Combined Population Dynamics and Entropy Modelling Supports Patient Stratification in Chronic Myeloid Leukemia

Area of Science:

• Oncology
• Computational Biology
• Genomics

Background:

• Multistep carcinogenesis modeling is crucial for understanding cancer progression and developing targeted therapies.
• Chronic myeloid leukemia (CML) serves as a model for hierarchical disease evolution.
• Accurate patient stratification is essential for personalized cancer treatment.

Purpose of the Study:

• To develop a novel approach for patient stratification in CML using population dynamic modeling and genomic analysis.
• To identify new biomarkers for disease progression and heterogeneity within CML stages.
• To enable quantitative approximation of individual patient disease history and risk assessment.

Main Methods:

• Combined population dynamic modeling with genomic analysis of CML patient biopsies.
• Utilized CD34(+) similarity as a disease progression marker.
• Applied patient-derived gene expression entropy analysis.

Main Results:

• Achieved unprecedented patient stratification resolution in CML.
• Linked CD34(+) similarity and gene expression entropy to separate CML progression stages.
• Uncovered significant heterogeneity within established CML disease stages.
• Developed a model that quantitatively approximates individual patient disease history in chronic phase (CP), distinguishing early from late CP.

Conclusions:

• The developed model offers a novel, genome-informed approach for personalized risk assessment in CML.
• This method is independent and complementary to existing measures of CML disease burden and prognosis.
• Findings pave the way for more precise and individualized CML management strategies.