Survivin regulation by HER2 through NF-κB and c-myc in irradiated breast cancer cells.

Radiotherapy is an important treatment modality against cancer resulting in apoptosis and inhibition of cell growth. Survivin is an important cancer biomarker conferring to tumour cells increased survival potential by inhibiting apoptosis. In the present study, we investigated the implication of breast cancer cells features, as hormone receptors and p53 status, in the radio-resistance of breast cancer cells and in the regulation of survivin's expression by nuclear factor (NF)-κB and c-myc. Six breast cancer cell lines Michigan Cancer Foundation (MCF-7), MCF-7/Human Epidermal Growth Factor Receptor (HER)2, M. D. Anderson - Metastatic Breast (MDA-MB-231), SK-BR-3, BT-474 and Human Breast Lactating (HBL-100) were irradiated and cell viability as well as cell cycle distribution were evaluated by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and flow cytometry, respectively. Survivin mRNA and protein levels were evaluated by real time PCR and Western blot analysis. Survivin and HER2 gene knockdown was performed with siRNA technology and investigation of transcription factors binding to survivin and c-myc gene promoters was assessed by chromatin immunoprecipitation. Student's t-test and F-statistics were used for statistical evaluation. Our results demonstrated that only HER2(+) breast cancer cells up-regulated survivin upon irradiation, whereas HER2 knockdown in HER2(+) cells led to survivin's down-regulation. Survivin and especially HER2 knockdown abolished the observed G2/M cell cycle checkpoint and reduced the radio-resistance of HER2 overexpressing breast cancer cells. Additionally, HER2 was found to regulate survivin's expression through NF-κB and c-myc transcription factors. This study revealed the significance of HER2 in the radio-resistance of HER2(+) breast cancer cells through induction of transcription factors NF-κB and c-myc, leading to activation of survivin, a downstream target oncogene preventing apoptosis.

On the use of published radiobiological parameters and the evaluation of NTCP models regarding lung pneumonitis in clinical breast radiotherapy.

In this study we sought to evaluate and accent the importance of radiobiological parameter selection and implementation to the normal tissue complication probability (NTCP) models. The relative seriality (RS) and the Lyman-Kutcher-Burman (LKB) models were studied. For each model, a minimum and maximum set of radiobiological parameter sets was selected from the overall published sets applied in literature and a theoretical mean parameter set was computed. In order to investigate the potential model weaknesses in NTCP estimation and to point out the correct use of model parameters, these sets were used as input to the RS and the LKB model, estimating radiation induced complications for a group of 36 breast cancer patients treated with radiotherapy. The clinical endpoint examined was Radiation Pneumonitis. Each model was represented by a certain dose-response range when the selected parameter sets were applied. Comparing the models with their ranges, a large area of coincidence was revealed. If the parameter uncertainties (standard deviation) are included in the models, their area of coincidence might be enlarged, constraining even greater their predictive ability. The selection of the proper radiobiological parameter set for a given clinical endpoint is crucial. Published parameter values are not definite but should be accompanied by uncertainties, and one should be very careful when applying them to the NTCP models. Correct selection and proper implementation of published parameters provides a quite accurate fit of the NTCP models to the considered endpoint.

Correlation between radiation-induced telomerase activity and human telomerase reverse transcriptase mRNA expression in HeLa cells.

