Browsing publications of the working group of computational biology for individualized medicine ([CiiM] BIIM) by Authors
Performance of Roche qualitative HEV assay on the cobas 6800 platform for quantitative measurement of HEV RNA.Thodou, Viktoria; Bremer, Birgit; Anastasiou, Olympia E; Cornberg, Markus; Maasoumy, Benjamin; Wedemeyer, Heiner; CIIM, Zentrum für individualisierte Infektionsmedizin, Feodor-Lynen-Str.7, 30625 Hannover. (Elsevier, 2020-06-27)Background: Hepatitis E virus (HEV) infection is an increasingly recognized cause of acute and chronic hepatitis in high-income countries and is the most frequent cause of acute viral hepatitis in many European countries. Appropriate tools to detect and quantify HEV RNA are needed. This study aimed to evaluate the performance of the Roche cobas® HEV assay and compare it with the Fast Track Diagnostics (FTD) Hepatitis E RNA assay. Methods: HEV viral load determination and lower limit of detection (LOD, defined as the lowest amount of viral copies that could be detected in 95 % of repeats) were assessed using a WHO standard dilution panel, testing 240 samples of various concentrations. Reproducibility was tested at three different concentration levels, for different genotypes, and with different sample types (serum, plasma) in 30 samples. Sample stability was analyzed after three freeze/thaw cycles in 25 samples. Results: Cobas HEV assay showed a strong linear relationship between log of HEV WHO dilution series and Ct values over the reportable range from 200-5000 IU/mL HEV RNA copies. The amplification efficiency was higher than 92 %. LOD was 22 IU/mL (95 % CI: 17.4-31.8) and reproducibility tests showed a 100 % nucleic acid test (NAT) reactivity of cobas HEV for WHO dilution series (range 200-5000 IU/mL, n = 90). Cobas HEV assay detected all different HEV genotypes from biobank samples irrespective of the sample type. NAT reactivity of cobas HEV was not affected by three freeze/thaw cycles. Conclusions: Roche cobas HEV assay is a powerful NAT tool in terms of robustness, reproducibility and linearity. It is a feasible alternative for high-volume testing.
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.