Prognostic value of the 6-gene OncoMasTR test in hormone receptor–positive HER2-negative early-stage breast cancer: Comparative analysis with standard clinicopathological factors

Aim: The aim of the study was to assess the prognostic performance of a 6-gene molecular score (OncoMasTR Molecular Score [OMm]) and a composite risk score (OncoMasTR Risk Score [OM]) and to conduct a within-patient comparison against four routinely used molecular and clinicopathological risk assessment tools: Oncotype DX Recurrence Score, Ki67, Nottingham Prognostic Index and Clinical Risk Category, based on the modified Adjuvant! Online definition and three risk factors: patient age, tumour size and grade. Methods: Biospecimens and clinicopathological information for 404 Irish women also previously enrolled in the Trial Assigning Individualized Options for Treatment [Rx] were provided by 11 participating hospitals, as the primary objective of an independent translational study. Gene expression measured via RT-qPCR was used to calculate OMm and OM. The prognostic value for distant recurrence-free survival (DRFS) and invasive disease-free survival (IDFS) was assessed using Cox proportional hazards models and Kaplan-Meier analysis. All statistical tests were two-sided ones. Results: OMm and OM (both with likelihood ratio statistic [LRS] P Discussion: Both OncoMasTR scores were significantly prognostic for DRFS and IDFS and provided additional prognostic information to the molecular and clinicopathological risk factors/tools assessed. OM was also the most accurate risk classification tool for identifying DR. A concise 6-gene signature with superior risk stratification was shown to increase prognosis reliability, which may help clinicians optimise treatment decisions. Trial registration: ClinicalTrials.gov NCT02050750 NCT00310180.</p

The critical role of ERK in death resistance and invasiveness of hypoxia-selected glioblastoma cells

<p>Abstract</p> <p>Background</p> <p>The rapid growth of tumor parenchyma leads to chronic hypoxia that can result in the selection of cancer cells with a more aggressive behavior and death-resistant potential to survive and proliferate. Thus, identifying the key molecules and molecular mechanisms responsible for the phenotypic changes associated with chronic hypoxia has valuable implications for the development of a therapeutic modality. The aim of this study was to identify the molecular basis of the phenotypic changes triggered by chronic repeated hypoxia.</p> <p>Methods</p> <p>Hypoxia-resistant T98G (HRT98G) cells were selected by repeated exposure to hypoxia and reoxygenation. Cell death rate was determined by the trypan blue exclusion method and protein expression levels were examined by western blot analysis. The invasive phenotype of the tumor cells was determined by the Matrigel invasion assay. Immunohistochemistry was performed to analyze the expression of proteins in the brain tumor samples. The Student T-test and Pearson Chi-Square test was used for statistical analyses.</p> <p>Results</p> <p>We demonstrate that chronic repeated hypoxic exposures cause T98G cells to survive low oxygen tension. As compared with parent cells, hypoxia-selected T98G cells not only express higher levels of anti-apoptotic proteins such as Bcl-2, Bcl-X_L, and phosphorylated ERK, but they also have a more invasive potential in Matrigel invasion chambers. Activation or suppression of ERK pathways with a specific activator or inhibitor, respectively, indicates that ERK is a key molecule responsible for death resistance under hypoxic conditions and a more invasive phenotype. Finally, we show that the activation of ERK is more prominent in malignant glioblastomas exposed to hypoxia than in low grade astrocytic glial tumors.</p> <p>Conclusion</p> <p>Our study suggests that activation of ERK plays a pivotal role in death resistance under chronic hypoxia and phenotypic changes related to the invasive phenotype of HRT98G cells compared to parent cells.</p

Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets

Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state “signature”. These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities

What's that gene (or protein)? Online resources for exploring functions of genes, transcripts, and proteins

The genomic era has enabled research projects that use approaches including genome-scale screens, microarray analysis, next-generation sequencing, and mass spectrometry–based proteomics to discover genes and proteins involved in biological processes. Such methods generate data sets of gene, transcript, or protein hits that researchers wish to explore to understand their properties and functions and thus their possible roles in biological systems of interest. Recent years have seen a profusion of Internet-based resources to aid this process. This review takes the viewpoint of the curious biologist wishing to explore the properties of protein-coding genes and their products, identified using genome-based technologies. Ten key questions are asked about each hit, addressing functions, phenotypes, expression, evolutionary conservation, disease association, protein structure, interactors, posttranslational modifications, and inhibitors. Answers are provided by presenting the latest publicly available resources, together with methods for hit-specific and data set–wide information retrieval, suited to any genome-based analytical technique and experimental species. The utility of these resources is demonstrated for 20 factors regulating cell proliferation. Results obtained using some of these are discussed in more depth using the p53 tumor suppressor as an example. This flexible and universally applicable approach for characterizing experimental hits helps researchers to maximize the potential of their projects for biological discovery

Bioinformatics for Human Genetics: Promises and Challenges

International audienceComputers have been used for decades to get practical work done in the field of human genetics for the benefit of patients and the advancement of science. To name only two examples, computer algorithms and programs for linkage analysis have formed the foundation of most disease-gene discovery projects, and databases of clinical findings have been widely used to support diagnostic decisions in the field of dysmorphology and general human genetics. A number of trends and recent developments, including next-generation sequencing (NGS), bio-ontologies and the Semantic Web, and the ever increasing role of hospital information technology (IT) systems and electronic health records are confronting scientists and clinicians with ever increasing amounts of data but also with ever improving tools to analyze these data for research and clinical care. Correspondingly, the field of bioinformatics is increasingly turning to research questions in the field of human genetics, and the field of human genetics is increasingly making use of bioinformatic algorithms and tools. The choice of "Bioinformatics and Human Genetics" as the topic of this special issue of Human Mutation reflects this new importance of bioinformatics and medical informatics in Human Genetics, and we have been fortunate to be able to recruit some of the leaders in this field as authors