Using a novel integrated modelling approach, combining population dynamics and patient biopsy genomic analysis, Brehme et al. provide a novel rationale for personalized and genome-informed disease progression risk assessment in Chronic Myeloid Leukemia (CML). Link to the paper: Brehme et al, Scientific Reports (2016)
The new study by researches at the Joint Research Center for Computational Biomedicine (JRC-COMBINE) of RWTH Aachen University, in collaboration with the Department of Hematology, Oncology, Hemostaseology and Stem Cell Transplantation of the University Medical Center RWTH Aachen, and co-authors at the Paul O'Gorman Leukaemia Research Centre, University of Glasgow and at the Fred Hutchinson Cancer Research Center in Seattle, addresses a challenging question at the interface of cancer modelling and the biology of Chronic Myeloid Leukemia (CML).
As a paradigm and likely the best characterized human cancer, CML has triggered the development of the first successful targeted cancer therapies. However, resistance to these therapies and patient relapse continue to challenge clinical disease management. Modelling the parameters of multistep carcinogenesis is key for a better understanding of cancer progression towards novel biomarkers for early patient stratification, timely risk assessment, and tailored treatment decisions.
This new study presents a novel integrated computational approach based on the combination of CML patient genomic data and mechanistic disease modelling. Driven by the goal to refine CML modelling for translational applicability and to more precisely pinpoint patient status within the early chronic phase of the disease, Brehme et al. show that combining a novel patient-derived CD34+ similarity score as disease progression marker with gene expression entropy using population dynamic models allows to associate clinical disease stages in patients with the time course of disease models, enabling individualized approximation of progression risk. The study reveals unprecedented patient heterogeneity in chronic phase CML, resolving an early sub-stratification of disease progression characteristics that is indicative of progression risk towards advanced fatal disease states. This novel approach is independent and complementary to conventional measures of CML disease burden and prognosis and carries potential as a novel exploratory tool with potential for wide applicability in clinical research.
Publication: Marc Brehme, Steffen Koschmieder, Maryam Montazeri, Mhairi Copland, Vivian G. Oehler, Jerald P. Radich, Tim H. Brümmendorf, and Andreas Schuppert. Combined Population Dynamics and Entropy Modelling Supports Patient Stratification in Chronic Myeloid Leukemia. Scientific Reports (2016), doi:10.1038/srep24057.
Funding: This work was supported by the Interdisciplinary Center for Clinical Research (IZKF) of the Medical Faculty of RWTH Aachen University, the Deutsche José Carreras Leukämiestiftung, Bayer Technology Services GmbH, the Scottish Funding Council and Bloodwise.