365969 Towards Personalized Treatments for Leukemia Based on Cell Cycle Heterogeneity: An Experimental/Modeling Approach

Monday, November 17, 2014: 3:33 PM
205 (Hilton Atlanta)
Maria Fuentes-Gari1,2, Ruth Misener1,2, David Garcia-Munzer1,2, Eirini Velliou1,2, Michael C. Georgiadis1, Margaritis Kostoglou3, Nicki Panoskaltsis4, Efstratios N. Pistikopoulos1 and Athanasios Mantalaris2, (1)Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College, London, United Kingdom, (2)Biological Systems Engineering Laboratory, Department of Chemical Engineering, Imperial College, London, United Kingdom, (3)Department of Chemistry, Aristotle University of Thessaloniki, Thessaloniki, Greece, (4)Department of Haematology, Northwick Park Hospital, London, United Kingdom

Leukemia arises when blood cell progenitors experience the wrong combination of genetic alterations; these heterogeneous clonal changes induce reduced cell death and increased cell proliferation. Most leukemia treatments focus on the simpler task of tumor debulking rather than restoring normal cell function; this motivates our study of the cell cycle.

A common type of chemotherapy treatment for highly proliferative cells relies on cell cycle phase-specific (CCS) drugs. CCS drugs selectively attack duplicating cells, by interfering with the processes carried out in only one of the cell cycle phases. Importantly, CCS drugs affect not only malignant cells but also normal cells in duplication; achieving a trade-off between eradicating the tumor and maintaining a sufficient number of healthy cells is crucial. However, clinical treatment protocols do not incorporate this constraint in their calculations from the start of treatment; instead, only factors that are believed to be related to drug tolerance are taken into account (patient's weight, height, other diseases etc.). One of the biggest challenges in this area is that of delivering truly personalized chemotherapy.

The heterogeneity of genetic modifications giving rise to the disease is one of the main sources of variation in treatment response between individual patients. One critical example of patient heterogeneity is the average duration of each cell cycle phase, which can vary by hours or days (Preisler et al., 1995). Biologically, not a single population of leukemic cells but a variety of them is what characterizes a tumor. Pefani et al. (2013, 2014) showed that cell cycle kinetics are one of the most significant variables affecting treatment outcomes. Since both inter- and intra- patient genetic heterogeneity are reflected in cell cycle kinetics, and drug action takes place at the cell cycle phase level, we hypothesize that a mathematical model that describes cell cycle kinetics for each single, homogeneous population can be useful in the calculation of optimal chemotherapy protocols.

In this work, we present a multi-stage population balance model of the cell cycle consisting of 3 compartments: lumped G0/G1 (quiescent/gap 1 phase); S (DNA synthesis phase); lumped G2/M (gap 2/mitosis phase). Each compartment is distributed according to a relevant state variable: cyclin E (G0/G1), DNA (S) and cyclin B (G2/M). Cyclins E and B are proteins that trigger cell cycle progression events in G1 and G2 phases respectively, and DNA duplicates in S phase. Cyclin and DNA production rates are assumed to be constant and the transitions are modeled as normal cumulative distribution functions. The model is discretized using a fully stable upwind scheme; care is taken to maintain sufficient discretization intervals to avoid loss of entities. Global sensitivity analysis was carried out in MATLAB and identified the cell cycle times (duration of each of the phases) as the most relevant variables for phase numbers, and cyclin thresholds (the average cyclin level at which cells transition) for cyclin kinetics.

The experimental determination of these parameters is presented for three different leukemic cell lines, derived from different types of leukemia: K-562 (Chronic Myeloid); MEC-1 (Chronic Lymphoid); MOLT-4 (Acute Lymphoblastic). The cell cycle times were obtained by following an EdU labeled population over time (EdU resembles one of the DNA building blocks and can be incorporated selectively in S phase cells only, resulting in both EdU positive and negative populations) and identifying the timings of entrance and exit from each cell cycle phase. The cyclin thresholds were found when cells reached the end of a particular phase and normalized as in García-Münzer et al. (2013). The model was then run in gPROMS (Process Systems Enterprise) and plotted against the experimental EdU negative cell cycle distribution over time. The agreement amongst the model prediction and the experimental data was extremely good for all three cell lines (the Chi-square test indicated there is no statistical difference between the model output and the experimental data). In parallel, the cyclin expression trends were well captured, which is measurable proof that the model is based on the same assumptions as the biological processes.

In order to validate the model independently and moving towards capturing cellular heterogeneity in patients, blind co-culture experiments were carried out. The purpose of the blind experiment was to prove that the model could differentiate between cell types based on heterogeneity in cell cycle kinetics. A second operator prepared an unknown mixture of 2 to 3 of the aforementioned cell lines (different cell types and ratios), which was put in culture. The cell cycle kinetics were recorded for each population. The model was then run assuming all possible scenarios (any combination of cell lines in 10% increment ratios), and the one presenting the smallest statistical error to the experimental data in every population was chosen. The model was capable of identifying and quantifying heterogeneous cell populations according to their cell cycle kinetics.

The development of more detailed models of the cell cycle that are experimentally validated is critical in the implementation of more advanced pharmacokinetic/pharmacodynamic models (Velliou et al., 2014). Additionally, connecting a small subset of measurable variables to individual characteristics is necessary for the personalization of treatments. The work presented here helps bridging the gap between both.

References

Garcia-Munzer D.G., Kostoglou M., Georgiadis M.C., Pistikopoulos E.N., Mantalaris A., Developing a Cyclin Blueprint as a Tool for Mapping the Cell Cycle in GS-NS0. Biochemical Engineering Journal, 2013, 81, 97-107

Pefani E., Panoskaltsis N., Mantalaris A., Georgiadis M.C., Pistikopoulos E.N.. Design of optimal patient-specific chemotherapy protocols for the treatment of acute myeloid leukemia (AML). Computers & Chemical Engineering, 2013, 57, 187-195

Pefani E., Panoskaltsis N., Mantalaris A., Georgiadis M.C., Pistikopoulos E.N.. Chemotherapy Drug Scheduling for the Induction Treatment of patients with Acute Myeloid Leukemia. IEEE Trans Biomed Eng, 2014, 99, doi: 10.1109/TBME.2014.2313226

Preisler H.D., Raza, A., Gopal V., Ahmad S., Bokhari J., Distribution of cell cycle times amongst the leukemia cells within individual patients with acute myelogenous leukemia. Leukemia Research, 1995, 19, 10, 693-698.

Velliou E., Fuentes-Garí M., Misener R., Pefani E., Rende M., Panoskaltsis N., Pistikopoulos E. N., Mantalaris A. A framework for the design, modeling and optimization of biomedical systems. In M. Eden, J. D. Siirola and G. P. Towler (Ed.), Proceedings of the 8th International Conference on Foundations of Computer-Aided Process Design – FOCAPD. Cle Elum, Washington, USA; 2014.

Acknowledgements

This work is supported by ERC-BioBlood (no. 340719), ERC-Mobile Project (no. 226462), by the EU 7th Framework Programme [MULTIMOD Project FP7/2007-2013, no 238013] and by the Richard Thomas Leukaemia Research Fund. R.M. is further thankful for a Royal Academy of Engineering Research Fellowship. ADDIN EN.REFLIST


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