an example application of the proposed method, we simulated six tumor phenotypes that contain various amounts of hypoxic and necrotic regions induced by a lack of oxygen and glucose, including phenotypes that are distinct on the microscopic level but visually similar in PET images. We computed 22 standardized Haralick texture features for each phenotype, and identified the features that could best discriminate the phenotypes with varying image noise levels. We demonstrated that "cluster shade" and "difference entropy" are the most effective and noise-resilient features for microscopic phenotype discrimination. Longitudinal analysis of the simulated tumor growth showed that radiomics analysis can be beneficial even in small lesions with a diameter of 3.5-4 resolution units, corresponding to 8.7-10.0 mm in modern PET scanners. Certain radiomics features were shown to change non-monotonically with tumor growth, which has implications for feature selection for tracking disease progression and therapy response.

Mathematical modeling of the synergistic interplay of radiotherapy and immunotherapy in anti-cancer treatments.

While radiotherapy has long been recognized for its ability to directly ablate cancer cells through necrosis or apoptosis, radiotherapy-induced abscopal effect suggests that its impact extends beyond local tumor destruction thanks to immune response. Cellular proliferation and necrosis have been extensively studied using mathematical models that simulate tumor growth, such as Gompertz law, and the radiation effects, such as the linear-quadratic model. However, the effectiveness of radiotherapy-induced immune responses may vary among patients due to individual differences in radiation sensitivity and other factors. We present a novel macroscopic approach designed to quantitatively analyze the intricate dynamics governing the interactions among the immune system, radiotherapy, and tumor progression. Building upon previous research demonstrating the synergistic effects of radiotherapy and immunotherapy in cancer treatment, we provide a comprehensive mathematical framework for understanding the underlying mechanisms driving these interactions. Our method leverages macroscopic observations and mathematical modeling to capture the overarching dynamics of this interplay, offering valuable insights for optimizing cancer treatment strategies. One shows that Gompertz law can describe therapy effects with two effective parameters. This result permits quantitative data analyses, which give useful indications for the disease progression and clinical decisions. Through validation against diverse data sets from the literature, we demonstrate the reliability and versatility of our approach in predicting the time evolution of the disease and assessing the potential efficacy of radiotherapy-immunotherapy combinations. This further supports the promising potential of the abscopal effect, suggesting that in select cases, depending on tumor size, it may confer full efficacy to radiotherapy.

An AI-based approach for modeling the synergy between radiotherapy and immunotherapy

Personalized, ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is designed to administer tumoricidal doses in a pulsed mode with extended intervals, spanning weeks or months. This approach leverages longer intervals to adapt the treatment plan based on tumor changes and enhance immune-modulated effects. In this investigation, we seek to elucidate the potential synergy between combined PULSAR and PD-L1 blockade immunotherapy using experimental data from a Lewis Lung Carcinoma (LLC) syngeneic murine cancer model. Employing a long short-term memory (LSTM) recurrent neural network (RNN) model, we simulated the treatment response by treating irradiation and anti-PD-L1 as external stimuli occurring in a temporal sequence. Our findings demonstrate that: (1) The model can simulate tumor growth by integrating various parameters such as timing and dose, and (2) The model provides mechanistic interpretations of a “causal relationship” in combined treatment, offering a completely novel perspective. The model can be utilized for in-silico modeling, facilitating exploration of innovative treatment combinations to optimize therapeutic outcomes. Advanced modeling techniques, coupled with additional efforts in biomarker identification, may deepen our understanding of the biological mechanisms underlying the combined treatment.

Hybrid Cellular Automata Modeling Reveals the Effects of Glucose Gradients on Tumour Spheroid Growth

Purpose: In recent years, mathematical models have become instrumental in cancer research, offering insights into tumor growth dynamics, and guiding the development of pharmacological strategies. These models, encompassing diverse biological and physical processes, are increasingly used in clinical settings, showing remarkable predictive precision for individual patient outcomes and therapeutic responses. Methods: Motivated by these advancements, our study introduces an innovative in silico model for simulating tumor growth and invasiveness. The automated hybrid cell emulates critical tumor cell characteristics, including rapid proliferation, heightened motility, reduced cell adhesion, and increased responsiveness to chemotactic signals. This model explores the potential evolution of 3D tumor spheroids by manipulating biological parameters and microenvironment factors, focusing on nutrient availability. Results: Our comprehensive global and local sensitivity analysis reveals that tumor growth primarily depends on cell duplication speed and cell-to-cell adhesion, rather than external chemical gradients. Conversely, tumor invasiveness is predominantly driven by chemotaxis. These insights illuminate tumor development mechanisms, providing vital guidance for effective strategies against tumor progression. Our proposed model is a valuable tool for advancing cancer biology research and exploring potential therapeutic interventions.

