A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin

Tumor heterogeneity contributes significantly to chemoresistance, a leading cause of treatment failure. To better personalize therapies, it is essential to develop tools capable of identifying and predicting intra- and inter-tumor heterogeneities. Biology-inspired mathematical models are capable of attacking this problem, but tumor heterogeneity is often overlooked in in-vivo modeling studies, while phenotypic considerations capturing spatial dynamics are not typically included in in-vitro modeling studies. We present a data assimilation-prediction pipeline with a two-phenotype model that includes a spatiotemporal component to characterize and predict the evolution of in-vitro breast cancer cells and their heterogeneous response to chemotherapy. Our model assumes that the cells can be divided into two subpopulations: surviving cells unaffected by the treatment, and irreversibly damaged cells undergoing treatment-induced death. MCF7 breast cancer cells were previously cultivated in wells for up to 1000 hours, treated with various concentrations of doxorubicin and imaged with time-resolved microscopy to record spatiotemporally-resolved cell count data. Images were used to generate cell density maps. Treatment response predictions were initialized by a training set and updated by weekly measurements. Our mathematical model successfully calibrated the spatiotemporal cell growth dynamics, achieving median [range] concordance correlation coefficients of > .99 [.88, >.99] and .73 [.58, .85] across the whole well and individual pixels, respectively. Our proposed data assimilation-prediction approach achieved values of .97 [.44, >.99] and .69 [.35, .79] for the whole well and individual pixels, respectively. Thus, our model can capture and predict the spatiotemporal dynamics of MCF7 cells treated with doxorubicin in an in-vitro setting.

Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data

Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.

A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model

Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer.

SIGNIFICANCE

Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.

Designing clinical trials for patients who are not average

The heterogeneity inherent in cancer means that even a successful clinical trial merely results in a therapeutic regimen that achieves, on average, a positive result only in a subset of patients. The only way to optimize an intervention for an individual patient is to reframe their treatment as their own, personalized trial. Toward this goal, we formulate a computational framework for performing personalized trials that rely on four mathematical techniques. First, mathematical models that can be calibrated with patient-specific data to make accurate predictions of response. Second, digital twins built on these models capable of simulating the effects of interventions. Third, optimal control theory applied to the digital twins to optimize outcomes. Fourth, data assimilation to continually update and refine predictions in response to therapeutic interventions. In this perspective, we describe each of these techniques, quantify their "state of readiness", and identify use cases for personalized clinical trials.

Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the underlying tumor biology in high-grade gliomas, where heterogeneity in the response to standard-of-care (SOC) radiotherapy contributes to sub-optimal patient outcomes. The digital twin is initialized through prior distributions derived from population-level clinical data in the literature for a mechanistic model's parameters. Then the digital twin is personalized using Bayesian model calibration for assimilating patient-specific magnetic resonance imaging data. The calibrated digital twin is used to propose optimal radiotherapy treatment regimens by solving a multi-objective risk-based optimization under uncertainty problem. The solution leads to a suite of patient-specific optimal radiotherapy treatment regimens exhibiting varying levels of trade-off between the two competing clinical objectives: (i) maximizing tumor control (characterized by minimizing the risk of tumor volume growth) and (ii) minimizing the toxicity from radiotherapy. The proposed digital twin framework is illustrated by generating an in silico cohort of 100 patients with high-grade glioma growth and response properties typically observed in the literature. For the same total radiation dose as the SOC, the personalized treatment regimens lead to median increase in tumor time to progression of around six days. Alternatively, for the same level of tumor control as the SOC, the digital twin provides optimal treatment options that lead to a median reduction in radiation dose by 16.7% (10 Gy) compared to SOC total dose of 60 Gy. The range of optimal solutions also provide options with increased doses for patients with aggressive cancer, where SOC does not lead to sufficient tumor control.

Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data

Despite the remarkable advances in cancer diagnosis, treatment, and management that have occurred over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires patient-specific information integrated into an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous, yet practical, mathematical theory of tumor initiation, development, invasion, and response to therapy. In this review, we begin by providing an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on ``big data" and artificial intelligence. Next, we present illustrative examples of mathematical models manifesting their utility and discussing the limitations of stand-alone mechanistic and data-driven models. We further discuss the potential of mechanistic models for not only predicting, but also optimizing response to therapy on a patient-specific basis. We then discuss current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.

