An Assessment of the Role of DNA Adenine Methyltransferase on Gene Expression Regulation in E coli

N6-Adenine methylation is an important epigenetic signal, which regulates various processes, such as DNA replication and repair and transcription. In γ-proteobacteria, Dam is a stand-alone enzyme that methylates GATC sites, which are non-randomly distributed in the genome. Some of these overlap with transcription factor binding sites. This work describes a global computational analysis of a published Dam knockout microarray alongside other publicly available data to throw insights into the extent to which Dam regulates transcription by interfering with protein binding. The results indicate that DNA methylation by DAM may not globally affect gene transcription by physically blocking access of transcription factors to binding sites. Down-regulation of Dam during stationary phase correlates with the activity of TFs whose binding sites are enriched for GATC sites

Applying unmixing to gene expression data for tumor phylogeny inference

<p>Abstract</p> <p>Background</p> <p>While in principle a seemingly infinite variety of combinations of mutations could result in tumor development, in practice it appears that most human cancers fall into a relatively small number of "sub-types," each characterized a roughly equivalent sequence of mutations by which it progresses in different patients. There is currently great interest in identifying the common sub-types and applying them to the development of diagnostics or therapeutics. Phylogenetic methods have shown great promise for inferring common patterns of tumor progression, but suffer from limits of the technologies available for assaying differences between and within tumors. One approach to tumor phylogenetics uses differences between single cells within tumors, gaining valuable information about intra-tumor heterogeneity but allowing only a few markers per cell. An alternative approach uses tissue-wide measures of whole tumors to provide a detailed picture of averaged tumor state but at the cost of losing information about intra-tumor heterogeneity.</p> <p>Results</p> <p>The present work applies "unmixing" methods, which separate complex data sets into combinations of simpler components, to attempt to gain advantages of both tissue-wide and single-cell approaches to cancer phylogenetics. We develop an unmixing method to infer recurring cell states from microarray measurements of tumor populations and use the inferred mixtures of states in individual tumors to identify possible evolutionary relationships among tumor cells. Validation on simulated data shows the method can accurately separate small numbers of cell states and infer phylogenetic relationships among them. Application to a lung cancer dataset shows that the method can identify cell states corresponding to common lung tumor types and suggest possible evolutionary relationships among them that show good correspondence with our current understanding of lung tumor development.</p> <p>Conclusions</p> <p>Unmixing methods provide a way to make use of both intra-tumor heterogeneity and large probe sets for tumor phylogeny inference, establishing a new avenue towards the construction of detailed, accurate portraits of common tumor sub-types and the mechanisms by which they develop. These reconstructions are likely to have future value in discovering and diagnosing novel cancer sub-types and in identifying targets for therapeutic development.</p

Apoptosis Induced by Piroxicam plus Cisplatin Combined Treatment Is Triggered by p21 in Mesothelioma

BACKGROUND: Malignant mesothelioma (MM) is a rare, highly aggressive tumor, associated to asbestos exposure. To date no chemotherapy regimen for MM has proven to be definitively curative, and new therapies for MM treatment need to be developed. We have previously shown in vivo that piroxicam/cisplatin combined treatment in MM, specifically acts on cell cycle regulation triggering apoptosis, with survival increase. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed, at molecular level, the apoptotic increase caused by piroxicam/cisplatin treatment in MM cell lines. By means of genome wide analyses, we analyzed transcriptional gene deregulation both after the single piroxicam or cisplatin and the combined treatment. Here we show that apoptotic increase following combined treatment is mediated by p21, since apoptotic increase in piroxicam/cisplatin combined treatment is abolished upon p21 silencing. CONCLUSIONS/SIGNIFICANCE: Piroxicam/cisplatin combined treatment determines an apoptosis increase in MM cells, which is dependent on the p21 expression. The results provided suggest that piroxicam/cisplatin combination might be tested in clinical settings in tumor specimens that express p21

Effect of Synthetic Dietary Triglycerides: A Novel Research Paradigm for Nutrigenomics

The effect of dietary fats on human health and disease are likely mediated by changes in gene expression. Several transcription factors have been shown to respond to fatty acids, including SREBP-1c, NF-kappaB, RXRs, LXRs, FXR, HNF4alpha, and PPARs. However, it is unclear to what extent these transcription factors play a role in gene regulation by dietary fatty acids in vivo

Individualized markers optimize class prediction of microarray data

BACKGROUND: Identification of molecular markers for the classification of microarray data is a challenging task. Despite the evident dissimilarity in various characteristics of biological samples belonging to the same category, most of the marker – selection and classification methods do not consider this variability. In general, feature selection methods aim at identifying a common set of genes whose combined expression profiles can accurately predict the category of all samples. Here, we argue that this simplified approach is often unable to capture the complexity of a disease phenotype and we propose an alternative method that takes into account the individuality of each patient-sample. RESULTS: Instead of using the same features for the classification of all samples, the proposed technique starts by creating a pool of informative gene-features. For each sample, the method selects a subset of these features whose expression profiles are most likely to accurately predict the sample's category. Different subsets are utilized for different samples and the outcomes are combined in a hierarchical framework for the classification of all samples. Moreover, this approach can innately identify subgroups of samples within a given class which share common feature sets thus highlighting the effect of individuality on gene expression. CONCLUSION: In addition to high classification accuracy, the proposed method offers a more individualized approach for the identification of biological markers, which may help in better understanding the molecular background of a disease and emphasize the need for more flexible medical interventions