Computational simulations of tumor growth and treatment response: Benefits of high-frequency, low-dose drug regimens and concurrent vascular normalization.

Implementation of effective cancer treatment strategies requires consideration of how the spatiotemporal heterogeneities within the tumor microenvironment (TME) influence tumor progression and treatment response. Here, we developed a multi-scale three-dimensional mathematical model of the TME to simulate tumor growth and angiogenesis and then employed the model to evaluate an array of single and combination therapy approaches. Treatments included maximum tolerated dose or metronomic (i.e., frequent low doses) scheduling of anti-cancer drugs combined with anti-angiogenic therapy. The results show that metronomic therapy normalizes the tumor vasculature to improve drug delivery, modulates cancer metabolism, decreases interstitial fluid pressure and decreases cancer cell invasion. Further, we find that combining an anti-cancer drug with anti-angiogenic treatment enhances tumor killing and reduces drug accumulation in normal tissues. We also show that combined anti-angiogenic and anti-cancer drugs can decrease cancer invasiveness and normalize the cancer metabolic microenvironment leading to reduced hypoxia and hypoglycemia. Our model simulations suggest that vessel normalization combined with metronomic cytotoxic therapy has beneficial effects by enhancing tumor killing and limiting normal tissue toxicity.

Mathematical Model for Evaluation of Tumor Response in Targeted Radionuclide Therapy with 211At Using Implanted Mouse Tumor.

Recent introduction of alpha-emitting radionuclides in targeted radionuclide therapy has stimulated the development of new radiopharmaceuticals. Preclinical evaluation using an animal experiment with an implanted tumor model is frequently used to examine the efficiency of the treatment method and to predict the treatment response before clinical trials. Here, we propose a mathematical model for evaluation of the tumor response in an implanted tumor model and apply it to the data obtained from the previous experiment of 211At treatment in a thyroid cancer mouse model. The proposed model is based on the set of differential equations, describing the kinetics of radiopharmaceuticals, the tumor growth, and the treatment response. First, the tumor growth rate was estimated from the control data without injection of 211At. The kinetic behavior of the injected radionuclide was used to estimate the radiation dose profile to the target tumor, which can suppress the tumor growth in a dose-dependent manner. An additional two factors, including the time delay for the reduction of tumor volume and the impaired recovery of tumor regrowth after the treatment, were needed to simulate the temporal changes of tumor size after treatment. Finally, the parameters obtained from the simulated tumor growth curve were able to predict the tumor response in other experimental settings. The model can provide valuable information for planning the administration dose of radiopharmaceuticals in clinical trials, especially to determine the starting dose at which efficacy can be expected with a sufficient safety margin.

Network Analysis Identifies Phase Transitions for Tumor With Interacting Cells.

Metastasis is the process by which cancer cells acquire the capability to leave the primary tumor and travel to distant sites. Recent experiments have suggested that the epithelial–mesenchymal transition can regulate invasion and metastasis. Another possible scenario is the collective motion of cells. Recent studies have also proposed a jamming–unjamming transition for epithelial cells based on physical forces. Here, we assume that there exists a short-range chemical attraction between cancer cells and employ the Brownian dynamics to simulate tumor growth. Applying the network analysis, we suggest three possible phases for a given tumor and study the transition between these phases by adjusting the attraction strength.

