Interaction between the immune system and acute myeloid leukemia: A model incorporating promotion of regulatory T cell expansion by leukemic cells

Population dynamics of regulatory T cells (Treg) are crucial for the underlying interplay between leukemic and immune cells in progression of acute myeloid leukemia (AML). The goal of this work is to elucidate the dynamics of a model that includes Treg, which can be qualitatively assessed by accumulating clinical findings on the impact of activated immune cell infusion after selective Treg depletion. We constructed an ordinary differential equation model to describe the dynamics of three components in AML: leukemic blast cells, mature regulatory T cells (Treg), and mature effective T cells (Teff), including cytotoxic T lymphocytes. The model includes promotion of Treg expansion by leukemic blast cells, leukemic stem cell and progenitor cell targeting by Teff, and Treg-mediated Teff suppression, and exhibits two coexisting, stable steady states, corresponding to high leukemic cell load at diagnosis or relapse, and to long-term complete remission. Our model is capable of explaining the clinical findings that the survival of patients with AML after allogeneic stem cell transplantation is influenced by the duration of complete remission, and that cut-off minimal residual disease thresholds associated with a 100% relapse rate are identified in AML.

[1]  N. Monk,et al.  The inheritance of process: a dynamical systems approach. , 2012, Journal of experimental zoology. Part B, Molecular and developmental evolution.

[2]  A. Krämer,et al.  Pretransplant NPM1 MRD levels predict outcome after allogeneic hematopoietic stem cell transplantation in patients with acute myeloid leukemia , 2016, Blood Cancer Journal.

[3]  H. Kantarjian,et al.  Blood counts at time of complete remission provide additional independent prognostic information in acute myeloid leukemia. , 2008, Leukemia research.

[4]  A. Perelson,et al.  Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis. , 1994, Bulletin of mathematical biology.

[5]  T. Haferlach,et al.  Monitoring of minimal residual disease in acute myeloid leukemia. , 2005, Critical reviews in oncology/hematology.

[6]  Dirk Hasenclever,et al.  Modelling Lymphoma Therapy and Outcome , 2014, Bulletin of mathematical biology.

[7]  Reka Albert,et al.  Biological switches and clocks , 2008, Journal of The Royal Society Interface.

[8]  S. Aksu,et al.  Rebound Thrombocytosis following Induction Chemotherapy Is an Independent Predictor of a Good Prognosis in Acute Myeloid Leukemia Patients Attaining First Complete Remission , 2015, Acta Haematologica.

[9]  Lei Wang,et al.  Bistable switches control memory and plasticity in cellular differentiation , 2009, Proceedings of the National Academy of Sciences.

[10]  R. Hills,et al.  Minimal residual disease monitoring by quantitative RT-PCR in core binding factor AML allows risk stratification and predicts relapse: results of the United Kingdom MRC AML-15 trial. , 2007, Blood.

[11]  Loise M. Francisco,et al.  PD-L1 regulates the development, maintenance, and function of induced regulatory T cells , 2009, The Journal of experimental medicine.

[12]  A. Radunskaya,et al.  Mixed Immunotherapy and Chemotherapy of Tumors: Modeling, Applications and Biological Interpretations , 2022 .

[13]  W. Hiddemann,et al.  Definition of refractoriness against conventional chemotherapy in acute myeloid leukemia: a proposal based on the results of retreatment by thioguanine, cytosine arabinoside, and daunorubicin (TAD 9) in 150 patients with relapse after standardized first line therapy. , 1990, Leukemia.

[14]  J. Dongen,et al.  Clinical significance of flowcytometric minimal residual disease detection in pediatric acute myeloid leukemia patients treated according to the DCOG ANLL97/MRC AML12 protocol , 2010, Leukemia.

[15]  Eshel Ben-Jacob,et al.  Modeling putative therapeutic implications of exosome exchange between tumor and immune cells , 2014, Proceedings of the National Academy of Sciences.

[16]  Ø. Bruserud,et al.  Intensive chemotherapy for acute myeloid leukemia differentially affects circulating TC1, TH1, TH17 and TREG cells , 2010, BMC Immunology.

[17]  D. Munn,et al.  Regulatory T cells in acute myelogenous leukemia: is it time for immunomodulation? , 2011, Blood.

[18]  Martin A. Nowak,et al.  Dynamics of chronic myeloid leukaemia , 2005, Nature.

[19]  Christopher G. Kanakry,et al.  Early lymphocyte recovery after intensive timed sequential chemotherapy for acute myelogenous leukemia: peripheral oligoclonal expansion of regulatory T cells. , 2011, Blood.

[20]  R. Hills,et al.  Increased CD200 expression in acute myeloid leukemia is linked with an increased frequency of FoxP3+ regulatory T cells , 2012, Leukemia.

[21]  C. Schürch,et al.  Regulation of hematopoietic and leukemic stem cells by the immune system , 2014, Cell Death and Differentiation.

