Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers
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 therap...
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| Format: | Article |
| Language: | English |
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Bioscientifica
2025-02-01
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| Series: | Endocrine Oncology |
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| Online Access: | https://eo.bioscientifica.com/view/journals/eo/5/1/EO-24-0025.xml |
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| author | John Metzcar Rachael Guenter Yafei Wang Kimberly M Baker Kate E Lines |
| author_facet | John Metzcar Rachael Guenter Yafei Wang Kimberly M Baker Kate E Lines |
| author_sort | John Metzcar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a7946e43d08f40a6b738671dad6f72cd |
| institution | Kabale University |
| issn | 2634-4793 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Bioscientifica |
| record_format | Article |
| series | Endocrine Oncology |
| spelling | doaj-art-a7946e43d08f40a6b738671dad6f72cd2025-02-10T15:34:04ZengBioscientificaEndocrine Oncology2634-47932025-02-015110.1530/EO-24-00251Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancersJohn Metzcar0Rachael Guenter1Yafei Wang2Kimberly M Baker3Kate E Lines4Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana, USADepartment of Surgery, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USADepartment of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana, USADepartment of Biology, Shaheen College of Arts and Sciences, University of Indianapolis, Indianapolis, Indiana, USAOCDEM, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Oxford, UKNeuroendocrine 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.https://eo.bioscientifica.com/view/journals/eo/5/1/EO-24-0025.xmlneuroendocrine tumorssystems biologymultiple neuroendocrine neoplasiavirtual clinical trialsclinical mathematical oncology modeling |
| spellingShingle | John Metzcar Rachael Guenter Yafei Wang Kimberly M Baker Kate E Lines Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers Endocrine Oncology neuroendocrine tumors systems biology multiple neuroendocrine neoplasia virtual clinical trials clinical mathematical oncology modeling |
| title | Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers |
| title_full | Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers |
| title_fullStr | Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers |
| title_full_unstemmed | Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers |
| title_short | Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers |
| title_sort | improving neuroendocrine tumor treatments with mathematical modeling lessons from other endocrine cancers |
| topic | neuroendocrine tumors systems biology multiple neuroendocrine neoplasia virtual clinical trials clinical mathematical oncology modeling |
| url | https://eo.bioscientifica.com/view/journals/eo/5/1/EO-24-0025.xml |
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