Brehme Marc, Koschmieder Steffen, Montazeri Maryam, Copland Mhairi, Oehler Vivian G, Radich Jerald P, Brümmendorf Tim H, Schuppert Andreas
Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, 52062 Aachen, Germany.
Department of Hematology, Oncology, Hemostaseology, and Stem Cell Transplantation, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany.
Sci Rep. 2016 Apr 6;6:24057. doi: 10.1038/srep24057.
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.
对多步骤致癌过程的参数进行建模，对于更好地理解癌症进展、生物标志物识别以及个性化治疗设计至关重要。以慢性髓性白血病（CML）作为分层疾病演变的范例，我们表明，结合群体动力学建模和CML患者活检基因组分析，能够以前所未有的分辨率对患者进行分层。将作为疾病进展标志物的CD34(+)相似性与患者来源的基因表达熵联系起来，区分了已确定的CML进展阶段，并揭示了疾病阶段内的额外异质性。重要的是，我们基于患者数据的模型能够对慢性期（CP）内个体患者的疾病史进行定量近似，并显著区分“早期”和“晚期”CP。我们的研究结果为个性化和基于基因组的疾病进展风险评估提供了新的理论依据，该评估独立于CML疾病负担和预后的传统测量方法，并与之互补。
