An introduction to low-level analysis methods of DNA microarray data

This article gives an overview over the methods used in the low--level analysis of gene expression data generated using DNA microarrays. This type of experiment allows to determine relative levels of nucleic acid abundance in a set of tissues or cell populations for thousands of transcripts or loci simultaneously. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. This includes the design of probes, the experimental design, the image analysis of microarray scanned images, the normalization of fluorescence intensities, the assessment of the quality of microarray data and incorporation of quality information in subsequent analyses, the combination of information across arrays and across sets of experiments, the discovery and recognition of patterns in expression at the single gene and multiple gene levels, and the assessment of significance of these findings, considering the fact that there is a lot of noise and thus random features in the data. For all of these components, access to a flexible and efficient statistical computing environment is an essential aspect

Recommendations for Discipline-Specific FAIRness Evaluation Derived from Applying an Ensemble of Evaluation Tools

From a research data repositories’ perspective, offering research data management services in line with the FAIR principles is becoming increasingly important. However, there exists no globally established and trusted approach to evaluate FAIRness to date. Here, we apply five different available FAIRness evaluation approaches to selected data archived in the World Data Center for Climate (WDCC). Two approaches are purely automatic, two approaches are purely manual and one approach applies a hybrid method (manual and automatic combined). The results of our evaluation show an overall mean FAIR score of WDCC-archived (meta) data of 0.67 of 1, with a range of 0.5 to 0.88. Manual approaches show higher scores than automated ones and the hybrid approach shows the highest score. Computed statistics indicate that the test approaches show an overall good agreement at the data collection level. We find that while neither one of the five valuation approaches is fully fit-forpurpose to evaluate (discipline-specific) FAIRness, all have their individual strengths. Specifically, manual approaches capture contextual aspects of FAIRness relevant for reuse, whereas automated approaches focus on the strictly standardised aspects of machine actionability. Correspondingly, the hybrid method combines the advantages and eliminates the deficiencies of manual and automatic evaluation approaches. Based on our results, we recommend future FAIRness evaluation tools to be based on a mature hybrid approach. Especially the design and adoption of the discipline-specific aspects of FAIRness will have to be conducted in concerted community efforts

Origin and pathogenesis of nodular lymphocyte–predominant Hodgkin lymphoma as revealed by global gene expression analysis

The pathogenesis of nodular lymphocyte–predominant Hodgkin lymphoma (NLPHL) and its relationship to other lymphomas are largely unknown. This is partly because of the technical challenge of analyzing its rare neoplastic lymphocytic and histiocytic (L&H) cells, which are dispersed in an abundant nonneoplastic cellular microenvironment. We performed a genome-wide expression study of microdissected L&H lymphoma cells in comparison to normal and other malignant B cells that indicated a relationship of L&H cells to and/or that they originate from germinal center B cells at the transition to memory B cells. L&H cells show a surprisingly high similarity to the tumor cells of T cell–rich B cell lymphoma and classical Hodgkin lymphoma, a partial loss of their B cell phenotype, and deregulation of many apoptosis regulators and putative oncogenes. Importantly, L&H cells are characterized by constitutive nuclear factor {kappa}B activity and aberrant extracellular signal-regulated kinase signaling. Thus, these findings shed new light on the nature of L&H cells, reveal several novel pathogenetic mechanisms in NLPHL, and may help in differential diagnosis and lead to novel therapeutic strategies

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

Origin and pathogenesis of nodular lymphocyte–predominant Hodgkin lymphoma as revealed by global gene expression analysis

The pathogenesis of nodular lymphocyte–predominant Hodgkin lymphoma (NLPHL) and its relationship to other lymphomas are largely unknown. This is partly because of the technical challenge of analyzing its rare neoplastic lymphocytic and histiocytic (L&H) cells, which are dispersed in an abundant nonneoplastic cellular microenvironment. We performed a genome-wide expression study of microdissected L&H lymphoma cells in comparison to normal and other malignant B cells that indicated a relationship of L&H cells to and/or that they originate from germinal center B cells at the transition to memory B cells. L&H cells show a surprisingly high similarity to the tumor cells of T cell–rich B cell lymphoma and classical Hodgkin lymphoma, a partial loss of their B cell phenotype, and deregulation of many apoptosis regulators and putative oncogenes. Importantly, L&H cells are characterized by constitutive nuclear factor κB activity and aberrant extracellular signal-regulated kinase signaling. Thus, these findings shed new light on the nature of L&H cells, reveal several novel pathogenetic mechanisms in NLPHL, and may help in differential diagnosis and lead to novel therapeutic strategies