SAMAY S24: a novel wireless 'online' device for real-time monitoring and analysis of volumetric capnography

Volumetric capnography (VCap) provides information about CO2 exhaled per breath (VCO2br) and physiologic dead space (VDphys). A novel wireless device with a high response time CO2 mainstream sensor coupled with a digital flowmeter was designed to monitor all VCap parameters online in rabbits (SAMAY S24).Ten New Zealand rabbits were anesthetized and mechanically ventilated. VCO2br corresponds to the area under the VCap curve. We used the modified Langley method to assess the airway VD (VDaw) and the alveolar CO2 pressure. VDphys was estimated using Bohr's formula, and the alveolar VD was calculated by subtracting VDaw from VDphys. We compared (Bland-Altman) the critical VCap parameters obtained by SAMAY S24 (Langley) with the Functional Approximation based on the Levenberg-Marquardt Algorithm (FA-LMA) approach during closed and opened chest conditions.SAMAY S24 could assess dead space volumes and VCap shape in real time with similar accuracy and precision compared to the 'offline' FA-LMA approach. The opened chest condition impaired CO2 kinetics, decreasing the phase II slope, which was correlated with the volume of CO2 exhaled per minute.

Dynamic parameterization of a modified SEIRD model to analyze and forecast the dynamics of COVID-19 outbreaks in the United States

The rapid spread of the numerous outbreaks of the coronavirus disease 2019 (COVID-19) pandemic has fueled interest in mathematical models designed to understand and predict infectious disease spread, with the ultimate goal of contributing to the decision making of public health authorities. Here, we propose a computational pipeline that dynamically parameterizes a modified SEIRD (susceptible-exposed-infected-recovered-deceased) model using standard daily series of COVID-19 cases and deaths, along with isolated estimates of population-level seroprevalence. We test our pipeline in five heavily impacted states of the US (New York, California, Florida, Illinois, and Texas) between March and August 2020, considering two scenarios with different calibration time horizons to assess the update in model performance as new epidemiologic data become available. Our results show a median normalized root mean squared error (NRMSE) of 2.38% and 4.28% in calibrating cumulative cases and deaths in the first scenario, and 2.41% and 2.30% when new data are assimilated in the second scenario, respectively. Then, 2-week (4-week) forecasts of the calibrated model resulted in median NRMSE of cumulative cases and deaths of 5.85% and 4.68% (8.60% and 17.94%) in the first scenario, and 1.86% and 1.93% (2.21% and 1.45%) in the second. Additionally, we show that our method provides significantly more accurate predictions of cases and deaths than a constant parameterization in the second scenario (p < 0.05). Thus, we posit that our methodology is a promising approach to analyze the dynamics of infectious disease outbreaks, and that our forecasts could contribute to designing effective pandemic-arresting public health policies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00366-023-01816-9.

Towards Patient-Specific Optimization of Neoadjuvant Treatment Protocols for Breast Cancer Based on Image-Guided Fluid Dynamics

OBJECTIVE

This study establishes a fluid dynamics model personalized with patient-specific imaging data to optimize neoadjuvant therapy (i.e., doxorubicin) protocols for breast cancers.

METHODS

Ten patients recruited at the University of Chicago were included in this study. Quantitative dynamic contrast-enhanced and diffusion weighted magnetic resonance imaging data are leveraged to estimate patient-specific hemodynamic properties, which are then used to constrain the mechanism-based drug delivery model. Then, computer simulations of this model yield the subsequent drug distribution throughout the breast. By systematically varying the dosing schedule, we identify an optimized regimen for each patient using the maximum safe therapeutic duration (MSTD), which is a metric balancing treatment efficacy and toxicity.

RESULTS

With an individually optimized dose (range = 12.11-15.11 mg/m2 per injection), a 3-week regimen consisting of a uniform daily injection significantly outperforms all other scheduling strategies (P < 0.001). In particular, the optimal protocol is predicted to significantly outperform the standard protocol (P < 0.001), improving the MSTD by an average factor of 9.93 (range = 6.63 to 14.17).

CONCLUSION

A clinical-mathematical framework was developed by integrating quantitative MRI data, advanced image processing, and computational fluid dynamics to predict the efficacy and toxicity of neoadjuvant therapy protocols, thus enabling the rational identification of an optimal therapeutic regimen on a patient-specific basis.

SIGNIFICANCE

Our clinical-computational approach has the potential to enable optimization of therapeutic regimens on a patient-specific basis and provide guidance for prospective clinical trials aimed at refining neoadjuvant therapy protocols for breast cancers.

Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin

The development of chemoresistance remains a significant cause of treatment failure in breast cancer. We posit that a mathematical understanding of chemoresistance could assist in developing successful treatment strategies. Towards that end, we have developed a model that describes the cytotoxic effects of the standard chemotherapeutic drug doxorubicin on the MCF-7 breast cancer cell line. We assume that treatment with doxorubicin induces a compartmentalization of the breast cancer cell population into surviving cells, which continue proliferating after treatment, and irreversibly damaged cells, which gradually transition from proliferating to treatment-induced death. The model is fit to experimental data including variations in drug concentration, inter-treatment interval, and number of doses. Our model recapitulates tumor cell dynamics in all these scenarios (as quantified by the concordance correlation coefficient, CCC > 0.95). In particular, superior tumor control is observed with higher doxorubicin concentrations, shorter inter-treatment intervals, and a higher number of doses (p < 0.05). Longer inter-treatment intervals require adapting the model parameterization after each doxorubicin dose, suggesting the promotion of chemoresistance. Additionally, we propose promising empirical formulas to describe the variation of model parameters as functions of doxorubicin concentration (CCC > 0.78). Thus, we conclude that our mathematical model could deepen our understanding of the cytotoxic effects of doxorubicin and could be used to explore practical drug regimens achieving optimal tumor control.

