Merging Metabolic Modeling and Imaging for Screening Therapeutic Targets in Colorectal Cancer.

Area of Science:

• Oncology
• Systems Biology
• Metabolic Engineering

Background:

• Cancer-associated fibroblasts (CAFs) are key drivers of metabolic reprogramming in colorectal cancer (CRC).
• CAFs contribute significantly to drug resistance in CRC, necessitating novel therapeutic strategies.
• Understanding the metabolic crosstalk between CAFs and CRC cells is crucial for developing effective treatments.

Purpose of the Study:

• To identify and validate key metabolic targets within the CRC-CAF metabolic crosstalk.
• To computationally screen enzyme perturbations using a machine learning-based approach and a CRC metabolic model.
• To experimentally validate the efficacy of targeting identified metabolic vulnerabilities in patient-derived tumor organoids.

Main Methods:

• Integrated systems biology approach combining computational modeling and experimental validation.
• Novel machine learning-based method for high-throughput computational screening of enzyme perturbations.
• Experimental validation using patient-derived tumor organoids (PDTOs), metabolic imaging, and viability assays.

Main Results:

• Hexokinase (HK) was identified as a crucial metabolic target through computational screening.
• PDTOs cultured in CAF-conditioned media showed increased sensitivity to HK inhibition.
• Experimental results confirmed the model's predictions regarding HK as a viable therapeutic target.

Conclusions:

• Integrating computational and experimental techniques is essential for exploring and exploiting CRC-CAF metabolic crosstalk.
• Targeting hexokinase represents a promising strategy to overcome drug resistance in colorectal cancer.
• This approach provides a framework for data-driven identification and validation of metabolic targets in cancer therapy.