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Introduction: Recent colorectal cancer trials with KRAS G12C inhibitors have faced resistance challenges, and a preclinical model is needed to test resistance inhibition strategies. Experimental Methods: We have developed a novel culturing system, ResCu, that allows for long-term culture of cells without the need for passaging, enabling us to determine treatment resistance that can develop over time in a physiologically relevant system that conserves the resistance pathways found in patients. Using this system, we evolved resistance to a KRAS G12C inhibitor, sotorasib, after only two weeks of culture. The evolved cells were characterized for cross-resistance and cross-sensitivity by exposing these cells to a panel of drugs for seven days. Cell viability was assessed by luminescence. Based on the drug screen results, the sotorasib evolved cells were further evolved to additional drug classes to identify novel therapeutic combinations. Whole population and single-cell transcriptomic analysis were used to identify resistance mechanisms and synthetic lethality. Results: Using the ResCu system, we evolved resistance to sotorasib, a KRAS G12C inhibitor, and other drugs in combination with sotorasib. We identified a synergistic combination of sotorasib and a secondary inhibitor that initially suppressed resistance in the sotorasib evolved cells. We identified potential mechanisms underlying this synergy using transcriptomic analysis to enable future mechanistic characterization. Subsequently, the ResCu system drove resistance evolution to the sotarasib synergistic combination. We identified splicing alterations associated with this resistance. Conclusion: We have developed a novel resistance culturing system, ResCu, that allows for preclinical testing of KRAS G12C inhibitors and determines the most promising compounds for inhibiting specific resistance pathways. This system allows for long-term culturing of cells without passaging, thereby maintaining the various phenotypes of the original populations that are commonly lost in traditional passaging cultures. Citation Format: Victor M. Ruiz, Lindsay Hill, Manasa Gadde, Christopher Bulow, Maurizio Morri, Nicholas Goldner. Using the ResCu system for preclinical testing of KRAS G12C inhibitors [abstract]. In: Proceedings of the AACR Special Conference: Targeting RAS; 2023 Mar 5-8; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Res 2023;21(5_Suppl):Abstract nr B030.

Introduction: 3D cellular models have become of increasing interest in anti-cancer drug discovery as a bridge between clinical models and traditional in vitro methods. An important component of this research is the comparison of drug-to-cell interactions and morphological characteristics between cells in mono-layer culture and the cells in 3D spheroids. Materials and Methods:The ResCu system is a novel long term, physiologically relevant culture system that avoids the need for passaging. It propagates tumoroids in a microenvironment that resembleshuman tissue. Cells with KRAS G12C mutation were cultured in the ResCu system for three months, during which they were constantly exposed to G12C inhibitors (G12Ci) for the evolution of resistant cell populations. Cells were also cultured in traditional 2D cultures and exposed to G12Ci. Transcriptomics was performed to determine resistant networks and identify different resistant subclones in each group. Results: We applied the ResCu system to model clinical resistance. We captured 95% of clinically identified on-target and pathway bypass resistance changes, and in the 2D system, only a fraction of mechanisms were discovered. Additionally, we noticed certain cell populations could tolerate higher doses of the G12Ci after resistance evolution in ResCu compared to 2D. A few had similar responses suggesting that the origin of the cell line plays a role in the microenvironment's impact on cellular response. Conclusion: By comparatively analyzing resistance evolution data for KRAS G12Ci in traditional 2D and ResCu, we were able to collect physiologically relevant data that can more confidently be applied to clinical studies than mono-layer cell culture data alone. Citation Format: Manasa Gadde, Elizabeth Schwabe, Kshithija Mulam, Lindsay Hill, Christopher Bulow. Bridging gaps in treatment resistance 2D models with the incorporation of 3D models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1682.

Inflammatory breast cancer (IBC), a rare form of breast cancer associated with increased angiogenesis and metastasis, is largely driven by tumor-stromal interactions with the vasculature and the extracellular matrix (ECM). However, there is currently a lack of understanding of the role these interactions play in initiation and progression of the disease. In this study, we developed the first three-dimensional, in vitro, vascularized, microfluidic IBC platform to quantify the spatial and temporal dynamics of tumor-vasculature and tumor-ECM interactions specific to IBC. Platforms consisting of collagen type 1 ECM with an endothelialized blood vessel were cultured with IBC cells, MDA-IBC3 (HER2+) or SUM149 (triple negative), and for comparison to non-IBC cells, MDA-MB-231 (triple negative). Acellular collagen platforms with endothelialized blood vessels served as controls. SUM149 and MDA-MB-231 platforms exhibited a significantly (p < .05) higher vessel permeability and decreased endothelial coverage of the vessel lumen compared to the control. Both IBC platforms, MDA-IBC3 and SUM149, expressed higher levels of vascular endothelial growth factor (p < .05) and increased collagen ECM porosity compared to non-IBCMDA-MB-231 (p < .05) and control (p < .01) platforms. Additionally, unique to the MDA-IBC3 platform, we observed progressive sprouting of the endothelium over time resulting in viable vessels with lumen. The newly sprouted vessels encircled clusters of MDA-IBC3 cells replicating a key feature of in vivo IBC. The IBC in vitro vascularized platforms introduced in this study model well-described in vivo and clinical IBC phenotypes and provide an adaptable, high throughput tool for systematically and quantitatively investigating tumor-stromal mechanisms and dynamics of tumor progression.