Applications of Microfluidics and Organ-on-a-Chip in Cancer Research

Taking the life of nearly 10 million people annually, cancer has become one of the major causes of mortality worldwide and a hot topic for researchers to find innovative approaches to demystify the disease and drug development. Having its root lying in microelectronics, microfluidics seems to hold great potential to explore our limited knowledge in the field of oncology. It offers numerous advantages such as a low sample volume, minimal cost, parallelization, and portability and has been advanced in the field of molecular biology and chemical synthesis. The platform has been proved to be valuable in cancer research, especially for diagnostics and prognosis purposes and has been successfully employed in recent years. Organ-on-a-chip, a biomimetic microfluidic platform, simulating the complexity of a human organ, has emerged as a breakthrough in cancer research as it provides a dynamic platform to simulate tumor growth and progression in a chip. This paper aims at giving an overview of microfluidics and organ-on-a-chip technology incorporating their historical development, physics of fluid flow and application in oncology. The current applications of microfluidics and organ-on-a-chip in the field of cancer research have been copiously discussed integrating the major application areas such as the isolation of CTCs, studying the cancer cell phenotype as well as metastasis, replicating TME in organ-on-a-chip and drug development. This technology’s significance and limitations are also addressed, giving readers a comprehensive picture of the ability of the microfluidic platform to advance the field of oncology.

Abstract 3362: Multiscale EGFR mutated NSCLC tumor heterogeneity knowledge-based model predicts tumor growth under gefitinib: An avenue to in silico clinical trial

Abstract Introduction: Non-small cell lung cancer (NSCLC) response to gefitinib depends on epidermal growth factor receptor (EGFR) mutational status. Response differs according to tumor heterogeneity, notably through clonal selection according to genetic background. We developed an in silico EGFR mutated lung adenocarcinoma (EGFR+ LUAD) model topredict the effect of EGFR mutations on tumor size in advanced LUAD patients using a mechanistic representation of tumor progression, including response to gefitinib. Tumor heterogeneity, age, gender, initial clinical stage, and smoking status are included as covariates. Methods: 5-step in silico model development: (i) Model Building: Biology of EGFR+LUAD was characterized by extracting biological features and their functional relationships from literature and translating them into ordinary differential equations (ODEs). Mutational burden, EGFR downstream-pathways, tumor growth and heterogeneity, gefitinib-PK/PD, treatment-induced resistance and clinical outcome were modeled in a computational simulation with 43 variables, 170 parameters and 18 to 83 ODEs reflecting intra-tumor heterogeneity. (ii) Calibration: Published spheroid, xenograft and clinical data were used for stepwise calibration. (iii) Virtual populations (VPOP): VPOPs were generated for validation and benchmarking respectively, adapting baseline characteristics of a real population.(iv) Validation: A VPOP with comparable baseline characteristics was tested against published patient data.(v) Benchmarking against a Bayesian reference model : (1) coverage of experimental interquartile range (IQR) with simulated IQR (precision) assesses model fit with experimental data, (2) coverage of simulated IQR with experimental IQR (overlap) assesses model fit with experimental variability. Results: Our model computed in silico data comparable to the reference model without use of original data for calibration (Figure 1B.2: experimental vs. simulated, precision of 68%, overlap of 91%). The reference model reported precision of 72% and overlap of 86%. Therefore, the ISELA model has a better percentage of the literature data area contained in the simulated one while the Bayesian model presents a better percentage of the simulated data contained in the literature one. Conclusion: We simulated tumor growth and treatment response in advanced EGFR+ LUAD patients and successfully validated results with published data, and compared it to an already published model with the same context of use. Both models successfully provide a reliable description of longitudinal tumor size when compared to each other or to observed data. Our model provides a benchmark for future in silico clinical trials. Citation Format: Michael Duruisseaux, Adèle L'Hostis, Emmanuel Peyronnet, Evgueni Jacob, Ben M.W. Illigens, Jim Bosley, Riad Kahoul, Jean-Louis Palgen, Claudio Monteiro. Multiscale EGFR mutated NSCLC tumor heterogeneity knowledge-based model predicts tumor growth under gefitinib: An avenue to in silico clinical trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3362.