Устройство автоматического регулирования жидкости

Although the significance of tumour site for estimating malignant potential in gastrointestinal stromal tumours (GISTs) has recently been recognized, site-specific genetic patterns have not to date been defined. This study examined 52 c-kit-positive primary GISTs (with a mean follow-up of 42.3 months in 51 cases) from three different locations (35 gastric, 12 small intestinal, and five colorectal) using comparative genomic hybridization (CGH). In general, tumour site correlated with key prognostic factors, including tumour size, mitotic rate, proliferative activity, and probable malignant potential. Furthermore, several DNA copy number changes showed a site-dependent pattern. These included losses at 14q (gastric 83%, intestinal 35%; p = 0.001), losses at 22q (gastric 46%, intestinal 82%; p = 0.02), losses at 1p (gastric 23%, intestinal 88%; p = 1 × 10-5), losses at 15q (gastric 14%, intestinal 59%; p = 0.002), losses at 9q (gastric 14%, intestinal 53%; p = 0.006), and gains at 5p (gastric 11%, intestinal 53%; p = 0.002). These data demonstrate strong site-dependent genetic heterogeneity in GISTs that may form a basis for subclassification. Prognostic evaluation of DNA copy number changes identified losses at 9q as a site-independent prognostic marker associated with shorter disease-free survival (p = 0.03) and overall survival (p = 0.002). Furthermore, 9q loss also appeared to carry prognostic value in predicting overall survival for patients with advanced or progressive GISTs (p = 0.003). Copyright © 2004 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd

Avelumab in patients with previously treated metastatic melanoma: phase 1b results from the JAVELIN Solid Tumor trial

Background We report phase 1b data from patients enrolled in the JAVELIN Solid Tumor clinical trial (NCT01772004) with unresectable stage IIIC or IV melanoma that had progressed after ≥1 line of therapy for metastatic disease. Patients and methods Patients received avelumab (10 mg/kg)—a human anti–PD-L1 antibody. Assessments included objective response rate (ORR), progression-free survival (PFS), overall survival (OS), and safety. Results As of December 31, 2016, 51 patients were treated and followed for a median of 24.2 months (range, 16.1–31.5). Most patients had cutaneous (n = 28 [54.9%]) or ocular (n = 16 [31.4%]) melanoma and had received a median of 2 prior lines of therapy (range, 0–4), including ipilimumab (n = 26 [51.0%]). The confirmed ORR was 21.6% (95% CI, 11.3–35.3; complete response, 7.8%; partial response, 13.7%). The median duration of response was not estimable (95% CI, 2.6 months-not estimable). Median PFS and OS were 3.1 months (95% CI, 1.4–6.3) and 17.2 months (95% CI, 6.6-not estimable), respectively. Subgroup analyses suggested meaningful clinical activity (ORR [95% CI]) in patients with non-ocular melanoma (31.4% [16.9–49.3]), PD-L1–positive tumors (42.1% [20.3–66.5]), or prior ipilimumab therapy (30.8% [14.3–51.8]). Thirty-nine patients (76.5%) had a treatment-related adverse event (TRAE), most commonly infusion-related reaction (29.4%), fatigue (17.6%), and chills (11.8%); 4 patients (7.8%) had a grade 3 TRAE. Five patients (9.8%) had an immune-related TRAE (all were grade 1/2). No grade 4 TRAEs or treatment-related deaths were reported. Conclusion Avelumab showed durable responses, promising survival outcomes, and an acceptable safety profile in patients with previously treated metastatic melanoma. Trial registration ClinicalTrials.gov identifier: NCT01772004This trial was sponsored by Merck KGaA, Darmstadt, Germany, and is part of an alliance between Merck KGaA and Pfizer, Inc., New York, NY, USA. Medical writing support was provided by ClinicalThinking, Inc., Hamilton, NJ, USA, and funded by Merck KGaA, and Pfizer, Inc

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

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