Analytical variables influencing the performance of a miRNA based laboratory assay for prediction of relapse in stage I non-small cell lung cancer (NSCLC)

<p>Abstract</p> <p>Background</p> <p>Laboratory assays are needed for early stage non-small lung cancer (NSCLC) that can link molecular and clinical heterogeneity to predict relapse after surgical resection. We technically validated two miRNA assays for prediction of relapse in NSCLC. Total RNA from seventy-five formalin-fixed and paraffin-embedded (FFPE) specimens was extracted, labeled and hybridized to Affymetrix miRNA arrays using different RNA input amounts, ATP-mix dilutions, array lots and RNA extraction- and labeling methods in a total of 166 hybridizations. Two combinations of RNA extraction- and labeling methods (assays I and II) were applied to a cohort of 68 early stage NSCLC patients.</p> <p>Results</p> <p>RNA input amount and RNA extraction- and labeling methods affected signal intensity and the number of detected probes and probe sets, and caused large variation, whereas different ATP-mix dilutions and array lots did not. Leave-one-out accuracies for prediction of relapse were 63% and 73% for the two assays. Prognosticator calls ("no recurrence" or "recurrence") were consistent, independent on RNA amount, ATP-mix dilution, array lots and RNA extraction method. The calls were not robust to changes in labeling method.</p> <p>Conclusions</p> <p>In this study, we demonstrate that some analytical conditions such as RNA extraction- and labeling methods are important for the variation in assay performance whereas others are not. Thus, careful optimization that address all analytical steps and variables can improve the accuracy of prediction and facilitate the introduction of microRNA arrays in the clinic for prediction of relapse in stage I non-small cell lung cancer (NSCLC).</p

A classifier driven approach to find biomarkers for affective disorders from transcription profiles in blood

Gene expression profiles in blood are increasingly being used to identify biomarkers for different affective disorders. We have selected a set of 29 genes to generate expression profiles for healthy control subjects as well as for patients diagnosed with acute post-traumatic stress disorder (PTSD) and with borderline personality disorder (BPD). Measurements were performed by quantitative polymerase chain reaction (qPCR). Using the actual data in an anonym-ous form we constructed a series of artificial data sets with known gene expression profiles. These sets were used to test 14 classification algorithms and feature selection methods for their ability to identify the correct expression patterns. Application of the three most effective algorithms to the actual expression data showed that control subjects can be dis-tinguished from BPD patients based on differential expression levels of the gene transcripts Gi2, GR and MAPK14, targets that may have links to stress related diseases. Controls can also be distinguished from acute PTSD patients by differential expression levels of the transcripts for ERK2 and RGS2 that are known to be associated with mood disord-ers and social anxiety. We conclude that it is possible to identify informative transcription profiles in blood samples from individuals with affective disorders

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

MicroRNAs and PTEN or TP53 mutations in NCI-60 cell-lines.

<p>Dot plots with medians and inter-quartile ranges are shown for the expression of microRNAs <i>miR-29b, −34a</i>, <i>−34a*</i> and <i>−769-3p</i> in 59 NCI-60 cell-lines grouped by mutation status for the <i>PTEN</i> or <i>TP53</i> genes.</p

Expression of MicroRNAs in the NCI-60 Cancer Cell-Lines

<div><p>The NCI-60 panel of 60 human cancer cell-lines of nine different tissues of origin has been extensively characterized in biological, molecular and pharmacological studies. Analyses of data from such studies have provided valuable information for understanding cellular processes and developing strategies for the diagnosis and treatment of cancer. Here, Affymetrix® GeneChip™ miRNA version 1 oligonucleotide microarrays were used to quantify 847 microRNAs to generate an expression dataset of 495 (58.4%) microRNAs that were identified as expressed in at least one cell-line of the NCI-60 panel. Accuracy of the microRNA measurements was partly confirmed by reverse transcription and polymerase chain reaction assays. Similar to that seen among the four existing NCI-60 microRNA datasets, the concordance of the new expression dataset with the other four was modest, with mean Pearson correlation coefficients of 0.37–0.54. In spite of this, comparable results with different datasets were noted in clustering of the cell-lines by their microRNA expression, differential expression of microRNAs by the lines’ tissue of origin, and correlation of specific microRNAs with the doubling-time of cells or their radiation sensitivity. Mutation status of the cell-lines for the <em>TP53, PTEN</em> and <em>BRAF</em> but not <em>CDKN2A</em> or <em>KRAS</em> cancer-related genes was found to be associated with changes in expression of specific microRNAs. The microRNA dataset generated here should be valuable to those working in the field of microRNAs as well as in integromic studies of the NCI-60 panel.</p> </div

Correlations between different NCI-60 cell-line microRNA expression datasets.

<p>Cumulative frequency distributions are shown for Pearson correlation coefficients (<i>r</i>) with a bin-size of 0.025 for microRNAs quantified in this study and in the studies of Blower, et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049918#pone.0049918-Blower2" target="_blank">[22]</a>, Gaur, et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049918#pone.0049918-Gaur1" target="_blank">[21]</a>, Liu, et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049918#pone.0049918-Liu1" target="_blank">[24]</a>, and Sokilde, et al <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049918#pone.0049918-Sokilde1" target="_blank">[23]</a>. The distribution of the coefficients with the expression measurements of Liu, et al. resampled is also shown.</p

MicroRNA expression in the NCI-60 cell-lines by tissue of origin.

<p><i>A</i>. Unsupervised clustering of 60 NCI-60 cell-lines by log₂-transformed microarray signal values of the 495 expressed microRNAs is shown as a dendrogram. The different types of tissues of origin of the cell-lines are indicated by their color (<i>Pr.</i>, prostate). The tree is drawn from uncentered Pearson correlations, with average linkages used for joining clusters. The scale on the left represents node-heights. <i>B</i>. Heat-map, with its pseudo-color scale underneath, of Z scaled microarray signal values of the 60 cell-lines for the sets of eight microRNAs each with lowest P values in tests of differential expression in cell-lines of a specific tissue of origin compared to all the other cell-lines. Both cell-lines and microRNAs are grouped by tissue of origin. Z scaling was done along rows (by microRNA).</p