Model-Based Inference and Classification of Immunologic Control Mechanisms from TKI Cessation and Dose Reduction in...

Area of Science:

• Immunology
• Mathematical Biology
• Hematology

Background:

• Recurrence risk after tyrosine kinase inhibitor (TKI) cessation in chronic myeloid leukemia (CML) is linked to immune response.
• Prospectively identifying patients for sustained treatment-free remission (TFR) remains challenging.

Purpose of the Study:

• To develop a mathematical model incorporating immunologic effects to explain TFR in CML.
• To classify patients into distinct immune configuration groups based on their response to TKI cessation.
• To investigate if TKI dose reduction dynamics can predict TFR outcomes.

Main Methods:

• An ordinary differential equation model for CML was developed, including an antileukemic immunologic effect.
• The model was applied to 21 CML patients with quantified BCR-ABL1/ABL1 time courses before and after TKI cessation.
• Patient-specific parameters were identified by fitting model simulations to clinical data.

Main Results:

• Immunologic control was essential to explain TFR in approximately half of the patients.
• Patients were classified into three groups based on their predicted "immunologic landscapes."
• Model simulations indicated that TKI dose reduction dynamics provide information about individual immune systems and predict TFR outcomes.

Conclusions:

• Different immunologic configurations in CML patients determine their response to therapy cessation.
• Mathematical modeling can identify distinct patient risk groups based on immune status.
• The approach of inferring immunologic configurations from treatment alterations may apply to other cancers involving immune support.