Abstract 2721: Accurate prediction of tumor growth and doubling times for triple negative breast cancer (TNBC) allows for patient-specific assessment of tumor aggressiveness

Abstract TNBC is the most aggressive of breast cancer subtypes and lacks targeted therapies. Thus, tumor aggressiveness is an important factor to determine escalation of care for TNBC patients. Currently, aggressiveness is evaluated based on tumor spread and markers of cell growth. Although these features are significantly associated with tumor volume doubling time (TVDT), TVDT distributions are not simply separated by any of these criteria. Therefore, for single patients, no clear predictor of TVDT (tumor aggressiveness) exists. We sought to test the SimBioSys TumorScope (TS), a biophysical modeling software, to determine whether it could be used for predicting tumor growth and TVDT in TNBC patients. We used the TS software to simulate tumor growth in 30 TNBC patients. We compared the calculated TVDTs from our simulations to values obtained from literature. Using our metabolic modeling platform, we simulated tumor average specific growth rates (SGRs) from RNA-seq data taken from the TCGA BRCA data set. We found that coordinated gene expression profiles act to regulate SGR differentially across breast cancer receptor subtypes (p = 2x10-16), with ER+/PR+ tumors growing slowest (SGR = 0.49 %/day +/- 0.33, median +/- stdev) and TNBC tumors growing fastest (SGR = 1.03 %/day +/- 0.35), which match well with previously reported values of SGR in breast cancers (reported SGR = 0.175 +/- 0.979 %/day for Luminal A and SGR = 1.003 +/- 1.121 %/day for TNBC, mean +/- stdev). We next tested how these growth rates are integrated with tumor biology to produce TVDTs on the order of 100-200 days in TNBC patients. We selected 30 patients with TNBC whose MRI data and electronic medical record (EMR) information are available in our TumorBank to use for this study. We then selected representative TNBC tumor metabolic models and mapped patients to these metabolic models based on patient phenotype. We used clinical measurements to predict unspecified model parameters for TS simulations, resulting in a model with a single free parameter (growing fraction of tumor cells). We tuned this parameter within reported values to simulate TVDT in our patient cohort. Simulating the growth of these tumors resulted in a median TVDT = 163 days with a standard deviation of 111 days, closely matching clinically measured TVDTs in TNBC patients (reported mean TVDT = 127 +/- 48 days). In summary, TS biophysical modeling software accurately predicts TVDTs in TNBC patients. Importantly, this was accomplished without parameter fitting outside of physiologically-measured values and serves as a powerful validation of the our modeling approach. In the future, this high accuracy modeling framework can be further personalized by accounting for additional patient features, including, but not limited to, patient-specific mitotic index, blood chemistry data, and tumor-specific RNA-sequencing. Citation Format: John Whitman, Daniel Cook, Snehal Patel, Lance Munn, John A. Cole, The SimBioSys Team. Accurate prediction of tumor growth and doubling times for triple negative breast cancer (TNBC) allows for patient-specific assessment of tumor aggressiveness [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2721.

Development and evaluation of a method for tumor growth simulation in virtual clinical trials of breast cancer screening.

Purpose: Image-based analysis of breast tumor growth rate may optimize breast cancer screening and diagnosis by suggesting optimal screening intervals and guide the clinical discussion regarding personalized screening based on tumor aggressiveness. Simulation-based virtual clinical trials (VCTs) can be used to evaluate and optimize medical imaging systems and design clinical trials. This study aimed to simulate tumor growth over multiple screening rounds. Approach: This study evaluates a preliminary method for simulating tumor growth. Clinical data on tumor volume doubling time (TVDT) was used to fit a probability distribution ("clinical fit") of TVDTs. Simulated tumors with TVDTs sampled from the clinical fit were inserted into 30 virtual breasts ("simulated cohort") and used to simulate mammograms. Based on the TVDT, two successive screening rounds were simulated for each virtual breast. TVDTs from clinical and simulated mammograms were compared. Tumor sizes in the simulated mammograms were measured by a radiologist in three repeated sessions to estimate TVDT. Results: The mean TVDT was 297days (standard deviation, SD, 169days) in the clinical fit and 322days (SD, 217days) in the simulated cohort. The mean estimated TVDT was 340days (SD, 287days). No significant difference was found between the estimated TVDTs from simulated mammograms and clinical TVDT values ( ). No significant difference ( ) was observed in the reproducibility of the tumor size measurements between the two screening rounds. Conclusions: The proposed method for tumor growth simulation has demonstrated close agreement with clinical results, supporting potential use in VCTs of temporal breast imaging.

