A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective

  • AbstractPancreatic 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 bothAbstractPancreatic 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.show moreshow less

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Metadaten
Author:Silke D. WerleORCiD, Nensi IkonomiORCiD, Ludwig Lausser, Annika M. T. U. Kestler, Felix M. WeidnerORCiD, Julian D. SchwabORCiD, Julia Maier, Malte Buchholz, Thomas M. Gress, Angelika M. R. Kestler, Hans A. KestlerORCiD
Language:English
Document Type:Article
Year of first Publication:2023
published in (English):npj Systems Biology and Applications
Publisher:Springer
Place of publication:Heidelberg
ISSN:2056-7189
Volume:9
Issue:1
Article Number:22
Review:peer-review
Open Access:ja
Version:published
URN:urn:nbn:de:bvb:573-38071
Related Identifier:https://doi.org/10.1038/s41540-023-00283-8
Faculties / Institutes / Organizations:Fakultät Informatik
Licence (German):License Logo Creative Commons BY 4.0
Release Date:2023/08/07