Data-Driven Simulation of Fisher-Kolmogorov Tumor Growth Models Using Dynamic Mode Decomposition

The computer simulation of organ-scale biomechanistic models of cancer personalized via routinely collected clinical and imaging data enables to obtain patient-specific predictions of tumor growth and treatment response over the anatomy of the patient's affected organ. However, the simulation of the underlying spatiotemporal models can entail a prohibitive computational cost, which constitutes a barrier to the successful development of clinically-actionable computational technologies for personalized tumor forecasting. Here we propose to utilize Dynamic-Mode Decomposition (DMD), an unsupervised machine learning method, to construct a low-dimensional representation of cancer models and accelerate their simulation. We show that DMD may be applied to Fisher-Kolmogorov models, which constitute an established formulation to represent untreated solid tumor growth that can further accommodate other relevant cancer phenomena. Our results show that a DMD implementation of this model over a clinically-relevant parameter space can yield impressive predictions, with short to medium-term errors remaining under 1% and long-term errors remaining under 20%, despite very short training periods. In particular, we have found that, for moderate to high tumor cell diffusivity and low to moderate tumor cell proliferation rate, DMD reconstructions provide accurate, bounded-error reconstructions for all tested training periods. We posit that this data-driven approach has the potential to greatly reduce the computational overhead of personalized simulations of cancer models, thereby facilitating tumor forecasting, parameter identification, uncertainty quantification, and treatment optimization.

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.

Abstract P1-08-20: In silico analysis of a novel mathematical model integrating in vitro and in vivo imaging data reveals driving mechanisms of breast cancer response to NAT for personalized tumor forecasting

The early assessment of neoadjuvant therapy (NAT) response in triple-negative breast cancer (TNBC) would enable a treating oncologist to adjust a therapeutic plan of a non-responding patient, and thereby enhance outcomes while preventing unnecessary toxicities. To address this challenge, we propose leveraging personalized, in silico forecasts of tumor response to therapeutic regimens via a mechanistic mathematical model calibrated with patient-specific longitudinal magnetic resonance imaging (MRI) data acquired early during NAT. Here, we focus on identifying the driving mechanisms involved in the model formulation through a global sensitivity analysis. Our model describes the dynamics of TNBC cell density as a combination of mobility, which is formulated as a diffusion process constrained by local tumor-induced mechanical stress, and net proliferation, which is represented with a logistic term. To model TNBC response to NAT drug combinations, we adjust the tumor cell proliferation rate with a recent pharmacodynamic model, MuSyC, which also accounts for synergy of potency and efficacy. Tumor cell density is estimated from diffusion-weighted MRI data, while tissue mechanical properties are defined from segmented contrast-enhanced T1-weighted MRI data. To model the heterogeneous intratumoral delivery of drugs, we use perfusion maps estimated from dynamic contrast-enhanced MRI data. NAT drug pharmacokinetics are approximated with a linear model, which reasonably represents their temporal decay during NAT. Sobol’s method is used for the global sensitivity analysis of the NAT response of two different tumors (one well-perfused and one poorly-perfused) on a 3D, tissue-scale domain. This allows us to assess the total effect (ST) of each model parameter on tumor volume and global cellularity. Here, we focus on two standard NAT regimens: doxorubicin plus cyclophosphamide, and paclitaxel plus carboplatin. The parameter space is constructed by integrating three approaches. First, we use prior patient-specific in silico estimates of tumor cell mobility and proliferation. Second, we experimentally constrain the parameters accounting for potential synergistic drug activity by using time-resolved, high-throughput, automated microscopy assays that capture drug-induced changes in proliferation rates of various TNBC lines (HCC1143, SUM149, MDAMB231, and MDAMB468; perfosfamide was used in lieu of the pro-drug cyclophosphamide). Third, we scale the resulting in vitro parameter ranges to be clinically-relevant through an in silico study with our mechanistic model. Our results show that out of the 15 model parameters considered in the sensitivity analysis only a minority exhibited a driving role (ST &gt; 0.1) in representing the dynamics of TNBC response to NAT, namely: the baseline tumor cell proliferation rate along with the effect and ratio of peak concentration to EC50 of doxorubicin, paclitaxel, and carboplatin. The other parameters have a marginal effect and can thus be fixed to any value within the parameter space. We select these constant parameters such that they contribute to simplifying the model formulation, leading to a surrogate reduced model. We further show that the reduced and the original model produce distributions of tumor volume and global cellularity that are in remarkable agreement both qualitatively (Dice similarity coefficient &gt; 0.90 and &gt; 0.94, respectively) and parameter-wise (concordance correlation coefficient &gt; 0.85 and &gt; 0.89, respectively). Thus, we conclude that our reduced model constitutes a feasible surrogate for future clinical calibration-forecasting studies, thereby facilitating personalized NAT response forecasting with patient-specific imaging datasets acquired in vivo.