A Differentiation-Based Phylogeny of Cancer Subtypes

Histopathological classification of human tumors relies in part on the degree of differentiation of the tumor sample. To date, there is no objective systematic method to categorize tumor subtypes by maturation. In this paper, we introduce a novel computational algorithm to rank tumor subtypes according to the dissimilarity of their gene expression from that of stem cells and fully differentiated tissue, and thereby construct a phylogenetic tree of cancer. We validate our methodology with expression data of leukemia, breast cancer and liposarcoma subtypes and then apply it to a broader group of sarcomas. This ranking of tumor subtypes resulting from the application of our methodology allows the identification of genes correlated with differentiation and may help to identify novel therapeutic targets. Our algorithm represents the first phylogeny-based tool to analyze the differentiation status of human tumors

Global Gene Expression Profiling Of Human Pleural Mesotheliomas: Identification of Matrix Metalloproteinase 14 (MMP-14) as Potential Tumour Target

BACKGROUND:The goal of our study was to molecularly dissect mesothelioma tumour pathways by mean of microarray technologies in order to identify new tumour biomarkers that could be used as early diagnostic markers and possibly as specific molecular therapeutic targets. METHODOLOGY:We performed Affymetrix HGU133A plus 2.0 microarray analysis, containing probes for about 39,000 human transcripts, comparing 9 human pleural mesotheliomas with 4 normal pleural specimens. Stringent statistical feature selection detected a set of differentially expressed genes that have been further evaluated to identify potential biomarkers to be used in early diagnostics. Selected genes were confirmed by RT-PCR. As reported by other mesothelioma profiling studies, most of genes are involved in G2/M transition. Our list contains several genes previously described as prognostic classifier. Furthermore, we found novel genes, never associated before to mesotheliom that could be involved in tumour progression. Notable is the identification of MMP-14, a member of matrix metalloproteinase family. In a cohort of 70 mesothelioma patients, we found by a multivariate Cox regression analysis, that the only parameter influencing overall survival was expression of MMP14. The calculated relative risk of death in MM patients with low MMP14 expression was significantly lower than patients with high MMp14 expression (P = 0.002). CONCLUSIONS:Based on the results provided, this molecule could be viewed as a new and effective therapeutic target to test for the cure of mesothelioma

Inferring tree causal models of cancer progression with probability raising

Existing techniques to reconstruct tree models of progression for accumulative processes, such as cancer, seek to estimate causation by combining correlation and a frequentist notion of temporal priority. In this paper, we define a novel theoretical framework called CAPRESE (CAncer PRogression Extraction with Single Edges) to reconstruct such models based on the notion of probabilistic causation defined by Suppes. We consider a general reconstruction setting complicated by the presence of noise in the data due to biological variation, as well as experimental or measurement errors. To improve tolerance to noise we define and use a shrinkage-like estimator. We prove the correctness of our algorithm by showing asymptotic convergence to the correct tree under mild constraints on the level of noise. Moreover, on synthetic data, we show that our approach outperforms the state-of-the-art, that it is efficient even with a relatively small number of samples and that its performance quickly converges to its asymptote as the number of samples increases. For real cancer datasets obtained with different technologies, we highlight biologically significant differences in the progressions inferred with respect to other competing techniques and we also show how to validate conjectured biological relations with progression models

Avelumab, an anti-PD-L1 antibody, in patients with locally advanced or metastatic breast cancer: a phase 1b JAVELIN Solid Tumor study

PURPOSE: Agents targeting programmed death receptor 1 (PD-1) or its ligand (PD-L1) have shown antitumor activity in the treatment of metastatic breast cancer (MBC). The aim of this study was to assess the activity of avelumab, a PD-L1 inhibitor, in patients with MBC. METHODS: In a phase 1 trial (JAVELIN Solid Tumor; NCT01772004), patients with MBC refractory to or progressing after standard-of-care therapy received avelumab intravenously 10 mg/kg every 2 weeks. Tumors were assessed every 6 weeks by RECIST v1.1. Adverse events (AEs) were graded by NCI-CTCAE v4.0. Membrane PD-L1 expression was assessed by immunohistochemistry (Dako PD-L1 IHC 73-10 pharmDx). RESULTS: A total of 168 patients with MBC, including 58 patients with triple-negative breast cancer (TNBC), were treated with avelumab for 2-50 weeks and followed for 6-15 months. Patients were heavily pretreated with a median of three prior therapies for metastatic or locally advanced disease. Grade >/= 3 treatment-related AEs occurred in 13.7% of patients, including two treatment-related deaths. The confirmed objective response rate (ORR) was 3.0% overall (one complete response and four partial responses) and 5.2% in patients with TNBC. A trend toward a higher ORR was seen in patients with PD-L1+ versus PD-L1- tumor-associated immune cells in the overall population (16.7% vs. 1.6%) and in the TNBC subgroup (22.2% vs. 2.6%). CONCLUSION: Avelumab showed an acceptable safety profile and clinical activity in a subset of patients with MBC. PD-L1 expression in tumor-associated immune cells may be associated with a higher probability of clinical response to avelumab in MBC

Error models for microarray intensities

We derive the additive-multiplicative error model for microarray intensities, and describe two applications. For the detection of differentially expressed genes, we obtain a statistic whose variance is approximately independent of the mean intensity. For the post hoc calibration (normalization) of data with respect to experimental factors, we describe a method for parameter estimation