The goal of this study is to calibrate a multiscale model of tumor angiogenesis with time-resolved data to allow for systematic testing of mathematical predictions of vascular sprouting. The multi-scale model consists of an agent-based description of tumor and endothelial cell dynamics coupled to a continuum model of vascular endothelial growth factor concentration. First, we calibrate ordinary differential equation models to time-resolved protein expression data to estimate the rates of secretion and consumption of vascular endothelial growth factor by endothelial and tumor cells, respectively. These parameters are then input into the multiscale tumor angiogenesis model, and the remaining model parameters are then calibrated to time resolved confocal microscopy images obtained within a 3D vascularized microfluidic platform. The microfluidic platform mimics a functional blood vessel with a surrounding collagen matrix seeded with inflammatory breast cancer cells, which induce tumor angiogenesis. Once the multi-scale model is fully parameterized, we forecast the spatiotemporal distribution of vascular sprouts at future time points and directly compare the predictions to experimentally measured data. We assess the ability of our model to globally recapitulate angiogenic vasculature density, resulting in an average relative calibration error of 17.7% ± 6.3% and an average prediction error of 20.2% ± 4% and 21.7% ± 3.6% using one and four calibrated parameters, respectively. We then assess the model’s ability to predict local vessel morphology (individualized vessel structure as opposed to global vascular density), initialized with the first time point and calibrated with two intermediate time points. To the best of our knowledge, this represents the first study to integrate well-controlled, experimental data into a mechanism-based, multiscale, mathematical model of angiogenic sprouting to make specific, testable predictions.

Summary 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., Graphical Abstract, Bioengineering; Systems Biology; Cancer; In Silico Biology

Introduction: Inflammatory breast cancer (IBC) is an aggressive and rare disease with poor prognosis, accounting for 10% of breast cancer mortality [1]. A primary factor contributing to the bleak prognosis is the lack of IBC specific treatments. There are currently no IBC specific therapies due to a lack of IBC specific diagnostic and targeting markers. Efforts focused on identifying driver mutations and tumor targets have implicated tumor stroma including stromal cells such as macrophages in mediating IBC-like symptoms. This highlights the significance of understanding the interactions of tumor cells with the tumor stroma in greater detail and the knowledge would enable determination of targetable biology from these interactions which would facilitate development of IBC specific treatments and therapeutics. What is needed is a model to capture the complexity of IBC, identify critical spatial hetero-cellular interactions and target them successfully in a physiologically relevant and high-throughput manner. Approach: To address this need, we developed a 3D IBC microfluidic platform, unique in its simultaneous integration of functional blood vessels, tumor cells, macrophages, and type I collagen whose density, stiffness, and porosity mimics cancerous breast stroma. The platform will be used to study the influence of macrophage-tumor-endothelial interactions on 2 key critical features of IBC: vascular sprouting and formation of IBC emboli surrounded by vascular sprouts. Results: The 3D IBC microfluidic platform composed of MDA-IBC3 cells and a functional endothelial blood vessel demonstrated both vascular sprouting and emboli formation, key features of IBC tumors seen in IBC patient derived xenograft (PDX) models. Additionally, we observed vascular nesting of MDA-IBC3 emboli, recreating a characteristic IBC phenomenon observed in Mary-X PDX models. Incorporation of macrophages significantly increased the number of new vascular sprouts, sprouting rate and resulted in sprouts forming at earlier time points. Additionally, the presence of macrophages resulted in the formation of a significantly more porous collagen matrix (p Conclusion: IBC is an aggressive and invasive breast cancer with a poor prognosis linked to tumor-stroma interactions. Current preclinical to study IBC consist primarily of PDX models where determining the influence of specific signaling pathways and microenvironmental stimuli on tumor progression is challenging. Here we present a novel 3D microfluidic IBC platform to study tumor stromal interactions in a controlled manner. The MDA-IBC3 breast tumor platform demonstrated both vascular sprouting and emboli formation, key features of IBC seen in PDX models and the presence of macrophages increased both angiogenic sprouting and remodeling of the collagen matrix. The stark differences in the tumor platform response associated with macrophage presence strengthens the hypothesis of tumor stroma as a key player driving the aggressive nature of IBC and reveals a potential target for IBC therapeutics. [1] Fernandez, S.V., et al., Breast cancer research and treatment, 140(1): p. 23-33, 2013 Citation Format: Manasa Gadde, Caleb Phillips, Omar Rahal, Wendy Woodward, Marissa Rylander, Thomas Yankeelov. An in vitro microfluidic tumor platform for modeling and investigating tumor stromal interactions in inflammatory breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-06-02.