PURPOSE: To quantify and correlate human telomerase reverse transcriptase (hTERT) mRNA expression with telomerase activity (TA) after ionizing irradiation of HeLa cells. MATERIALS AND METHODS: TA and hTERT mRNA expression were evaluated, at 24-h intervals, in HeLa cells cultured for up to 144 h, before and after treatment with increasing doses of 6 MV photon ionizing radiation (5 - 20 Gy), using the telomeric repeat amplification protocol (TRAP) assay and real-time reverse transcriptase polymerase chain reaction (RT-PCR), respectively. Cell viability was determined using the 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. A prototype phantom was constructed for accurate irradiation of HeLa cells. RESULTS: Treated cells showed a decrease in viability with increasing radiation dose, and a correlation was observed with post-treatment period. TA and hTERT mRNA expression of HeLa cells increased for the first 24 h after irradiation. The maximal increases were approximately two times the un-irradiated cell levels at 24 h post-irradiation, followed by a decrease and a return to the control levels 72 h post-irradiation. The time-course of telomerase activation after 24 h, differed among radiation doses. A dose-dependent G2/M arrest was observed 24 h post-irradiation, along with an increase in polyploidy 48 h post-irradiation and afterwards. CONCLUSION: A correlation between TA and hTERT mRNA expression and a radiation induced cell cycle dependent modification of hTERT mRNA expression was established for the first 24 h post-irradiation.

Prospective PET image quality gain calculation method by optimizing detector parameters.

BACKGROUND: Lutetium-based scintillators with high-performance electronics introduced time-of-flight (TOF) reconstruction in the clinical setting. Let G' be the total signal to noise ratio gain in a reconstructed image using the TOF kernel compared with conventional reconstruction modes. G' is then the product of G1 gain arising from the reconstruction process itself and (n-1) other gain factors (G2, G3, … Gn) arising from the inherent properties of the detector. METHODS: We calculated G2 and G3 gains resulting from the optimization of the coincidence and energy window width for prompts and singles, respectively. Both quantitative and image-based validated Monte Carlo models of Lu2SiO5 (LSO) TOF-permitting and Bi4Ge3O12 (BGO) TOF-nonpermitting detectors were used for the calculations. RESULTS: G2 and G3 values were 1.05 and 1.08 for the BGO detector and G3 was 1.07 for the LSO. A value of almost unity for G2 of the LSO detector indicated a nonsignificant optimization by altering the energy window setting. G' was found to be ∼1.4 times higher for the TOF-permitting detector after reconstruction and optimization of the coincidence and energy windows. CONCLUSION: The method described could potentially predict image noise variations by altering detector acquisition parameters. It could also further contribute toward a long-lasting debate related to cost-efficiency issues of TOF scanners versus the non-TOF ones. Some vendors re-engage nowadays to non-TOF product line designs in an effort to reduce crystal costs. Therefore, exploring the limits of image quality gain by altering the parameters of these detectors remains a topical issue.

Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data.

INTRODUCTION: A clinical decision support system (CDSS) for brain tumor classification can be used to assist in the diagnosis and grading of brain tumors. A Fast Spectroscopic Multiple Analysis (FASMA) system that uses combinations of multiparametric MRI data sets was developed as a CDSS for brain tumor classification. METHODS: MRI metabolic ratios and spectra, from long and short TE, respectively, as well as diffusion and perfusion data were acquired from the intratumoral and peritumoral area of 126 patients with untreated intracranial tumors. These data were categorized based on the pathology, and different machine learning methods were evaluated regarding their classification performance for glioma grading and differentiation of infiltrating versus non-infiltrating lesions. Additional databases were embedded to the system, including updated literature values of the related MR parameters and typical tumor characteristics (imaging and histological), for further comparisons. Custom Graphical User Interface (GUI) layouts were developed to facilitate classification of the unknown cases based on the user's available MR data. RESULTS: The highest classification performance was achieved with a support vector machine (SVM) using the combination of all MR features. FASMA correctly classified 89 and 79% in the intratumoral and peritumoral area, respectively, for cases from an independent test set. FASMA produced the correct diagnosis, even in the misclassified cases, since discrimination between infiltrative versus non-infiltrative cases was possible. CONCLUSIONS: FASMA is a prototype CDSS, which integrates complex quantitative MR data for brain tumor characterization. FASMA was developed as a diagnostic assistant that provides fast analysis, representation and classification for a set of MR parameters. This software may serve as a teaching tool on advanced MRI techniques, as it incorporates additional information regarding typical tumor characteristics derived from the literature.