Prostate cancers that progress during androgen-deprivation therapy often overexpress the androgen receptor (AR) and depend on AR signaling for growth. In most cases, increased AR expression occurs ...without gene amplification and may be due to altered transcriptional regulation. The transcription factor nuclear factor (NF)-kappaB, which is implicated in tumorigenesis, functions as an important downstream substrate of mitogen-activated protein kinase, phosphatidylinositol 3-kinase, AKT, and protein kinase C and plays a role in other cancer-associated signaling pathways. NF-kappaB is an important determinant of prostate cancer clinical biology, and therefore we investigated its role in the regulation of AR expression. We found that NF-kappaB expression in prostate cancer cells significantly increased AR mRNA and protein levels, AR transactivation activity, serum prostate-specific antigen levels, and cell proliferation. NF-kappaB inhibitors decrease AR expression levels, prostate-specific antigen secretion, and proliferation of prostate cancer cells in vitro. Furthermore, inhibitors of NF-kappaB demonstrated anti-tumor activity in androgen deprivation-resistant prostate cancer xenografts. In addition, levels of both NF-kappaB and AR were strongly correlated in human prostate cancer. Our data suggest that NF-kappaB can regulate AR expression in prostate cancer and that NF-kappaB inhibitors may have therapeutic potential.
Glioblastoma (GBM) is one of the most common primary brain tumours in adults, with a dismal prognosis despite aggressive multimodality treatment by a combination of surgery and adjuvant ...radiochemotherapy. A detailed knowledge of the spreading of glioma cells in the brain might allow for more targeted escalated radiotherapy, aiming to reduce locoregional relapse. Recent years have seen the development of a large variety of mathematical modelling approaches to predict glioma migration.
The aim of this study is hence to evaluate the clinical applicability of a detailed micro- and meso-scale mathematical model in radiotherapy. First and foremost, a clinical workflow is established, in which the tumour is automatically segmented as input data and then followed in time mathematically based on the diffusion tensor imaging data. The influence of several free model parameters is individually evaluated, then the full model is retrospectively validated for a collective of 3 GBM patients treated at our institution by varying the most important model parameters to achieve optimum agreement with the tumour development during follow-up. Agreement of the model predictions with the real tumour growth as defined by manual contouring based on the follow-up MRI images is analyzed using the dice coefficient.
The tumour evolution over 103-212 days follow-up could be predicted by the model with a dice coefficient better than 60% for all three patients. In all cases, the final tumour volume was overestimated by the model by a factor between 1.05 and 1.47.
To evaluate the quality of the agreement between the model predictions and the ground truth, we must keep in mind that our gold standard relies on a single observer's (CB) manually-delineated tumour contours. We therefore decided to add a short validation of the stability and reliability of these contours by an inter-observer analysis including three other experienced radiation oncologists from our department. In total, a dice coefficient between 63% and 89% is achieved between the four different observers. Compared with this value, the model predictions (62-66%) perform reasonably well, given the fact that these tumour volumes were created based on the pre-operative segmentation and DTI.
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