In vivo measurement of vascular modulation in experimental tumors using a fluorescent contrast agent.

We compared the effectiveness of three optical techniques based on fluorescence imaging and spectroscopy with indocyanine green (ICG) contrast agent to evaluate in vivo the disruption of the active vasculature induced by a vascular targeting agent. The blood perfusion of the MDA-MB-435 tumor model transplanted in nude mice was estimated from the signal of the contrast agent measured immediately after its systemic injection in mice. Optical measurements were performed using a fluorescence imaging setup and a fiber-based time correlated single photon counting (TCSPC) apparatus. This latter apparatus was used to measure the tumor fluorescence in transmittance geometry and the change in the basal optical absorption induced by the contrast agent, thus providing an alternative estimation of the blood content in the tumor. Mice were divided into four groups. Three groups were treated with different doses of the vascular disrupting agent ZD6126, the fourth group (control group) received the drug vehicle only. Optical measurements were carried out 3 h after pharmacologic treatment. After 24 h, mice were killed, tumors were excised and the extent of necrosis was evaluated with standard histologic analysis. On fluorescence imaging ICG emission from tumors of mice treated with ZD6126 significantly was lower compared with the emission from control mice. The histologic sections also showed a significantly higher amount of necrosis in tumors of treated mice. Both these findings, which correlate with each other, indicate an effective vascular shutdown induced by the drug. However, ICG fluorescence measured with the TCSPC apparatus in transmittance geometry and the estimate of the change in optical absorption did not allow a statistically significant differentiation between treated and control groups.

Interpreting cell cycle effects of drugs: the case of melphalan.

Multiple effects usually occur in the cell cycle, during and after the exposure to a drug, while treated cells flowing through the cycle encounter G1, S and G2M checkpoints. We developed a simulation tool connecting the microscopic level of the cellular response in G1, S and G2M with the experimental data of growth inhibition and flow cytometry. We found that multiple-often not intuitive-combinations of cytostatic and cytotoxic effects can be in keeping with the observations. This multiplicity of interpretation can be strongly reduced by considering together data with different methods, ideally reaching a reconstruction of the underlying cell cycle perturbations. Here, we propose an experimental plan including a time course of DNA flow cytometry and absolute cell count measurements with several drug concentrations and a limited number of flow cytometric DNA-Bromodeoxyuridine and TUNEL analyses, coupled with computer simulation. We showed its use in the attempt to define the complete time course of the effects of melphalan on three cancer cell lines. After drug treatment, each subset of cells experienced blocks and lethality in all phases of the cell cycle, but the dynamics was different, the differences being strongly dose-dependent. Our approach allows a better appreciation of the complexity of the cell cycle phenomena associated with drug treatment. It is expected that such level of understanding of the time- and dose-dependence of the cytostatic and cytotoxic effects of a drug might support rational therapeutic design.

Quantitative assessment of the complex dynamics of G1, S, and G2-M checkpoint activities.

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 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.

The contribution of p53 in the dynamics of cell cycle response to DNA damage interpreted by a mathematical model.

Despite numerous studies on the tumor suppressor p53, a complete picture of its role in cell arrest and killing in G(1), S and G(2)M phases after drug treatment is lacking. We tackled the analysis of the complexity of cell cycle effects combining the time-course measures with different techniques with the aid of a computer program simulating cell cycle progression. This mixed experimental-simulation approach enabled us to decode the dynamics of the cytostatic and cytotoxic responses to cisplatin and doxorubicin treatments in a p53--proficient colon carcinoma cell line (HCT-116) and in its p53-deficient counterpart. We achieved a separate evaluation of the activity of each cell cycle control and we connected these results with measures of p53 level in G(1), S and G(2)M. We confirmed the action of p53 in all cell cycle phases, but also the presence of strong p53-independent cytostatic and cytotoxic activities exerted by both drugs. In G(1) phase, p53 was responsible for a medium/long term block, distinct from the short-term block, which was p53-independent. The delay in traversing S phase was reduced by the presence of p53. In G(2)M phase, despite a strong p53-independent block, there was a weaker but more persistent p53-dependent block. At cytotoxic concentrations, p53-dependent and p53-independent cell death was observed. The former was poorly phase-specific, occurred earlier and exploited the apoptotic mechanism more than p53-independent death. Computer simulation produced a framework where previous partial and sometimes apparently contradictory observations of the p53-mediated effects could be reconciled and explained.