Three methods for optimization of cross-laboratory and cross-platform microarray expression data

Development and Validation of a Prognostic Autophagy-Related Gene Pair Index Related to Tumor-Infiltrating Lymphocytes in Early-Stage Lung Adenocarcinoma

The role of autophagy in lung cancer is context-dependent and complex. Recent studies have reported the important role of autophagy in tumor immune escape. However, the association between autophagy and tumor-infiltrating lymphocytes (TILs) in early-stage lung adenocarcinoma (LUAD) remains unclear. In this study, we aimed to develop and validate the autophagy-related gene pair index (ATGPI) and autophagy clinical prognostic index (ACPI) in multiple LUAD cohorts, including The Cancer Genome Atlas (TCGA) cohort, Gene Expression Omnibus cohorts, and one cohort from Union Hospital, Wuhan (UH cohort), using a Cox proportional hazards regression model with the least absolute shrinkage and selection operator. Multivariate Cox regression analysis demonstrated that there was a significant difference in overall survival (OS) between patients with high and low ATGPI in the testing [hazard ratio (HR) = 1.97; P < 0.001] and TCGA validation (HR = 2.25; P < 0.001) cohorts. Time-dependent receiver operating characteristic curve analysis was also performed. We found that high ATGPI could accurately identify patients with early-stage LUAD with shorter OS, with the areas under the curve of 0.703 and 0.676 in the testing and TCGA validation cohorts, respectively. Concordance index (C-index) was used to evaluate the efficiency of ATGPI and ACPI. The C-index of ACPI was higher than that of ATGPI in the testing (0.71 vs. 0.66; P < 0.001), TCGA validation (0.69 vs. 0.65; P = 0.028), and UH (0.80 vs. 0.70; P = 0.015) cohorts. TIL analysis demonstrated that the proportions of tumor-infiltrating CD4⁺ T cells were lower in the high-ATGPI group than in the low-ATGPI group in both the TCGA validation and UH cohorts. These results indicate the potential clinical use of ATG signatures which are associated with TILs, in identifying patients with early-stage LUAD with different OS.

Predicted Immunogenicity of CDK12 Biallelic Loss-of-Function Tumors Varies across Cancer Types

CDK12 biallelic inactivation is associated with a distinct genomic signature of focal tandem duplications (FTDs). Gene fusions resulting from FTDs increase neoantigen load, raising interest in CDK12 as a biomarker of response to immune checkpoint inhibitors. Despite evidence of FTDs in multiple CDK12-altered cancer types, notably prostate and ovarian, report of fusion-associated neoantigen load is limited to prostate cancer. Molecular profiles were retrospectively reviewed for CDK12-biallelic (CDK12-biLOF) and -monoallelic loss-of-function (CDK12-monoLOF) in a primary cohort of >9000 tumors, representing 39 cancer types, and immune epitopes were predicted from fusions detected by whole transcriptome sequencing. CDK12-biLOF was identified for 0.3% tumors overall, most frequently in prostate cancer (4.7%). CDK12-biLOF tumors had higher mean fusion rates and fusion-associated neoantigen load than CDK12-monoLOF and CDK12-WT tumors (P < 0.01). However, concurrent mismatch repair deficiency/microsatellite instability with CDK12-biLOF associated with low fusion rates. Among CDK12-biLOF tumors, fusion-associated neoantigen load was highest in prostate and ovarian cancers, which correlated with distinct immune profiles. In a validation cohort, CDK12-biLOF tumors (0.4%) exhibited high mean fusion rates, particularly for prostate and ovarian tumors. Low fusion rates in other CDK12-biLOF tumor types warrant further investigation and highlight the value of quantitative biomarkers. Fusion rate and fusion-associated neoantigen load are linked to CDK12-biLOF in select cancers and may help to identify responders of immune checkpoint inhibitor therapy.

Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type

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CUP occurs in as many as 3–5% of patients when standard diagnostic tests are not able to determine the origin of cancer.

