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Mathematical Modeling of Tumor Organoids: Toward Personalized Medicine

  • Aleksandra Karolak
  • Katarzyna A. Rejniak
Chapter
Part of the Cancer Drug Discovery and Development book series (CDD&D)

Abstract

Three-dimensional organoid and organoidal cell cultures can recreate certain aspects of in vivo tumors and tumor microenvironments, and thus can be used to test intratumoral interactions and tumor response to treatments. In silico organoid models, when based on biological or clinical data, are an invaluable tool for hypothesis testing, and provide an opportunity to explore experimental conditions beyond what is feasible experimentally. In this chapter, three different approaches to building in silico organoids are described together with methods for integration with experimental or clinical data. The first model will be used to determine the mechanisms of development of breast tumor acini, based on their in vitro morphology. The second model will be used to predict conditions for the most effective cellular uptake of therapies targeting pancreatic cancers that incorporate intravital microscopy data. The third model will provide a procedure for assessing patients’ response to chemotherapeutic treatments, based on the biopsy data. For each of the models, a protocol will be proposed indicating how it can be used to generate testable hypotheses or predictions. These models can help biologists in determining what experiments should be performed in the laboratory. They can also assist clinicians in assessing cancer patients’ response to a given therapy and their risk of tumor recurrence.

Keywords

In silico organoids Digitized tissue Organotypic cultures Mammary acini Drug penetration Single-cell delivery Targeted therapy Personalized medicine Mathematical modeling 

Notes

Acknowledgements

This work was supported in part by the U01-CA20229-01 grant from the National Institute of Health (NIH) via the National Cancer Institute—Physical Science Oncology Network (NCI-PSON). Data collection and analysis were supported by the Cancer Center Support Grant P30-CA-076292 from NIH to H. Lee Moffitt Cancer Center & Research Institute, an NCI-designated Comprehensive Cancer Center.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Integrated Mathematical Oncology DepartmentH. Lee Moffitt Cancer Center & Research InstituteTampaUSA
  2. 2.Department of Oncologic Sciences, College of MedicineUniversity of South FloridaTampaUSA

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