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

Area of Science:

• Oncology
• Systems Biology
• Computational Biology

Background:

• Pancreatic neuroendocrine tumors (PanNETs) are rare, with unpredictable progression and rising incidence.
• Molecular drivers and biomarkers for PanNETs remain largely unknown.
• PanNET heterogeneity complicates treatment, and targeted therapies often show limited response.

Purpose of the Study:

• To predict PanNET progression and identify resistance mechanisms to targeted therapies.
• To model the impact of key genetic drivers (MEN1, DAXX, TSC) in PanNET development.
• To forecast patient response to mammalian target of rapamycin complex 1 (mTORC1) inhibitors.

Main Methods:

• Integrated dynamic modeling with patient expression profiles and classifier approaches.
• Developed a computational model representing common PanNET genetic alterations.
• Simulated tumor progression and treatment response based on molecular profiles.

Main Results:

• Identified potential first and second genetic hits driving PanNET progression after MEN1 loss.
• Predicted differential benefits of mTORC1 inhibitors across various PanNET mutation profiles.
• Hypothesized mechanisms of resistance to mTORC1 inhibitors in PanNETs.

Conclusions:

• Systems biology modeling offers a powerful tool for understanding PanNETs.
• Predictive models can guide personalized treatment strategies for PanNET patients.
• This approach enhances prediction and treatment for diverse PanNET molecular phenotypes.