How to predict relapse in leukemia using time series data: A comparative in silico study.

Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes ca...

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Autores principales:	Helene Hoffmann, Christoph Baldow, Thomas Zerjatke, Andrea Gottschalk, Sebastian Wagner, Elena Karg, Sebastian Niehaus, Ingo Roeder, Ingmar Glauche, Nico Scherf
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fb2c2107ef474a52add0aeb0a6fbdb49
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spelling	oai:doaj.org-article:fb2c2107ef474a52add0aeb0a6fbdb492021-12-02T20:13:10ZHow to predict relapse in leukemia using time series data: A comparative in silico study.1932-620310.1371/journal.pone.0256585https://doaj.org/article/fb2c2107ef474a52add0aeb0a6fbdb492021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256585https://doaj.org/toc/1932-6203Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients' time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients.Helene HoffmannChristoph BaldowThomas ZerjatkeAndrea GottschalkSebastian WagnerElena KargSebastian NiehausIngo RoederIngmar GlaucheNico ScherfPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0256585 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Helene Hoffmann Christoph Baldow Thomas Zerjatke Andrea Gottschalk Sebastian Wagner Elena Karg Sebastian Niehaus Ingo Roeder Ingmar Glauche Nico Scherf How to predict relapse in leukemia using time series data: A comparative in silico study.
description	Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients' time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients.
format	article
author	Helene Hoffmann Christoph Baldow Thomas Zerjatke Andrea Gottschalk Sebastian Wagner Elena Karg Sebastian Niehaus Ingo Roeder Ingmar Glauche Nico Scherf
author_facet	Helene Hoffmann Christoph Baldow Thomas Zerjatke Andrea Gottschalk Sebastian Wagner Elena Karg Sebastian Niehaus Ingo Roeder Ingmar Glauche Nico Scherf
author_sort	Helene Hoffmann
title	How to predict relapse in leukemia using time series data: A comparative in silico study.
title_short	How to predict relapse in leukemia using time series data: A comparative in silico study.
title_full	How to predict relapse in leukemia using time series data: A comparative in silico study.
title_fullStr	How to predict relapse in leukemia using time series data: A comparative in silico study.
title_full_unstemmed	How to predict relapse in leukemia using time series data: A comparative in silico study.
title_sort	how to predict relapse in leukemia using time series data: a comparative in silico study.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/fb2c2107ef474a52add0aeb0a6fbdb49
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How to predict relapse in leukemia using time series data: A comparative in silico study.

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