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Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers
John Metzcar1,2,3, Rachael Guenter4, Yafei Wang1
1Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana, USA.
Abstract:
Neuroendocrine tumors (NETs) occur sporadically or as part of rare endocrine tumor syndromes (RETSs) such as multiple endocrine neoplasia 1 and von Hippel-Lindau syndromes. Due to their relative rarity and lack of model systems, NETs and RETSs are difficult to study, hindering advancements in therapeutic development. Causal or mechanistic mathematical modeling is widely deployed in disease areas such as breast and prostate cancers, aiding the understanding of observations and streamlining in vitro and in vivo modeling efforts. Mathematical modeling, while not yet widely utilized in NET research, offers an opportunity to accelerate NET research and therapy development. To illustrate this, we highlight examples of how mathematical modeling associated with more common endocrine cancers has been successfully used in the preclinical, translational and clinical settings. We also provide a scope of the limited work that has been done in NETs and map how these techniques can be utilized in NET research to address specific outstanding challenges in the field. Finally, we include practical details such as hardware and data requirements, present advantages and disadvantages of various mathematical modeling approaches and discuss challenges of using mathematical modeling. Through a cross-disciplinary approach, we believe that many currently difficult problems can be made more tractable by applying mathematical modeling and that the field of rare diseases in endocrine oncology is well poised to take advantage of these techniques.
Insights
Mathematical modeling can accelerate research into rare endocrine tumor syndromes (RETSs) and neuroendocrine tumors (NETs). This approach, proven in other cancers, offers new ways to understand and treat these rare diseases.
Area of Science:
- Endocrine Oncology
- Computational Biology
- Translational Medicine
Background:
- Neuroendocrine tumors (NETs) and rare endocrine tumor syndromes (RETSs) are challenging to study due to their rarity and limited model systems.
- Therapeutic development for NETs and RETSs is hindered by these research difficulties.
- Mathematical modeling is successfully used in common cancers like breast and prostate cancer for research and development.
Purpose of the Study:
- To highlight the potential of mathematical modeling to accelerate NET and RETS research and therapy development.
- To illustrate successful applications of mathematical modeling in preclinical, translational, and clinical settings for common endocrine cancers.
- To outline how mathematical modeling can address specific challenges in NET research.
Main Methods:
- Review of mathematical modeling applications in common endocrine cancers.
- Exploration of existing mathematical modeling work in NETs.
- Mapping potential applications of mathematical modeling techniques to NET research challenges.
- Discussion of practical aspects including hardware, data requirements, and modeling approaches.
Main Results:
- Mathematical modeling offers a powerful, underutilized tool for advancing NET and RETS research.
- Examples demonstrate the successful translation of mathematical modeling from basic research to clinical application in other endocrine cancers.
- Specific strategies for applying mathematical modeling to NET research challenges are identified.
Conclusions:
- Mathematical modeling can significantly improve the tractability of complex problems in endocrine oncology, particularly for rare diseases.
- The field of rare endocrine tumors is well-positioned to benefit from a cross-disciplinary approach integrating mathematical modeling.
- Adoption of mathematical modeling techniques promises to accelerate therapeutic development for NETs and RETSs.
