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Patient-specific molecular response dynamics can predict the possibility of relapse during the second treatment-free remission attempt in chronic myelogenous leukemia
In chronic myelogenous leukemia (CML), treatment-free remission (TFR) is defined as maintaining a major molecular response (MMR) without a tyrosine kinase inhibitor (TKI), such as imatinib (IM). Several studies have investigated the safety of the first TFR (TFR(1)) attempt and suggested recommendati...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Neoplasia Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9309666/ https://www.ncbi.nlm.nih.gov/pubmed/35878453 http://dx.doi.org/10.1016/j.neo.2022.100817 |
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author | Kim, Eunjung Hwang, Eo-Jin Lee, Junghye Kim, Dae-Young Kim, Jae-Young Kim, Dong-Wook |
author_facet | Kim, Eunjung Hwang, Eo-Jin Lee, Junghye Kim, Dae-Young Kim, Jae-Young Kim, Dong-Wook |
author_sort | Kim, Eunjung |
collection | PubMed |
description | In chronic myelogenous leukemia (CML), treatment-free remission (TFR) is defined as maintaining a major molecular response (MMR) without a tyrosine kinase inhibitor (TKI), such as imatinib (IM). Several studies have investigated the safety of the first TFR (TFR(1)) attempt and suggested recommendation guidelines for such an attempt. However, the plausibility and predictive factors for a second TFR (TFR(2)) have yet to be reported. The present study included 21 patients in chronic myeloid leukemia who participated in twice repeated treatment stop attempts. We develop a mathematical model to analyze and explain the outcomes of TFR(2). Our mathematical model framework can explain patient-specific molecular response dynamics. Fitting the model to longitudinal BCR-ABL1 transcripts from the patients generated patient-specific parameters. Binary tree decision analyses of the model parameters suggested a model based predictive binary classification factor that separated patients into low- and high-risk groups of TFR(2) attempts with an overall accuracy of 76.2% (sensitivity of 81.1% and specificity of 69.9%). The low-risk group maintained a median TFR(2) of 28.2 months, while the high-risk group relapsed at a median time of 3.25 months. Further, our model predicted a patient-specific optimal IM treatment duration before the second IM stop that could achieve the desired TFR(2) (e.g., 5 years). |
format | Online Article Text |
id | pubmed-9309666 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Neoplasia Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-93096662022-08-01 Patient-specific molecular response dynamics can predict the possibility of relapse during the second treatment-free remission attempt in chronic myelogenous leukemia Kim, Eunjung Hwang, Eo-Jin Lee, Junghye Kim, Dae-Young Kim, Jae-Young Kim, Dong-Wook Neoplasia Original Research In chronic myelogenous leukemia (CML), treatment-free remission (TFR) is defined as maintaining a major molecular response (MMR) without a tyrosine kinase inhibitor (TKI), such as imatinib (IM). Several studies have investigated the safety of the first TFR (TFR(1)) attempt and suggested recommendation guidelines for such an attempt. However, the plausibility and predictive factors for a second TFR (TFR(2)) have yet to be reported. The present study included 21 patients in chronic myeloid leukemia who participated in twice repeated treatment stop attempts. We develop a mathematical model to analyze and explain the outcomes of TFR(2). Our mathematical model framework can explain patient-specific molecular response dynamics. Fitting the model to longitudinal BCR-ABL1 transcripts from the patients generated patient-specific parameters. Binary tree decision analyses of the model parameters suggested a model based predictive binary classification factor that separated patients into low- and high-risk groups of TFR(2) attempts with an overall accuracy of 76.2% (sensitivity of 81.1% and specificity of 69.9%). The low-risk group maintained a median TFR(2) of 28.2 months, while the high-risk group relapsed at a median time of 3.25 months. Further, our model predicted a patient-specific optimal IM treatment duration before the second IM stop that could achieve the desired TFR(2) (e.g., 5 years). Neoplasia Press 2022-07-22 /pmc/articles/PMC9309666/ /pubmed/35878453 http://dx.doi.org/10.1016/j.neo.2022.100817 Text en © 2022 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Research Kim, Eunjung Hwang, Eo-Jin Lee, Junghye Kim, Dae-Young Kim, Jae-Young Kim, Dong-Wook Patient-specific molecular response dynamics can predict the possibility of relapse during the second treatment-free remission attempt in chronic myelogenous leukemia |
title | Patient-specific molecular response dynamics can predict the possibility of relapse during the second treatment-free remission attempt in chronic myelogenous leukemia |
title_full | Patient-specific molecular response dynamics can predict the possibility of relapse during the second treatment-free remission attempt in chronic myelogenous leukemia |
title_fullStr | Patient-specific molecular response dynamics can predict the possibility of relapse during the second treatment-free remission attempt in chronic myelogenous leukemia |
title_full_unstemmed | Patient-specific molecular response dynamics can predict the possibility of relapse during the second treatment-free remission attempt in chronic myelogenous leukemia |
title_short | Patient-specific molecular response dynamics can predict the possibility of relapse during the second treatment-free remission attempt in chronic myelogenous leukemia |
title_sort | patient-specific molecular response dynamics can predict the possibility of relapse during the second treatment-free remission attempt in chronic myelogenous leukemia |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9309666/ https://www.ncbi.nlm.nih.gov/pubmed/35878453 http://dx.doi.org/10.1016/j.neo.2022.100817 |
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