Accumulation of Lipid Droplets as an Underlying Cause of the Progression of Fatty Liver Disease to Liver Cancer

Condition(s) studied

Investigational drug(s) / intervention(s)

Comprehensive Genomic and Transcriptomic Profiling: DNA isolation followed by Whole Exome Sequencing (WES) to identify rare and common genetic variants. This intervention includes single-cell transcriptomics for precise immunophenotyping of liver resident cells and the mapping of cellular heterogeneity across the MASLD spectrum.

Patient-Derived Human Liver Organoid (HLO) Development: Generation and analysis of 3D Human Liver Organoids (HLOs) from patient biological samples. These models are utilized to study lipid droplet (LD) biology, hepatocyte function, and the mechanisms of disease progression in a controlled, patient-specific environment.

AI-Driven Digital Pathology (HistoIndex): Use of a non-invasive, stain-free imaging system based on second-harmonic generation (SHG) microscopy. This device provides automated, AI-driven quantification of liver fibrosis and detailed morphological assessment of lipid droplets.

Targeted LD Lipidomics: Advanced lipidomic profiling of lipid droplets (LD) conducted on liver samples to identify specific lipid signatures associated with the transition from simple steatosis to hepatocellular carcinoma (HCC).

AI-Integrated Predictive Risk Modeling: Application of artificial intelligence algorithms to integrate multi-omic data (genomic, transcriptomic, lipidomic) with clinical outcomes. This intervention focuses on developing refined risk stratification tools and identifying novel therapeutic targets for cirrhosis and HCC.

Study summary

Metabolic dysfunction-associated steatotic liver disease (MASLD) is currently the leading cause of chronic liver disease, accounting for an increasing burden of cirrhosis, hepatocellular carcinoma (HCC), and related mortality, thus representing a major emerging public health threat. Currently, the primary unmet clinical needs in progressive MASLD remain the development of non-invasive biomarkers and effective therapeutic options.

The objective is to delineate the pathogenic mechanisms driving the transition from hepatic lipid accumulation to steatohepatitis, fibrosis, and HCC, based on the hypothesis that alterations in lipid droplet (LD) biology within hepatocytes and resident liver cells are early, decisive factors in disease progression. To test this, human genetic studies from well-characterized cohorts will be combined with human liver organoids (HLOs) and artificial intelligence (AI) tools.

Specifically, common and rare genetic variants will be integrated into partitioned polygenic risk scores (pPRS) to link genetic predisposition to specific LD morphological and functional traits. Furthermore, an innovative high-throughput screening platform using multi-omic approaches will be developed to deconvolve the genetic diversity of MASLD through LD profiling. Finally, these data will be integrated via AI algorithms to refine risk stratification, develop new diagnostic and prognostic tools for cirrhosis and HCC, and identify novel therapeutic targets. Ultimately, the identification of high-risk MASLD subtypes through specific LD pathways will enable precision medicine strategies, significantly improving clinical management.

Eligibility

Primary outcome measure(s)

• Incidence of High-Risk MASLD with Advanced Liver Fibrosis — up to 24 months
  Presence of significant to advanced liver fibrosis, defined as histological or non-invasive stage \>= F2 (evaluated via transient elastography/FibroScan liver stiffness \>7.9 kPa, liver biopsy, or validated non-invasive scoring systems such as NAFLD Fibrosis Score, APRI, or FIB-4). The predictive accuracy of multi-level polygenic risk scores (PRS/pPRS) and multi-omic models in stratifying this risk will be evaluated using area under the receiver operating characteristic curve (AUC-ROC) and logistic regression odds ratios.
• Incidence of Hepatocellular Carcinoma (HCC) — up to 24 months
  Diagnosis of new or existing Hepatocellular Carcinoma (HCC) confirmed according to AASLD/EASL guidelines using dynamic imaging (Contrast-Enhanced Computed Tomography \[CT\] or Magnetic Resonance Imaging \[MRI\]) or histological examination. The performance of genetic (PRS/pPRS) and multi-omic predictive algorithms in identifying individuals at high risk for developing HCC within the MASLD population will be assessed.

Trial sites (1)

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