gene expression profiling in breast cancer a clinical perspective

Gene expression profiling tests are used in an attempt to determine the right treatment for the right person with early-stage breast cancer that may have spread to nearby lymph nodes but not to distant parts of the body. These new diagnostic approaches are designed to spare people who do not need additional treatment (adjuvant therapy) the side effects of unnecessary treatment, and allow people who may benefit from adjuvant therapy to receive it. In the present review we discuss in detail the major diagnostic tests available such as MammaPrint dx, Oncotype dx, PAM50, Mammostrat, IHC4, MapQuant DX, Theros-Breast Cancer Gene Expression Ratio Assay, and their potential clinical applications

Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Although 58 genomic regions have been associated with CAD thus far, most of the heritability is unexplained, indicating that additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and performed meta-analysis of results with 194,427 participants previously genotyped, totaling 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD associations (P < 5 × 10(-8), in fixed-effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leukocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 (p.Ser219Gly)) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell-type-specific gene expression and plasma protein levels sheds light on potential disease mechanisms

Posttranscriptional Regulation of the Human LDL Receptor by the U2-Spliceosome

Background: The low-density lipoprotein receptor (LDLR) in the liver is the major determinant of LDL-cholesterol levels in human plasma. The discovery of genes that regulate the activity of LDLR helps to identify pathomechanisms of hypercholesterolemia and novel therapeutic targets against atherosclerotic cardiovascular disease.Methods: We performed a genome-wide RNA interference screen for genes limiting the uptake of fluorescent LDL into Huh-7 hepatocarcinoma cells. Top hit genes were validated by in vitro experiments as well as analyses of datasets on gene expression and variants in human populations.Results: The knockdown of 54 genes significantly inhibited LDL uptake. Fifteen of them encode for components or interactors of the U2-spliceosome. Knocking down any one of 11 out of 15 genes resulted in the selective retention of intron 3 of LDLR. The translated LDLR fragment lacks 88% of the full length LDLR and is detectable neither in non-transfected cells nor in human plasma. The hepatic expression of the intron 3 retention transcript is increased in non-alcoholic fatty liver disease as well as after bariatric surgery. Its expression in blood cells correlates with LDL-cholesterol and age. Single nucleotide polymorphisms and three rare variants of one spliceosome gene, RBM25, are associated with LDL-cholesterol in the population and familial hypercholesterolemia, respectively. Compared to overexpression of wild type RBM25, overexpression of the three rare RBM25 mutants in Huh-7 cells led to lower LDL uptake.Conclusions: We identified a novel mechanism of post-transcriptional regulation of LDLR activity in humans and associations of genetic variants of RBM25 with LDL-cholesterol levels.</p

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field

Radiology for PET/CT Reporting

VII, 149 p. 258 illus., 128 illus. in color.onlin

New radiopharmaceutical markers for metabolism and receptor

Recently new radiopharmaceuticals have been proposed for investigating prostate cancer patients, including metabolic radiotracer such as anti1-amino-3-18F-fluorocyclobutane-1-carboxylic acid (18F-FACBC) or probe targeting the prostate-specific membrane antigen (PSMA). These radiotracers showed in literature better performance in the detection of prostate cancer recurrence as compared to choline PET/CT imaging [1, 2]

Endocytosis of lipoproteins

During their metabolism, all lipoproteins undergo endocytosis, either to be degraded intracellularly, for example in hepatocytes or macrophages, or to be re-secreted, for example in the course of transcytosis by endothelial cells. Moreover, there are several examples of internalized lipoproteins sequestered intracellularly, possibly to exert intracellular functions, for example the cytolysis of trypanosoma. Endocytosis and the subsequent intracellular itinerary of lipoproteins hence are key areas for understanding the regulation of plasma lipid levels as well as the biological functions of lipoproteins. Indeed, the identification of the low-density lipoprotein (LDL)-receptor and the unraveling of its transcriptional regulation led to the elucidation of familial hypercholesterolemia as well as to the development of statins, the most successful therapeutics for lowering of cholesterol levels and risk of atherosclerotic cardiovascular diseases. Novel limiting factors of intracellular trafficking of LDL and the LDL receptor continue to be discovered and to provide drug targets such as PCSK9. Surprisingly, the receptors mediating endocytosis of high-density lipoproteins or lipoprotein(a) are still a matter of controversy or even new discovery. Finally, the receptors and mechanisms, which mediate the uptake of lipoproteins into non-degrading intracellular itineraries for re-secretion (transcytosis, retroendocytosis), storage, or execution of intracellular functions, are largely unknown

Smells like Teen Spirit: Improving Bug Prediction Performance using the Intensity of Code Smells

Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes. Specifically, we evaluate the contribution of a measure of the severity of code smells (i.e., code smell intensity) by adding it to existing bug prediction models and comparing the results of the new model against the baseline model. Results indicate that the accuracy of a bug prediction model increases by adding the code smell intensity as predictor. We also evaluate the actual gain provided by the intensity index with respect to the other metrics in the model, including the ones used to compute the code smell intensity. We observe that the intensity index is much more important as compared to other metrics used for predicting the buggyness of smelly classes

Toward a Smell-aware Bug Prediction Model

Code smells are symptoms of poor design choices. Previous studies assessed the impact of smells on bug-proneness of classes. In this paper, we build a specialized bug prediction model for smelly classes. We evaluate the contribution of a measure of the severity of code smells by adding it to existing models based on both product and process metrics, and comparing the results of the new model against the baseline ones. Results indicate that the accuracy of a bug prediction model increases by adding the code smell intensity as predictor. We also compare our results with the ones of an alternative technique which considers historical metrics of code smells, finding that our model works better. By evaluating the information gain provided by the intensity index with respect to the other metrics in the model, we found that the intensity index is a relevant feature for both product and process metrics-based models. At the same time, the metric counting the average number of code smells in previous versions of a class considered by the alternative model is also able to reduce the entropy of the model. Thus, we devised a combined bug prediction. We observed an improvement of 13% of the baselines