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PET and MRI Guided Irradiation of a Glioblastoma Rat Model Using a Micro-irradiator
Published on: December 28, 2017
Spatially fractionated x-rays integrated with live-cell microscopy to study early responses to minibeam radiation
Romain Pinon1,2, Arnaud Quentel1,2, Maria Oval3
1Laboratoire InGenO, Institut de Cancérologie de l'Ouest, F-49055 Angers, France.
Abstract:
Objective.Spatially fractionated radiotherapy (SFRT) reduces normal tissue toxicity while retaining antitumor effects in preclinical models. A challenge to clinical translation is the multitude of dosimetric factors involved. Our objective was to develop a flexible system combining spatial x-ray fractionation and live-cell microscopy to study early SFRT cellular effects, which are determinant for therapeutic outcomes.Approach.A miniature 50 kV x-ray source was mounted on an inverted microscope, characterized, modeled by Monte Carlo (MC) simulations, and collimated to produce a range of planar minibeams and isolateral spot mini-GRID geometries. Spatial patterns of p53 activation and DNA double-strand break (DSB) induction were detected in cell cultures by immunostaining. The dynamics of DSB induction and cell proliferation were captured with live-cell imaging.Results.The MC model accurately predicted percent depth dose in RW3 phantoms and in a biological matrix. Separation of peaks and valleys was maintained over a depth of 7.5 mm. Peak-to-valley dose ratios (PVDRs) were 22.7, 12.4, and 2.3 for 1, 0.5, and 0.3 mm minibeams, respectively. For 1 mm spot mini-GRID lattices with 30% and 50% extruded surfaces, PVDRs were 23.4 and 8.2. Phosphorylation of p53 and amounts of DSB foci labeled withγH2AX tightly matched dose peaks and valleys for all collimation geometries. Cells in peaks and valleys showed distinct kinetics of DSB foci accumulation, and spatial effects on cell proliferation were observed.Significance.We developed a method to evaluate SFRT parameters on cellular outcomes in preclinicalin situmodels. The approach enables rapid exploration of dosimetric parameters and an exquisite temporal resolution for live-cell analyses. We anticipate that this approach will yield a better mechanistic understanding of SFRT for parameter optimization.
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