Повышение доходности лесоохотничьих хозяйств на основе развития новых туристических услуг

The comprehensive transcriptomic analysis of clinically annotated human tissue has found widespread use in oncology, cell biology, immunology, and toxicology. In cancer research, microarray-based gene expression profiling has successfully been applied to subclassify disease entities, predict therapy response, and identify cellular mechanisms. Public accessibility of raw data, together with corresponding information on clinicopathological parameters, offers the opportunity to reuse previously analyzed data and to gain statistical power by combining multiple datasets. However, results and conclusions obviously depend on the reliability of the available information. Here, we propose gene expression-based methods for identifying sample misannotations in public transcriptomic datasets. Sample mix-up can be detected by a classifier that differentiates between samples from male and female patients. Correlation analysis identifies multiple measurements of material from the same sample. The analysis of 45 datasets (including 4913 patients) revealed that erroneous sample annotation, affecting 40 % of the analyzed datasets, may be a more widespread phenomenon than previously thought. Removal of erroneously labelled samples may influence the results of the statistical evaluation in some datasets. Our methods may help to identify individual datasets that contain numerous discrepancies and could be routinely included into the statistical analysis of clinical gene expression data

Characterization and clinical evaluation of a novel 2D detector array for conventional and flattening filter free (FFF) IMRT pre-treatment verification

Background and purpose: The novel MatriXXFFF (IBA Dosimetry, Germany) detector is a new 2D ionization chamber detector array designed for patient specific IMRT-plan verification including flattening-filter-free (FFF) beams. This study provides a detailed analysis of the characterization and clinical evaluation of the new detector array. Material and methods: The verification of the MatriXXFFF was subdivided into (i) physical dosimetric tests including dose linearity, dose rate dependency and output factor measurements and (ii) patient specific IMRT pre-treatment plan verifications. The MatriXXFFF measurements were compared to the calculated dose distribution of a commissioned treatment planning system by gamma index and dose difference evaluations for 18 IMRT-sequences. All IMRT-sequences were measured with original gantry angles and with collapsing all beams to 0° gantry angle to exclude the influence of the detector's angle dependency. Results: The MatriXXFFF was found to be linear and dose rate independent for all investigated modalities (deviations ≤0.6%). Furthermore, the output measurements of the MatriXXFFF were in very good agreement to reference measurements (deviations ≤1.8%). For the clinical evaluation an average pixel passing rate for γ(3%,3 mm) of (98.5 ± 1.5)% was achieved when applying a gantry angle correction. Also, with collapsing all beams to 0° gantry angle an excellent agreement to the calculated dose distribution was observed (γ(3%,3 mm) = (99.1 ± 1.1)%). Conclusions: The MatriXXFFF fulfills all physical requirements in terms of dosimetric accuracy. Furthermore, the evaluation of the IMRT-plan measurements showed that the detector particularly together with the gantry angle correction is a reliable device for IMRT-plan verification including FFF

Identification of sample annotation errors in gene expression datasets

The comprehensive transcriptomic analysis of clinically annotated human tissue has found widespread use in oncology, cell biology, immunology, and toxicology. In cancer research, microarray-based gene expression profiling has successfully been applied to subclassify disease entities, predict therapy response, and identify cellular mechanisms. Public accessibility of raw data, together with corresponding information on clinicopathological parameters, offers the opportunity to reuse previously analyzed data and to gain statistical power by combining multiple datasets. However, results and conclusions obviously depend on the reliability of the available information. Here, we propose gene expression-based methods for identifying sample misannotations in public transcriptomic datasets. Sample mix-up can be detected by a classifier that differentiates between samples from male and female patients. Correlation analysis identifies multiple measurements of material from the same sample. The analysis of 45 datasets (including 4913 patients) revealed that erroneous sample annotation, affecting 40 % of the analyzed datasets, may be a more widespread phenomenon than previously thought. Removal of erroneously labelled samples may influence the results of the statistical evaluation in some datasets. Our methods may help to identify individual datasets that contain numerous discrepancies and could be routinely included into the statistical analysis of clinical gene expression data

Interferon-inducible guanylate binding protein (GBP2) is associated with better prognosis in breast cancer and indicates an efficient T cell response.

