No abstract

Autophagy has been reported to be increased in irradiated cancer cells resistant to various apoptotic stimuli. We therefore hypothesized that induction of autophagy via mTOR inhibition could enhance radiosensitization in apoptosis-inhibited H460 lung cancer cells in vitro and in a lung cancer xenograft model. To test this hypothesis, combinations of Z-DEVD (caspase-3 inhibitor), RAD001 (mTOR inhibitor) and irradiation were tested in cell and mouse models. The combination of Z-DEVD and RAD001 more potently radiosensitized H460 cells than individual treatment alone. The enhancement in radiation response was not only evident in clonogenic survival assays, but also was demonstrated through markedly reduced tumor growth, cellular proliferation (Ki67 staining), apoptosis (TUNEL staining) and angiogenesis (vWF staining) in vivo. Additionally, upregulation of autophagy as measured by increased GFP-LC3-tagged autophagosome formation accompanied the noted radiosensitization in vitro and in vivo. The greatest induction of autophagy and associated radiation toxicity was exhibited in the tri-modality treatment group. Autophagy marker, LC-3-II, was reduced by 3-methyladenine (3-MA), a known inhibitor of autophagy, but further increased by the addition of lysosomal protease inhibitors (pepstatin A and E64d), demonstrating that there is autophagic induction through type III PI3 kinase during the combined therapy. Knocking down of ATG5 and beclin-1, two essential autophagic molecules, resulted in radiation resistance of lung cancer cells. Our report suggests that combined inhibition of apoptosis and mTOR during radiotherapy is a potential therapeutic strategy to enhance radiation therapy in patients with non-small cell lung cancer.

The purpose of this work was to characterize expert variation in segmentation of intracranial structures pertinent to radiation therapy, and to assess a registration-driven atlas-based segmentation algorithm in that context. Eight experts were recruited to segment the brainstem, optic chiasm, optic nerves, and eyes, of 20 patients who underwent therapy for large space-occupying tumors. Performance variability was assessed through three geometric measures: volume, Dice similarity coefficient, and Euclidean distance. In addition, two simulated ground truth segmentations were calculated via the simultaneous truth and performance level estimation (STAPLE) algorithm and a novel application of probability maps. The experts and automatic system were found to generate structures of similar volume, though the experts exhibited higher variation with respect to tubular structures. No difference was found between the mean Dice coefficient (DSC) of the automatic and expert delineations as a group at a 5% significance level over all cases and organs. The larger structures of the brainstem and eyes exhibited mean DSC of approximately 0.8–0.9, whereas the tubular chiasm and nerves were lower, approximately 0.4–0.5. Similarly low DSC have been reported previously without the context of several experts and patient volumes. This study, however, provides evidence that experts are similarly challenged. The average maximum distances (maximum inside, maximum outside) from a simulated ground truth ranged from (−4.3, +5.4) mm for the automatic system to (−3.9, +7.5) mm for the experts considered as a group. Over all the structures in a rank of true positive rates at a 2 mm threshold from the simulated ground truth, the automatic system ranked second of the nine raters. This work underscores the need for large scale studies utilizing statistically robust numbers of patients and experts in evaluating quality of automatic algorithms.