Periodic Reporting for period 2 - GENIAL (Understanding Gene ENvironment Interaction in ALcohol-related hepatocellular carcinoma)
Berichtszeitraum: 2024-07-01 bis 2025-12-31
The GENIAL project addresses a critical European health challenge: alcohol-related hepatocellular carcinoma (ALD-HCC), the most common cause of liver cancer. This disease, directly caused by alcohol consumption, significantly burdens public health systems and patient quality of life as it is often diagnosed late when treatment options are limited.
GENIAL's core objective is to deepen our understanding of ALD-HCC development mechanisms, focusing specifically on the interplay between genetic predisposition and environmental factors, By examining this gene-environment interaction, the project aims to uncover why some individuals who consume alcohol develop ALD-HCC while others do not and try to uncover potential therapeutic targets.While GENIAL's primary focus is on understanding mechanisms, its findings have the potential to impact prevention strategies and early detection through improved risk stratification.
Given that ALD-HCC is the most common cause of liver cancer, the third leading cause of cancer death globally, GENIAL's potential impact is substantial. By integrating diverse data types with AI technologies to study gene-environment interactions, GENIAL could provide crucial insights into ALD-HCC development, potentially benefiting thousands of patients across Europe. This aligns with EU Cancer Mission objectives to improve understanding of cancer mechanisms and risk factors, with implications for prevention and early detection strategies.
GENIAL's core objective is to deepen our understanding of ALD-HCC development mechanisms, focusing specifically on the interplay between genetic predisposition and environmental factors, By examining this gene-environment interaction, the project aims to uncover why some individuals who consume alcohol develop ALD-HCC while others do not and try to uncover potential therapeutic targets.While GENIAL's primary focus is on understanding mechanisms, its findings have the potential to impact prevention strategies and early detection through improved risk stratification.
Given that ALD-HCC is the most common cause of liver cancer, the third leading cause of cancer death globally, GENIAL's potential impact is substantial. By integrating diverse data types with AI technologies to study gene-environment interactions, GENIAL could provide crucial insights into ALD-HCC development, potentially benefiting thousands of patients across Europe. This aligns with EU Cancer Mission objectives to improve understanding of cancer mechanisms and risk factors, with implications for prevention and early detection strategies.
WP1: The consortium successfully completed the largest-ever case-control dataset for ALD-HCC genetic discovery (4,418 cases/6,655 controls across 20 cohorts), enabling a genome-wide association study that replicated five known risk loci (PNPLA3, TM6SF2, TERT, WNT3A, HSD17B13) and identified a novel genome-wide significant signal at EPHA2. A polygenic risk score (PRS) incorporating these six loci demonstrated a two-fold increased HCC risk in high-risk UK Biobank cirrhotic patients with moderate-to-high alcohol consumption (sHR 2.08-2.26) with 10-year absolute risk differences up to 12.2% in heavy drinkers. Spatial transcriptomics analyses further revealed EPHA2 enrichment in biliary tract lineage cells, providing novel mechanistic insights.
WP2: Histopathological assessment of 45 ALD liver specimens was completed, with spatial omics and single-nuclei datasets now available for integration via UPCIT's MetaMAP and Histomap methods. Champalimaud uncovered novel neuro-immune axes in hepatic transformation, identifying potential first-in-class therapeutic targets for MASLD/HCC. The SCherlock method for robust cell-type marker identification in single-cell data has been finalized and is ready for submission alongside its validation paper.
WP3: FASTRAK trial recruitment reached 637 patients (102% of M36 target across 34 centers), confirming a 3% annual HCC incidence in this high-risk cohort. SERENA cohort (n=524 advanced MASLD/MetALD patients) reported a 7.6% 5-year HCC incidence, with FIB-4 emerging as a strong independent predictor (HR 1.34/unit AUC 0.81). Machine learning models benchmarked on CIRRAL/CirVir/NASH-AVC cohorts (n=2,544) achieved AUC 0.81 when combining clinical scores and pre-GENIAL PRS; ongoing integration of WP1 genetics, pathology, radiology, and FASTRAK/SERENA data targets AUC>0.90. Biobanks for circulating samples, digital pathology, and imaging are operationa
WP2: Histopathological assessment of 45 ALD liver specimens was completed, with spatial omics and single-nuclei datasets now available for integration via UPCIT's MetaMAP and Histomap methods. Champalimaud uncovered novel neuro-immune axes in hepatic transformation, identifying potential first-in-class therapeutic targets for MASLD/HCC. The SCherlock method for robust cell-type marker identification in single-cell data has been finalized and is ready for submission alongside its validation paper.
WP3: FASTRAK trial recruitment reached 637 patients (102% of M36 target across 34 centers), confirming a 3% annual HCC incidence in this high-risk cohort. SERENA cohort (n=524 advanced MASLD/MetALD patients) reported a 7.6% 5-year HCC incidence, with FIB-4 emerging as a strong independent predictor (HR 1.34/unit AUC 0.81). Machine learning models benchmarked on CIRRAL/CirVir/NASH-AVC cohorts (n=2,544) achieved AUC 0.81 when combining clinical scores and pre-GENIAL PRS; ongoing integration of WP1 genetics, pathology, radiology, and FASTRAK/SERENA data targets AUC>0.90. Biobanks for circulating samples, digital pathology, and imaging are operationa
GENIAL has already successfully constituted several databases related to genetic and clinical risk factors for ALD-HCC and developed new methods, yielding promising preliminary results. Our genetic study has exceeded patient recruitment targets, enhancing our ability to identify novel risk factors. Comprehensive analysis of multiple cohorts with patients followed prospectively, integrating diverse data types, will advance our predictive modeling capabilities. While these achievements position GENIAL at the forefront of liver cancer research, more effort is still needed to fully realize the project's ambitious goals.