Modelling acute myeloid leukaemia in a continuum of differentiation states

Cho, Heyrim; Ayers, Kimberly; DePills, L; Kuo, Yao‐Haur; Park, J; Radunskaya, Ami; Rockne, Russell C.

doi:10.1080/23737867.2018.1472532

Cited by 11 publications

(8 citation statements)

References 53 publications

(107 reference statements)

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“…Further, AML is a heterogeneous disease harboring multiple mutations and multiple clones. In future studies, it will be valuable to perform such mathematical modeling at the single-cell level (20) and with differentiation along a continuum rather than in discrete states to capture clonal and genetic heterogeneity and to understand how the differences in degree of differentiation of individual cells could be the result of a different set of cooperating mutations and regulatory feedback. Such modeling should also consider how the various growth and differentiation rates among healthy cells and various tumor subclones affect the clonal heterogeneity and system behavior.…”

Section: Resultsmentioning

confidence: 99%

Differentiation of leukemic blasts is not completely blocked in acute myeloid leukemia

Agarwal

Bolosky

Wilson

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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Hematopoiesis, the formation of blood cells, involves the hierarchical differentiation of immature blast cells into mature, functional cell types and lineages of the immune system. Hematopoietic stem cells precisely regulate self-renewal versus differentiation to balance the production of blood cells and maintenance of the stem cell pool. The canonical view of acute myeloid leukemia (AML) is that it results from a combination of molecular events in a hematopoietic stem cell that block differentiation and drive proliferation. These events result in the accumulation of primitive hematopoietic blast cells in the blood and bone marrow. We used mathematical modeling to determine the impact of varying differentiation rates on myeloblastic accumulation. Our model shows that, instead of the commonly held belief that AML results from a complete block of differentiation of the hematopoietic stem cell, even a slight skewing of the fraction of cells that differentiate would produce an accumulation of blasts. We confirmed this model by interphase fluorescent in situ hybridization (FISH) and sequencing of purified cell populations from patients with AML, which showed that different leukemia-causing molecular abnormalities typically thought to block differentiation were consistently present in mature myeloid cells such as neutrophils and monocytes at similar levels to those in immature myeloid cells. These findings suggest reduced or skewed, rather than blocked, differentiation is responsible for the development of AML. Approaches that restore normal regulation of hematopoiesis could be effective treatment strategies.

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Section: Resultsmentioning

confidence: 99%

Differentiation of leukemic blasts is not completely blocked in acute myeloid leukemia

Agarwal

Bolosky

Wilson

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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“…A few works have already integrated this data in the modelling process (see e.g. [17] or [79]), exposing at the same time the challenge that this recently unveiled heterogeneity poses. The use of multi-omics data implies several challenges from the clinical point of view, such as the need for standardized protocols for data collection, panel creation and data storage.…”

Section: Discussionmentioning

confidence: 99%

“…1. In [17], a PDE model of haematopoiesis was parametrised on a graph using publicly available RNA-Seq data in a high-dimensional space. The high-dimensional data were later reduced to R 2 or R 3 using reduction techniques, such as principal component analysis, diffusion maps and t-distributed stochastic neighbour embedding, and a PDE model on a graph G was constructed.…”

Section: Other Studies Of Myeloid Leukaemiasmentioning

confidence: 99%

Mathematical models of leukaemia and its treatment: a review

Chulián

Rubio

Rosa

et al. 2022

SeMA

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Leukaemia accounts for around 3% of all cancer types diagnosed in adults, and is the most common type of cancer in children of paediatric age (typically ranging from 0 to 14 years). There is increasing interest in the use of mathematical models in oncology to draw inferences and make predictions, providing a complementary picture to experimental biomedical models. In this paper we recapitulate the state of the art of mathematical modelling of leukaemia growth dynamics, in time and response to treatment. We intend to describe the mathematical methodologies, the biological aspects taken into account in the modelling, and the conclusions of each study. This review is intended to provide researchers in the field with solid background material, in order to achieve further breakthroughs in the promising field of mathematical biology.