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MI GPSai (Genomic Prevalence Score) is an AI that uses genomic and transcriptomic data to elucidate tumor origin.

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The algorithm was trained on molecular data from 57,489 cases and validated on 19,555 cases.

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MI GPSai predicted the tumor type out of 21 options in the labeled data set with an accuracy of over 94% on 93% of cases.

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When also considering the second highest prediction, the accuracy increases to 97%.

Cancer of Unknown Primary (CUP) occurs in 3–5% of patients when standard histological diagnostic tests are unable to determine the origin of metastatic cancer. Typically, a CUP diagnosis is treated empirically and has very poor outcomes, with median overall survival less than one year. Gene expression profiling alone has been used to identify the tissue of origin but struggles with low neoplastic percentage in metastatic sites which is where identification is often most needed. MI GPSai, a Genomic Prevalence Score, uses DNA sequencing and whole transcriptome data coupled with machine learning to aid in the diagnosis of cancer. The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai predicted the tumor type in the labeled data set with an accuracy of over 94% on 93% of cases while deliberating amongst 21 possible categories of cancer. When also considering the second highest prediction, the accuracy increases to 97%. Additionally, MI GPSai rendered a prediction for 71.7% of CUP cases. Pathologist evaluation of discrepancies between submitted diagnosis and MI GPSai predictions resulted in change of diagnosis in 41.3% of the time. MI GPSai provides clinically meaningful information in a large proportion of CUP cases and inclusion of MI GPSai in clinical routine could improve diagnostic fidelity. Moreover, all genomic markers essential for therapy selection are assessed in this assay, maximizing the clinical utility for patients within a single test.

Scalable high-density peptide arrays for comprehensive health monitoring

There is an increasing awareness that health care must move from post-symptomatic treatment to presymptomatic intervention. An ideal system would allow regular inexpensive monitoring of health status using circulating antibodies to report on health fluctuations. Recently, we demonstrated that peptide microarrays can do this through antibody signatures (immunosignatures). Unfortunately, printed microarrays are not scalable. Here we demonstrate a platform based on fabricating microarrays (~10 M peptides per slide, 330,000 peptides per assay) on silicon wafers using equipment common to semiconductor manufacturing. The potential of these microarrays for comprehensive health monitoring is verified through the simultaneous detection and classification of six different infectious diseases and six different cancers. Besides diagnostics, these high-density peptide chips have numerous other applications both in health care and elsewhere.

Aptamer-guided gene targeting in yeast and human cells

Gene targeting is a genetic technique to modify an endogenous DNA sequence in its genomic location via homologous recombination (HR) and is useful both for functional analysis and gene therapy applications. HR is inefficient in most organisms and cell types, including mammalian cells, often limiting the effectiveness of gene targeting. Therefore, increasing HR efficiency remains a major challenge to DNA editing. Here, we present a new concept for gene correction based on the development of DNA aptamers capable of binding to a site-specific DNA binding protein to facilitate the exchange of homologous genetic information between a donor molecule and the desired target locus (aptamer-guided gene targeting). We selected DNA aptamers to the I-SceI endonuclease. Bifunctional oligonucleotides containing an I-SceI aptamer sequence were designed as part of a longer single-stranded DNA molecule that contained a region with homology to repair an I-SceI generated double-strand break and correct a disrupted gene. The I-SceI aptamer-containing oligonucleotides stimulated gene targeting up to 32-fold in yeast Saccharomyces cerevisiae and up to 16-fold in human cells. This work provides a novel concept and research direction to increase gene targeting efficiency and lays the groundwork for future studies using aptamers for gene targeting.

