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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
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, virol...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789842/ https://www.ncbi.nlm.nih.gov/pubmed/33437904 http://dx.doi.org/10.1002/hep4.1626 |
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author | 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 Wedemeyer, Heiner Jeng, Wen‐Juei Meyer Hermann, Michael Cornberg, Markus Höner zu Siederdissen, Christoph |
author_facet | 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 Wedemeyer, Heiner Jeng, Wen‐Juei Meyer Hermann, Michael Cornberg, Markus Höner zu Siederdissen, Christoph |
author_sort | Wübbolding, Maximilian |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7789842 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77898422021-01-11 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 Wedemeyer, Heiner Jeng, Wen‐Juei Meyer Hermann, Michael Cornberg, Markus Höner zu Siederdissen, Christoph Hepatol Commun Original Articles 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. John Wiley and Sons Inc. 2020-11-05 /pmc/articles/PMC7789842/ /pubmed/33437904 http://dx.doi.org/10.1002/hep4.1626 Text en © 2020 The Authors. Hepatology Communications published by Wiley Periodicals LLC on behalf of the American Association for the Study of Liver Diseases. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Articles 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 Wedemeyer, Heiner Jeng, Wen‐Juei Meyer Hermann, Michael Cornberg, Markus Höner zu Siederdissen, Christoph 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789842/ https://www.ncbi.nlm.nih.gov/pubmed/33437904 http://dx.doi.org/10.1002/hep4.1626 |
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