[22]  Eduardo Sontag,et al.  Untangling the wires: A strategy to trace functional interactions in signaling and gene networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Yi-xiang Han,et al.  Elevated frequencies of CD4+CD25+CD127lo regulatory T cells is associated to poor prognosis in patients with acute myeloid leukemia , 2011, International journal of cancer.

[24]  R. Mesa,et al.  Absolute lymphocyte count recovery after induction chemotherapy predicts superior survival in acute myelogenous leukemia , 2005, Leukemia.

[25]  C. Schürch,et al.  Cytotoxic T cells induce proliferation of chronic myeloid leukemia stem cells by secreting interferon-γ , 2013, The Journal of experimental medicine.

[26]  Yunxiao Xu,et al.  Clinical significance of Treg cell frequency in acute myeloid leukemia , 2013, International Journal of Hematology.

[27]  G. Schuurhuis,et al.  Peripheral blood minimal residual disease may replace bone marrow minimal residual disease as an immunophenotypic biomarker for impending relapse in acute myeloid leukemia , 2016, Leukemia.

[28]  M. A. Curotto de Lafaille,et al.  Control of homeostatic proliferation by regulatory T cells. , 2005, The Journal of clinical investigation.

[29]  D. Glass,et al.  Autoantigen-Specific TGFβ-Induced Foxp3+ Regulatory T Cells Prevent Autoimmunity by Inhibiting Dendritic Cells from Activating Autoreactive T Cells1 , 2007, The Journal of Immunology.

[30]  P. Thall,et al.  Effect of circulating blasts at time of complete remission on subsequent relapse-free survival time in newly diagnosed AML. , 2003, Blood.

[31]  J. Collins,et al.  Construction of a genetic toggle switch in Escherichia coli , 2000, Nature.

[32]  M. Mackey,et al.  Periodic Oscillations of Blood Cell Populations in Chronic Myelogenous Leukemia , 2004, SIAM J. Math. Anal..

[33]  Ingo Roeder,et al.  Dynamic modeling of imatinib-treated chronic myeloid leukemia: functional insights and clinical implications , 2006, Nature Medicine.

[34]  Bin Zhang,et al.  Clearance of acute myeloid leukemia by haploidentical natural killer cells is improved using IL-2 diphtheria toxin fusion protein. , 2014, Blood.

[35]  Doron Levy,et al.  Dynamics and Potential Impact of the Immune Response to Chronic Myelogenous Leukemia , 2008, PLoS Comput. Biol..

[36]  M. Kester,et al.  Myeloid leukemic progenitor cells can be specifically targeted by minor histocompatibility antigen LRH-1-reactive cytotoxic T cells. , 2009, Blood.

[37]  Katherine Meyer,et al.  A MATHEMATICAL REVIEW OF RESILIENCE IN ECOLOGY , 2016 .

[38]  Sarah Filippi,et al.  The ecology in the hematopoietic stem cell niche determines the clinical outcome in chronic myeloid leukemia , 2014, Proceedings of the National Academy of Sciences.

[39]  J. Lipton,et al.  Duration of first remission, hematopoietic cell transplantation-specific comorbidity index and patient age predict survival of patients with AML transplanted in second CR , 2013, Bone Marrow Transplantation.

[40]  G. Hill,et al.  CD4+CD25+ regulatory T cells control CD8+ T-cell effector differentiation by modulating IL-2 homeostasis , 2011, Proceedings of the National Academy of Sciences.

[41]  Ami Radunskaya,et al.  A mathematical tumor model with immune resistance and drug therapy: an optimal control approach , 2001 .

[42]  Artur C. Fassoni,et al.  An ecological resilience perspective on cancer: insights from a toy model , 2016, 1604.08921.

[43]  M. Peakman,et al.  Defective suppressor function in CD4(+)CD25(+) T-cells from patients with type 1 diabetes. , 2005, Diabetes.

[44]  M. Baccarani,et al.  Modulation of tryptophan catabolism by human leukemic cells results in the conversion of CD25- into CD25+ T regulatory cells. , 2007, Blood.

[45]  Naveen Garg,et al.  Kinetics of CLL cells in tissues and blood during therapy with the BTK inhibitor ibrutinib. , 2013, Blood.

[46]  Thomas Stiehl,et al.  Cell division patterns in acute myeloid leukemia stem-like cells determine clinical course: a model to predict patient survival. , 2015, Cancer research.

[47]  E. Estey Treatment of relapsed and refractory acute myelogenous leukemia , 2000, Leukemia.

[48]  E. Hoster,et al.  Early assessment of minimal residual disease in AML by flow cytometry during aplasia identifies patients at increased risk of relapse , 2014, Leukemia.

[49]  E. Estey Treatment of refractory AML. , 1996, Leukemia.

[50]  T. Whiteside,et al.  Increased Frequency and Suppression by Regulatory T Cells in Patients with Acute Myelogenous Leukemia , 2009, Clinical Cancer Research.