title: Combined Population Dynamics and Entropy Modelling Supports Patient Stratification in Chronic Myeloid Leukemia.
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 patient-derived 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.
authors.affiliation: Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, 52062 Aachen, Germany.
authors.forename: Marc
authors.initials: M
authors.lastname: Brehme
authors.affiliation: Department of Hematology, Oncology, Hemostaseology, and Stem Cell Transplantation, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany.
authors.forename: Steffen
authors.initials: S
authors.lastname: Koschmieder
authors.affiliation: Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, 52062 Aachen, Germany.
authors.forename: Maryam
authors.initials: M
authors.lastname: Montazeri
authors.affiliation: Paul O'Gorman Leukaemia Research Centre, Institute of Cancer Sciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow G12 0ZD, United Kingdom.
authors.forename: Mhairi
authors.initials: M
authors.lastname: Copland
authors.affiliation: Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
authors.forename: Vivian G
authors.initials: VG
authors.lastname: Oehler
authors.affiliation: Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
authors.forename: Jerald P
authors.initials: JP
authors.lastname: Radich
authors.affiliation: Department of Hematology, Oncology, Hemostaseology, and Stem Cell Transplantation, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany.
authors.forename: Tim H
authors.initials: TH
authors.lastname: Brümmendorf
authors.affiliation: Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, 52062 Aachen, Germany.|Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, 52062 Aachen, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Schuppert
mesh_terms: D018952:Antigens, CD34 / Q000378:metabolism; D014408:Biomarkers, Tumor / Q000378:metabolism; D001706:Biopsy; D015331:Cohort Studies; D018450:Disease Progression; D019008:Drug Resistance, Neoplasm; D019277:Entropy; D020869:Gene Expression Profiling; D015973:Gene Expression Regulation, Leukemic; D023281:Genomics; D006801:Humans; D015464:Leukemia, Myelogenous, Chronic, BCR-ABL Positive / Q000145:classification / Q000378:metabolism* / Q000473:pathology*; D008954:Models, Biological; D011157:Population Dynamics; D011379:Prognosis; D018570:Risk Assessment; D013234:Stem Cells / Q000166:cytology
chemical_list: D018952:Antigens, CD34; D014408:Biomarkers, Tumor