A mathematical model for IL-6-mediated, stem cell driven tumor growth and targeted treatment

Nazari, Fereshteh; Pearson, Alexander T.; Jackson, Trachette L.

doi:10.1371/journal.pcbi.1005920

Cited by 26 publications

(26 citation statements)

References 68 publications

(105 reference statements)

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“…While other mathematical models have been developed to describe tumor growth dynamics and chemoresistance relative to CSC proportion, none have modeled CSC emergence in serial passaging of epithelial ovarian cancer spheroids [27], [28], [29], [30], [89], [90], [91], which can provide easily discernable, experimentally derived parameter-values to enhance biological relevance of the model. Similar to our model, Fornari et al [28] proposed an experimentally informed system of equations to investigate CSC-driven tumorigenesis and analyze tumor growth dynamics based on CSC population proportions at passage 1, 2, and 3.…”

Section: Discussionmentioning

confidence: 99%

Engineered 3D Model of Cancer Stem Cell Enrichment and Chemoresistance

et al. 2019

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Intraperitoneal dissemination of ovarian cancers is preceded by the development of chemoresistant tumors with malignant ascites. Despite the high levels of chemoresistance and relapse observed in ovarian cancers, there are no in vitro models to understand the development of chemoresistance in situ . Method: We describe a highly integrated approach to establish an in vitro model of chemoresistance and stemness in ovarian cancer, using the 3D hanging drop spheroid platform. The model was established by serially passaging non-adherent spheroids. At each passage, the effectiveness of the model was evaluated via measures of proliferation, response to treatment with cisplatin and a novel ALDH1A inhibitor. Concomitantly, the expression and tumor initiating capacity of cancer stem-like cells (CSCs) was analyzed. RNA-seq was used to establish gene signatures associated with the evolution of tumorigenicity, and chemoresistance. Lastly, a mathematical model was developed to predict the emergence of CSCs during serial passaging of ovarian cancer spheroids. Results: Our serial passage model demonstrated increased cellular proliferation, enriched CSCs, and emergence of a platinum resistant phenotype. In vivo tumor xenograft assays indicated that later passage spheroids were significantly more tumorigenic with higher CSCs, compared to early passage spheroids. RNA-seq revealed several gene signatures supporting the emergence of CSCs, chemoresistance, and malignant phenotypes, with links to poor clinical prognosis. Our mathematical model predicted the emergence of CSC populations within serially passaged spheroids, concurring with experimentally observed data. Conclusion: Our integrated approach illustrates the utility of the serial passage spheroid model for examining the emergence and development of chemoresistance in ovarian cancer in a controllable and reproducible format.

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Section: Discussionmentioning

confidence: 99%

Engineered 3D Model of Cancer Stem Cell Enrichment and Chemoresistance

et al. 2019

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“…These TACs can then divide a certain number of times before they reach senescence, and become Terminally Differentiated Cells (TDs). This simple model, and extensions of it, have been used to study feedback control 39,40 , cancer therapy with chemotherapy 15 , as well as targeted therapy 11,41,42 , and hematopoesis 43 . Regardless of the utility, even basic aspects of this model, such as the number of divisions before progression from one compartment to another, are currently experimentally inaccessible and can only be inferred theoretically 44 .…”

Section: Existing Modifications To the Canonical Model Cannot Capturementioning

confidence: 99%

Recasting the Cancer Stem Cell Hypothesis: Unification Using a Continuum Model of Microenvironmental Forces

et al. 2019

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Purpose of review: Here, we identify shortcomings of standard compartment-based mathematical models of cancer stem-cells, and propose a continuous formalism which includes the tumor microenvironment. Recent findings: Stem-cell models of tumor growth have provided explanations for various phenomena in oncology including, metastasis, drug-and radio-resistance, and functional heterogeneity in the face of genetic homogeneity. While some of the newer models allow for plasticity, or de-differentiation, there is no consensus on the mechanisms driving this. Recent experimental evidence suggests that tumor microenvironment factors like hypoxia, acidosis and nutrient deprivation have causative roles. Summary: To settle the dissonance between the mounting experimental evidence surrounding the effects of the microenvironment on tumor stemness, We propose a continuous mathematical model where we model microenvironmental perturbations like forces, which then shape the distribution of stemness within the tumor. We propose methods by which to systematically measure and charaterize these forces, and show results of a simple experiment which support our claims.

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“…The coupled, nonlinear dynamics of intracellular and extracellular signaling are represented by cascades of tens of ordinary differential equations to model the onset of tuberculosis [101]. The same approach is applied to modeling the interaction of the immune system and drugs in the mathematical biology of cancer [79]. In this case, the major challenge is system identification.…”

Section: Applications and Opportunitiesmentioning

confidence: 99%

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Peng

Alber

Tepole³

et al. 2020

Arch Computat Methods Eng

220

103

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Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. This review serves as introduction to a special issue on Uncertainty Quantification, Machine Learning, and Data-Driven Modeling of Biological Systems that will help identify current roadblocks and areas where computational mechanics, as a discipline, can play a significant role. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

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A mathematical model for IL-6-mediated, stem cell driven tumor growth and targeted treatment

Cited by 26 publications

References 68 publications

Engineered 3D Model of Cancer Stem Cell Enrichment and Chemoresistance

Engineered 3D Model of Cancer Stem Cell Enrichment and Chemoresistance

Recasting the Cancer Stem Cell Hypothesis: Unification Using a Continuum Model of Microenvironmental Forces

Multiscale Modeling Meets Machine Learning: What Can We Learn?

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