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Detection of Low Copy Number Integrated Viral DNA Formed by In Vitro Hepatitis B Infection
Published on: November 7, 2018
Machine-learning based patient classification using Hepatitis B virus full-length genome quasispecies from Asian and
Alan J Mueller-Breckenridge1, Fernando Garcia-Alcalde2, Steffen Wildum2
1Roche Innovation Centre, Basel, Switzerland F. Hoffmann-La Roche AG, Grenzacherstrasse 124, CH-4070, Basel, Switzerland. alan_james.mueller-breckenridge@roche.com.
Insights
Hepatitis B virus (HBV) quasispecies diversity was comprehensively surveyed using ultra-deep sequencing. Machine learning models identified viral variants that accurately classify Hepatitis B e antigen (HBeAg) status, aiding chronic HBV infection management.
Area of Science:
- Virology
- Genomics
- Hepatology
Background:
- Chronic Hepatitis B virus (HBV) infection is a leading cause of liver disease, including fibrosis, cirrhosis, and hepatocellular carcinoma (HCC).
- Understanding the role of viral genetic diversity in disease progression is crucial for effective management.
- Current tools for analyzing complex HBV patient viral profiles are limited.
Purpose of the Study:
- To conduct the first comprehensive survey of HBV quasispecies diversity across European and Asian patient cohorts.
- To develop a machine learning model for classifying Hepatitis B e antigen (HBeAg) seroconversion status based on viral variants.
- To explore the utility of advanced analytics for clinical decision support in chronic HBV infection.
Main Methods:
- Ultra-deep sequencing of the complete HBV genome from European and Asian patient samples.
- Application of machine learning algorithms to identify viral variants associated with HBeAg status.
- Comparative analysis of HBV quasispecies populations and their clinical implications.
Main Results:
- Detailed characterization of HBV quasispecies diversity across diverse patient populations.
- Development of a predictive model accurately classifying HBeAg status using viral variant signatures.
- Identification of specific viral mutants correlating with disease progression markers.
Conclusions:
- Ultra-deep sequencing provides unprecedented insight into HBV quasispecies dynamics.
- Machine learning effectively leverages viral genetic data for clinical stratification of HBV patients.
- Advanced analysis of HBV quasispecies can inform therapeutic strategies and improve patient outcomes in chronic infection.
Abstract:
Chronic infection with Hepatitis B virus (HBV) is a major risk factor for the development of advanced liver disease including fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The relative contribution of virological factors to disease progression has not been fully defined and tools aiding the deconvolution of complex patient virus profiles is an unmet clinical need. Variable viral mutant signatures develop within individual patients due to the low-fidelity replication of the viral polymerase creating 'quasispecies' populations. Here we present the first comprehensive survey of the diversity of HBV quasispecies through ultra-deep sequencing of the complete HBV genome across two distinct European and Asian patient populations. Seroconversion to the HBV e antigen (HBeAg) represents a critical clinical waymark in infected individuals. Using a machine learning approach, a model was developed to determine the viral variants that accurately classify HBeAg status. Serial surveys of patient quasispecies populations and advanced analytics will facilitate clinical decision support for chronic HBV infection and direct therapeutic strategies through improved patient stratification.
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