A comparative analysis of transcription factor expression during metazoan embryonic development

During embryonic development, a complex organism is formed from a single starting cell. These processes of growth and differentiation are driven by large transcriptional changes, which are following the expression and activity of transcription factors (TFs). This study sought to compare TF expression during embryonic development in a diverse group of metazoan animals: representatives of vertebrates (Danio rerio, Xenopus tropicalis), a chordate (Ciona intestinalis) and invertebrate phyla such as insects (Drosophila melanogaster, Anopheles gambiae) and nematodes (Caenorhabditis elegans) were sampled, The different species showed overall very similar TF expression patterns, with TF expression increasing during the initial stages of development. C2H2 zinc finger TFs were over-represented and Homeobox TFs were under-represented in the early stages in all species. We further clustered TFs for each species based on their quantitative temporal expression profiles. This showed very similar TF expression trends in development in vertebrate and insect species. However, analysis of the expression of orthologous pairs between more closely related species showed that expression of most individual TFs is not conserved, following the general model of duplication and diversification. The degree of similarity between TF expression between Xenopus tropicalis and Danio rerio followed the hourglass model, with the greatest similarity occuring during the early tailbud stage in Xenopus tropicalis and the late segmentation stage in Danio rerio. However, for Drosophila melanogaster and Anopheles gambiae there were two periods of high TF transcriptome similarity, one during the Arthropod phylotypic stage at 8-10 hours into Drosophila development and the other later at 16-18 hours into Drosophila development.

StRAP: an integrated resource for profiling high-throughput cancer genomic data from stress response studies

The increasing availability and maturity of DNA microarray technology has led to an explosion of cancer profiling studies for identifying cancer biomarkers, and predicting treatment response. Uncovering complex relationships, however, remains the most challenging task as it requires compiling and efficiently querying data from various sources. Here, we describe the Stress Response Array Profiler (StRAP), an open-source, web-based resource for storage, profiling, visualization, and sharing of cancer genomic data. StRAP houses multi-cancer microarray data with major emphasis on radiotherapy studies, and takes a systems biology approach towards the integration, comparison, and cross-validation of multiple cancer profiling studies. The database is a comprehensive platform for comparative analysis of gene expression data. For effective use of arrays, we provide user-friendly and interactive visualization tools that can display the data and query results. StRAP is web-based, platform-independent, and freely accessible at http://strap.nci.nih.gov/.

Robust microarray meta-analysis identifies differentially expressed genes for clinical prediction

Combining multiple microarray datasets increases sample size and leads to improved reproducibility in identification of informative genes and subsequent clinical prediction. Although microarrays have increased the rate of genomic data collection, sample size is still a major issue when identifying informative genetic biomarkers. Because of this, feature selection methods often suffer from false discoveries, resulting in poorly performing predictive models. We develop a simple meta-analysis-based feature selection method that captures the knowledge in each individual dataset and combines the results using a simple rank average. In a comprehensive study that measures robustness in terms of clinical application (i.e., breast, renal, and pancreatic cancer), microarray platform heterogeneity, and classifier (i.e., logistic regression, diagonal LDA, and linear SVM), we compare the rank average meta-analysis method to five other meta-analysis methods. Results indicate that rank average meta-analysis consistently performs well compared to five other meta-analysis methods.

Comparative study of classification algorithms for immunosignaturing data

BACKGROUND

High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data.

RESULTS

We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found 'Naïve Bayes' far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy.

CONCLUSIONS

'Naïve Bayes' algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties.

Multipurpose instantaneous microarray detection of acute encephalitis causing viruses and their expression profiles

