flunarizine and Fibrosarcoma

T2*-weighted gradient-echo magnetic resonance imaging (T2*-weighted GRE MRI) was used to investigate spontaneous fluctuations in tumour vasculature non-invasively. FSa fibrosarcomas, implanted intramuscularly (i.m.) in the legs of mice, were imaged at 4.7 T, over a 30 min or 1 h sampling period. On a voxel-by-voxel basis, time courses of signal intensity were analysed using a power spectrum density (PSD) analysis to isolate voxels for which signal changes did not originate from Gaussian white noise or linear drift. Under baseline conditions, the tumours exhibited spontaneous signal fluctuations showing spatial and temporal heterogeneity over the tumour. Statistically significant fluctuations occurred at frequencies ranging from 1 cycle/3 min to 1 cycle/h. The fluctuations were independent of the scanner instabilities. Two categories of signal fluctuations were reported: (i) true fluctuations (TFV), i.e., sequential signal increase and decrease, and (ii) profound drop in signal intensity with no apparent signal recovery (SDV). No temporal correlation between tumour and contralateral muscle fluctuations was observed. Furthermore, treatments aimed at decreasing perfusion-limited hypoxia, such as carbogen combined with nicotinamide and flunarizine, decreased the incidence of tumour T2*-weighted GRE fluctuations. We also tracked dynamic changes in T2* using multiple GRE imaging. Fluctuations of T2* were observed; however, fluctuation maps using PSD analysis could not be generated reliably. An echo-time dependency of the signal fluctuations was observed, which is typical to physiological noise. Finally, at the end of T2*-weighted GRE MRI acquisition, a dynamic contrast-enhanced MRI was performed to characterize the microenvironment in which tumour signal fluctuations occurred in terms of vessel functionality, vascularity and microvascular permeability. Our data showed that TFV were predominantly located in regions with functional vessels, whereas SDV occurred in regions with no contrast enhancement as the result of vessel functional impairment. Furthermore, transient fluctuations appeared to occur preferentially in neoangiogenic hyperpermeable vessels. The present study suggests that spontaneous T2*-weighted GRE fluctuations are very likely to be related to the spontaneous fluctuations in blood flow and oxygenation associated with the pathophysiology of acute hypoxia in tumours. The disadvantage of the T2*-weighted GRE MRI technique is the complexity of signal

Topics: Animals; Biomarkers, Tumor; Carbon Dioxide; Cell Hypoxia; Cell Line, Tumor; Computer Simulation; Fibrosarcoma; Flunarizine; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Male; Mice; Mice, Inbred C3H; Models, Biological; Niacinamide; Oxygen; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Stochastic Processes

BOLD-contrast functional MRI (fMRI) has been used to assess the evolution of tumor oxygenation and blood flow after treatment. The aim of this study was to evaluate K-means-based cluster analysis as a exploratory, data-driven method. The advantage of this approach is that it can be used to extract information without the need for prior knowledge concerning the hemodynamic response function. Two data sets were acquired to illustrate different types of BOLD fMRI response inside tumors: the first set following a respiratory challenge with carbogen, and the second after pharmacological modulation of tumor blood flow using flunarizine. To improve the efficiency of the clustering, a power density spectrum analysis was first used to isolate voxels for which signal changes did not originate from noise or linear drift. The technique presented here can be used to assess hemodynamic response to treatment, and especially to display areas of the tumor with heterogeneous responses.

Topics: Animals; Calcium Channel Blockers; Cluster Analysis; Data Interpretation, Statistical; Fibrosarcoma; Flunarizine; Hindlimb; Magnetic Resonance Imaging; Male; Mice; Oxygen Consumption; Regional Blood Flow

Verapamil had previously been shown to increase cellular melphalan uptake and cytotoxicity in fibrosarcomas, and increased the area under the blood concentration versus time curve (AUC) for melphalan in CBA mice. Verapamil (10 mg kg-1 i.p.) had no effect on the fractional distribution of cardiac output (FDCO), measured with 86Rb-rubidium chloride, to subcutaneous fibrosarcomas. 14C-Melphalan uptake by FS13 fibrosarcomas was increased 60 min after verapamil (10 mg kg-1 i.p.), but not after lower doses which did not affect the AUC. Flunarizine (5 mg kg-1 i.p.) also had no effect on FDCO to FS13 fibrosarcomas, and tended to increase 14C-melphalan content of blood and the fibrosarcomas and to promote growth delay by melphalan. Alcohol increased FDCO to FS13 fibrosarcomas, maximally at a 1:20 dilution in saline, but had no effect on 14C-melphalan uptake or growth delay. Thus, melphalan cytotoxicity correlated with tumour melphalan uptake, and both followed changes in the AUC for melphalan but not changes in FDCO. In these murine fibrosarcomas melphalan uptake and cytotoxicity were not limited by blood flow. In subcutaneous human melanoma HX46 xenografts, verapamil had no effect on the FDCO, nor on 14C-melphalan uptake, and did not affect blood 14C-melphalan levels, suggesting absence of effects on the AUC and on cellular uptake. Alcohol did not increase the FDCO to HX46 xenografts, providing evidence for a different vascular supply.

Topics: Animals; Cinnarizine; Drug Synergism; Ethanol; Female; Fibrosarcoma; Flunarizine; Humans; Male; Melanoma; Melphalan; Mice; Mice, Inbred CBA; Regional Blood Flow; Verapamil