Characterizing the tumor microenvironment of MASH-related HCC to unravel determinants of response and/or resistance to atezolizumab + bevacizumab

• Applicant: Dr. Tiago Almeida Valadas De Castro
• Subject Area: Gastroenterology
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 549129192

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer (~90% of cases), which represents the 3rd-highest cause of cancer mortality worldwide. In advanced, unresectable stages of disease, the combination of atezolizumab (an immune checkpoint inhibitor (ICI) targeting the Programmed Death-Ligand 1 (PD-L1)) with bevacizumab (an anti-angiogenic targeting the Vascular Endothelial Growth Factor (VEGF)) has recently become the new standard of care. Risk factors for HCC development include cirrhosis, hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, alcohol abuse and metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD represents the fastest rising cause of HCC in the western world. Importantly, recent experimental and human data indicate that immunotherapies are significantly less effective in non-viral HCC etiologies, such as MASLD. Several studies have proposed that the tumor microenvironment (TME) may mediate response and/or resistance to immunotherapy. Single-cell sequencing-based technologies have greatly increased our understanding of intra-tumor heterogeneity in both tumor and stromal/immune cells, and how it relates to ICI response.The aim of this project is to apply single-cell sequencing technologies in the context of advanced MASH-HCC to map the TME of tumors of this etiology and unravel its molecular determinants of response/resistance to atezolizumab + bevacizumab. Firstly, an atlas of the pre-treatment TME of both advanced MASH and advanced viral HCC patients treated with atezolizumab + bevacizumab will be created. Stratifying for response to atezolizumab + bevacizumab will provide insights into potential mechanisms of response and/or resistance to this combination treatment in MASH-HCC vs non-MASH HCC. In addition, as single-cell technologies are neither established nor cost-efficient in routine clinical care, bulk transcriptome data will be generated to identify effective, predictive biomarkers of response to atezolizumab + bevacizumab in advanced MASH-HCC, ensuring that the identified biomarkers align closely with techniques applicable in the clinical practice. Finally, a deep learning-based pipeline will be employed to develop predictors of response to the combination therapy based on the analysis of high-resolution H&E scans.

• DFG Programme: WBP Fellowship
• International Connection: USA
• Host: Professor Dr. Josep M. Llovet