Detection of multiple viruses is important for global analysis of gene or protein content and expression, opening up new prospects in terms of molecular and physiological systems for pathogenic diagnosis. Early diagnosis is crucial for disease treatment and control as it reduces inappropriate use of antiviral therapy and focuses surveillance activity. This requires the ability to detect and accurately diagnose infection at or close to the source/outbreak with minimum delay and the need for specific, accessible point-of-care diagnosis able to distinguish causative viruses and their subtypes. None of the available viral diagnostic assays combine a point-of-care format with the complex capability to identify a large range of human and animal viruses. Microarray detection provides a useful, labor-saving tool for detection of multiple viruses with several advantages, such as convenience and prevention of cross-contamination of polymerase chain reaction (PCR) products, which is of foremost importance in such applications. Recently, real-time PCR assays with the ability to confirm the amplification product and quantitate the target concentration have been developed. Furthermore, nucleotide sequence analysis of amplification products has facilitated epidemiological studies of infectious disease outbreaks and monitoring of treatment outcomes for infections, in particular for viruses that mutate at high frequency. This review discusses applications of microarray technology as a potential new tool for detection and identification of acute encephalitis-causing viruses in human serum, plasma, and cell cultures.

Gene and protein expression signature of endometrial glandular and stromal compartments during the window of implantation

OBJECTIVE

To map the changes in messenger RNA (mRNA) and protein abundance during the window of implantation in specifically endometrial stromal and glandular epithelial cells obtained using laser microdissection microscopy (LDM).

DESIGN

Endometrial samples were collected from two menstrual cycles at 2 and 7 days after first significant rise in blood LH, and separate cell populations were obtained using LDM. A new generation linear polymerase chain reaction (PCR) amplified the mRNA, which were hybridized to both Affymetrix U133 Plus2 and Agilent 4x44K microarrays followed by gene set analysis. Immunohistochemistry assessed protein expression between the two collection times.

SETTING

In vitro fertilization clinic.

PATIENT(S)

Nine Caucasian, fertile, cycling women.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Cycle dating using blood markers; microarrays on laser microdissected glands and stroma; dual platform microarray confirmation; immunohistochemical analysis of cell cycle proteins.

RESULT(S)

The two microarray platforms showed concordance. During the window of implantation, a statistically significant network of 22 mRNA associated with the cell cycle was down-regulated. Immunohistochemistry identified altered localization in stroma.

CONCLUSION(S)

Microarrays demonstrated glands and stroma have distinct mRNA signatures, each dependent on the day of the cycle. We characterized two compartments of the receptive endometrium with a transcriptomic signature identifying regulation of only the cell cycle. Immunohistochemical analysis of cell cycle proteins identified a signature staining pattern of nuclear relocalization of a group of cyclins of stromal cells, which may be clinically applicable.

Physical characterization of the "immunosignaturing effect"

Identifying new, effective biomarkers for diseases is proving to be a challenging problem. We have proposed that antibodies may offer a solution to this problem. The physical features and abundance of antibodies make them ideal biomarkers. Additionally, antibodies are often elicited early in the ontogeny of different chronic and infectious diseases. We previously reported that antibodies from patients with infectious disease and separately those with Alzheimer's disease display a characteristic and reproducible "immunosignature" on a microarray of 10,000 random sequence peptides. Here we investigate the physical and chemical parameters underlying how immunosignaturing works. We first show that a variety of monoclonal and polyclonal antibodies raised against different classes of antigens produce distinct profiles on this microarray and the relative affinities are determined. A proposal for how antibodies bind the random sequences is tested. Sera from vaccinated mice and people suffering from a fugal infection are individually assayed to determine the complexity of signals that can be distinguished. Based on these results, we propose that this simple, general and inexpensive system could be optimized to generate a new class of antibody biomarkers for a wide variety of diseases.

Evaluation of biological sample preparation for immunosignature-based diagnostics

To address the need for a universal system to assess health status, we previously described a method termed "immunosignaturing" which splays the entire humoral antibody repertoire across a peptide microarray. Two important issues relative to the potential broad use of immunosignatures are sample preparation and stability. In the present study, we compared the immunosignatures developed from serum, plasma, saliva, and antibodies eluted from blood dried onto filter paper. We found that serum and plasma provide identical immunosignatures. Immunosignatures derived from dried blood also correlated well with those from nondried serum from the same individual. Immunosignatures derived from dried blood were capable of distinguishing naïve mice from those infected with influenza virus. Saliva was applied to the arrays, and the IgA immunosignature correlated strongly with that from dried blood. Finally, we demonstrate that dried blood retains immunosignature information even when exposed to high temperature. This work expands the potential diagnostic uses for immunosignatures. These features suggest that different forms of archival samples can be used for diagnosis development and that in prospective studies samples can be easily procured.

