• HBV-RNA Co-amplification May Influence HBV DNA Viral Load Determination.

      Maasoumy, Benjamin; Geretti, Anna Maria; Frontzek, André; Austin, Harrison; Aretzweiler, Gudrun; Garcia-Álvarez, Monica; Leuchter, Susanne; Simon, Christian O; Marins, Ed G; Canchola, Jesse A; et al. (Wiley, 2020-05-26)
      Despite effective hepatitis B virus (HBV)-DNA suppression, HBV RNA can circulate in patients receiving nucleoside/nucleotide analogues (NAs). Current assays quantify HBV DNA by either real-time polymerase chain reaction (PCR), which uses DNA polymerase, or transcription-mediated amplification, which uses reverse-transcriptase (RT) and RNA polymerase. We assessed the effect of RT capability on HBV-DNA quantification in samples from three cohorts, including patients with quantified HBV RNA. We compared the HBV-DNA levels by real-time PCR (cobas HBV, Roche 6800/8800; Xpert HBV, Cepheid), transcription-mediated amplification (Aptima HBV, Hologic), and real-time PCR with added RT capability (cobas HBV+RT). In the first cohort (n = 45) followed over 192 weeks of NA therapy, on-treatment HBV-DNA levels were higher with cobas HBV+RT than cobas HBV (mean difference: 0.14 log10 IU/mL). In a second cohort (n = 50) followed over 96 weeks of NA therapy, HBV-DNA viral load was significantly higher with the cobas HBV+RT and Aptima HBV compared with the cobas HBV test at all time points after initiation of NA therapy (mean difference: 0.65-1.16 log10 IU/mL). A clinically significant difference was not detected between the assays at baseline. In a third cohort (n = 53), after a median of 2.2 years of NA therapy, we detected HBV RNA (median 5.6 log10 copies/mL) in 23 patients (43.4%). Median HBV-DNA levels by Aptima HBV were 2.4 versus less than 1 log10 IU/mL in samples with HBV RNA and without HBV RNA, respectively (P = 0.0006). In treated patients with HBV RNA, Aptima HBV measured higher HBV-DNA levels than Xpert HBV and cobas HBV. Conclusion: Tests including an RT step may overestimate HBV DNA, particularly in samples with low viral loads as a result of NA therapy. This overestimation is likely due to amplification of HBV RNA and may have an impact on clinical decisions.
    • Pilot Study Using Machine Learning to Identify Immune Profiles for the Prediction of Early Virological Relapse After Stopping Nucleos(t)ide Analogues in HBeAg-Negative CHB.

      Wübbolding, Maximilian; Lopez Alfonso, Juan Carlos; Lin, Chun-Yen; Binder, Sebastian; Falk, Christine; Debarry, Jennifer; Gineste, Paul; Kraft, Anke R M; Chien, Rong-Nan; Maasoumy, Benjamin; et al. (Wiley, 2020-11-05)
      Treatment with nucleos(t)ide analogues (NAs) may be stopped after 1-3 years of hepatitis B virus DNA suppression in hepatitis B e antigen (HBeAg)-negative patients according to Asian Pacific Association for the Study of Liver and European Association for the Study of Liver guidelines. However, virological relapse (VR) occurs in most patients. We aimed to analyze soluble immune markers (SIMs) and use machine learning to identify SIM combinations as predictor for early VR after NA discontinuation. A validation cohort was used to verify the predictive power of the SIM combination. In a post hoc analysis of a prospective, multicenter therapeutic vaccination trial (ABX-203, NCT02249988), hepatitis B surface antigen, hepatitis B core antigen, and 47 SIMs were repeatedly determined before NA was stopped. Forty-three HBeAg-negative patients were included. To detect the highest predictive constellation of host and viral markers, a supervised machine learning approach was used. Data were validated in a different cohort of 49 patients treated with entecavir. VR (hepatitis B virus DNA ≥ 2,000 IU/mL) occurred in 27 patients. The predictive value for VR of single SIMs at the time of NA stop was best for interleukin (IL)-2, IL-17, and regulated on activation, normal T cell expressed and secreted (RANTES/CCL5) with a maximum area under the curve of 0.65. Hepatitis B core antigen had a higher predictive power than hepatitis B surface antigen but lower than the SIMs. A supervised machine-learning algorithm allowed a remarkable improvement of early relapse prediction in patients treated with entecavir. The combination of IL-2, monokine induced by interferon γ (MIG)/chemokine (C-C motif) ligand 9 (CCL9), RANTES/CCL5, stem cell factor (SCF), and TNF-related apoptosis-inducing ligand (TRAIL) was reliable in predicting VR (0.89; 95% confidence interval: 0.5-1.0) and showed viable results in the validation cohort (0.63; 0.1-0.99). Host immune markers such as SIMs appear to be underestimated in guiding treatment cessation in HBeAg-negative patients. Machine learning can help find predictive SIM patterns that allow a precise identification of patients particularly suitable for NA cessation.