Dynamics of hematopoiesis in health and disease - from governing principles to clinical implications

Wednesday, June 16 at 09:30am (PDT)
Wednesday, June 16 at 05:30pm (BST)
Thursday, June 17 01:30am (KST)

SMB2021 SMB2021 Follow Tuesday (Wednesday) during the "MS13" time block.
Note: this minisymposia has multiple sessions. The second session is MS12-MMPB (click here).

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Peter Ashcroft (ETH Zurich, Switzerland), Tony Humphries (McGill University, Canada), Morten Andersen (Roskilde University, Denmark)


Blood cells dominate cell turnover in the human body. This advocates for strong regulating mechanisms of blood production (hematopoiesis). Many diseases of the hematopoietic system can be diagnosed from blood samples while the malfunction itself may be located at the less-accessible stem cell level in the bone marrow – the breeding ground for blood cells. This calls for mechanism-based mathematical modelling and analysis bridging biological knowledge and data from the stem cell level to mature blood cells. In this minisymposium the precise language of mathematics is used to formulate the governing principles of hematopoiesis. Difficult problems arise, such as including nonlinear feedback mechanisms, interaction of hematopoiesis with the immune system and dealing with heterogeneous subclone formation in the case of blood cancers. The use of mathematical modelling for diagnosis and patient-specific treatment protocols for blood malignancies will be explored.

Lora Bailey

(Grand Valley State University, USA)
"The resilience of hematopoietic feedback networks against mutations"
In hematopoietic systems, cell fate decisions such as stem cell differentiation or differentiated cell death may be controlled by cell populations through cell-to-cell signaling to keep the system in a state of homeostasis. By examining different feedback networks mathematically, we can determine not only which feedback networks are possible, but which have greater resilience against mutations. While networks with exactly one feedback loop are sufficient for maintaining homeostasis, they are all equally vulnerable to dangerous mutations that alter the present feedback and can lead to unlimited growth of cancerous populations. Therefore, a network with multiple, redundant feedback loops appears evolutionarily advantageous. We discovered that these redundant networks have varying degrees of resilience against mutations. For some redundant networks, any mutation that weakens or eliminates one of the existing feedback loops results in the growth of the cancerous stem cell population, while for other redundant networks this same type of alteration can lead to a depletion of the cancerous stem cell population and may slow down further unwanted evolution.

Mia Brunetti

(Université de Montréal, Centre de recherche du CHU Sainte-Justine, Canada)
"Mathematical modelling of the pre-leukemic phase of AML to evaluate clonal reduction therapeutic strategies"
Acute myeloid leukemia (AML) is an aggressive blood cancer subtype characterized by the uncontrolled proliferation of myeloblasts in the bone marrow and the blood. While rare, this disease has one of the highest mortality rates of any leukemias. The inefficiency of standard therapies, which target leukemic cells directly, highlights the need for a new approach to treating AML. Previous studies identified a premalignant phase preceding the onset of AML orchestrated by pre-leukemic stem cells (pre-LSCs). Pre-LSCs outcompete healthy hematopoietic stem cells and allow for AML to develop through their clonal expansion and the acquisition of secondary mutations. More recently, studies have suggested that different approved medications target pre-LSCs. These clonal reduction strategies could completely prevent the evolution of AML; however a better understanding of their impact on hematopoiesis is required. In response, we developed a Moran model of hematopoietic stem cells dynamics in the pre-leukemic phase. To this model, we integrated population pharmacokinetic-pharmacodynamics (PK-PD) models to investigate the clonal reduction potential of several candidate drugs. Our results suggest that three cardiac glycosides (proscillaridin A, digoxin and ouabain) reduce the expansion of premalignant stem cells through a decrease in pre-LSC viability, underlining the prospect of these treatments for AML.

Derek Park

(Department of Integrated Mathematical Oncology, Moffitt Cancer Center, USA)
"Deep Reinforcement Learning of Optimal Chemotherapy Scheduling Demonstrates a Robustness vs. Performance Tradeoff in Patient Outcomes"
Hematopoietic and immune dynamics are a complex system that often underpins success or failure for cancer chemotherapy. While multiple mathematical models exist for simulating cancer treatment and response, there remains a significant deficit in regards to optimization and getting cohesive, generalizable strategies. Here, we present a deep reinforcement learning framework to optimize previously established models of hematopoietic and immune dynamics during chemotherapy. By testing differing reward mechanisms and training on biased cohorts, we demonstrate a robustness-performance trade-off when it comes to treating aggressive versus less-aggressive tumors. Finally, we present how this framework can be generalized to other hematopoietic models in cancer treatment settings.

John Higgins

(Department of Systems Biology, Harvard Medical School; Department of Pathology, Massachusetts General Hospital, USA)
"Population dynamics of circulating blood cells in the pathogenesis and diagnosis of some common diseases"
Circulating populations of red (RBC) and white blood cells and platelets in humans are tightly regulated, and rates of production, maturation, and turnover are modulated in response to disease. Anemia or low red blood cell count is a common early finding in diseases ranging from infection to cancer to malnutrition, and persistence of anemia is associated with poor patient outcomes. The age distribution of the circulating cell populations provides a history of disease-induced perturbations and homeostatic responses, but it is not currently feasible to measure these distributions. Standard clinical blood counts (CBCs) report only a handful of blood cell population statistics, but CBCs usually involve thousands of single-cell measurements. Building on existing theory and analysis, we have developed models of the RBC age distribution that use these and other routine clinical data sets to enable inferences about the RBC age distribution and how it is altered in common disease states. These models suggest for instance that the healthy response to blood loss often entails not only the recognized compensatory increase in production of new cells but also an unappreciated decrease in turnover of old cells, a response which would also serve to mitigate the effects of the loss.

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.