Brehme, M., Koschmieder, S., Montazeri, M., Copland, M. , Oehler, V. G., Radich, J. P., Brümmendorf, T. H., and Schuppert, A. (2016) Combined population dynamics and entropy modelling supports patient stratification in chronic myeloid leukemia. Scientific Reports, 6, 24057. (doi: 10.1038/srep24057) (PMID:27048866) (PMCID:PMC4822142)
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Abstract
Modelling the parameters of multistep carcinogenesis is key for a better understanding of cancer progression, biomarker identification and the design of individualized therapies. Using chronic myeloid leukemia (CML) as a paradigm for hierarchical disease evolution we show that combined population dynamic modelling and CML patient biopsy genomic analysis enables patient stratification at unprecedented resolution. Linking CD34+ similarity as a disease progression marker to patientderived gene expression entropy separated established CML progression stages and uncovered additional heterogeneity within disease stages. Importantly, our patient data informed model enables quantitative approximation of individual patients’ disease history within chronic phase (CP) and significantly separates “early” from “late” CP. Our findings provide a novel rationale for personalized and genome-informed disease progression risk assessment that is independent and complementary to conventional measures of CML disease burden and prognosis.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Copland, Professor Mhairi |
Authors: | Brehme, M., Koschmieder, S., Montazeri, M., Copland, M., Oehler, V. G., Radich, J. P., Brümmendorf, T. H.,, and Schuppert, A. |
College/School: | College of Medical Veterinary and Life Sciences > School of Cancer Sciences |
Journal Name: | Scientific Reports |
Publisher: | Nature Publishing Group |
ISSN: | 2045-2322 |
ISSN (Online): | 2045-2322 |
Copyright Holders: | Copyright © 2016 The Authors |
First Published: | First published in Scientific Reports 6:24057 |
Publisher Policy: | Reproduced under a Creative Commons License |
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