Citation Format: Guillermo Lorenzo, Angela M. Jarrett, Christian T. Meyer, Darren R. Tyson, Vito Quaranta, Thomas E. Yankeelov. In silico analysis of a novel mathematical model integrating in vitro and in vivo imaging data reveals driving mechanisms of breast cancer response to NAT for personalized tumor forecasting [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-08-20.

Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data

Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2-3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.

Abstract PS13-44: Identifying relevant parameters that characterize the early response to NAT in breast cancer patients using a novel personalized mechanistic model integrating in vitro and in vivo imaging data

The early determination of response to neoadjuvant therapy (NAT) in triple-negative breast cancer would enable the treating oncologist to adapt the therapeutic regimen of a non-responding patient (e.g., by changing dosage, dose schedule, prescribed drugs), and thereby improve treatment outcomes while avoiding unnecessary toxicities. To address this challenge, we propose to use personalized, in silico forecasts of tumor response to therapeutic regimens via a mechanistic mathematical model calibrated with patient-specific longitudinal multi-parametric magnetic resonance imaging (MRI) data acquired early in the course of NAT.

Here, we extend our mechanistic model to include a new term describing the synergistic effects of NAT drug combinations and identify the driving parameters involved in its formulation by means of a sensitivity analysis. Our model describes tumor cell dynamics as a combination of proliferation, which is regulated by a logistic term, and mobility, which is described as a diffusion process constrained by the local tumor-induced mechanical stress. Tumor cell density is extracted from diffusion-weighted MRI data, while tissue mechanical properties are defined from segmented T1-weighted MRI data. We adjust the tumor proliferation rate in response to NAT drug combinations with a recent model of drug synergy, MuSyC, which accounts for distinct types of synergistic drug effects (synergy of potency vs. synergy of efficacy). We also consider the heterogeneous intratumoral delivery of drugs by means of perfusion maps estimated from dynamic contrast-enhanced MRI data.

We use Sobol’s method for the sensitivity analysis of two different tumors - one well-perfused and one poorly-perfused. We simulate a four-cycle NAT protocol in which NAT drugs are delivered every 14 days, and assess the total effect (ST) of each parameter on the mean relative difference of tumor cell density with respect to a control simulation of tumor growth without NAT. Sensitivity analysis results directly depend on the definition of the parameter space, which we construct by combining two approaches. First, we experimentally constrain parameter ranges using time-resolved, high-throughput, automated microscopy assays to capture the changes in proliferation rates of various breast cancer lines (HCC1143, SUM149, MDAMB231, and MDAMB468) caused by two standard drug combinations: paclitaxel with carboplatin and doxorubicin with perfosfamide (metabolic derivative of the pro-drug cyclophosphamide), and fitting the MuSyC model to these data. Second, we scale the resulting in vitro parameter ranges to clinically-relevant in vivo ranges by running an in silico study with our mechanistic model of breast cancer growth and NAT response.

Our results show that, out of the ten parameters involved in the synergy term, three have a dominant role in the dynamics of breast cancer during NAT (ST &gt; 0.1): synergistic potency, the maximal change in tumor cell proliferation by the slowest decaying drug, and its concentration producing half of maximal effects. The other parameters have marginal (0.02 &lt; ST &lt; 0.1) to negligible effect (ST &lt; 0.02). Ongoing studies are assessing the ability of our mechanistic model to forecast NAT response over a small patient cohort after patient-specific calibration of the driving parameters identified in the present study.

Citation Format: Guillermo Lorenzo, Angela M. Jarrett, Christian T. Meyer, Darren R. Tyson, Vito Quaranta, Thomas E. Yankeelov. Identifying relevant parameters that characterize the early response to NAT in breast cancer patients using a novel personalized mechanistic model integrating in vitro and in vivo imaging data [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS13-44.

