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Werle SD, Ikonomi N, Lausser L, Kestler AMTU, Weidner FM, Schwab JD, Maier J, Buchholz M, Gress TM, Kestler AMR, Kestler HA. A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective. NPJ Syst Biol Appl 2023; 9:22. [PMID: 37270586 DOI: 10.1038/s41540-023-00283-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/17/2023] [Indexed: 06/05/2023] Open
Abstract
Pancreatic neuroendocrine tumors (PanNETs) are a rare tumor entity with largely unpredictable progression and increasing incidence in developed countries. Molecular pathways involved in PanNETs development are still not elucidated, and specific biomarkers are missing. Moreover, the heterogeneity of PanNETs makes their treatment challenging and most approved targeted therapeutic options for PanNETs lack objective responses. Here, we applied a systems biology approach integrating dynamic modeling strategies, foreign classifier tailored approaches, and patient expression profiles to predict PanNETs progression as well as resistance mechanisms to clinically approved treatments such as the mammalian target of rapamycin complex 1 (mTORC1) inhibitors. We set up a model able to represent frequently reported PanNETs drivers in patient cohorts, such as Menin-1 (MEN1), Death domain associated protein (DAXX), Tuberous Sclerosis (TSC), as well as wild-type tumors. Model-based simulations suggested drivers of cancer progression as both first and second hits after MEN1 loss. In addition, we could predict the benefit of mTORC1 inhibitors on differentially mutated cohorts and hypothesize resistance mechanisms. Our approach sheds light on a more personalized prediction and treatment of PanNET mutant phenotypes.
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Affiliation(s)
- Silke D Werle
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany
| | - Nensi Ikonomi
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany
| | - Ludwig Lausser
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany
- Faculty of Computer Science, Technische Hochschule Ingolstadt, 85049, Ingolstadt, Germany
| | | | - Felix M Weidner
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany
| | - Julian D Schwab
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany
| | - Julia Maier
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany
- Institute of Pathology, University Hospital Ulm, 89081, Ulm, Germany
| | - Malte Buchholz
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043, Marburg, Germany
| | - Thomas M Gress
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043, Marburg, Germany
| | | | - Hans A Kestler
- Institute of Medical Systems Biology, Ulm University, 89081, Ulm, Germany.
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Werle SD, Ikonomi N, Schwab JD, Kraus JM, Weidner FM, Lenhard Rudolph K, Pfister AS, Schuler R, Kühl M, Kestler HA. Identification of dynamic driver sets controlling phenotypical landscapes. Comput Struct Biotechnol J 2022; 20:1603-1617. [PMID: 35465155 PMCID: PMC9010550 DOI: 10.1016/j.csbj.2022.03.034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/30/2022] [Accepted: 03/30/2022] [Indexed: 11/03/2022] Open
Abstract
Controlling phenotypical landscapes is of vital interest to modern biology. This task becomes highly demanding because cellular decisions involve complex networks engaging in crosstalk interactions. Previous work on control theory indicates that small sets of compounds can control single phenotypes. However, a dynamic approach is missing to determine the drivers of the whole network dynamics. By analyzing 35 biologically motivated Boolean networks, we developed a method to identify small sets of compounds sufficient to decide on the entire phenotypical landscape. These compounds do not strictly prefer highly related compounds and show a smaller impact on the stability of the attractor landscape. The dynamic driver sets include many intervention targets and cellular reprogramming drivers in human networks. Finally, by using a new comprehensive model of colorectal cancer, we provide a complete workflow on how to implement our approach to shift from in silico to in vitro guided experiments.
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