Molecular knowledge of liver cancer will improve the lives of patients
Liver cancer is a major public health issue, with an estimated incidence of one million cases from 2025. Surgery and local-regional treatments cure the disease in a small number of cases, but most patients progress to advanced stages, in which molecular therapies obtain survival rates of up to one year.
Molecular and genomic studies have defined the molecular and immunological classification of the tumour and the main therapeutic targets. However, such knowledge has not yet given rise to any clinical application that could prove beneficial to patients.
The group has defined the molecular and immunological classification of hepatocellular carcinoma and has characterised certain therapeutic targets. The most pressing challenges to improve the treatment of patients with hepatocellular carcinoma and cholangiocarcinoma are: identification of molecular markers – with liquid biopsy – for early diagnosis; identification of the pro-carcinogenic microenvironment as the therapeutic target in patients with cirrhosis of the liver; examination of genomic alterations to detect the presence of new genotoxic agents; identification of the biomarkers denoting response or primary resistance to new therapies, basically immunological treatments; identification of new therapeutic targets by methylation deregulation; identification of drugs which, when combined, have synergistic effects, and improving knowledge about the mechanisms involved in the origin, progression and dissemination of these cancers.
The long-term objective is to improve survival rates and quality of life in patients with liver cancer, as well as to improve prevention. Secondary objectives include increasing knowledge of the way in which the disease develops, molecular alterations and treatment of these tumours.
The impact of the group’s studies may help improve the prevention and treatment of liver cancer by identifying the biomarkers that predict the response to immunological therapies, and those that predict resistance. This will also make it possible to identify the best therapeutic combinations for use in clinical trials and to identify new treatment paradigms.