Simulating the spread of COVID-19 via a spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion

We present an early version of a Susceptible-Exposed-Infected-Recovered-Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatio-temporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features. To test the model, we compare the outputs generated by a finite-element solver with measured data over the Italian region of Lombardy, which has been heavily impacted by this crisis between February and April 2020. Our results show a strong qualitative agreement between the simulated forecast of the spatio-temporal COVID-19 spread in Lombardy and epidemiological data collected at the municipality level. Additional simulations exploring alternative scenarios for the relaxation of lockdown restrictions suggest that reopening strategies should account for local population densities and the specific dynamics of the contagion. Thus, we argue that data-driven simulations of our model could ultimately inform health authorities to design effective pandemic-arresting measures and anticipate the geographical allocation of crucial medical resources.

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.

Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics

The overall goal of this study is to employ quantitative magnetic resonance imaging (MRI) data to constrain a patient-specific, computational fluid dynamics (CFD) model of blood flow and interstitial transport in breast cancer. We develop image processing methodologies to generate tumor-related vasculature-interstitium geometry and realistic material properties, using dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) data. These data are used to constrain CFD simulations for determining the tumor-associated blood supply and interstitial transport characteristics unique to each patient. We then perform a proof-of-principle statistical comparison between these hemodynamic characteristics in 11 malignant and 5 benign lesions from 12 patients. Significant differences between groups (i.e., malignant versus benign) were observed for the median of tumor-associated interstitial flow velocity ( P = 0.028 ), and the ranges of tumor-associated blood pressure (P = 0.016) and vascular extraction rate (P = 0.040). The implication is that malignant lesions tend to have larger magnitude of interstitial flow velocity, and higher heterogeneity in blood pressure and vascular extraction rate. Multivariable logistic models based on combinations of these hemodynamic data achieved excellent differentiation between malignant and benign lesions with an area under the receiver operator characteristic curve of 1.0, sensitivity of 1.0, and specificity of 1.0. This image-based model system is a fundamentally new way to map flow and pressure fields related to breast tumors using only non-invasive, clinically available imaging data and established laws of fluid mechanics. Furthermore, the results provide preliminary evidence for this methodology's utility for the quantitative characterization of breast cancer.

Abstract 5483: An image-based mechanistic computational model for early prediction of organ-confined untreated prostate cancer growth

According to the American Cancer Society, prostate cancer (PCa) is the most common newly-diagnosed cancer and the second leading cancer-related cause of death among men in the US in 2019. The current clinical protocols of PCa are based on two key strategies: regular screening of men over fifty and patient triaging in risk groups. Prostatic tumors are usually detected at an early organ-confined stage, which poses a low to intermediate risk to the patient and may not produce any symptoms or require treatment for long time. However, most patients are prescribed a radical treatment immediately after diagnosis (e.g., surgery or radiation therapy), which may adversely impact their lives without necessarily prolonging their longevity or quality of life. Moreover, some patients who initially delay their treatment ultimately succumb to PCa due to an inaccurate initial diagnosis. Thus, while regular screening enables the detection of the majority of tumors at an early and mild stage, the limited individualization of patient monitoring and treatment has led to significant rates of both overtreatment and undertreatment.

To overcome these fundamental limitations in the clinical management of PCa, we propose the use of a mechanistic mathematical model for which we can perform computer simulations to forecast patient-specific tumor growth. This computational model is based on a set of partial differential equations that describe the main mechanisms involved in organ-confined PCa growth. The model is parameterized using longitudinal multiparametric magnetic resonance (MR) images and the available clinical data for each patient. Tumor growth is simulated over the patient's prostate extracted from T2-weighted MR images. We use isogeometric analysis to accurately and efficiently address the computational challenges arising in this application.

Our preliminary simulation results show that our computational technology can predict tumor growth and associated serum Prostate Specific Antigen (PSA, a key biomarker in clinical management of PCa) with reasonable accuracy. We also explore the potential of model parameters and variables to characterize tumor aggressivity. Thus, we believe that our imaging-based modeling approach could be a promising tool capable of being implemented in current PCa protocols to assist physicians in the clinical management of PCa.

Citation Format: Guillermo Lorenzo, Thomas J. Hughes, Alessandro Reali, Hector Gomez, Thomas E. Yankeelov. An image-based mechanistic computational model for early prediction of organ-confined untreated prostate cancer growth [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5483.

Diffusion-reaction compartmental models formulated in a continuum mechanics framework: application to COVID-19, mathematical analysis, and numerical study

The outbreak of COVID-19 in 2020 has led to a surge in interest in the research of the mathematical modeling of epidemics. Many of the introduced models are so-called compartmental models, in which the total quantities characterizing a certain system may be decomposed into two (or more) species that are distributed into two (or more) homogeneous units called compartments. We propose herein a formulation of compartmental models based on partial differential equations (PDEs) based on concepts familiar to continuum mechanics, interpreting such models in terms of fundamental equations of balance and compatibility, joined by a constitutive relation. We believe that such an interpretation may be useful to aid understanding and interdisciplinary collaboration. We then proceed to focus on a compartmental PDE model of COVID-19 within the newly-introduced framework, beginning with a detailed derivation and explanation. We then analyze the model mathematically, presenting several results concerning its stability and sensitivity to different parameters. We conclude with a series of numerical simulations to support our findings.

