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Objectives: Our aim was to evaluate changes in prostate cancer diagnosis and management and to examine changes in the stage and grade of newly diagnosed prostate cancer in the North West of England over a 10-year period. Materials and methods: Data was collected concerning the diagnosis (including stage and grade) and management of newly diagnosed prostate cancer in the North West of England. There were three time points: 2003, 2007 and 2011 including a total of 648 patients. For assessment of median time changes Spearman’s Rank correlation test was used, for the assessment of changes in Gleason grade and clinical stage Mann–Whitney U test was used, and assessment of positive margin rates was done with Fisher’s test. Results: Median time from management decision to surgery has reduced from 46 (2003), 34 (2007) to 27 days (2011) (p=0.074). The proportion of patients managed with active surveillance has remained relatively constant over time (18%, 16% and 21% respectively). More minimally invasive, nerve-sparing prostatectomies are now performed, and positive margin rates have significantly reduced from 53% (2003) to 23% (2011) (p<0.001). Gleason grade significantly increased over time (p<0.001); Gleason 7 disease was diagnosed in 23% of patients in 2003, 32% in 2007 and 49% in 2011 (p<0.001). There was an increase in Gleason 8 disease; 6% (2003) to 8.6% (2011), but this was not significant (p=0.27). Increase in clinical stage was also noted over time; identification of T3 disease rose from 2% (2003 and 2007) to 5% (2011) (p=0.045) (excluding cases with non-recorded stage). Conclusion: Prostate cancer management in the North West of England has evolved over the last decade, with overall improvements in management quality. We have demonstrated an increase in the presenting stage and grade of prostate cancer over a 10-year period. [ABSTRACT FROM AUTHOR]

Background:A high percentage of stroma predicts poor survival in triple-negative breast cancers but is diminished in studies of unselected cases. We determined the prognostic significance of tumour-stroma ratio (TSR) in oestrogen receptor (ER)-positive male and female breast carcinomas.Methods:TSR was measured in haematoxylin and eosin-stained tissue sections (118 female and 62 male). Relationship of TSR (cutoff 49%) to overall survival (OS) and relapse-free survival (RFS) was analysed.Results:Tumours with 49% stroma were associated with better survival in female (OS P=0.008, HR=0.2-0.7; RFS P=0.006, HR=0.1-0.6) and male breast cancer (OS P=0.005, HR=0.05-0.6; RFS P=0.01, HR=0.87-5.6), confirmed in multivariate analysis.Conclusions:High stromal content was related to better survival in ER-positive breast cancers across both genders, contrasting data in triple-negative breast cancer and highlighting the importance of considering ER status when interpreting the prognostic value of TSR. [ABSTRACT FROM AUTHOR]

