Supporting data for “Hepatitis B virus integration in hepatocellular carcinoma: from detection and mechanistic insights to clinical translation”

<p dir="ltr">Hepatitis B virus (HBV) infection is a significant risk factor for hepatocellular carcinoma (HCC), primarily through viral integration into the host genome and other mechanisms, which can lead to genomic instability and tumorigenesis. This study aimed to deepen the understanding of HBV integration in HCC from event detection, landscape characterization and mechanistic determination to translational application. To enhance HBV integration detection and support our subsequent HBV-associated HCC investigation, AVID (Accurate Viral Integration Detector) algorithm was developed with improved sensitivity and accuracy for viral integration detection. It outperformed other existing tools and was applicable beyond HCC, with demonstrated performance in various oncovirus-associated cancers. Utilizing AVID, we further investigated the mechanistic implications of HBV integration in HCC, revealing notable enrichment and co-involvement of HBV integrations at telomeres and centromeres, which may contribute to genomic instability and poor clinical outcomes. Notably, since <i>TERT </i>is the most frequently integrated gene and its specific role in telomere maintenance and tumorigenesis, we pinpointed our investigation on <i>TERT </i>events and identified the orientation and relative distance of HBV integration concurrently modulate TERT transcription activation. These above findings highlight both gene-independent and gene-dependent mechanisms by which HBV integration promotes HCC progression. Given the critical role of clonal expansion in various oncogenic processes, we further focused on the clonal enrichment of HBV integration, revealing such enrichment may vary across different geographical regions and patients’ viral infection history. A clonal disparity score was designed to quantify the diverse clonal enrichment and served as a potential prognostic indicator for patients. Recognizing the significant role of clonal HBV integration in HCC, we finally explored its potential as a blood-based biomarker through circulating cell-free DNA (cfDNA). Clonal disparity score was further applied on cfDNA data and alteration profile including HBV integration and HBV fragment size was employed to construct a machine learning model for early HCC detection, highlighting the potential of using HBV integration in guiding clinical management strategies for HCC.</p>