An Integrative multi-lineage model of variation in leukopoiesis and acute myelogenous leukemia

Joyatee M. Sarker, Serena M. Pearce, Robert Nelson, Tamara L. Kinzer-Ursem, David M. Umulis, Ann E. Rundell

Research output: Contribution to journalArticle

Abstract

Background: Acute myelogenous leukemia (AML) progresses uniquely in each patient. However, patients are typically treated with the same types of chemotherapy, despite biological differences that lead to differential responses to treatment. Results: Here we present a multi-lineage multi-compartment model of the hematopoietic system that captures patient-to-patient variation in both the concentration and rates of change of hematopoietic cell populations. By constraining the model against clinical hematopoietic cell recovery data derived from patients who have received induction chemotherapy, we identified trends for parameters that must be met by the model; for example, the mitosis rates and the probability of self-renewal of progenitor cells are inversely related. Within the data-consistent models, we found 22,796 parameter sets that meet chemotherapy response criteria. Simulations of these parameter sets display diverse dynamics in the cell populations. To identify large trends in these model outputs, we clustered the simulated cell population dynamics using k-means clustering and identified thirteen 'representative patient' dynamics. In each of these patient clusters, we simulated AML and found that clusters with the greatest mitotic capacity experience clinical cancer outcomes more likely to lead to shorter survival times. Conversely, other parameters, including lower death rates or mobilization rates, did not correlate with survival times. Conclusions: Using the multi-lineage model of hematopoiesis, we have identified several key features that determine leukocyte homeostasis, including self-renewal probabilities and mitosis rates, but not mobilization rates. Other influential parameters that regulate AML model behavior are responses to cytokines/growth factors produced in peripheral blood that target the probability of self-renewal of neutrophil progenitors. Finally, our model predicts that the mitosis rate of cancer is the most predictive parameter for survival time, followed closely by parameters that affect the self-renewal of cancer stem cells; most current therapies target mitosis rate, but based on our results, we propose that additional therapeutic targeting of self-renewal of cancer stem cells will lead to even higher survival rates.

Original languageEnglish (US)
Article number78
JournalBMC Systems Biology
Volume11
Issue number1
DOIs
StatePublished - Aug 25 2017

Fingerprint

Leukopoiesis
Multi-model
Leukemia
Acute Myeloid Leukemia
Acute
Renewal
Mitosis
Survival Time
Cell Population
Cancer
Chemotherapy
Neoplastic Stem Cells
Stem Cells
Survival
Cells
Stem cells
Patient Advocacy
Hematopoietic System
Drug Therapy
Hematopoiesis

Keywords

  • Acute myelogenous leukemia
  • Hematopoiesis
  • Leukopoiesis
  • Mathematical model
  • Personalized medicine

ASJC Scopus subject areas

  • Structural Biology
  • Modeling and Simulation
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

An Integrative multi-lineage model of variation in leukopoiesis and acute myelogenous leukemia. / Sarker, Joyatee M.; Pearce, Serena M.; Nelson, Robert; Kinzer-Ursem, Tamara L.; Umulis, David M.; Rundell, Ann E.

In: BMC Systems Biology, Vol. 11, No. 1, 78, 25.08.2017.

Research output: Contribution to journalArticle

Sarker, Joyatee M. ; Pearce, Serena M. ; Nelson, Robert ; Kinzer-Ursem, Tamara L. ; Umulis, David M. ; Rundell, Ann E. / An Integrative multi-lineage model of variation in leukopoiesis and acute myelogenous leukemia. In: BMC Systems Biology. 2017 ; Vol. 11, No. 1.
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AB - Background: Acute myelogenous leukemia (AML) progresses uniquely in each patient. However, patients are typically treated with the same types of chemotherapy, despite biological differences that lead to differential responses to treatment. Results: Here we present a multi-lineage multi-compartment model of the hematopoietic system that captures patient-to-patient variation in both the concentration and rates of change of hematopoietic cell populations. By constraining the model against clinical hematopoietic cell recovery data derived from patients who have received induction chemotherapy, we identified trends for parameters that must be met by the model; for example, the mitosis rates and the probability of self-renewal of progenitor cells are inversely related. Within the data-consistent models, we found 22,796 parameter sets that meet chemotherapy response criteria. Simulations of these parameter sets display diverse dynamics in the cell populations. To identify large trends in these model outputs, we clustered the simulated cell population dynamics using k-means clustering and identified thirteen 'representative patient' dynamics. In each of these patient clusters, we simulated AML and found that clusters with the greatest mitotic capacity experience clinical cancer outcomes more likely to lead to shorter survival times. Conversely, other parameters, including lower death rates or mobilization rates, did not correlate with survival times. Conclusions: Using the multi-lineage model of hematopoiesis, we have identified several key features that determine leukocyte homeostasis, including self-renewal probabilities and mitosis rates, but not mobilization rates. Other influential parameters that regulate AML model behavior are responses to cytokines/growth factors produced in peripheral blood that target the probability of self-renewal of neutrophil progenitors. Finally, our model predicts that the mitosis rate of cancer is the most predictive parameter for survival time, followed closely by parameters that affect the self-renewal of cancer stem cells; most current therapies target mitosis rate, but based on our results, we propose that additional therapeutic targeting of self-renewal of cancer stem cells will lead to even higher survival rates.

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