Multiscale in silico knowledge-based model predicts tumor growth in advanced EGFR+ NSCLC patients on gefitinib.

e20565 Background: Tumor heterogeneity and treatment resistance remains a continued threat to patients with advanced non-small cell lung cancer (NSCLC), despite the emergence of targeted medicines, e.g. against epidermal growth factor receptor (EGFR) alterations. We developed an in silico EGFR mutated lung adenocarcinoma (EGFR+ LUAD) model to predict the effect of known oncogenic EGFR mutations (common EGFR mutations, EGFR exon 20 insertions). The model provides mechanistic representation of tumor progression, including response to gefitinib and captures tumor heterogeneity, patient age, gender, initial clinical disease stage, and smoking status. Methods: 5-step in silico model development: Model building: biology of EGFR+LUAD was characterized by extracting biological features and their functional relationships from > 300 published papers and translating them into ordinary differential equations (ODEs). Mutational burden, EGFR-downstream-pathways, tumor growth and heterogeneity, gefitinib-PK/PD, treatment-induced resistance and clinical outcome were incorporated into a knowledge-based model containing 27-97 variables, 108-258 parameters and 13-83 ODEs reflecting intra-tumor clonal heterogeneity in a mechanistic manner. Calibration: published spheroid, xenograft and clinical data were used for stepwise calibration to find the correct parameter values. Relevant virtual populations (VPOPs) matching real patients baseline characteristics were generated for model benchmarking and validation. Benchmarking against a published data-based model: coverage of experimental interquartile range (IQR) with simulated IQR (precision) assesses model fit with experimental data, coverage of simulated IQR with experimental IQR (overlap) assesses model fit with experimental variability. Validation: a VPOP with comparable baseline characteristics was simulated and results compared against a published patient dataset that wasn’t used in any step during calibration. Matching of prediction over clinical data was done using both coverage and bootstrapped log-rank metrics. Results: Our model provides comparable outputs to the data-based model without having access to the exact original data (our model: precision of 62%, overlap of 91% VS data-based model: precision of 72% and an overlap of 86%). Besides, as a validation criterion, our model also successfully reproduces the time to progression observed in an independent clinical trial (coverage > 99%, negative log-rank tests > 98%). Conclusions: We simulated tumor growth and treatment response in advanced EGFR+ LUAD patients and successfully validated results both against an existing data-based model and a published clinical data. Our model highlights the potential of in silico modeling to better understand complex diseases progression and support efficient development of innovative therapies.

Geometry-Aware Neural Solver for Fast Bayesian Calibration of Brain Tumor Models.

Modeling of brain tumor dynamics has the potential to advance therapeutic planning. Current modeling approaches resort to numerical solvers that simulate the tumor progression according to a given differential equation. Using highly-efficient numerical solvers, a single forward simulation takes up to a few minutes of compute. At the same time, clinical applications of tumor modeling often imply solving an inverse problem, requiring up to tens of thousands of forward model evaluations when used for a Bayesian model personalization via sampling. This results in a total inference time prohibitively expensive for clinical translation. While recent data-driven approaches become capable of emulating physics simulation, they tend to fail in generalizing over the variability of the boundary conditions imposed by the patient-specific anatomy. In this paper, we propose a learnable surrogate for simulating tumor growth which maps the biophysical model parameters directly to simulation outputs, i.e. the local tumor cell densities, whilst respecting patient geometry. We test the neural solver in a Bayesian model personalization task for a cohort of glioma patients. Bayesian inference using the proposed surrogate yields estimates analogous to those obtained by solving the forward model with a regular numerical solver. The near real-time computation cost renders the proposed method suitable for clinical settings. The code is available at https://github.com/IvanEz/tumor-surrogate.

Distinguishing excess mutations and increased cell death based on variant allele frequencies.

Tumors often harbor orders of magnitude more mutations than healthy tissues. The increased number of mutations may be due to an elevated mutation rate or frequent cell death and correspondingly rapid cell turnover, or a combination of the two. It is difficult to disentangle these two mechanisms based on widely available bulk sequencing data, where sequences from individual cells are intermixed and, thus, the cell lineage tree of the tumor cannot be resolved. Here we present a method that can simultaneously estimate the cell turnover rate and the rate of mutations from bulk sequencing data. Our method works by simulating tumor growth and finding the parameters with which the observed data can be reproduced with maximum likelihood. Applying this method to a real tumor sample, we find that both the mutation rate and the frequency of death may be high.