Mechanistic modelling of prostate-specific antigen dynamics shows potential for personalized prediction of radiation therapy outcome

External beam radiation therapy is a widespread treatment for prostate cancer. The ensuing patient follow-up is based on the evolution of the prostate-specific antigen (PSA). Serum levels of PSA decay due to the radiation-induced death of tumour cells and cancer recurrence usually manifest as a rising PSA. The current definition of biochemical relapse requires that PSA reaches nadir and starts increasing, which delays the use of further treatments. Also, these methods do not account for the post-radiation tumour dynamics that may contain early information on cancer recurrence. Here, we develop three mechanistic models of post-radiation PSA evolution. Our models render superior fits of PSA data in a patient cohort and provide a biological justification for the most common empirical formulation of PSA dynamics. We also found three model-based prognostic variables: the proliferation rate of the survival fraction, the ratio of radiation-induced cell death rate to the survival proliferation rate, and the time to PSA nadir since treatment termination. We argue that these markers may enable the early identification of biochemical relapse, which would permit physicians to subsequently adapt patient monitoring to optimize the detection and treatment of cancer recurrence.

Realization in biology?

It is widely assumed that functional and dispositional properties are not identical to their physical base, but that there is some kind of asymmetrical ontological dependence between them. In this regard, a popular idea is that the former are realized by the latter, which, under the non-identity assumption, is generally understood to be a non-causal, constitutive relation. In this paper we examine two of the most widely accepted approaches to realization, the so-called 'flat view' and the 'dimensioned view', and we analyze their explanatory relevance in the light of a number of examples from the life sciences, paying special attention to developmental phenomena. Our conclusion is that the emphasis placed by modern-day biology on such properties as variability, evolvability, and a whole collection of phenomena like modularity, robustness, and developmental constraint or developmental bias requires the adoption of a much more dynamic perspective than traditional realization frameworks are able to capture.

[Endoscopic incision of intravesical ureteroceles in patients with duplex system]

PURPOSE

To evaluate the clinical status and renal function of pa- tients with duplex system and intravesical ureterocele after drainage by cystoscopy.

MATERIAL AND METHODS

In 9 patients with duplex system and intravesical ureterocele drainage was performed to present recurrent urinary tract infections (7 children with episodes of pyelonephritis and sepsis) or obstruction of the urinary drainage. The mean age was 33 months (range 8-108 months). The thecnique was done under general anesthesia in the operating room and puncture of the ureterocele was performed using cystourethroscopy with loop electrode. The minimum follow-up was 12 months (range 12-48 months) and includes renal ultrasound, renal isotopic study (Mag3 with furosemide) and-echocystography study in patients with preoperative vesicoureteral reflux or postoperative urinary tract infection.

RESULTS

The average operative time was 60 minutes. Mean hospital stay was 48 hours. There were no complications during the procedure. In five patients urinary infection episodes disappeared. Ultrasound demonstrated decreased hydronephrosis and megaureter. In two patients the differential renal function following the technique improved. One patient with preoperative split renal function of 14% required nephrectomy. One patient had postoperative vesicoureteral reflux.

CONCLUSIONS

The drainage of intravesical ureterocele in patients with duplex system reduces episodes of urinary tract infection and urinary tract dilation.

Richard Owen on the mind/body problem

Contrary to the received view of Richard Owen as a Platonic and conservative naturalist, we document that he held a radically physicalist worldview that extended to so tough a matter as the Mind/Body Problem. We argue that if viewed from the perspective of his overall comparative project, Owen's reflections on the nature of mind at the end of volume III of On the anatomy of vertebrates can be read as an anticipation of some of the main tenets of the Brain State Theory of mind developed since the mid 20th century.

The archaeological record speaks: bridging anthropology and linguistics

This paper examines the origins of language, as treated within Evolutionary Anthropology, under the light offered by a biolinguistic approach. This perspective is presented first. Next we discuss how genetic, anatomical, and archaeological data, which are traditionally taken as evidence for the presence of language, are circumstantial as such from this perspective. We conclude by discussing ways in which to address these central issues, in an attempt to develop a collaborative approach to them.

[First results of the orchiopexy via scrotal approach]

PURPOSE

We present our first results with the technique described by Bianchi and Squire in 1989 for the surgical treatment of undescended testis by scrotal incision as an alternative to the traditional inguinal approach.

MATERIALS AND METHODS

Prospective study of patients operated with the diagnosis of cryptorchidism with scrotal orchidopexy from October 2008 through July 2009.

