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

Biophysics Unit, Laboratory of Anticancer Pharmacology, Department of Oncology, Istituto di Ricerche Farmacologiche "Mario Negri," Milan, Italy

Requests for reprints: Paolo Ubezio, Istituto di Ricerche Farmacologiche "Mario Negri," Via La Masa, 19, 20156, Milan, Italy. Phone: 39-02-39014438; Fax: 39-02-39014734; E-mail: ubezio{at}marionegri.it.

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 population-based 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 G₁, S, and G₂-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]