Multiscale integration of -omic, imaging, and clinical data in biomedical informatics

This paper reviews challenges and opportunities in multiscale data integration for biomedical informatics. Biomedical data can come from different biological origins, data acquisition technologies, and clinical applications. Integrating such data across multiple scales (e.g., molecular, cellular/tissue, and patient) can lead to more informed decisions for personalized, predictive, and preventive medicine. However, data heterogeneity, community standards in data acquisition, and computational complexity are big challenges for such decision making. This review describes genomic and proteomic (i.e., molecular), histopathological imaging (i.e., cellular/tissue), and clinical (i.e., patient) data; it includes case studies for single-scale (e.g., combining genomic or histopathological image data), multiscale (e.g., combining histopathological image and clinical data), and multiscale and multiplatform (e.g., the Human Protein Atlas and The Cancer Genome Atlas) data integration. Numerous opportunities exist in biomedical informatics research focusing on integration of multiscale and multiplatform data.

Multi-platform data integration in microarray analysis

An increasing number of studies have profiled gene expressions in tumor specimens using distinct microarray platforms and analysis techniques. One challenging task is to develop robust statistical models in order to integrate multi-platform findings. We compare some methodologies on the field with respect to estrogen receptor (ER) status, and focus on a unified-among-platforms scale implemented by Shen et al. in 2004, which is based on a Bayesian mixture model. Under this scale, we study the ER intensity similarities between four breast cancer datasets derived from various platforms. We evaluate our results with an independent dataset in terms of ER sample classification, given the derived gene ER signatures of the integrated data. We found that integrated multi-platform gene signatures and fold-change variability similarities between different platform measurements can assist the statistical analysis of independent microarray datasets in terms of ER classification.

Application of immunosignatures to the assessment of Alzheimer's disease

OBJECTIVE

Accurate assessment of Alzheimer's disease (AD), both presymptomatically and at different disease stages, will become increasingly important with the expanding elderly population. There are a number of indications that the immune system is engaged in AD. Here we explore the ability of an antibody-profiling technology to characterize AD and screen for peptides that may be used for a simple diagnostic test.

METHODS

We developed an array-based system to profile the antibody repertoire of transgenic mice with cerebral amyloidosis (TG) and elderly individuals with or without AD. The array consists of 10,000 random sequence peptides (20-mers) capable of detecting antibody binding patterns, allowing the identification of peptides that mimic epitopes targeted by a donor's serum.

RESULTS

TG mice exhibited a distinct immunoprofile compared to nontransgenic littermates. Further, we show that dementia patients, including autopsy-confirmed AD subjects, have distinguishable profiles compared to age-matched nondemented people. Using antibodies to different forms of Aβ peptide and blocking protocols, we demonstrate that most of this signature is not due to the subject's antibodies raised against Aβ.

INTERPRETATION

We propose that "immunosignaturing" technology may have potential as a diagnostic tool in AD.

Evaluating methods for ranking differentially expressed genes applied to microArray quality control data

Background

Statistical methods for ranking differentially expressed genes (DEGs) from gene expression data should be evaluated with regard to high sensitivity, specificity, and reproducibility. In our previous studies, we evaluated eight gene ranking methods applied to only Affymetrix GeneChip data. A more general evaluation that also includes other microarray platforms, such as the Agilent or Illumina systems, is desirable for determining which methods are suitable for each platform and which method has better inter-platform reproducibility.