INCLUSION CRITERIA

patients with inguinal palpable testis, scrotal orchidopexy, testicular position was assessed at 6 months and/or one year after surgery. All procedures were performed by the same surgeon. Retractile testes were excluded. We studied the preoperative localization of the testis, the average surgical time, presence or absence of the processus vaginalis, conversions to the traditional inguinal orchiopexy, complications and location of six months and one year after surgery.

RESULTS

A total of 50 orchidopexy were performed in 39 patients during this period. Aged between 1 and 12 years (mean 5 years, median 4 years). Were located in the intraoperative exam under anesthesia, fifteen testes in the inguinal canal and 35 in the external inguinal ring. Operative times ranged from 15 to 60 minutes (mean 34 minutes). The processus vaginalis was patent in 25 procedures (50%) and were ligated via the scrotal incision. Two patients required conversion to a traditional inguinal approach. All testes were satisfactorily positioned in the scrotum and there were no cases de testicular atrophy or ascent, hernia o hydrocele formation with followup that ranged from 6 months to 1 year.

CONCLUSIONS

The technique of orchiopexy with scrotal approach is a safe, well tolerated and reliable method.

[A case of papilledema and Arnold-Chiari type I malformation]

CLINICAL CASE

A 12-year old girl was brought to the emergency ward because of headache and diplopia for 4 days. Bilateral papilledema was observed. Ancillary studies showed Arnold-Chiari Type I malformation without hydrocephalia. Cranial decompression treatment was performed but papilledema persisted and a progressive visual field deterioration was assessed. One month later, an optic nerve sheath fenestration was performed.

DISCUSSION

Arnold-Chiari I malformation is characterized by downward displacement of cerebellar tonsils below the foramen magnum plane. It usually remains asymptomatic or appears in adulthood with brainstem compression-related symptoms. Surgical decompression of the posterior cranial fossa is mandatory in symptomatic cases. In our case, intracranial hypertension persisted because of postoperative subdural hygromas. Visual field deterioration was resolved by optic nerve sheath fenestration (Arch Soc Esp Oftalmol 2002; 77: 449-454).

Molecular evidence for the existence of two species of Marteilia in Europe

Marteilia refringens is one of the most significant pathogens of bivalve molluscs. Previous sequencing of the small subunit ribosomal RNA gene of M. refringens isolates derived from the infected mussels (Mytilus edulis and Mytilus galloprovinciallis) and the oyster (Ostrea edulis) in Europe did not reveal genetic polymorphisms despite indications from epizootiological data that distinct types may exist. We investigated the existence of polymorphisms in the internal transcribed spacer region of the ribosomal RNA genes. The sequences of this region proved to be clearly dimorphic among Marteilia from five sampling sites. The distribution of the two genetic types, named "O" and "M", appeared to be linked to the host species, oysters and mussels, respectively. We therefore support the recognition of two species of Marteilia in Europe and propose that the "O" type corresponds to M. refringens and the "M" type to M. maurini.

A mitochondrial tRNA(Lys) gene mutation (T8316C) in a patient with mitochondrial myopathy, lactic acidosis, and stroke-like episodes

We studied a patient with mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes who had morphologically and biochemically abnormal muscle mitochondria. Molecular analysis revealed a T8316C transition in the mitochondrial DNA tRNA(Lys) gene. The mutation was homoplasmic in muscle from the proposita, heteroplasmic in her blood, and still less abundant in blood from her asymptomatic maternal relatives. The T8316C mutation affects a highly conserved base pair and was not found in controls, thus satisfying the accepted criteria for pathogenicity. Our data document the genetic heterogeneity in mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes syndrome, underlining that the same syndrome may be associated with mutations of different genes.

Mapping of linear antibody epitopes of the glycoprotein of VHSV, a salmonid rhabdovirus

Antibody linear epitopes of the glycoprotein G (gpG) of the viral haemorrhagic septicaemia virus (VHSV), a rhabdovirus of salmonids, were mapped by pepscan using overlapping 15-mer peptides covering the entire gpG sequence and ELISA with polyclonal and monoclonal murine and polyclonal trout antibodies. Among the regions recognized in the pepscan by the polyclonal antibodies (PAbs) were the previously identified phosphatidylserine binding heptad-repeats (Estepa & Coll 1996; Virology 216:60-70) and leucocyte stimulating peptides (Lorenzo et al. 1995; Virology 212:348-355). Among 17 monoclonal antibodies (MAbs), only 2 non-neutralizing MAbs, 110 (aa 139-153) and IP1H3 (aa 399-413), could be mapped to specific peptides in the pepscan of the gpG. Mapping of these MAbs was confirmed by immunoblotting with recombinant proteins and/or other synthetic peptides covering those sequences. None of the neutralizing MAbs tested reacted with any of the gpG peptides. Previously mapped MAb resistant mutants in the gpG did not coincide with any of the linear epitopes defined by the pepscan strategy, suggesting the complementarity of the 2 methods for the identification of antibody recognition sites.

