Prediction of individual response to anticancer therapy: historical and future perspectives

Since the introduction of chemotherapy for cancer treatment in the early 20th century considerable efforts have been made to maximize drug efficiency and at the same time minimize side effects. As there is a great interpatient variability in response to chemotherapy, the development of predictive biomarkers is an ambitious aim for the rapidly growing research area of personalized molecular medicine. The individual prediction of response will improve treatment and thus increase survival and life quality of patients. In the past, cell cultures were used as in vitro models to predict in vivo response to chemotherapy. Several in vitro chemosensitivity assays served as tools to measure miscellaneous endpoints such as DNA damage, apoptosis and cytotoxicity or growth inhibition. Twenty years ago, the development of high-throughput technologies, e.g. cDNA microarrays enabled a more detailed analysis of drug responses. Thousands of genes were screened and expression levels were correlated to drug responses. In addition, mutation analysis became more and more important for the prediction of therapeutic success. Today, as research enters the area of -omics technologies, identification of signaling pathways is a tool to understand molecular mechanism underlying drug resistance. Combining new tissue models, e.g. 3D organoid cultures with modern technologies for biomarker discovery will offer new opportunities to identify new drug targets and in parallel predict individual responses to anticancer therapy. In this review, we present different currently used chemosensitivity assays including 2D and 3D cell culture models and several –omics approaches for the discovery of predictive biomarkers. Furthermore, we discuss the potential of these assays and biomarkers to predict the clinical outcome of individual patients and future perspectives.     

Maximum Transfer Distance Between Partitions

In this paper, we study a distance defined over the partitions of a finite set. Given two partitions P and Q, this distance is defined as the minimum number of transfers of an element from one class to another, required to transform P into Q. We recall the algorithm to evaluate this distance and we give some formulae for the maximum distance value between two partitions having exactly or at most p and q classes, for given p and q.     

Interferon modulation of cellular microRNAs as an antiviral mechanism

RNA interference through non-coding microRNAs (miRNAs) represents a vital component of the innate antiviral immune response in plants and invertebrate animals; however, a role for cellular miRNAs in the defence against viral infection in mammalian organisms has thus far remained elusive. Here we show that interferon beta (IFNbeta) rapidly modulates the expression of numerous cellular miRNAs, and that eight of these IFNbeta-induced miRNAs have sequence-predicted targets within the hepatitis C virus (HCV) genomic RNA. The introduction of synthetic miRNA-mimics corresponding to these IFNbeta-induced miRNAs reproduces the antiviral effects of IFNbeta on HCV replication and infection, whereas neutralization of these antiviral miRNAs with anti-miRNAs reduces the antiviral effects of IFNbeta against HCV. In addition, we demonstrate that IFNbeta treatment leads to a significant reduction in the expression of the liver-specific miR-122, an miRNA that has been previously shown to be essential for HCV replication. Therefore, our findings strongly support the notion that mammalian organisms too, through the interferon system, use cellular miRNAs to combat viral infections.     

Enhancing Validity and Reliability Through Feedback-Driven Exploration: A Study in the Context of Conjoint Analysis

This study proposes a research design for the enhancement of validity and reliability in conjoint analysis research. For this purpose, we are applying the concept of feedback-driven exploration to conjoint analysis and assess the proposed research design concerning its benefits and limitations in respect of validity and reliability of results. The article is of interest for the field of preference elicitation through stated preference methods, and for model validation in transdisciplinary research. By applying the principle of feedback-driven exploration, we allow for feedback loops between researchers, industry experts and survey participants in order to strengthen both validity and reliability. A multi-case study of the agricultural markets in Switzerland illustrates the functioning of the proposed research design. We find that feedback-driven exploration significantly increases validity and reliability by enhancing methodological rigor and implementing an error-correcting mechanism. Additionally, a better understanding of the underlying decision processes is supported by the design due to increased interaction between researchers, industry experts and market participants.     

Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma

Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with concentrations of liver enzymes in plasma, of which 32 are new associations (P = 10(-8) to P = 10(-190)). We used functional genomic approaches including metabonomic profiling and gene expression analyses to identify probable candidate genes at these regions. We identified 69 candidate genes, including genes involved in biliary transport (ATP8B1 and ABCB11), glucose, carbohydrate and lipid metabolism (FADS1, FADS2, GCKR, JMJD1C, HNF1A, MLXIPL, PNPLA3, PPP1R3B, SLC2A2 and TRIB1), glycoprotein biosynthesis and cell surface glycobiology (ABO, ASGR1, FUT2, GPLD1 and ST3GAL4), inflammation and immunity (CD276, CDH6, GCKR, HNF1A, HPR, ITGA1, RORA and STAT4) and glutathione metabolism (GSTT1, GSTT2 and GGT), as well as several genes of uncertain or unknown function (including ABHD12, EFHD1, EFNA1, EPHA2, MICAL3 and ZNF827). Our results provide new insight into genetic mechanisms and pathways influencing markers of liver function.     

Revisiting the metabolic syndrome: the emerging role of aquaglyceroporins

The metabolic syndrome (MetS) includes a group of medical conditions such as insulin resistance (IR), dyslipidemia and hypertension, all associated with an increased risk for cardiovascular disease. Increased visceral and ectopic fat deposition are also key features in the development of IR and MetS, with pathophysiological sequels on adipose tissue, liver and muscle. The recent recognition of aquaporins (AQPs) involvement in adipose tissue homeostasis has opened new perspectives for research in this field. The members of the aquaglyceroporin subfamily are specific glycerol channels implicated in energy metabolism by facilitating glycerol outflow from adipose tissue and its systemic distribution and uptake by liver and muscle, unveiling these membrane channels as key players in lipid balance and energy homeostasis. Being involved in a variety of pathophysiological mechanisms including IR and obesity, AQPs are considered promising drug targets that may prompt novel therapeutic approaches for metabolic disorders such as MetS. This review addresses the interplay between adipose tissue, liver and muscle, which is the basis of the metabolic syndrome, and highlights the involvement of aquaglyceroporins in obesity and related pathologies and how their regulation in different organs contributes to the features of the metabolic syndrome.     

Post-translational add-ons mark the path in exosomal protein sorting

Extracellular vesicles (EVs) are released by cells to the extracellular environment to mediate inter-cellular communication. Proteins, lipids, nucleic acids and metabolites shuttled in these vesicles modulate specific functions in recipient cells. The enrichment of selected sets of proteins in EVs compared with global cellular levels suggests the existence of specific sorting mechanisms to specify EV loading. Diverse post-translational modifications (PTMs) of proteins participate in the loading of specific elements into EVs. In this review, we offer a perspective on PTMs found in EVs and discuss the specific role of some PTMs, specifically Ubiquitin and Ubiquitin-like modifiers, in exosomal sorting of protein components. The understanding of these mechanisms will provide new strategies for biomedical applications. Examples include the presence of defined PTM marks on EVs as novel biomarkers for the diagnosis and prognosis of certain diseases, or the specific import of immunogenic components into EVs for vaccine generation.     

Chfr is required for tumor suppression and Aurora A regulation

Tumorigenesis is a consequence of loss of tumor suppressors and activation of oncogenes. Expression of the mitotic checkpoint protein Chfr is lost in 20-50% of primary tumors and tumor cell lines. To explore whether downregulation of Chfr contributes directly to tumorigenesis, we generated Chfr knockout mice. Chfr-deficient mice are cancer-prone, develop spontaneous tumors and have increased skin tumor incidence after treatment with dimethylbenz(a)anthracene. Chfr deficiency leads to chromosomal instability in embryonic fibroblasts and regulates the mitotic kinase Aurora A, which is frequently upregulated in a variety of tumors. Chfr physically interacts with Aurora A and ubiquitinates Aurora A both in vitro and in vivo. Collectively, our data suggest that Chfr is a tumor suppressor and ensures chromosomal stability by controlling the expression levels of key mitotic proteins such as Aurora A.