Dettaglio Progetto Finanziato

PRECURSOR-HCC

L’Ente Capofila del progetto è la Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico.  Il Responsabile Scientifico del progetto è il Professor Luca Vittorio Carlo Valenti.

Patologia:La steatoepatite associata a disfunzione metabolica
Area Tematica:Fisiopatologia endocrino-metabolica
Data di Inizio Progetto:30.01.2026
Data di Fine Progetto:29.01.2029
Finanziamento:€ 1.910.368,00
PartenariatoFondazione IRCCS Cà Granda Ospedale Maggiore Policlinico 
IRCCS Istituto Clinico Humanitas
ASST Grande Ospedale metropolitano Niguarda

Metabolic dysfunction associated steatotic liver disease (MASLD) is now the leading cause of liver disease leading to cirrhosis, hepatocellular carcinoma (HCC) and increased mortality, representing a looming public health threat. Development on non-invasive biomarkers and effective treatments are main unmet clinical needs for progressive MASLD. Aim of this project will be to pinpoint the mechanisms leading the transition from liver fat accumulation to steatohepatitis, fibrosis and HCC. We hypothesize that alterations in lipid droplets (LDs) biology in liver cells are the precursor to progressive MASLD. To test this, we propose to combine human genetics in well characterized clinical cohorts with disease modelling in human liver organoids (HLOs) and artificial intelligence. Specifically, we will: a) integrate common and rare genetic determinants of MASLD into partitioned polygenic risk scores (pPRS) linking genetic predisposition to specific LDs features with liver-related outcomes; b) create a novel experimental platform for high-throughput LDs screening (morphology, kinetics, composition, transcriptome) through multi-omics, disentangling MASLD genetic diversity; c) integrate results by artificial intelligence to improve risk stratification, yielding  novel diagnostic/prognostic tools for cirrhosis and HCC and therapeutic targets. By isolating high-risk MASLD subtypes through distinct LDs pathways, we will enable a precision medicine improving clinical management efficacy.