Cellular automata modelling of leukaemic stem cell dynamics in acute myeloid leukaemia: insights into predictive outcomes and targeted therapies
Acute myeloid leukaemia (AML) is a haematologic malignancy with high relapse rates in both adults and children. Leukaemic stem cells (LSCs) are central to leukaemopoiesis, treatment response and relapse and frequently associated with measurable residual disease (MRD). However, the dynamics of LSCs w...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
The Royal Society
2025-01-01
|
| Series: | Royal Society Open Science |
| Subjects: | |
| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.241202 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590606726070272 |
|---|---|
| author | Yutaka Saikawa Toshihiko Komatsuzaki Nobuaki Nishiyama Toshihisa Hatta |
| author_facet | Yutaka Saikawa Toshihiko Komatsuzaki Nobuaki Nishiyama Toshihisa Hatta |
| author_sort | Yutaka Saikawa |
| collection | DOAJ |
| description | Acute myeloid leukaemia (AML) is a haematologic malignancy with high relapse rates in both adults and children. Leukaemic stem cells (LSCs) are central to leukaemopoiesis, treatment response and relapse and frequently associated with measurable residual disease (MRD). However, the dynamics of LSCs within the AML microenvironment is not fully understood. This study utilized three-dimensional cellular automata (CA) modelling to simulate LSC behaviour and treatment response under induction chemotherapy. Our study revealed: (i) a correlation between LSC persistence post-induction chemotherapy and risk of AML relapse; (ii) MRD negativity based on LSC count may not reliably predict outcomes, supporting clinical evidence that patients with MRD-negative status can still be at risk of relapse; (iii) prolonged persistence of LSCs post-chemotherapy without disruption of normal haematopoiesis, aligning with clinical observations of dormant AML clones; (iv) early LSC dynamics post-induction chemotherapy, characterized by stochastic behaviours and movement velocities, are insufficient predictors of long-term prognosis; and (v) a distinct spatiotemporal organization of LSCs in later phases post-induction chemotherapy is correlated with long-term outcomes. Our modelling results provide a theoretical and clinical framework for AML research, and future clinical data validation could refine the utility of CA modelling for oncological studies. |
| format | Article |
| id | doaj-art-66eb209017ac40478009734529bb462a |
| institution | Kabale University |
| issn | 2054-5703 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj-art-66eb209017ac40478009734529bb462a2025-01-23T10:15:28ZengThe Royal SocietyRoyal Society Open Science2054-57032025-01-0112110.1098/rsos.241202Cellular automata modelling of leukaemic stem cell dynamics in acute myeloid leukaemia: insights into predictive outcomes and targeted therapiesYutaka Saikawa0Toshihiko Komatsuzaki1Nobuaki Nishiyama2Toshihisa Hatta3Department of Pediatrics, Kanazawa Medical University, Uchinada, Ishikawa 9200293, JapanFaculty of Frontier Engineering, Institute of Science and Engineering, Kanazawa University, Kakuma, Ishikawa 9201192, JapanGraduate School of Natural Science and Technology, Kanazawa University, Kakuma, Ishikawa 9201192, JapanDepartment of Anatomy, Kanazawa Medical University, Uchinada, Ishikawa 9200293, JapanAcute myeloid leukaemia (AML) is a haematologic malignancy with high relapse rates in both adults and children. Leukaemic stem cells (LSCs) are central to leukaemopoiesis, treatment response and relapse and frequently associated with measurable residual disease (MRD). However, the dynamics of LSCs within the AML microenvironment is not fully understood. This study utilized three-dimensional cellular automata (CA) modelling to simulate LSC behaviour and treatment response under induction chemotherapy. Our study revealed: (i) a correlation between LSC persistence post-induction chemotherapy and risk of AML relapse; (ii) MRD negativity based on LSC count may not reliably predict outcomes, supporting clinical evidence that patients with MRD-negative status can still be at risk of relapse; (iii) prolonged persistence of LSCs post-chemotherapy without disruption of normal haematopoiesis, aligning with clinical observations of dormant AML clones; (iv) early LSC dynamics post-induction chemotherapy, characterized by stochastic behaviours and movement velocities, are insufficient predictors of long-term prognosis; and (v) a distinct spatiotemporal organization of LSCs in later phases post-induction chemotherapy is correlated with long-term outcomes. Our modelling results provide a theoretical and clinical framework for AML research, and future clinical data validation could refine the utility of CA modelling for oncological studies.https://royalsocietypublishing.org/doi/10.1098/rsos.241202acute myeloid leukaemialeukaemic stem cellsmeasurable residual diseasecellular automata |
| spellingShingle | Yutaka Saikawa Toshihiko Komatsuzaki Nobuaki Nishiyama Toshihisa Hatta Cellular automata modelling of leukaemic stem cell dynamics in acute myeloid leukaemia: insights into predictive outcomes and targeted therapies Royal Society Open Science acute myeloid leukaemia leukaemic stem cells measurable residual disease cellular automata |
| title | Cellular automata modelling of leukaemic stem cell dynamics in acute myeloid leukaemia: insights into predictive outcomes and targeted therapies |
| title_full | Cellular automata modelling of leukaemic stem cell dynamics in acute myeloid leukaemia: insights into predictive outcomes and targeted therapies |
| title_fullStr | Cellular automata modelling of leukaemic stem cell dynamics in acute myeloid leukaemia: insights into predictive outcomes and targeted therapies |
| title_full_unstemmed | Cellular automata modelling of leukaemic stem cell dynamics in acute myeloid leukaemia: insights into predictive outcomes and targeted therapies |
| title_short | Cellular automata modelling of leukaemic stem cell dynamics in acute myeloid leukaemia: insights into predictive outcomes and targeted therapies |
| title_sort | cellular automata modelling of leukaemic stem cell dynamics in acute myeloid leukaemia insights into predictive outcomes and targeted therapies |
| topic | acute myeloid leukaemia leukaemic stem cells measurable residual disease cellular automata |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.241202 |
| work_keys_str_mv | AT yutakasaikawa cellularautomatamodellingofleukaemicstemcelldynamicsinacutemyeloidleukaemiainsightsintopredictiveoutcomesandtargetedtherapies AT toshihikokomatsuzaki cellularautomatamodellingofleukaemicstemcelldynamicsinacutemyeloidleukaemiainsightsintopredictiveoutcomesandtargetedtherapies AT nobuakinishiyama cellularautomatamodellingofleukaemicstemcelldynamicsinacutemyeloidleukaemiainsightsintopredictiveoutcomesandtargetedtherapies AT toshihisahatta cellularautomatamodellingofleukaemicstemcelldynamicsinacutemyeloidleukaemiainsightsintopredictiveoutcomesandtargetedtherapies |