Data and implication based comparison of two chronic myeloid leukemia models
Chronic myeloid leukemia, a disorder of hematopoietic stem cells, is currently treated using targeted molecular therapy with imatinib. We compare two models that describe the treatment of CML, a multi-scale model (Model 1) and a simple cell competition model (Model 2).Both models describe the compet...
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| Format: | Article |
| Language: | English |
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AIMS Press
2013-07-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2013.10.1501 |
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| author | R. A. Everett Y. Zhao K. B. Flores Yang Kuang |
| author_facet | R. A. Everett Y. Zhao K. B. Flores Yang Kuang |
| author_sort | R. A. Everett |
| collection | DOAJ |
| description | Chronic myeloid leukemia, a disorder of hematopoietic stem cells, is currently treated using targeted molecular therapy with imatinib. We compare two models that describe the treatment of CML, a multi-scale model (Model 1) and a simple cell competition model (Model 2).Both models describe the competition of leukemic and normal cells, however Model 1 also describes the dynamics of BCR-ABL, the oncogene targeted by imatinib, at the sub-cellular level. Using clinical data, we analyze the differences in estimated parameters between the models and the capacity for each model to predict drug resistance. We found that while both models fit the data well, Model 1 is more biologically relevant. The estimated parameter ranges for Model 2 are unrealistic, whereas the parameter ranges for Model 1 are close to values found in literature. We also found that Model 1 predicts long-term drug resistance from patient data, which is exhibited by both an increase in the proportion of leukemic cells as well as an increase in BCR-ABL/ABL%. Model 2, however, is not able to predict resistance and accurately model the clinical data. These results suggest that including sub-cellular mechanisms in a mathematical model of CML can increase the accuracy of parameter estimation and may help to predict long-term drug resistance. |
| format | Article |
| id | doaj-art-ade438f05c3e45b7aff7e5039288d3aa |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2013-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-ade438f05c3e45b7aff7e5039288d3aa2025-01-24T02:26:34ZengAIMS PressMathematical Biosciences and Engineering1551-00182013-07-01105&61501151810.3934/mbe.2013.10.1501Data and implication based comparison of two chronic myeloid leukemia modelsR. A. Everett0Y. Zhao1K. B. Flores2Yang Kuang3School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287Department of Mathematics, North Carolina State University, Raleigh, NC 27695School of Mathematics and Statistical Sciences, Arizona State University, Tempe, AZ 85281Chronic myeloid leukemia, a disorder of hematopoietic stem cells, is currently treated using targeted molecular therapy with imatinib. We compare two models that describe the treatment of CML, a multi-scale model (Model 1) and a simple cell competition model (Model 2).Both models describe the competition of leukemic and normal cells, however Model 1 also describes the dynamics of BCR-ABL, the oncogene targeted by imatinib, at the sub-cellular level. Using clinical data, we analyze the differences in estimated parameters between the models and the capacity for each model to predict drug resistance. We found that while both models fit the data well, Model 1 is more biologically relevant. The estimated parameter ranges for Model 2 are unrealistic, whereas the parameter ranges for Model 1 are close to values found in literature. We also found that Model 1 predicts long-term drug resistance from patient data, which is exhibited by both an increase in the proportion of leukemic cells as well as an increase in BCR-ABL/ABL%. Model 2, however, is not able to predict resistance and accurately model the clinical data. These results suggest that including sub-cellular mechanisms in a mathematical model of CML can increase the accuracy of parameter estimation and may help to predict long-term drug resistance.https://www.aimspress.com/article/doi/10.3934/mbe.2013.10.1501clinical datachronic myeloid leukemiacancer modeling.drug resistance |
| spellingShingle | R. A. Everett Y. Zhao K. B. Flores Yang Kuang Data and implication based comparison of two chronic myeloid leukemia models Mathematical Biosciences and Engineering clinical data chronic myeloid leukemia cancer modeling. drug resistance |
| title | Data and implication based comparison of two chronic myeloid leukemia models |
| title_full | Data and implication based comparison of two chronic myeloid leukemia models |
| title_fullStr | Data and implication based comparison of two chronic myeloid leukemia models |
| title_full_unstemmed | Data and implication based comparison of two chronic myeloid leukemia models |
| title_short | Data and implication based comparison of two chronic myeloid leukemia models |
| title_sort | data and implication based comparison of two chronic myeloid leukemia models |
| topic | clinical data chronic myeloid leukemia cancer modeling. drug resistance |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2013.10.1501 |
| work_keys_str_mv | AT raeverett dataandimplicationbasedcomparisonoftwochronicmyeloidleukemiamodels AT yzhao dataandimplicationbasedcomparisonoftwochronicmyeloidleukemiamodels AT kbflores dataandimplicationbasedcomparisonoftwochronicmyeloidleukemiamodels AT yangkuang dataandimplicationbasedcomparisonoftwochronicmyeloidleukemiamodels |