Diagnosis of hepatocellular adenomas and malignancy determination by proteomic patterns matching (1)

Biopsies are underrated and underused and our goal was to demonstrate that their inherent expression proteomic pattern could give them an added value for diagnosis. As proof of concept, we used as model hepatocellular adenomas (HCA), well characterized benign liver tumors. From a collection of 260 cases, we selected 55 typical cases to build the first HCA proteomic database. Biopsies proteomic patterns allowed HCA classification, even for complex cases. In addition, these data gave access to a malignancy pattern identifying the HCA transformation. This pioneering work proposes a proteomic based machine learning tool, operational on fixed biopsies, to improve HCA diagnosis and therefore patient’s management.