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Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B‐precursor acute lymphoblastic leukaemia

Relapsed acute lymphoblastic leukaemia (ALL) remains a prevalent paediatric cancer and one of the most common causes of mortality from malignancy in children. Tailoring the intensity of therapy according to early stratification is a promising strategy but remains a major challenge due to heterogenei...

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Autores principales: Huang, Qingsheng, Zhong, Jiayong, Gao, Huan, Li, Kuanrong, Liang, Huiying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8178509/
https://www.ncbi.nlm.nih.gov/pubmed/33987975
http://dx.doi.org/10.1002/cam4.3842
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author Huang, Qingsheng
Zhong, Jiayong
Gao, Huan
Li, Kuanrong
Liang, Huiying
author_facet Huang, Qingsheng
Zhong, Jiayong
Gao, Huan
Li, Kuanrong
Liang, Huiying
author_sort Huang, Qingsheng
collection PubMed
description Relapsed acute lymphoblastic leukaemia (ALL) remains a prevalent paediatric cancer and one of the most common causes of mortality from malignancy in children. Tailoring the intensity of therapy according to early stratification is a promising strategy but remains a major challenge due to heterogeneity and subtyping difficulty. In this study, we subgroup B‐precursor ALL patients by gene expression profiles, using non‐negative matrix factorization and minimum description length which unsupervisedly determines the number of subgroups. Within each of the four subgroups, logistic and Cox regression with elastic net regularization are used to build models predicting minimal residual disease (MRD) and relapse‐free survival (RFS) respectively. Measured by area under the receiver operating characteristic curve (AUC), subgrouping improves prediction of MRD in one subgroup which mostly overlaps with subtype TCF3‐PBX1 (AUC = 0·986 in the training set and 1·0 in the test set), compared to a global model published previously. The models predicting RFS displayed acceptable concordance in training set and discriminate high‐relapse‐risk patients in three subgroups of the test set (Wilcoxon test p = 0·048, 0·036, and 0·016). Genes playing roles in the models are specific to different subgroups. The improvement of subgrouped MRD prediction and the differences of genes in prediction models of subgroups suggest that the heterogeneity of B‐precursor ALL can be handled by subgrouping according to gene expression profiles to improve the prediction accuracy.
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spelling pubmed-81785092021-06-15 Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B‐precursor acute lymphoblastic leukaemia Huang, Qingsheng Zhong, Jiayong Gao, Huan Li, Kuanrong Liang, Huiying Cancer Med Cancer Prevention Relapsed acute lymphoblastic leukaemia (ALL) remains a prevalent paediatric cancer and one of the most common causes of mortality from malignancy in children. Tailoring the intensity of therapy according to early stratification is a promising strategy but remains a major challenge due to heterogeneity and subtyping difficulty. In this study, we subgroup B‐precursor ALL patients by gene expression profiles, using non‐negative matrix factorization and minimum description length which unsupervisedly determines the number of subgroups. Within each of the four subgroups, logistic and Cox regression with elastic net regularization are used to build models predicting minimal residual disease (MRD) and relapse‐free survival (RFS) respectively. Measured by area under the receiver operating characteristic curve (AUC), subgrouping improves prediction of MRD in one subgroup which mostly overlaps with subtype TCF3‐PBX1 (AUC = 0·986 in the training set and 1·0 in the test set), compared to a global model published previously. The models predicting RFS displayed acceptable concordance in training set and discriminate high‐relapse‐risk patients in three subgroups of the test set (Wilcoxon test p = 0·048, 0·036, and 0·016). Genes playing roles in the models are specific to different subgroups. The improvement of subgrouped MRD prediction and the differences of genes in prediction models of subgroups suggest that the heterogeneity of B‐precursor ALL can be handled by subgrouping according to gene expression profiles to improve the prediction accuracy. John Wiley and Sons Inc. 2021-05-13 /pmc/articles/PMC8178509/ /pubmed/33987975 http://dx.doi.org/10.1002/cam4.3842 Text en © 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Cancer Prevention
Huang, Qingsheng
Zhong, Jiayong
Gao, Huan
Li, Kuanrong
Liang, Huiying
Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B‐precursor acute lymphoblastic leukaemia
title Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B‐precursor acute lymphoblastic leukaemia
title_full Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B‐precursor acute lymphoblastic leukaemia
title_fullStr Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B‐precursor acute lymphoblastic leukaemia
title_full_unstemmed Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B‐precursor acute lymphoblastic leukaemia
title_short Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B‐precursor acute lymphoblastic leukaemia
title_sort subgrouping by gene expression profiles to improve relapse risk prediction in paediatric b‐precursor acute lymphoblastic leukaemia
topic Cancer Prevention
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8178509/
https://www.ncbi.nlm.nih.gov/pubmed/33987975
http://dx.doi.org/10.1002/cam4.3842
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