The detection of quantitative changes in genomic DNA, i.e. deletions and duplications or Copy Number Variants (CNVs), has recently gained considerable interest. First, detailed analysis of the human genome showed a surprising amount of CNVs, involving thousands of genes. Second, it was realised that the detection of CNVs as a cause of genetic disease was often neglected, but should be an essential part of a complete screening strategy. In both cases new efficient CNV screening methods, covering the entire range from specific loci to genome-wide, were behind these developments. This paper will briefly review the methods that are available to detect CNVs, discuss their strong and weak points, show some new developments and look ahead. Methods covered include microscopy, fluorescence in situ hybridization (including fiber-FISH), Southern blotting, PCR-based methods (including MLPA), array technology and massive parallel sequencing. In addition, we will show some new developments, including a 1400-plex CNV bead assay, fast-MLPA (from DNA to result in ∼6 h) and a simple Melting Curve Analysis assay to confirm potential CNVs. Using the 1400-plex CNV bead assay, targeting selected chromosomal regions only, we detected confirmed rearrangements in 9% of 320 mental retardation patients studied. Copyright © 2009 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Abstract Background Serial Analysis of Gene Expressions (SAGE) produces gene expression measurements on a discrete scale, due to the finite number of molecules in the sample. This means that part of the variance in SAGE data should be understood as the sampling error in a binomial or Poisson distribution, whereas other variance sources, in particular biological variance, should be modeled using a continuous distribution function, i.e. a prior on the intensity of the Poisson distribution. One challenge is that such a model predicts a large number of genes with zero counts, which cannot be observed. Results We present a hierarchical Poisson model with a gamma prior and three different algorithms for estimating the parameters in the model. It turns out that the rate parameter in the gamma distribution can be estimated on the basis of a single SAGE library, whereas the estimate of the shape parameter becomes unstable. This means that the number of zero counts cannot be estimated reliably. When a bivariate model is applied to two SAGE libraries, however, the number of predicted zero counts becomes more stable and in approximate agreement with the number of transcripts observed across a large number of experiments. In all the libraries we analyzed there was a small population of very highly expressed tags, typically 1% of the tags, that could not be accounted for by the model. To handle those tags we chose to augment our model with a non-parametric component. We also show some results based on a log-normal distribution instead of the gamma distribution. Conclusion By modeling SAGE data with a hierarchical Poisson model it is possible to separate the sampling variance from the variance in gene expression. If expression levels are reported at the gene level rather than at the tag level, genes mapped to multiple tags must be kept separate, since their expression levels show a different statistical behavior. A log-normal prior provided a better fit to our data than the gamma prior, but except for a small subpopulation of tags with very high counts, the two priors are similar.

Abstract Background In experimental biology, including retrovirology and molecular biology, replicate measurement sessions very often show similar proportional differences between experimental conditions, but different absolute values, even though the measurements were presumably carried out under identical circumstances. Although statistical programs enable the analysis of condition effects despite this replication error, this approach is hardly ever used for this purpose. On the contrary, most researchers deal with such between-session variation by normalisation or standardisation of the data. In normalisation all values in a session are divided by the observed value of the 'control' condition, whereas in standardisation, the sessions' means and standard deviations are used to correct the data. Normalisation, however, adds variation because the control value is not without error, while standardisation is biased if the data set is incomplete. Results In most cases, between-session variation is multiplicative and can, therefore, be removed by division of the data in each session with a session-specific correction factor. Assuming one level of multiplicative between-session error, unbiased session factors can be calculated from all available data through the generation of a between-session ratio matrix. Alternatively, these factors can be estimated with a maximum likelihood approach. The effectiveness of this correction method, dubbed "factor correction", is demonstrated with examples from the field of molecular biology and retrovirology. Especially when not all conditions are included in every measurement session, factor correction results in smaller residual error than normalisation and standardisation and therefore allows the detection of smaller treatment differences. Factor correction was implemented into an easy-to-use computer program that is available on request at: biolab-services@amc.uva.nl?subject=factor. Conclusion Factor correction is an effective and efficient way to deal with between-session variation in multi-session experiments.

Abstract Background Recent analyses of prokaryotic genome sequences have demonstrated the important force horizontal gene transfer constitutes in genome evolution. Horizontally acquired sequences are detectable by, among others, their dinucleotide composition (genome signature) dissimilarity with the host genome. Genomic islands (GIs) comprise important and interesting horizontally transferred sequences, but information about acquisition events or relatedness between GIs is scarce. In Vibrio vulnificus CMCP6, 10 and 11 GIs have previously been identified in the sequenced chromosomes I and II, respectively. We assessed the compositional similarity and putative acquisition account of these GIs using the genome signature. For this analysis we developed a new algorithm, available as a web application. Results Of 21 GIs, VvI-1 and VvI-10 of chromosome I have similar genome signatures, and while artificially divided due to a linear annotation, they are adjacent on the circular chromosome and therefore comprise one GI. Similarly, GIs VvI-3 and VvI-4 of chromosome I together with the region between these two islands are compositionally similar, suggesting that they form one GI (making a total of 19 GIs in chromosome I + chromosome II). Cluster analysis assigned the 19 GIs to 11 different branches above our conservative threshold. This suggests a limited number of compositionally similar donors or intragenomic dispersion of ancestral acquisitions. Furthermore, 2 GIs of chromosome II cluster with chromosome I, while none of the 19 GIs group with chromosome II, suggesting an unidirectional dispersal of large anomalous gene clusters from chromosome I to chromosome II. Conclusion From the results, we infer 10 compositionally dissimilar donors for 19 GIs in the V. vulnificus CMCP6 genome, including chromosome I donating to chromosome II. This suggests multiple transfer events from individual donor types or from donors with similar genome signatures. Applied to other prokaryotes, this approach may elucidate the acquisition account in their genome sequences, and facilitate donor identification of GIs.

