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GENIAL

Understanding Gene ENvironment Interaction in ALcohol-related hepatocellular carcinoma

Grant period2023-01-01 - 2027-12-31
Funding bodyEuropean Union
Call numberHORIZON-MISS-2021-CANCER-02
Grant number101096312
IdentifierG:(EU-Grant)101096312

Note: 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 towards prevention and early-stage detection of ALD-HCC in at-risk individuals. This action is part of the Cancer Mission cluster of projects on ‘‘Understanding’.
   

Recent Publications

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Deep Learning Reveals Liver MRI Features Associated With PNPLA3 I148M in Steatotic Liver Disease
Liver international 45(7), e70164 () [10.1111/liv.70164]  GO OpenAccess  Download fulltext Files BibTeX | EndNote: XML, Text | RIS

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Medical large language models are susceptible to targeted misinformation attacks
npj digital medicine 7(1), 288 () [10.1038/s41746-024-01282-7]  GO OpenAccess  Download fulltext Files BibTeX | EndNote: XML, Text | RIS

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Reconstruction of patient-specific confounders in AI-based radiologic image interpretation using generative pretraining
Cell reports / Medicine 5(9), 101713 () [10.1016/j.xcrm.2024.101713]  GO OpenAccess  Download fulltext Files BibTeX | EndNote: XML, Text | RIS

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A pilot study on the efficacy of GPT-4 in providing orthopedic treatment recommendations from MRI reports
Scientific reports 13, 20159 () [10.1038/s41598-023-47500-2]  GO OpenAccess  Download fulltext Files BibTeX | EndNote: XML, Text | RIS

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Denoising diffusion probabilistic models for 3D medical image generation
Scientific reports 13, 7303 () [10.1038/s41598-023-34341-2]  GO OpenAccess  Download fulltext Files BibTeX | EndNote: XML, Text | RIS

All known publications ...
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 Record created 2023-02-21, last modified 2023-02-22



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