Contributions
Abstract: PB1906
Type: Publication Only
Background
There is increasing evidence in patients with Chronic Myeloid Leukemia (CML) pointing to the role of the immune system in the sustained control of residual leukemic cells after tyrosine-kinase inhibitors (TKI) treatment cessation. It has been speculated that, once the treatment has reduced the leukemic cell burden below a certain threshold, the immune cells are capable to control the disease, or even to completely eliminate the leukemic clone. However, at the moment these mechanisms are poorly understood.
Aims
It is our aim to contribute to a quantitative, mechanistic understanding of the immune response in TKI-treated CML. Specifically, we address the questions if and why the immune response appears to be effective only at a low level of leukemic cells. Here it is of particular interest to understand the interaction of two dynamic processes: (i) the stimulation of immune cells by the presence of leukemic cells and (ii) the immune system-mediated elimination of these cells.
Methods
We developed a mathematical framework describing CML progression and treatment in terms of ordinary differential equations. Within this model framework we consider different assumptions about the mechanisms (i) by which immune cells are stimulated by leukemic cells and (ii) of how leukemic cells are targeted by immune cells. The combination of the different assumptions leads to several structurally different models, which are characterized by different systems dynamics (such as unavoidable relapse, low-level control of leukemic cells, potential cure, etc.).
Results
We compare our conceptual results with available data sets on the BCR-ABL1 levels from CML patients after TKI cessation, thereby allowing us to assess and critically discuss the plausibility of the particular assumptions. Specifically, we show that both the recruitment of specific immune cells as well as the immune response-related kill of leukemic cells need to be determined by non-constant and even non-linear regulation processes to consistently explain clinically observed outcomes after TKI cessation, as there are, early molecular relapse, fluctuating but extremely low BCR-ABL1 levels, and long-term BCR-ABL1 negativity.
Conclusion
The model analyses suggest that the shape of the dose-response relationship of leukemic cell burden and immune response is essential to understand and predict the molecular relapse dynamics after TKI cessation. To identify these relationships, one needs to define relevant readouts of immune response and to quantify these over time and in relation to the tumor load. Our results highlight the necessity to understand the mechanisms of immune control in leukemia therapy to employ this effect for optimal treatment results and to identify clinical measures that allow to derive better predictions about the outcome of treatment cessation.
Session topic: 7. Chronic myeloid leukemia – Biology & Translational Research
Keyword(s): Chronic myeloid leukemia, Immune response, Minimal residual disease (MRD), Prediction
Abstract: PB1906
Type: Publication Only
Background
There is increasing evidence in patients with Chronic Myeloid Leukemia (CML) pointing to the role of the immune system in the sustained control of residual leukemic cells after tyrosine-kinase inhibitors (TKI) treatment cessation. It has been speculated that, once the treatment has reduced the leukemic cell burden below a certain threshold, the immune cells are capable to control the disease, or even to completely eliminate the leukemic clone. However, at the moment these mechanisms are poorly understood.
Aims
It is our aim to contribute to a quantitative, mechanistic understanding of the immune response in TKI-treated CML. Specifically, we address the questions if and why the immune response appears to be effective only at a low level of leukemic cells. Here it is of particular interest to understand the interaction of two dynamic processes: (i) the stimulation of immune cells by the presence of leukemic cells and (ii) the immune system-mediated elimination of these cells.
Methods
We developed a mathematical framework describing CML progression and treatment in terms of ordinary differential equations. Within this model framework we consider different assumptions about the mechanisms (i) by which immune cells are stimulated by leukemic cells and (ii) of how leukemic cells are targeted by immune cells. The combination of the different assumptions leads to several structurally different models, which are characterized by different systems dynamics (such as unavoidable relapse, low-level control of leukemic cells, potential cure, etc.).
Results
We compare our conceptual results with available data sets on the BCR-ABL1 levels from CML patients after TKI cessation, thereby allowing us to assess and critically discuss the plausibility of the particular assumptions. Specifically, we show that both the recruitment of specific immune cells as well as the immune response-related kill of leukemic cells need to be determined by non-constant and even non-linear regulation processes to consistently explain clinically observed outcomes after TKI cessation, as there are, early molecular relapse, fluctuating but extremely low BCR-ABL1 levels, and long-term BCR-ABL1 negativity.
Conclusion
The model analyses suggest that the shape of the dose-response relationship of leukemic cell burden and immune response is essential to understand and predict the molecular relapse dynamics after TKI cessation. To identify these relationships, one needs to define relevant readouts of immune response and to quantify these over time and in relation to the tumor load. Our results highlight the necessity to understand the mechanisms of immune control in leukemia therapy to employ this effect for optimal treatment results and to identify clinical measures that allow to derive better predictions about the outcome of treatment cessation.
Session topic: 7. Chronic myeloid leukemia – Biology & Translational Research
Keyword(s): Chronic myeloid leukemia, Immune response, Minimal residual disease (MRD), Prediction