Understanding Gene ENvironment Interaction in ALcohol-related hepatocellular carcinoma

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Unité de recherche

SEFRI

Numéro de projet

23.00087

Titre du projet

Understanding Gene ENvironment Interaction in ALcohol-related hepatocellular carcinoma

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	Allemand	Français	Italien	Anglais
Résumé des résultats (Abstract)	-	-	-

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Résumé des résultats (Abstract) (Anglais)	Alcohol-related hepatocellular carcinoma (ALD-HCC) is, in Europe, the leading cause of liver cancer (2nd most common cause of cancer-related death worldwide, affecting both men and women). ALD-HCC has a median 5-year survival rate of 15%. Yet, the prognosis is driven by the tumour stage, with curative options providing a 5-year survival exceeding 70% for early-stage HCC (<20% of cases). Therefore, interventions aiming to improve prevention and early detection are key. ALD-HCC results from the interplay between environmental determinants and genetic variations. A comprehensive characterisation of environmental factors (e.g. diet, lifestyle) linked to ALD-HCC is still lacking. We recently performed the 1st genome-wide association study of ALD-HCC and identified predisposing genetic variations. However, their role on alcohol-related liver carcinogenesis needs clarification and the genetic architecture of ALD-HCC remains mostly unknown. GENIAL brings together partners with unique expertise in clinical hepatology, single-cell and spatial multi-omics, artificial intelligence (AI) and communication and dissemination capacities. Our aim is to 1) portray genetic and environmental determinants promoting ALD-HCC; 2) evaluate how they interact at cellular level in human samples and preclinical models to get novel insights into liver carcinogenesis, and identify chemopreventive targets; and 3) assess how these determinants modulate the ALD-HCC risk in prospective cohorts of patients included in HCC surveillance programs. Environmental factors will be comprehensively characterised in an ongoing clinical trial designed to evaluate alternative methods for early-stage HCC detection. Finally, AI models, reaching the minimal viable product stage by the end of GENIAL, will be used to integrate genetic and non-genetic information (including digital imaging) to develop novel cost-effective strategies fowards prevention and early-stage detection of ALDHCC in at-risk individuals. This action is part of the Cancer Mission cluster of projects on ‘‘Understanding’.

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Texte

Résumé des résultats (Abstract)
(Anglais)

Alcohol-related hepatocellular carcinoma (ALD-HCC) is, in Europe, the leading cause of liver cancer (2nd most common cause of cancer-related death worldwide, affecting both men and women). ALD-HCC has a median 5-year survival rate of 15%. Yet, the prognosis is driven by the tumour stage, with curative options providing a 5-year survival exceeding 70% for early-stage HCC (<20% of cases). Therefore, interventions aiming to improve prevention and early detection are key. ALD-HCC results from the interplay between environmental determinants and genetic variations. A comprehensive characterisation of environmental factors (e.g. diet, lifestyle) linked to ALD-HCC is still lacking. We recently performed the 1st genome-wide association study of ALD-HCC and identified predisposing genetic variations. However, their role on alcohol-related liver carcinogenesis needs clarification and the genetic architecture of ALD-HCC remains mostly unknown. GENIAL brings together partners with unique expertise in clinical hepatology, single-cell and spatial multi-omics, artificial intelligence (AI) and communication and dissemination capacities. Our aim is to 1) portray genetic and environmental determinants promoting ALD-HCC; 2) evaluate how they interact at cellular level in human samples and preclinical models to get novel insights into liver carcinogenesis, and identify chemopreventive targets; and 3) assess how these determinants modulate the ALD-HCC risk in prospective cohorts of patients included in HCC surveillance programs. Environmental factors will be comprehensively characterised in an ongoing clinical trial designed to evaluate alternative methods for early-stage HCC detection. Finally, AI models, reaching the minimal viable product stage by the end of GENIAL, will be used to integrate genetic and non-genetic information (including digital imaging) to develop novel cost-effective strategies fowards prevention and early-stage detection of ALDHCC in at-risk individuals. This action is part of the Cancer Mission cluster of projects on ‘‘Understanding’.