Abstract Background Correlation between the expression levels of genes which are located close to each other on the genome has been found in various organisms, including yeast, drosophila and humans. Since such a correlation could be explained by several biochemical, evolutionary, genetic and technological factors, there is a need for statistical models that correspond to specific biological models for the correlation structure. Results We modelled the pairwise correlation between the expressions of the genes in a Drosophila microarray experiment as a normal mixture under Fisher's z-transform, and fitted the model to the correlations of expressions of adjacent as well as non-adjacent genes. We also analyzed simulated data for comparison. The model provided a good fit to the data. Further, correlation between the activities of two genes could, in most cases, be attributed to either of two factors: the two genes both being active in the same age group (adult or embryo), or the two genes being in proximity of each other on the chromosome. The interaction between these two factors was weak. Conclusions Correlation between the activities of adjacent genes is higher than between non-adjacent genes. In the data we analyzed, this appeared, for the most part, to be a constant effect that applied to all pairs of adjacent genes.

Abstract Background When DNA microarray data are used for gene clustering, genotype/phenotype correlation studies, or tissue classification the signal intensities are usually transformed and normalized in several steps in order to improve comparability and signal/noise ratio. These steps may include subtraction of an estimated background signal, subtracting the reference signal, smoothing (to account for nonlinear measurement effects), and more. Different authors use different approaches, and it is generally not clear to users which method they should prefer. Results We used the ratio between biological variance and measurement variance (which is an F-like statistic) as a quality measure for transformation methods, and we demonstrate a method for maximizing that variance ratio on real data. We explore a number of transformations issues, including Box-Cox transformation, baseline shift, partial subtraction of the log-reference signal and smoothing. It appears that the optimal choice of parameters for the transformation methods depends on the data. Further, the behavior of the variance ratio, under the null hypothesis of zero biological variance, appears to depend on the choice of parameters. Conclusions The use of replicates in microarray experiments is important. Adjustment for the null-hypothesis behavior of the variance ratio is critical to the selection of transformation method.

Abstract: Array comparative genomic hybridisation (aCGH) profiling is currently the gold standard for genetic diagnosis of copy number. Next generation sequencing technologies provide an alternative and adaptable method of detecting copy number by comparing the number of sequence reads in non-overlapping windows between patient and control samples. Detection of copy number using the BlueGnome 8×60k oligonucleotide aCGH platform was compared with low resolution next generation sequencing using the Illumina GAIIx on 39 patients with developmental delay and/or learning difficulties who were referred to the Leeds Clinical Cytogenetics Laboratory. Sensitivity and workflow of the two platforms were compared. Customised copy number algorithms assessed sequence counts and detected changes in copy number. Imbalances detected on both platforms were compared. Of the thirty-nine patients analysed, all eleven imbalances detected by array CGH and confirmed by FISH or Q-PCR were also detected by CNV-seq. In addition, CNV-seq reported one purported pathogenic copy number variant that was not detected by array CGH. Non-pathogenic, unconfirmed copy number calls were detected by both platforms; however few were concordant between the two. CNV-seq offers an alternative to array CGH for copy number analysis with resolution and future costs comparable to conventional array CGH platforms and with less stringent sample requirements. [Copyright &y& Elsevier]