Results

We compared the eight gene ranking methods using the MicroArray Quality Control (MAQC) datasets produced by five manufacturers: Affymetrix, Applied Biosystems, Agilent, GE Healthcare, and Illumina. The area under the curve (AUC) was used as a measure for both sensitivity and specificity. Although the highest AUC values can vary with the definition of "true" DEGs, the best methods were, in most cases, either the weighted average difference (WAD), rank products (RP), or intensity-based moderated t statistic (ibmT). The percentages of overlapping genes (POGs) across different test sites were mainly evaluated as a measure for both intra- and inter-platform reproducibility. The POG values for WAD were the highest overall, irrespective of the choice of microarray platform. The high intra- and inter-platform reproducibility of WAD was also observed at a higher biological function level.

Conclusion

These results for the five microarray platforms were consistent with our previous ones based on 36 real experimental datasets measured using the Affymetrix platform. Thus, recommendations made using the MAQC benchmark data might be universally applicable.

The ketogenic diet reverses gene expression patterns and reduces reactive oxygen species levels when used as an adjuvant therapy for glioma

BACKGROUND

Malignant brain tumors affect people of all ages and are the second leading cause of cancer deaths in children. While current treatments are effective and improve survival, there remains a substantial need for more efficacious therapeutic modalities. The ketogenic diet (KD) - a high-fat, low-carbohydrate treatment for medically refractory epilepsy - has been suggested as an alternative strategy to inhibit tumor growth by altering intrinsic metabolism, especially by inducing glycopenia.

METHODS

Here, we examined the effects of an experimental KD on a mouse model of glioma, and compared patterns of gene expression in tumors vs. normal brain from animals fed either a KD or a standard diet.

RESULTS

Animals received intracranial injections of bioluminescent GL261-luc cells and tumor growth was followed in vivo. KD treatment significantly reduced the rate of tumor growth and prolonged survival. Further, the KD reduced reactive oxygen species (ROS) production in tumor cells. Gene expression profiling demonstrated that the KD induces an overall reversion to expression patterns seen in non-tumor specimens. Notably, genes involved in modulating ROS levels and oxidative stress were altered, including those encoding cyclooxygenase 2, glutathione peroxidases 3 and 7, and periredoxin 4.

CONCLUSIONS

Our data demonstrate that the KD improves survivability in our mouse model of glioma, and suggests that the mechanisms accounting for this protective effect likely involve complex alterations in cellular metabolism beyond simply a reduction in glucose.

Quality assessment of transcriptome data using intrinsic statistical properties

In view of potential application to biomedical diagnosis, tight transcriptome data quality control is compulsory. Usually, quality control is achieved using labeling and hybridization controls added at different stages throughout the processing of the biologic RNA samples. These control measures, however, only reflect the performance of the individual technical manipulations during the entire process and have no bearing as to the continued integrity of the RNA sample itself. Here we demonstrate that intrinsic statistical properties of the resulting transcriptome data signal and signal-variance distributions and their invariance can be identified independently of the animal species studied and the labeling protocol used. From these invariant properties we have developed a data model, the parameters of which can be estimated from individual experiments and used to compute relative quality measures based on similarity with large reference datasets. These quality measures add supplementary, non-redundant information to standard quality control estimates based on spike-in and hybridization controls, and are exploitable in data analysis. A software application for analyzing datasets as well as a reference dataset for AB1700 arrays are provided. They should allow AB1700 users to easily integrate this method into their analysis pipeline, and might instigate similar developments for other transcriptome platforms.

Cross-platform analysis of global microRNA expression technologies

Background

Although analysis of microRNAs (miRNAs) by DNA microarrays is gaining in popularity, these new technologies have not been adequately validated. We examined within and between platform reproducibility of four miRNA array technologies alongside TaqMan PCR arrays.