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“…Moreover, a central feature of contemporary analysis 114 of scRNA-seq data is clustering and inferring relationships between clusters of known 115 cell types [4]. Therefore, we develop a model that can describe cell state-transition 116 dynamics on a graph that represents relationships between known cell types identified 117 with clusters, extended from our previous work in [12]. An immediate advantage of this 118 approach is that it is convenient to employ biological insights from well-known classical 119 discrete cell states.…”

mentioning

confidence: 99%

“…(4) and (7) 294 that controls the local cell capacity. In addition to the over-proliferation, another aspect 295 of the leukemia pathogenesis of our interest is the impaired differentiation of erythroid 296 lineage differentiation, where it can be modeled by reducing the cell differentiation V (θ) 297 in Eq (5) and V k (x) in Eq (1) toward Ery (See S1 Appendix and [12] for the details).…”

mentioning

confidence: 99%

Mathematical modeling with single-cell sequencing data

Cho¹,

Rockne²

2019

Preprint

Self Cite

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Single-cell sequencing technologies have revolutionized molecular and cellular biology and stimulated the development of computational tools to analyze the data generated from these technology platforms. However, despite the recent explosion of computational analysis tools, relatively few mathematical models have been developed to utilize these data. Here we compare and contrast two approaches for building mathematical models of cell state-transitions with single-cell RNA-sequencing data with hematopoeisis as a model system; by solving partial differential equations on a graph representing discrete cell state relationships, and by solving the equations on a continuous cell state-space. We demonstrate how to calibrate model parameters from single or multiple time-point single-cell sequencing data, and examine the effects of data processing algorithms on the model calibration and predictions. As an application of our approach, we demonstrate how the calibrated models may be used to mathematically perturb normal hematopoeisis to simulate, predict, and study the emergence of novel cell types during the pathogenesis of acute myeloid leukemia. The mathematical modeling framework we present is general and can be applied to study cell state-transitions in any single-cell genome sequencing dataset. Author summaryHere we compare and contrast graph-and continuum-based approaches for constructing mathematical models of cell state-transitions using single-cell RNA-sequencing data. Using two publicly available datasets, we demonstrate how to calibrate mathematical models of hematopoeisis and how to use the models to predict dynamics of acute myeloid leukemia pathogenesis by mathematically perturbing the process of cellular proliferation and differentiation. We apply these modeling approaches to study the effects of perturbing individual or sets of genes in subsets of cells, or by modeling the dynamics of cell state-transitions directly in a reduced dimensional space. We examine the effects of different graph abstraction and trajectory inference algorithms on calibrating the models and the subsequent model predictions. We conclude that both the graph-and continuum-based modeling approaches can be equally well calibrated to data and discuss situations in which one method may be preferable over the other. This work presents a general mathematical modeling framework, applicable to any single-cell sequencing dataset where cell state-transitions are of interest.July 21, 2019 1/32 47 impact of various graph construction and trajectory inference methods on the geometry 48 of the cell state space, and solve the model on these geometries. We then use the model 49 the study the effects of perturbing 1) the graph structure 2) expression of select subsets 50 of genes 3) and cell state transition dynamics by perturbing flow on the graph or by 51 modifying the dynamics in the continuous space. We predict novel dynamics of leukemia 52July 21, 2019 2/32 65 embedding. While different techniques provide different shapes and differentiation 66 sp...

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Modelling acute myeloid leukaemia in a continuum of differentiation states

Cited by 11 publications

References 53 publications

Differentiation of leukemic blasts is not completely blocked in acute myeloid leukemia

Differentiation of leukemic blasts is not completely blocked in acute myeloid leukemia

Mathematical models of leukaemia and its treatment: a review

Mathematical modeling with single-cell sequencing data

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