These studies aim to develop a new computationally driven platform to examine complex physical and chemical microenvironments utilizing organ-on-chip microfluidic bioreactor technology coupled with a predictive mathematical model of tumor growth and therapeutic response. Malignant breast tumors are highly heterogeneous in terms of their cellular composition, varying levels of oxygenation, acidity, and nutrients, as well as local changes in the extracellular matrix. Furthermore, tumor tissue and tumor microenvironment properties can dynamically evolve not only during tumor growth but also when anticancer treatments are administered. Despite this, nearly all pre-clinical assessments of drug efficacy and optimal dosing are performed using homogeneous 2D cell cultures that do not resemble the cellular, metabolic, and physical features manifest in tumors in vivo. Such approach suffered from overly reductionist ex vivo / in vitro studies may not fully recapitulate th complexity of cancers especially the physical and chemical microenvironment. To address these issues we propose to develop an integrated quantitative platform that combines the power of organ-on-chip 3D tissue bioreactor, developed to include non-uniform fully controlled physical and chemical microenvironments, together with a 3DMultiCel math model that allows predictive testing of a broad range of microenvironmental combinations around the experimentally validated baseline. To achieve this goal in a quantitative way we have formed a transdisciplinary team consisting of cancer biologists, biomedical engineers and mathematicians, who will develop an experimental platform for individualized anticancer treatment based on physical science principles. Our long-term goal is to provide a computationally driven lab-on-chip platform for 3D organotypic cultures derived from patients' tumor biopsies that will be exposed to fully controlled but dynamically variable microenvironments that will be used to optimize personalized therapeutic treatments that effectively provoke breast tumor regression with minimized harmful side effects for surrounding normal tissue. Outcomes of this study will be: (i) an improved experimental platform that combines 3D culture of tumor organoids coupled with validated predictive mathematical models for the growth and response of human breast tumor organoids within realistic microenvironments; and (ii) quantitative methods that allow one to assess the dynamics of breast tumor organoid development and response to anti-tumor treatments, using mathematical modeling.
Our aims are: 1. Develop a predictive methodology to assess effects of defined microenvironments on the dynamics of normal and tumorigenic breast organoids and their sensitivity to therapeutics; 2. Construct and validate in silico model-guided complex spatial and temporal microenvironmental gradients established within TTb-G reactor, and assess breast tumor organoids response to chemotherapeutics. 3. Apply our integrated computational/engineering approach to guide therapy and predict therapeutic response ex vivo and in vivo.
We are developing a 'lab-on-chip' platform that combines 3D organotypic cultures exposed to fully controlled and dynamically variable heterogeneous microenvironments with mathematical modeling of tumor growth, development and response to treatments. The whole system will be validated in vitro and in vivo using breast cancer cell lines of various metastatic potential, as well as cultured primary tumor-derived organoids. This methodology coupled with predictive mathematical models will allow to assess the effects of defined microenvironments on the dynamics of normal and tumorigenic breast organoids and their sensitivity to therapeutics.
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| Karolak, Aleksandra; Rejniak, Katarzyna A (2018) Micropharmacology: An In Silico Approach for Assessing Drug Efficacy Within a Tumor Tissue. Bull Math Biol : |
| Hövener, Jan-Bernd; Pravdivtsev, Andrey N; Kidd, Bryce et al. (2018) Parahydrogen-Based Hyperpolarization for Biomedicine. Angew Chem Int Ed Engl 57:11140-11162 |
| Karolak, Aleksandra; Estrella, Veronica C; Huynh, Amanda S et al. (2018) Targeting Ligand Specificity Linked to Tumor Tissue Topological Heterogeneity via Single-Cell Micro-Pharmacological Modeling. Sci Rep 8:3638 |
| Karolak, Aleksandra; Markov, Dmitry A; McCawley, Lisa J et al. (2018) Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues. J R Soc Interface 15: |
| Zhou, Zijian; Yu, Jin; Colell, Johannes F P et al. (2017) Long-Lived 13C2 Nuclear Spin States Hyperpolarized by Parahydrogen in Reversible Exchange at Microtesla Fields. J Phys Chem Lett 8:3008-3014 |
| Colell, Johannes F P; Logan, Angus W J; Zhou, Zijian et al. (2017) Generalizing, Extending, and Maximizing Nitrogen-15 Hyperpolarization Induced by Parahydrogen in Reversible Exchange. J Phys Chem C Nanomater Interfaces 121:6626-6634 |
| Pérez-Velázquez, Judith; Gevertz, Jana L; Karolak, Aleksandra et al. (2016) Microenvironmental Niches and Sanctuaries: A Route to Acquired Resistance. Adv Exp Med Biol 936:149-164 |
| Shah, Ami B; Rejniak, Katarzyna A; Gevertz, Jana L (2016) Limiting the development of anti-cancer drug resistance in a spatial model of micrometastases. Math Biosci Eng 13:1185-1206 |
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