Background & Purpose. Hepatocellular carcinoma (HCC) is the second leading and fastest rising cause of cancer death worldwide. HCC is often diagnosed when it is not curable. Thus, there are many challenges associated with the treatment of HCC. Until now there is no simple and robust system for reliable pre-treatment screening of approved drugs.
Methods. We developed a simple and robust patient-derived multicellular cell clustering model from 18 liver resections. We applied this technology on 9 HCC liver tissues and assessed FDA-approved drug responses.
Results. The success rate of our model is 100% independently from the type of liver underlying disease. The model shows differential responses to HCC targeted- and immune-based treatments with a high reproducibility. Our work highlights that preserving the integrity of the primary liver architecture permits to perpetuate the physiopathology.
Conclusion. This model system will help to understand the biological role of the tumor microenvironment and to accelerate the clinical research program for personalized liver cancer treatment based on functional screening.