Scanner calibration revisited

<p>Abstract</p> <p>Background</p> <p>Calibration of a microarray scanner is critical for accurate interpretation of microarray results. Shi et al. (<it>BMC Bioinformatics</it>, 2005, <b>6</b>, Art. No. S11 Suppl. 2.) reported usage of a Full Moon BioSystems slide for calibration. Inspired by the Shi et al. work, we have calibrated microarray scanners in our previous research. We were puzzled however, that most of the signal intensities from a biological sample fell below the sensitivity threshold level determined by the calibration slide. This conundrum led us to re-investigate the quality of calibration provided by the Full Moon BioSystems slide as well as the accuracy of the analysis performed by Shi et al.</p> <p>Methods</p> <p>Signal intensities were recorded on three different microarray scanners at various photomultiplier gain levels using the same calibration slide from Full Moon BioSystems. Data analysis was conducted on raw signal intensities without normalization or transformation of any kind. Weighted least-squares method was used to fit the data.</p> <p>Results</p> <p>We found that initial analysis performed by Shi et al. did not take into account autofluorescence of the Full Moon BioSystems slide, which led to a grossly distorted microarray scanner response. Our analysis revealed that a power-law function, which is explicitly accounting for the slide autofluorescence, perfectly described a relationship between signal intensities and fluorophore quantities.</p> <p>Conclusions</p> <p>Microarray scanners respond in a much less distorted fashion than was reported by Shi et al. Full Moon BioSystems calibration slides are inadequate for performing calibration. We recommend against using these slides.</p

Structural similarity assessment for drug sensitivity prediction in cancer

<p>Abstract</p> <p>Background</p> <p>The ability to predict drug sensitivity in cancer is one of the exciting promises of pharmacogenomic research. Several groups have demonstrated the ability to predict drug sensitivity by integrating chemo-sensitivity data and associated gene expression measurements from large anti-cancer drug screens such as NCI-60. The general approach is based on comparing gene expression measurements from sensitive and resistant cancer cell lines and deriving drug sensitivity profiles consisting of lists of genes whose expression is predictive of response to a drug. Importantly, it has been shown that such profiles are generic and can be applied to cancer cell lines that are not part of the anti-cancer screen. However, one limitation is that the profiles can not be generated for untested drugs (i.e., drugs that are not part of an anti-cancer drug screen). In this work, we propose using an existing drug sensitivity profile for drug A as a substitute for an untested drug B given high structural similarities between drugs A and B.</p> <p>Results</p> <p>We first show that structural similarity between pairs of compounds in the NCI-60 dataset highly correlates with the similarity between their activities across the cancer cell lines. This result shows that structurally similar drugs can be expected to have a similar effect on cancer cell lines. We next set out to test our hypothesis that we can use existing drug sensitivity profiles as substitute profiles for untested drugs. In a cross-validation experiment, we found that the use of substitute profiles is possible without a significant loss of prediction accuracy if the substitute profile was generated from a compound with high structural similarity to the untested compound.</p> <p>Conclusion</p> <p>Anti-cancer drug screens are a valuable resource for generating omics-based drug sensitivity profiles. We show that it is possible to extend the usefulness of existing screens to untested drugs by deriving substitute sensitivity profiles from structurally similar drugs part of the screen.</p

The particle carriers of field-aligned currents in the Earth's magnetotail during a substorm

Although the particle carriers of field-aligned currents (FACs) in the Earth's magnetotail play an important role in the transfer of momentum and energy between the solar wind, magnetosphere, and ionosphere, the characteristics of the FAC carriers have been poorly understood. Taking advantage of multiinstrument magnetic field and plasma data collected by the four spacecraft of the Cluster constellation as they traversed the northern plasma sheet boundary layer in the magnetotail on 14 September 2004, we identified the species type and energy range of the FAC carriers for the first time. The results indicate that part of tailward FACs is carried by energetic keV ions, which are probably originated from the ionosphere through outflow, and they are not too small (~2 nA/m2) to be ignored. The earthward (tailward) FACs are mainly carried by the dominant tailward (earthward) motion of electrons, and higher-energy electrons (from ~0.5 to 26 keV) are the main carriers

Cross-Platform Comparison of Microarray-Based Multiple-Class Prediction

High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets

Criteria for the use of omics-based predictors in clinical trials.

The US National Cancer Institute (NCI), in collaboration with scientists representing multiple areas of expertise relevant to 'omics'-based test development, has developed a checklist of criteria that can be used to determine the readiness of omics-based tests for guiding patient care in clinical trials. The checklist criteria cover issues relating to specimens, assays, mathematical modelling, clinical trial design, and ethical, legal and regulatory aspects. Funding bodies and journals are encouraged to consider the checklist, which they may find useful for assessing study quality and evidence strength. The checklist will be used to evaluate proposals for NCI-sponsored clinical trials in which omics tests will be used to guide therapy

The EDKB: an established knowledge base for endocrine disrupting chemicals

<p>Abstract</p> <p>Background</p> <p>Endocrine disruptors (EDs) and their broad range of potential adverse effects in humans and other animals have been a concern for nearly two decades. Many putative EDs are widely used in commercial products regulated by the Food and Drug Administration (FDA) such as food packaging materials, ingredients of cosmetics, medical and dental devices, and drugs. The Endocrine Disruptor Knowledge Base (EDKB) project was initiated in the mid 1990’s by the FDA as a resource for the study of EDs. The EDKB database, a component of the project, contains data across multiple assay types for chemicals across a broad structural diversity. This paper demonstrates the utility of EDKB database, an integral part of the EDKB project, for understanding and prioritizing EDs for testing.</p> <p>Results</p> <p>The EDKB database currently contains 3,257 records of over 1,800 EDs from different assays including estrogen receptor binding, androgen receptor binding, uterotropic activity, cell proliferation, and reporter gene assays. Information for each compound such as chemical structure, assay type, potency, etc. is organized to enable efficient searching. A user-friendly interface provides rapid navigation, Boolean searches on EDs, and both spreadsheet and graphical displays for viewing results. The search engine implemented in the EDKB database enables searching by one or more of the following fields: chemical structure (including exact search and similarity search), name, molecular formula, CAS registration number, experiment source, molecular weight, etc. The data can be cross-linked to other publicly available and related databases including TOXNET, Cactus, ChemIDplus, ChemACX, Chem Finder, and NCI DTP. </p> <p>Conclusion</p> <p>The EDKB database enables scientists and regulatory reviewers to quickly access ED data from multiple assays for specific or similar compounds. The data have been used to categorize chemicals according to potential risks for endocrine activity, thus providing a basis for prioritizing chemicals for more definitive but expensive testing. The EDKB database is publicly available and can be found online at <url>http://edkb.fda.gov/webstart/edkb/index.html</url>.</p> <p><b>Disclaimer:</b><it>The views presented in this article do not necessarily reflect those of the US Food and Drug Administration.</it></p

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs

This article provides an overview of methods for reliability assessment of quantitative structure–activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation. Key words: QSAR acceptability criteria, QSAR applicability domain, QSAR reliability, QSAR uncertainty estimation, QSAR validation