Epigenetics in radiotherapy: Where are we heading?

Radiotherapy is an important component of anti-cancer treatment. However, not all cancer patients respond to radiotherapy, and with current knowledge clinicians are unable to predict which patients are at high risk of recurrence after radiotherapy. There is therefore an urgent need for biomarkers to guide clinical decision-making. Although the importance of epigenetic alterations is widely accepted, their application as biomarkers in radiotherapy has not been studied extensively. In addition, it has been suggested that radiotherapy itself introduces epigenetic alterations. As epigenetic alterations can potentially be reversed by drug treatment, they are interesting candidate targets for anticancer therapy or radiotherapy sensitizers. The application of demethylating drugs or histone deacetylase inhibitors to sensitize patients for radiotherapy has been studied in vitro, in vivo as well as in clinical trials with promising results. This review describes the current knowledge on epigenetics in radiotherapy

Hypoxia promotes stem cell phenotypes and poor prognosis through epigenetic regulation of DICER

MicroRNAs are small regulatory RNAs that post-transcriptionally control gene expression. Reduced expression of DICER, the enzyme involved in microRNA processing, is frequently observed in cancer and is associated with poor clinical outcome in various malignancies. Yet the underlying mechanisms are not well understood. Here, we identify tumor hypoxia as a regulator of DICER expression in large cohorts of breast cancer patients. We show that DICER expression is suppressed by hypoxia through an epigenetic mechanism that involves inhibition of oxygen-dependent H3K27me3 demethylases KDM6A/B and results in silencing of the DICER promoter. Subsequently, reduced miRNA processing leads to derepression of the miR-200 target ZEB1, stimulates the epithelial to mesenchymal transition and ultimately results in the acquisition of stem cell phenotypes in human mammary epithelial cells. Our study uncovers a previously unknown relationship between oxygen-sensitive epigenetic regulators, miRNA biogenesis and tumor stem cell phenotypes that may underlie poor outcome in breast cancer

A Simple but Highly Effective Approach to Evaluate the Prognostic Performance of Gene Expression Signatures

BACKGROUND: Highly parallel analysis of gene expression has recently been used to identify gene sets or 'signatures' to improve patient diagnosis and risk stratification. Once a signature is generated, traditional statistical testing is used to evaluate its prognostic performance. However, due to the dimensionality of microarrays, this can lead to false interpretation of these signatures. PRINCIPAL FINDINGS: A method was developed to test batches of a user-specified number of randomly chosen signatures in patient microarray datasets. The percentage of random generated signatures yielding prognostic value was assessed using ROC analysis by calculating the area under the curve (AUC) in six public available cancer patient microarray datasets. We found that a signature consisting of randomly selected genes has an average 10% chance of reaching significance when assessed in a single dataset, but can range from 1% to ∼40% depending on the dataset in question. Increasing the number of validation datasets markedly reduces this number. CONCLUSIONS: We have shown that the use of an arbitrary cut-off value for evaluation of signature significance is not suitable for this type of research, but should be defined for each dataset separately. Our method can be used to establish and evaluate signature performance of any derived gene signature in a dataset by comparing its performance to thousands of randomly generated signatures. It will be of most interest for cases where few data are available and testing in multiple datasets is limited

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

REST is a hypoxia-responsive transcriptional repressor

Cellular exposure to hypoxia results in altered gene expression in a range of physiologic and pathophysiologic states. Discrete cohorts of genes can be either up- or down-regulated in response to hypoxia. While the Hypoxia-Inducible Factor (HIF) is the primary driver of hypoxia-induced adaptive gene expression, less is known about the signalling mechanisms regulating hypoxia-dependent gene repression. Using RNA-seq, we demonstrate that equivalent numbers of genes are induced and repressed in human embryonic kidney (HEK293) cells. We demonstrate that nuclear localization of the Repressor Element 1-Silencing Transcription factor (REST) is induced in hypoxia and that REST is responsible for regulating approximately 20% of the hypoxia-repressed genes. Using chromatin immunoprecipitation assays we demonstrate that REST-dependent gene repression is at least in part mediated by direct binding to the promoters of target genes. Based on these data, we propose that REST is a key mediator of gene repression in hypoxia

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS

Efficiency Effects of Cross-Border Medical Demand

There is a growing interest in cross-border medical care and its comparative advantages. In addition, medical care can be defined as a local assurance good. Little research is being carried out in this field. This paper discusses the individual considerations for medical treatment offered at home and abroad within a micro-economical framework. Specific assumptions as mistrust, monetary and non-monetary transaction-costs, a price and cost gradient, illness severity as well as a lump-sum insurance are discussed. We show that a demand abroad can be utility maximizing, however, only second best. There are inefficiencies in the dimensions of ex-post demand and income risk either on the side of gross-income or of costs. Furthermore, the foreign demand is restricted for low health stages driven by mistrust and restrictions in quality. Higher stages are more capable if fixed costs are low. To demand abroad the marginal treatment costs abroad must fall short of a threshold level. Finally, an out-of-pocket payment can reduce the moral hazard when treatment takes place abroad