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