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Updated: Dec 17, 2025

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
Published on: September 30, 2021
Molecular perspectives for the treatment of hepatocellular carcinoma
A Demory1,2,3,4, J C Nault1,2,3,4
1Service d'hépatologie, Hôpital Jean Verdier, Hôpitaux Universitaires Paris-Seine-Saint-Denis, Assistance-Publique Hôpitaux de Paris, Bondy, France.
Abstract:
Major advances have been performed in the understanding of genomic dysregulation of hepatocellular carcinoma. A median of 40 to 60 somatic mutations in coding sequence per tumor was identified including 2 to 6 mutations per tumor in genes driving liver carcinogenesis. The main genetic alterations target the key signaling pathways of liver carcinogenesis : telomere maintenance, cell cycle gene, Wnt/beta-catenin pathway, epigenetic modifier gene, oxidative stress pathway, AKT/mTOR and Ras/Raf MAP kinase pathways. A genotype/phenotype classification between these genetic drivers the tumor and patient's features have been also described and was correlated with transcriptomic profiling. These data will be helpful to identify subgroups of HCC that will respond or resist to systemic treatments already used in clinical practice such as tyrosine kinase inhibitors, anti-VEGFR antibody or checkpoint inhibitors and will be useful to identify new therapeutic targets tested in future clinical trials.
Insights
Researchers identified key genetic mutations driving liver cancer (hepatocellular carcinoma). Understanding these genomic alterations helps predict treatment response and discover new therapeutic targets for HCC.
Area of Science:
- Genomics
- Hepatocellular Carcinoma Research
- Cancer Signaling Pathways
Background:
- Hepatocellular carcinoma (HCC) is a complex malignancy with significant genomic dysregulation.
- Understanding the genetic landscape of HCC is crucial for developing effective therapies.
Purpose of the Study:
- To elucidate the major genomic alterations in hepatocellular carcinoma.
- To correlate genetic drivers with tumor and patient phenotypes.
- To identify potential biomarkers for treatment response and new therapeutic targets.
Main Methods:
- Somatic mutation analysis of coding sequences in HCC tumors.
- Identification of frequently mutated genes and affected signaling pathways.
- Genotype/phenotype classification and correlation with transcriptomic profiling.
Main Results:
- A median of 40-60 somatic mutations per tumor, with 2-6 mutations in key liver carcinogenesis genes.
- Identified alterations in critical pathways: telomere maintenance, cell cycle, Wnt/beta-catenin, epigenetic modifiers, oxidative stress, AKT/mTOR, and Ras/Raf MAP kinase.
- Established a genotype/phenotype classification correlated with transcriptomic data.
Conclusions:
- Genomic profiling provides insights into HCC heterogeneity.
- This classification aids in predicting patient response to current systemic treatments (e.g., tyrosine kinase inhibitors, checkpoint inhibitors).
- Identified genetic drivers offer avenues for novel therapeutic strategies in future clinical trials.
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