[Low grade disseminated astrocytoma in childhood]

INTRODUCTION

Leptomeningeal dissemination is a common event in some kinds of cerebral tumours, but in low-grade astrocytomas who classically have been conferred a benign course. However, multifocal affectation is described each time more frequently in these group of tumours.

CLINIC CASES

We present three patients aged respectively 8 and 10 months and 10 years, who were diagnosed of low-grade astrocytoma with multicentric spread. They were treated with systemic chemotherapy and controlled with MRI. At the end of the treatment an important reduction in tumours' size was observed in the three cases.

DISCUSSION

Biological behaviour of low-grade astrocytoma as been reviewed in last years. By one side, it has been seen that certain histological characteristics are associated with an aggressive behaviour: on the other hand, long-term evolution of these tumours can be complicated with tumoral recurrence, malignization and leptomeningeal dissemination. This last one is observed each time more frequently since the use of MRI in the diagnose of cerebral tumours has become routine. From the therapeutic point of view, chemotherapy represents an effective choice for these patients' management, when surgery is not possible and radiotherapy not advisable, allowing in many cases to stop tumoral growth and sometimes to reduce it's size strongly.

Fast neutralization/immunoperoxidase assay for viral haemorrhagic septicaemia with anti-nucleoprotein monoclonal antibody

An enzyme-immunohistochemical procedure was employed to facilitate neutralization/diagnostic tests for viral haemorrhagic septicaemia virus (VHSV), a significant pathogen in trout farms throughout Europe. The method described can be used for trout or mice antibodies; increases speed (1 day), simplicity, and minimizes the use of reagents compared to other neutralization assays. Furthermore, the test requires a minimum handling of the cell cultures under sterile conditions, decreasing frequent contamination due to the non-sterile conditions of the fish pathological samples. Foci of 5-20 infected epithelioma papillosum carp (EPC) cells are detected and counted with an inverted microscope in under 16 h after infection of EPC monolayers using a high titre anti-N VHSV monoclonal antibody (MAb) 2C9. MAb 2C9 recognizes different viral haemorrhagic septicaemia virus serotypes and VHSV isolates from different host species (trout, salmon and barbel) and Spanish geographical locations. The high titre and specificity of MAb 2C9 favour its conjugation to peroxidase and also make it possible to use in direct immunoperoxidase staining of the VHSV infected EPC monolayers. This neutralization/immunoperoxidase assay should improve diagnostics that use currently agarose or methylcellulose plaque reduction neutralization assays.

Sporadic MERRF/MELAS overlap syndrome associated with the 3243 tRNA(Leu(UUR)) mutation of mitochondrial DNA

We studied a patient with a mitochondrial encephalomyopathy characterized by the presence of all the cardinal features of both myoclonic epilepsy and ragged-red fibers (MERRF) and mitochondrial encephalomyopathy, lactic acidosis, and strokelike episodes (MELAS) syndromes. Muscle biopsy showed ragged-red fibers (RRF). Some RRF were cytochrome c oxidase (COX)-negative while some others stained positive for COX. Muscle biochemistry revealed defects of complexes I and IV of the respiratory chain. Both muscle and blood mitochondrial DNA from the patient showed the presence of the mutation at nucleotide position 3243 in the tRNA(Leu(UUR)) gene and the absence of point mutations related to MERRF syndrome. The proportions of mutant mtDNA were 70% in muscle and 30% in blood. The mutation was absent in blood from all maternal relatives, in hair follicles from the mother, and in muscle from one sister of the proband. Therefore, there was no evidence of maternal inheritance.

Variable clinical and biochemical presentation of seven Spanish cases with glutaryl-CoA-dehydrogenase deficiency

In this report, we describe seven new patients with a severe deficiency of glutaryl-CoA dehydrogenase in cultured skin fibroblasts. Three of the patients studied excreted high levels of glutaric acid. The remaining four patients presented a lack of significant glutaric aciduria. However, glutaric acid was found in increased levels in CSF. In both groups of patients, the urine glutaric acid levels were not related to their metabolic condition at the time of sampling. Hypocarnitinemia was a common finding. Some patients also showed defects on respiratory chain complexes in muscle biopsy. Only one patient has a normal psychomotor development. The other six patients are severely handicapped despite the attempts of different therapies. In patients with progressive neurological deterioration with dystonia and cerebellar signs associated with temporal lobe atrophy and bilateral basal ganglia damage on MRI, a glutaric aciduria type I (GA I) should always be investigated. The presence of glutaric acid in body fluids, especially in CSF, as well as plasma carnitine levels, should be determined. These procedures can lead to the diagnosis of glutaric aciduria type I.