Microfluidic technology has led to the development of advanced in vitro tumor platforms that overcome the challenges of in vivo animal and in vitro two dimensional models. This paper presents platform designs and methods used to develop complex vascularized in vitro models to mimic the tumor microenvironment. Features of these platforms include a continuous, aligned endothelium that allows for cell-cell interactions between vasculature and tumor cells. A novel platform for fabrication of a single endothelialized microchannel encased within a collagen platform hosting breast cancer cells was developed and utilized to study the influence of cellular interaction on transport phenomenon through vasculature in a hyperpermeable tumor microenvironment. This platform relies on subtractive tissue engineering fabrication techniques. Through confocal imaging we have demonstrated that the platform produces enhanced vessel leakiness recapitulating physiological features of the tumor microenvironment. The influence of tumor endothelial interactions on transport of particles was also demonstrated. Additionally, we designed two more complex and intricate endothelialized microfluidic networks by combining lithographic techniques with additive tissue engineering methods. We created a network platform consisting of interconnected microchannels to model a highly vascularized system and successfully perfused the system with fluorescent particles. Finally, we developed a physiologically representative in vitro microfluidic platform with vasculature patterned from in vivo data showing the versatility of these systems to replicate the complex geometries of tumor microvasculature and dynamically measured particle transport. Overall, we have shown the ability to develop functional microfluidic vascular tumor platforms of varying complexities and demonstrated their utility for studying spatial particle transport within these systems.

New directions in medical and biomedical sciences have gradually emerged over recent years that will change the way diseases are diagnosed and treated and are leading to the redirection of medicine toward patient-specific treatments. We refer to these new approaches for studying biomedical systems as predictive medicine, a new version of medical science that involves the use of advanced computer models of biomedical phenomena, high-performance computing, new experimental methods for model data calibration, modern imaging technologies, cutting-edge numerical algorithms for treating large stochastic systems, modern methods for model selection, calibration, validation, verification, and uncertainty quantification, and new approaches for drug design and delivery, all based on predictive models. The methodologies are designed to study events at multiple scales, from genetic data, to sub-cellular signaling mechanisms, to cell interactions, to tissue physics and chemistry, to organs in living human subjects. The present document surveys work on the development and implementation of predictive models of vascular tumor growth, covering aspects of what might be called modeling-and-experimentally based computational oncology. The work described is that of a multi-institutional team, centered at ICES with strong participation by members at M. D. Anderson Cancer Center and University of Texas at San Antonio. This exposition covers topics on signaling models, cell and cell-interaction models, tissue models based on multi-species mixture theories, models of angiogenesis, and beginning work of drug effects. A number of new parallel computer codes for implementing finite-element methods, multi-level Markov Chain Monte Carlo sampling methods, data classification methods, stochastic PDE solvers, statistical inverse algorithms for model calibration and validation, models of events at different spatial and temporal scales is presented. Importantly, new methods for model selection in the presence of uncertainties fundamental to predictive medical science, are described which are based on the notion of Bayesian model plausibilities. Also, as part of this general approach, new codes for determining the sensitivity of model outputs to variations in model parameters are described that provide a basis for assessing the importance of model parameters and controlling and reducing the number of relevant model parameters. Model specific data is to be accessible through careful and model-specific platforms in the Tumor Engineering Laboratory. We describe parallel computer platforms on which large-scale calculations are run as well as specific time-marching algorithms needed to treat stiff systems encountered in some phase-field mixture models. We also cover new non-invasive imaging and data classification methods that provide in vivo data for model validation. The study concludes with a brief discussion of future work and open challenges.

Traditional experimental platforms to study cancer biology consist of two-dimensional (2D) cell culture systems and animal models. Although 2D cell cultures have yielded fundamental insights into cancer biology, they do not provide a physiologically representative three-dimensional (3D) volume for cell attachment and infiltration. These systems also cannot recapitulate critical features of the tumor microenvironment including hemodynamics, matrix mechanics, cellular crosstalk, and matrix interactions in a dynamic manner, or impose chemical and mechanical gradients. While animal models provide physiologic fidelity, they can be highly variable and cost prohibitive for extensive biological investigation and therapeutic optimization. Furthermore, the interplay of many different microenvironmental variables, such as growth factors, immune reaction, and stromal interactions, make it difficult to isolate the effect of a specific stimulus on cell response using animal models. Due to these limitations, 3D in vitro tumor models have recently emerged as valuable tools for the study of cancer progression as these systems have the ability to overcome many of the limitations of static 2D monolayers and mammalian systems. Initial 3D in vitro models have consisted of static 3D co-culture platforms and have been successful in providing a deeper insight compared to animal and static 2D systems. However, the majority of these existing systems lack the presence of physiological flow, a pivotal stimuli in tumor growth and metastasis and important consideration for transport of diagnostic or therapeutic agents. In order to consider the influence of flow on cancer progression microfluidic platforms are being widely used. The integration of microfluidic technology and microfabrication techniques with tumor biology has resulted in complex 3D microfluidic platforms capable of investigating various key stages in cancer evolution including angiogenesis and metastasis. 3D microfluidic platforms are able to provide a physiologically representative tumor environment while allowing for dynamic monitoring and simultaneous control of multiple factors such as cellular and extracellular matrix composition, fluid velocity and wall shear stress, and both biochemical and mechanical gradients.