Biomechanics of human optic chiasmal compression: ex vivo experiment and finite element modelling

The mechanism of bitemporal hemianopia arising as a result of chiasmal compression is unknown. In this study, we combined an ex vivo experiment and finite element modelling (FEM) to investigate its potential mechanism. A cadaveric human optic chiasm was scanned using micro-CT before and after deformation by inflation of Foley catheter, to simulate tumour growth from beneath. The geometry of the same chiasm was reconstructed and simulated using finite element analysis. Chiasmal deformations were extracted from the simulation and compared with those observed during micro-CT scanning. In addition, nerve fibre models examining variation in local fibre distribution patterns of the chiasm were incorporated to investigate the strain (deformation) distributions of the chiasm at an axonal level. The FEM model matched the micro-CT scans well both qualitatively and quantitatively. Compression of the chiasm induced high strains in the paracentral portions of the chiasm where the crossing optic nerve fibres are located. At an axonal level, the magnitude of strains affecting crossed fibres were greater than those affecting uncrossed fibres. The high strains in the paracentral portions of the chiasm, combined with the differences in strain between crossed and uncrossed nerve fibres, are consistent with a biomechanical explanation for the pattern of visual field loss seen in chiasmal compression.

Simulating tumor growth using mathematical and agent-based modeling

In this paper, two different approaches for tumor growth modeling are presented and implemented. In the first part of the paper, a macroscopic approach using a PDE model, where the tumor is viewed as a cell mass, is implemented using the level-set method to track the tumor moving boundary in one hand by using Darcy’s law to compute the normal velocity of the free boundary and on the other hand using the shape optimization to draw the normal velocity. In the second part of the paper, a microscopic approach, which focuses on the cellular scale, is presented. A hybrid model using agent-based modeling for the cell behavior and a PDE for the description of the tumor environment is presented. A sensitivity analysis is performed on the hybrid model for a better understanding of its impact on the tumor growth. Numerical experiments are provided for the proposed approaches.

P71.05 Use of a Multiscale NSCLC Tumor Heterogeneity Model to Predict Tumor Growth Under Gefitinib

Novel high-throughput techniques, advanced tumor micro-environment analyses and new insights in tumor cell heterogeneity have significantly increased understanding of regulatory processes in Non-small cell lung cancer (NSCLC): NSCLC treatment response to Gefitinib depends on the epidermal growth factor receptor (EGFR), but differs depending on EGFR gene mutations making clinical prognosis challenging. We therefore developed an in silico EGFR+ lung adenocarcinoma (LUAD) model to predict the effect of EGFR-related mutations on tumor size in advanced-stage adenocarcinoma patients (IIIb or higher), using a mechanistic representation of tumor evolution, including response to Gefitinib. Tumor heterogeneity, age, gender, initial clinical stage, and smoking status are included as covariates. 5-step in silico model development: 1. Model Building: Pathophysiology of EGFR+LUAD was characterized by extracting biological features and their functional relationships from literature and translating them into ordinary differential equations (ODEs). Mutational burden, EGFR downstream-pathways, tumor growth and heterogeneity, Gefitinib-PK/PD, treatment-induced resistance and clinical outcome were modeled in a computational simulation with 43 variables, 170 parameters and 18 to 83 ODEs reflecting intra-tumor heterogeneity. 2. Calibration: Published spheroid, xenograft and clinical data were used for stepwise calibration. 3. Virtual populations (VPOP): VPOPs were generated for validation and benchmarking respectively, adapting baseline characteristics of a real population. 4. Validation: A VPOP with comparable baseline characteristics was tested against published patient data[1]. 5. Benchmarking against a Bayesian reference model[2]: (1) coverage of experimental interquartile range (IQR) with simulated IQR (precision) assesses model fit with experimental data, (2) coverage of simulated IQR with experimental IQR (overlap) assesses model fit with experimental variability. Our model computed in silico data comparable to the reference model[2] without use of original data for calibration (Figure 1B.2: experimental vs. simulated, precision of 68%, overlap of 91%). The reference model reported precision of 72% and overlap of 86%. We simulated tumor growth and treatment response in advanced-stage adenocarcinoma patients and successfully validated results with a published study[2]. Access to patient-level data for calibration would have improved precision of our model.