(717a) Towards a Personalised Treatment of Acute Myeloid Leukaemia: The Impact of Considering the Cell Cycle

Fuentes-Gari, M., Imperial College
Pistikopoulos, E. N., Imperial College London
Mantalaris, A., Imperial College London
Misener, R., Imperial College
Panoskaltsis, N., Northwick Park Hospital
Velliou, E., Imperial College
Brito Dos Santos, S., Imperial College London

Towards a personalised treatment of Acute Myeloid Leukaemia:

The impact of considering the cell cycle

M. Fuentes Garí1,2, E. G. Velliou1,2 , R. Misener1,2, S. Brito Dos Santos2, N. Panoskaltsis3, A. Mantalaris2, E.N. Pistikopoulos1

1Centre for Process Systems Engineering (CPSE), Department of Chemical Engineering, Imperial Imperial College London, South Kensington Campus, London SW7 2AZ, UK

2Biological Systems Engineering Laboratory (BSEL), Department of Chemical Engineering, Imperial Imperial College London, South Kensington Campus, London SW7 2AZ, UK

3Department of Hematology, Imperial College London, Northwick Park & St. Mark’s Campus, London HA1 3UJ, UK

Acute Myeloid Leukaemia (AML) is a type of blood cancer characterized by the release into the blood of abnormal amounts of immature cells of the myeloid lineage that preserve the ability to produce new cells. Patients diagnosed with AML are typically treated with chemotherapeutic drugs. These drugs (cytarabine, daunorubicin) prevent highly proliferative cells from progressing in their duplication and irreversibly program those cells to undergo apoptosis. A major challenge with chemotherapy is to eradicate a big proportion of the malignant cells while keeping enough healthy cells for the patient to survive. Indeed, these drugs are only selective for proliferative cells, but make no distinction between normal and abnormal cell types. Therefore, it is crucial to assess the response of both cell types to chemotherapy to be able to target (at least partially) the malignant cells by their kinetic behaviour in favour of the healthy cells. Our challenge is two-fold: in vitro, we need to reproduce an environment where the cells will grow as they would in the bone marrow (BM); and in silico, we need to model the system keeping in mind the real processes occurring in the cell and the existence of techniques to quantify them.

The BM is a highly porous and vascularized environment that supports the growth and differentiation of hematopoietic stem cells (HSCs). These cells are sensitive to both chemical and mechanical cues. They are stimulated chemically by the presentation of integrins (attachment proteins) by other cells or the BM matrix, and by the presence/absence of nutrients and metabolites. HSCs reside in “niche” arrangements, which provide the mechanical signals for their settlement. These niches consist of a high wall surface for cell attachment as well as an extensive network of interconnected channels, for the transport of signals and the accommodation of daughter cells (Baker and Chen 2012). The BM microenvironment is maintained by the production and regulation of growth factors and extracellular matrix components from the stromal cells (Panoskaltsis et al., 2005; (Kim 2005)). To assess cell growth kinetics, it is important to mimic as closely as possible this environment, by keeping a 3D structure with similar mechanical characteristics and reproducing the extracellular matrix properties, such as the presence of collagen type I for cell attachment. Such a system has previously been developed by our group in the form of a polyurethane (PU) porous scaffold coated with collagen type I, and has proved to be successful in recapitulating the BM properties (Mortera et al., 2010). Mononuclear stem cells from cord blood have successfully been cultured in this 3D platform for periods of over a month, in the absence of exogenous growth factors (Mortera et al., 2011).

Proliferation kinetics has its roots in the cell cycle. The cell cycle is an organized process by which cells duplicate their DNA and divide into two new cells. It consists of four different phases, highly regulated by the timed expression of cell cycle proteins (cyclins) and their partner cdks (cyclin-dependent kinases) (Darzynkiewicz et al., 1996). By following experimentally the cyclin expression over time, the cell cycle kinetics can be predicted mathematically, and optimal treatment strategies can be derived. Our goal is to capture individual cell cycle kinetics in the 3D environment and introduce them as parameters into the chemotherapy model.

In this work, we compare 2D and 3D kinetics and the changes in cell cycle and metabolic response, as a first step to justify the use of the 3D platform. K-562 (a widely used leukemic cell line) cells are cultured in parallel under 2D (liquid suspension contained in wells/flasks) and 3D (collagen-coated PU scaffolds dipped in growth medium) conditions for up to 4 weeks. Growth kinetics are captured indirectly in situ with the MTS assay ((3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, which is a substance that can be degraded enzymatically by cells and changes colour in the process), and by DNA quantification of the lysate. All of the in situanalyses are done in triplicates by three different operators (9 total replicates per condition). In addition, cell counts are performed weekly (2 replicates x 3 operators per condition) on 2D cells without further processing, and on 3D cells extracted from the scaffolds, giving the cell numbers and viability. Those cells are further analysed via flow cytometry for the characterization of: intracellular cyclin proteins, DNA content and proliferation markers.  Bi- and tri-variate analysis provides information on: (A) the cell cycle regulatory patterns in 2D or 3D conditions, and (B) the cell cycle distribution.  Finally, the metabolic activity of the cells is obtained by analysing the levels of glucose, lactate, glutamine and glutamate.

Faster proliferation kinetics were observed in the 2D samples, possibly due to the 2D-to-3D adaptation period (after all, cell lines are 2D-adapted populations) but also to intrinsic slower kinetics in the three-dimensional environment, as previously reported for other cancer types (Chitcholtan K. et al., 2013). This was not due to overcrowding of the cells, as the space availability in the 3D environment was high even after several weeks of culture (as observed in scanning electron microscopy images). We have to consider whether slower kinetics might indicate a closer behaviour to the one the cells have inside the patient. The cyclin expression profiles in both 2D and 3D looked similar, suggesting that the levels of protein expression stay the same for a particular cell line.


Baker, B. M. and C. S. Chen (2012). Deconstructing the third dimension: how 3D culture microenvironments alter cellular cues. Journal of Cell Science 125(Pt 13): 3015-3024.

Chitcholtan K, E. Asselin, et al. (2013). Differences in growth properties of endometrial cancer in three dimensional (3D) culture and 2D cell monolayer. Exp Cell Res 319(1): 75-87.

Darzynkiewicz, Z., J. P. Gong, et al. (1996). Cytometry of cyclin proteins. Cytometry 25(1): 1-13.

Kim, J. B. (2005). Three-dimensional tissue culture models in cancer biology. Seminars in Cancer Biology 15(5): 365-377.

Mortera-Blanco T, A. Mantalaris, A. Bismarck, N. Panoskaltsis (2010). Development of a three-dimensional biomimicry of human acute myeloid leukemia ex vivo. Biomaterials, 31: 2243-2251.

Mortera-Blanco T, Mantalaris A, Bismarck A, et al., Long-term cytokine-free expansion of cord blood mononuclear cells in three-dimensional scaffolds (2011). Biomaterials, 32: 9263-9270.

Panoskaltsis, N., Mantalaris, S., Wu, D. (2005). Engineering a mimicry of bone marrow tissue Ex vivo. Journal of Bioscience and Bioengineering, 100: 28-35.



The research leading to these results has received funding from the European Union Seventh Framework Programme [FP7/2007-2013] under grant agreement n° 238013 (MULTIMOD ITN). It has additionally been supported by ERC-Mobile Project (no 226462) and the Richard Thomas Leukemia Research Fund. R.M. is further thankful for a Royal Academy of Engineering Research Fellowship (RAEng 10216/118).