In vitro vascularized tumor platform for modeling tumor‐vasculature interactions of inflammatory breast cancer

Gadde, Manasa; Phillips, Caleb M.; Ghousifam, Neda; Sorace, Anna G.; Wong, Enoch; Krishnamurthy, Savitri; Syed, Anum; Rahal, Omar; Yankeelov, Thomas E.; Woodward, Wendy A.; Rylander, Marissa Nichole

doi:10.1002/bit.27487

Cited by 23 publications

(40 citation statements)

References 115 publications

(196 reference statements)

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“…These devices can play a powerful role in the study of tumor angiogenesis and vasculature by providing a controlled, repeatable experimental platform in vitro that can isolate specific processes that are not easily studied individually in vivo. Many microfluidic devices are widely reproducible and allow for a systematic investigation of vasculogenesis [ 110 , 111 , 112 , 113 ], angiogenesis [ 114 , 115 , 116 , 117 ], and response to anti-angiogenic therapies [ 118 ].…”

Section: Approaches For Modeling Tumor Vasculature At the Cell Scalementioning

confidence: 99%

“…Phillips et al [ 18 , 79 , 100 ] have proposed integrating confocal microscopy data from an in vitro vascularized tumor platform [ 117 ] with an agent-based mathematical model of tumor angiogenesis [ 100 ]. In their framework, time-resolved confocal measurements of individual angiogenic sprouts are used to calibrate and validate a multiscale agent-based model.…”

Section: Approaches For Modeling Tumor Vasculature At the Cell Scalementioning

confidence: 99%

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

Hormuth

Phillips

et al. 2021

Cancers

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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.

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Section: Approaches For Modeling Tumor Vasculature At the Cell Scalementioning

confidence: 99%

Section: Approaches For Modeling Tumor Vasculature At the Cell Scalementioning

confidence: 99%

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

Hormuth

Phillips

et al. 2021

Cancers

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“…In this contribution, we aim to calibrate an agent-based model of tumor angiogenesis [49] with experimental data obtained from the in vitro vascularized tumor platform [48] described above to predict vasculature sprouting. First, we perform experiments that isolate key components in the process of angiogenesis and use these data to calibrate important parameters in mathematical models of increasing complexity.…”

Section: Introductionmentioning

confidence: 99%

“…We do note other in vitro works in experimental angiogenesis [43][44][45][46][47]. In the present effort, we address the data demands via a 3D in vitro microfluidic platform [48]. This system has the advantage of emulating the complex architecture of the tumor microenvironment observed in vivo, as well as capturing the spatiotemporal behavior of the cells.…”

Section: Introductionmentioning

confidence: 99%

“…Spatiotemporal measurements of cell proliferation and apoptosis, branch lengths and number, anastomosis, and lumen formation in the platform will provide key parameters that drive the mathematical model. In this contribution, we aim to calibrate an agent-based model of tumor angiogenesis [49] with experimental data obtained from the in vitro vascularized tumor platform [48] described above to predict vasculature sprouting. First, we perform experiments that isolate key components in the process of angiogenesis and use these data to calibrate important parameters in mathematical models of increasing complexity.…”

Section: Introductionmentioning

confidence: 99%

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Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis

Phillips

Eabf

Gadde

et al. 2021

Preprint

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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.

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Accumulation of amyloid beta (Aβ) and amyloid precursor protein (APP) in tumors formed by a mouse xenograft model of inflammatory breast cancer

et al. 2021

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Accumulation of amyloid in breast cancer is a well-known phenomenon, but only immunoglobulin light-chain amyloidosis (AL) or transthyretin (TTR) amyloid had been detected in human breast tumor samples previously. We recently reported that another amyloidogenic peptide, amyloid beta (Ab), is present in an aggregated form in animal and human highgrade gliomas and suggested that it originates systemically from the blood, possibly generated by platelets. To study whether breast cancers are also associated with these Ab peptides and in what form, we used a nude mouse model inoculated with triple-negative inflammatory breast cancer cell (SUM-149) xenografts, which develop noticeable tumors. Immunostaining with two types of specific antibodies for Ab identified the clear presence of Ab peptides associated with (a) carcinoma cells and (b) extracellular aggregated amyloid (also revealed by Congo red and thioflavin S staining). Ab peptides, in both cells and in aggregated amyloid, were distributed in clear gradients, with maximum levels near blood vessels. We detected significant presence of amyloid precursor protein (APP) in the walls of blood vessels of tumor samples, as well as in carcinoma cells. Finally, we used ELISA to confirm the presence of elevated levels of mouse-generated Ab40 in tumors. We conclude that Ab in inflammatory breast cancer tumors, at least in a mouse model, is always present and is concentrated near blood vessels. We also discuss here the possible pathways of Ab accumulation in tumors and whether this phenomenon could represent the specific signature of highgrade cancers.Inflammatory breast cancer (IBC) is one of the most aggressive types of this disease; it is highly metastatic and usually fatal (as it is commonly diagnosed at T4, according to the tumor-nodes-metastasis classification of cancers (TNM) classification). Various IBC carcinoma cell subtypes exist, with the triple-negative subtype the most frequently diagnosed. These tumors lack receptors for estrogen, progesterone, or human epidermal growth factor receptor 2 (proto-oncogene) (HER2/neu), and thus, IBC is often treated with chemotherapy, radiation, or Abbreviations AD, Alzheimer's disease; AL, light-chain amyloidosis; APP, amyloid precursor protein; Ab, amyloid beta; DAPI-4 0 , 6-diamidino-2-phenylindole; ELISA, enzyme-linked immunosorbent assay; HER2/neu, human epidermal growth factor receptor 2 (proto-oncogene); IBC, inflammatory breast cancer; SUM-149-IBC cell line; MALT, mucosa-associated lymphoid tissue; MAPK, mitogen-activated protein kinases; PDGF, plateletderived growth factor; SCID, severe combined immunodeficient (mice strain); TNM, tumor-nodes-metastasis classification of cancers; TTR, transthyretin.

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In vitro vascularized tumor platform for modeling tumor‐vasculature interactions of inflammatory breast cancer

Cited by 23 publications

References 115 publications

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

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

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis

Accumulation of amyloid beta (Aβ) and amyloid precursor protein (APP) in tumors formed by a mouse xenograft model of inflammatory breast cancer

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