BACKGROUND: Recently, interferon-inducible guanylate binding protein (GBP2) has been discussed as a possible control factor in tumor development, which is controlled by p53, and inhibits NF-Kappa B and Rac protein as well as expression of matrix metalloproteinase 9. However, the potential role that GBP2 plays in tumor development and prognosis has not yet been studied. METHODS: We analyzed whether GBP2 mRNA levels are associated with metastasis-free interval in 766 patients with node negative breast carcinomas who did not receive systemic chemotherapy. Furthermore, response to anthracycline-based chemotherapy was studied in 768 breast cancer patients. RESULTS: High expression of GBP2 in breast carcinomas was associated with better prognosis in the univariate (P < 0.001, hazard ratio 0.763, 95 % CI 0.650-0.896) as well as in the multivariate Cox analysis (P = 0.008, hazard ratio 0.731, 95 % CI 0.580-0.920) adjusted to the established clinical factors age, pT stage, grading, hormone and ERBB2 receptor status. The association was particularly strong in subgroups with high proliferation and positive estrogen receptor status but did not reach significance in carcinomas with low expression of proliferation associated genes. Besides its prognostic capacity, GBP2 also predicted pathologically complete response to anthracycline-based chemotherapy (P = 0.0037, odds ratio 1.39, 95 % CI 1.11-1.74). Interestingly, GBP2 correlated with a recently established T cell signature, indicating tumor infiltration with T cells (R = 0.607, P < 0.001). CONCLUSION: GBP2 is associated with better prognosis in fast proliferating tumors and probably represents a marker of an efficient T cell response

Surface guidance compared with ultrasound-based monitoring and diaphragm position in cone-beam computed tomography during abdominal stereotactic radiotherapy in breath-hold

Background and purpose: Spirometry induced deep-inspiration-breath-hold (DIBH) reduces intrafractional motion during upper abdominal stereotactic body radiotherapy (SBRT). The aim of this prospective study was to evaluate whether surface scanning (SGRT) is an adequate surrogate for monitoring residual internal motion during DIBH. Residual motion detected by SGRT was compared with experimental 4D-ultrasound (US) and an internal motion detection benchmark (diaphragm-dome-position in kV cone-beam computed tomography (CBCT) projections). Materials and methods: Intrafractional monitoring was performed with SGRT and US in 460 DIBHs of 12 patients. Residual motion detected by all modalities (SGRT (anterior-posterior (AP)), US (AP, craniocaudal (CC)) and CBCT (CC)) was analyzed. Agreement analysis included Wilcoxon signed rank test, Maloney and Rastogi’s test, Pearson’s correlation coefficient (PCC) and interclass correlation coefficient (ICC). Results: Interquartile range was 0.7 mm (US(AP)), 0.8 mm (US(CC)), 0.9 mm (SGRT) and 0.8 mm (CBCT). SGRT(AP) vs. CBCT(CC) and US(CC) vs. CBCT(CC) showed comparable agreement (PCCs 0.53 and 0.52, ICCs 0.51 and 0.49) with slightly higher precision of CBCT(CC). Most agreement was observed for SGRT(AP) vs. US(AP) with largest PCC (0.61) and ICC (0.60), least agreement for SGRT(AP) vs. US(CC) with smallest PCC (0.44) and ICC (0.42). Conclusions: Residual motion detected during spirometry induced DIBH is small. SGRT alone is no sufficient surrogate for residual internal motion in all patients as some high velocity motion could not be detected. Observed patient-specific residual errors may require individualized PTV-margins

Aberrantly activated claudin 6 and 18.2 as potential therapy targets in non-small-cell lung cancer

Claudins (CLDNs) are central components of tight junctions that regulate epithelial-cell barrier function and polarity. Altered CLDN expression patterns have been demonstrated in numerous cancer types and lineage-specific CLDNs have been proposed as therapy targets. The objective of this study was to assess which fraction of patients with non-small-cell lung cancer (NSCLC) express CLDN6 and CLDN18 isoform 2 (CLDN18.2). Protein expression of CLDN6 and CLDN18.2 was examined by immunohistochemistry on a tissue microarray (n=355) and transcript levels were supportively determined based on gene expression microarray data from fresh-frozen NSCLC tissues (n=196). Both were analyzed with regard to frequency, distribution and association with clinical parameters. Immunohistochemical analysis of tissue sections revealed distinct membranous positivity of CLDN6 (6.5%) and CLDN18.2 (3.7%) proteins in virtually non-overlapping subgroups of adenocarcinomas and large-cell carcinomas. Pneumocytes and bronchial epithelial cells were consistently negative. Corresponding to the protein expression, in subsets of non-squamous lung carcinoma high mRNA levels of CLDN6 (7-16%) and total CLDN18 (5-12%) were observed. Protein expression correlated well with total mRNA expression of the corresponding gene (rho=0.4-0.8). CLDN18.2 positive tumors were enriched among slowly proliferating, thyroid transcription factor 1 (TTF-1)-negative adenocarcinomas, suggesting that isoform-specific CLDN expression may delineate a specific subtype. Noteworthy, high CLDN6 protein expression was associated with worse prognosis in lung adenocarcinoma in the univariate [hazard ratio (HR): 1.8; p=0.03] and multivariate COX regression model (HR: 1.9; p=0.02). These findings encourage further clinical exploration of targeting ectopically activated CLDN expression as a valuable treatment concept in NSCLC