Assessment and optimisation of normalisation methods for dual-colour antibody microarrays

<p>Abstract</p> <p>Background</p> <p>Recent advances in antibody microarray technology have made it possible to measure the expression of hundreds of proteins simultaneously in a competitive dual-colour approach similar to dual-colour gene expression microarrays. Thus, the established normalisation methods for gene expression microarrays, e.g. loess regression, can in principle be applied to protein microarrays. However, the typical assumptions of such normalisation methods might be violated due to a bias in the selection of the proteins to be measured. Due to high costs and limited availability of high quality antibodies, the current arrays usually focus on a high proportion of regulated targets. Housekeeping features could be used to circumvent this problem, but they are typically underrepresented on protein arrays. Therefore, it might be beneficial to select invariant features among the features already represented on available arrays for normalisation by a dedicated selection algorithm.</p> <p>Results</p> <p>We compare the performance of several normalisation methods that have been established for dual-colour gene expression microarrays. The focus is on an invariant selection algorithm, for which effective improvements are proposed. In a simulation study the performances of the different normalisation methods are compared with respect to their impact on the ability to correctly detect differentially expressed features. Furthermore, we apply the different normalisation methods to a pancreatic cancer data set to assess the impact on the classification power.</p> <p>Conclusions</p> <p>The simulation study and the data application demonstrate the superior performance of the improved invariant selection algorithms in comparison to other normalisation methods, especially in situations where the assumptions of the usual global loess normalisation are violated.</p

A semi-nonparametric mixture model for selecting functionally consistent proteins

Background High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experimental and statistical issues that have yet to be addressed, and that will carry over to new high-throughput technologies unless they are investigated. One of the largest of these challenges is the selection of functionally consistent proteins. Results We present a novel semi-nonparametric mixture model for classifying proteins as consistent or inconsistent while controlling the false discovery rate and the false non-discovery rate. The performance of the proposed approach is compared to current methods via simulation under a variety of experimental conditions. Conclusions We provide a statistical method for selecting functionally consistent proteins in the context of protein microarray experiments, but the proposed semi-nonparametric mixture model method can certainly be generalized to solve other mixture data problems. The main advantage of this approach is that it provides the posterior probability of consistency for each protein

Microfluidic processor allows rapid HER2 immunohistochemistry of breast carcinomas and significantly reduces ambiguous (2+) read-outs.

Biomarker analysis is playing an essential role in cancer diagnosis, prognosis, and prediction. Quantitative assessment of immunohistochemical biomarker expression on tumor tissues is of clinical relevance when deciding targeted treatments for cancer patients. Here, we report a microfluidic tissue processor that permits accurate quantification of the expression of biomarkers on tissue sections, enabled by the ultra-rapid and uniform fluidic exchange of the device. An important clinical biomarker for invasive breast cancer is human epidermal growth factor receptor 2 [(HER2), also known as neu], a transmembrane tyrosine kinase that connotes adverse prognostic information for the patients concerned and serves as a target for personalized treatment using the humanized antibody trastuzumab. Unfortunately, when using state-of-the-art methods, the intensity of an immunohistochemical signal is not proportional to the extent of biomarker expression, causing ambiguous outcomes. Using our device, we performed tests on 76 invasive breast carcinoma cases expressing various levels of HER2. We eliminated more than 90% of the ambiguous results (n = 27), correctly assigning cases to the amplification status as assessed by in situ hybridization controls, whereas the concordance for HER2-negative (n = 31) and -positive (n = 18) cases was 100%. Our results demonstrate the clinical potential of microfluidics for accurate biomarker expression analysis. We anticipate our technique will be a diagnostic tool that will provide better and more reliable data, onto which future treatment regimes can be based