Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets
<i>FLT3</i>-mutant acute myeloid leukemia (AML) is an aggressive form of leukemia with poor prognosis. Treatment with <i>FLT3</i> inhibitors frequently produces a clinical response, but the disease nevertheless often recurs. Recent studies have revealed system-wide gene expre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/11/3/193 |
| _version_ | 1797541792384548864 |
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| author | David J. Wooten Melat Gebru Hong-Gang Wang Réka Albert |
| author_facet | David J. Wooten Melat Gebru Hong-Gang Wang Réka Albert |
| author_sort | David J. Wooten |
| collection | DOAJ |
| description | <i>FLT3</i>-mutant acute myeloid leukemia (AML) is an aggressive form of leukemia with poor prognosis. Treatment with <i>FLT3</i> inhibitors frequently produces a clinical response, but the disease nevertheless often recurs. Recent studies have revealed system-wide gene expression changes in <i>FLT3</i>-mutant AML cell lines in response to drug treatment. Here we sought a systems-level understanding of how these cells mediate these drug-induced changes. Using RNAseq data from AML cells with an internal tandem duplication <i>FLT3</i> mutation (<i>FLT3</i>-ITD) under six drug treatment conditions including quizartinib and dexamethasone, we identified seven distinct gene programs representing diverse biological processes involved in AML drug-induced changes. Based on the literature knowledge about genes from these modules, along with public gene regulatory network databases, we constructed a network of <i>FLT3</i>-ITD AML. Applying the BooleaBayes algorithm to this network and the RNAseq data, we created a probabilistic, data-driven dynamical model of acquired resistance to these drugs. Analysis of this model reveals several interventions that may disrupt targeted parts of the system-wide drug response. We anticipate co-targeting these points may result in synergistic treatments that can overcome resistance and prevent eventual recurrence. |
| first_indexed | 2024-03-10T13:20:45Z |
| format | Article |
| id | doaj.art-e45566c08bcf4609825b1eb57065f3ce |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-10T13:20:45Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-e45566c08bcf4609825b1eb57065f3ce2023-11-21T10:05:15ZengMDPI AGJournal of Personalized Medicine2075-44262021-03-0111319310.3390/jpm11030193Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic TargetsDavid J. Wooten0Melat Gebru1Hong-Gang Wang2Réka Albert3Department of Physics, Pennsylvania State University, University Park, PA 16802, USADepartment of Pediatrics, Penn State College of Medicine, Hershey, PA 17033, USADepartment of Pediatrics, Penn State College of Medicine, Hershey, PA 17033, USADepartment of Physics, Pennsylvania State University, University Park, PA 16802, USA<i>FLT3</i>-mutant acute myeloid leukemia (AML) is an aggressive form of leukemia with poor prognosis. Treatment with <i>FLT3</i> inhibitors frequently produces a clinical response, but the disease nevertheless often recurs. Recent studies have revealed system-wide gene expression changes in <i>FLT3</i>-mutant AML cell lines in response to drug treatment. Here we sought a systems-level understanding of how these cells mediate these drug-induced changes. Using RNAseq data from AML cells with an internal tandem duplication <i>FLT3</i> mutation (<i>FLT3</i>-ITD) under six drug treatment conditions including quizartinib and dexamethasone, we identified seven distinct gene programs representing diverse biological processes involved in AML drug-induced changes. Based on the literature knowledge about genes from these modules, along with public gene regulatory network databases, we constructed a network of <i>FLT3</i>-ITD AML. Applying the BooleaBayes algorithm to this network and the RNAseq data, we created a probabilistic, data-driven dynamical model of acquired resistance to these drugs. Analysis of this model reveals several interventions that may disrupt targeted parts of the system-wide drug response. We anticipate co-targeting these points may result in synergistic treatments that can overcome resistance and prevent eventual recurrence.https://www.mdpi.com/2075-4426/11/3/193acute myeloid leukemiaBoolean modeldrug resistancenetwork |
| spellingShingle | David J. Wooten Melat Gebru Hong-Gang Wang Réka Albert Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets Journal of Personalized Medicine acute myeloid leukemia Boolean model drug resistance network |
| title | Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets |
| title_full | Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets |
| title_fullStr | Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets |
| title_full_unstemmed | Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets |
| title_short | Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets |
| title_sort | data driven math model of flt3 itd acute myeloid leukemia reveals potential therapeutic targets |
| topic | acute myeloid leukemia Boolean model drug resistance network |
| url | https://www.mdpi.com/2075-4426/11/3/193 |
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