Modelling hepatocellular carcinoma microenvironment phenotype to evaluate drug efficacy

Area of Science:

• Oncology
• Biotechnology
• Drug Discovery

Background:

• Hepatocellular carcinoma (HCC) presents significant global mortality, with treatment hindered by poor drug efficacy and response prediction challenges.
• Existing models often fail to fully recapitulate HCC complexity and individual patient variability.

Purpose of the Study:

• To develop a patient-centric spheroid model that mimics hepatocellular carcinoma (HCC) phenotype and molecular classification.
• To establish a novel strategy for monitoring therapeutic response in HCC models.
• To validate the predictive capability of the model for tyrosine kinase inhibitor (TKI) treatments.

Main Methods:

• Co-culture of HepG2, TWNT-1, and THP-1 cells to create patient-centric HCC spheroids.
• Implementation of a target-independent cell killing (TICK) exclusion strategy for therapeutic response monitoring.
• Testing the model's response to TKIs (sorafenib, cabozantinib, lenvatinib) and comparing predictions with clinical outcomes.

Main Results:

• The developed spheroid model successfully reproduced the Barcelona Clinic Liver Cancer (BCLC) molecular classification and epigenetic alterations.
• The model demonstrated patient-dependent responses to various TKIs, mirroring clinical observations.
• The model accurately predicted 34 out of 37 clinical outcomes for TKI treatments in HCC patients.

Conclusions:

• Patient-centric HCC spheroids combined with the TICK strategy offer a robust platform for drug discovery.
• This approach shows significant potential for predicting patient response to TKIs, paving the way for personalized HCC treatment strategies.