Quantitative Assessment of the Complex Dynamics of G1, S, and G2-M Checkpoint Activities

Abstract

Although studies of cell cycle perturbation and growth inhibition are common practice, they are unable to properly measure the activity of cell cycle checkpoints and frequently convey misinterpretation or incomplete pictures of the response to anticancer treatment. A measure of the strength of the treatment response of all checkpoints, with their time and dose dependence, provides a new way to evaluate the antiproliferative activity of the drugs, fully accounting for variation of the cell fates within a cancer cell line. This is achieved with an interdisciplinary approach, joining information from independent experimental platforms and interpreting all data univocally with a simple mathematical model of cell cycle proliferation. The model connects the dynamics of checkpoint activities at the molecular level with populationbased flow cytometric and growth inhibition time course measures. With this method, the response to five drugs, characterized by different molecular mechanisms of action, was studied in a synoptic way, producing a publicly available database of time course measures with different techniques in a range of drug concentrations, from sublethal to frankly cytotoxic. Using the computer simulation program, we were able to closely reproduce all the measures in the experimental database by building for each drug a scenario of the time and dose dependence of G 1 , S, and G 2 -M checkpoint activities. We showed that the response to each drug could be described as a combination of a few types of activities, each with its own strength and concentration threshold. The results gained from this method provide a means for exploring new concepts regarding the drug-cell cycle interaction. [Cancer Res 2009;69(12):5234-40]

Optimisation of Cancer Drug Treatments Using Cell Population Dynamics

“…Ubezio and co-workers [94,95,104,135] based themselves on an age and phase structured PDE model to develop a discrete age-structured model of cell cycle describing the time evolution of the number of cells of age a at time t in the phases G 1 , S and G 2 /M of the cell cycle. This model also takes into account the inter-cell variability in phase duration.…”

“…In [94,95], based on experimental data, Lupi et al investigated the effects of topotecan and melphalan respectively, on ovarian cells. Later, Ubezio et al [135] deepened the previous works of their team by examining the effects of five drugs (doxorubicin, cisplatin, topotecan, paclitaxel and melphalan) on ovarian cancer cells using several doses.…”

“…They are linked to cell population samples, either with observation on a global population, such as given by flow cytometry (or FACS, for fluorescence activated cell sorting) [16,17,18,19,20,95,135], or by observations on individual cells and statistics performed on the sampled population of individual cells marked with fluorescent proteins, such as FUCCI [125] in [25,26]. These methods require previous cell staining, e.g.…”

Interoperability of time series cytometric data: A cross platform approach for modeling tumor heterogeneity

“…After this desynchronization routine, the G1, S and G2M age distributions were normalised according to the overall cell number measured at the start of the experiment, providing the time-zero (or initiating) cell distribution. Then, percentages of cells in G1, S and G2M, equivalent to flow cytometric data derived from DNA histograms (34)(35)(36), were calculated by integration of G1, S and G2M age distributions.…”

“…Understanding the complexity of cell proliferation in terms of the underlying molecular control system is a major challenge (43) and the current proclivity for molecular profiling of bulk cell populations often can be misleading even when particular care been taken at single cell level. This is more pertinent when it is understood that the cellular population under study is heterogeneous where stochastic behavior is an inherent property of the system, and is prominent when responding to perturbation (34). Ignoring or averaging these differential or stochastic behaviors develops a cul-de-sac for many cell-based analyses (44) in the drug discovery and development process, as such approach fails to reveal the behavior of ''informative cells'' within a cell-based assay (6).…”