Results

Two distinct pools of reference materials were selected in order to maximize differences in miRNA content. Filtering for miRNA that yielded signal above background revealed 54 miRNA probes (matched by sequence) across all platforms. Using this probeset as well as all probes that were present on an individual platform, within-platform analyses revealed Spearman correlations of >0.9 for most platforms. Comparing between platforms, rank analysis of the log ratios of the two reference pools also revealed high correlation (range 0.663-0.949). Spearman rank correlation and concordance correlation coefficients for miRNA arrays against TaqMan qRT-PCR arrays were similar for all of the technologies. Platform performances were similar to those of previous cross-platform exercises on mRNA and miRNA microarray technologies.

Conclusions

These data indicate that miRNA microarray platforms generated highly reproducible data and can be recommended for the study of changes in miRNA expression.

A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

BACKGROUND

Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for the observed discrepancies are the measurement error associated with each feature and the choice of preprocessing method. Microarray data are known to be subject to technical variation and the confidence intervals around individual point estimates of expression levels can be wide. Furthermore, the estimated expression values also vary depending on the selected preprocessing scheme. In microarray breast cancer classification studies, however, these two forms of feature variability are almost always ignored and hence their exact role is unclear.

RESULTS

We have performed a comprehensive sensitivity analysis of microarray breast cancer classification under the two types of feature variability mentioned above. We used data from six state of the art preprocessing methods, using a compendium consisting of eight different datasets, involving 1131 hybridizations, containing data from both one and two-color array technology. For a wide range of classifiers, we performed a joint study on performance, concordance and stability. In the stability analysis we explicitly tested classifiers for their noise tolerance by using perturbed expression profiles that are based on uncertainty information directly related to the preprocessing methods. Our results indicate that signature composition is strongly influenced by feature variability, even if the array platform and the stratification of patient samples are identical. In addition, we show that there is often a high level of discordance between individual class assignments for signatures constructed on data coming from different preprocessing schemes, even if the actual signature composition is identical.

CONCLUSION

Feature variability can have a strong impact on breast cancer signature composition, as well as the classification of individual patient samples. We therefore strongly recommend that feature variability is considered in analyzing data from microarray breast cancer expression profiling experiments.

Development and evaluation of normalization methods for label-free relative quantification of endogenous peptides

The performances of 10 different normalization methods on data of endogenous brain peptides produced with label-free nano-LC-MS were evaluated. Data sets originating from three different species (mouse, rat, and Japanese quail), each consisting of 35-45 individual LC-MS analyses, were used in the study. Each sample set contained both technical and biological replicates, and the LC-MS analyses were performed in a randomized block fashion. Peptides in all three data sets were found to display LC-MS analysis order-dependent bias. Global normalization methods will only to some extent correct this type of bias. Only the novel normalization procedure RegrRun (linear regression followed by analysis order normalization) corrected for this type of bias. The RegrRun procedure performed the best of the normalization methods tested and decreased the median S.D. by 43% on average compared with raw data. This method also produced the smallest fraction of peptides with interblock differences while producing the largest fraction of differentially expressed peaks between treatment groups in all three data sets. Linear regression normalization (Regr) performed second best and decreased median S.D. by 38% on average compared with raw data. All other examined methods reduced median S.D. by 20-30% on average compared with raw data.

Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips

Interlaboratory comparison of microarray data, even when using the same platform, imposes several challenges to scientists. RNA quality, RNA labeling efficiency, hybridization procedures and data-mining tools can all contribute variations in each laboratory. In Affymetrix GeneChips, about 11–20 different 25-mer oligonucleotides are used to measure the level of each transcript. Here, we report that ‘labeling extension values (LEVs)’, which are correlation coefficients between probe intensities and probe positions, are highly correlated with the gene expression levels (GEVs) on eukayotic Affymetrix microarray data. By analyzing LEVs and GEVs in the publicly available 2414 cel files of 20 Affymetrix microarray types covering 13 species, we found that correlations between LEVs and GEVs only exist in eukaryotic RNAs, but not in prokaryotic ones. Surprisingly, Affymetrix results of the same specimens that were analyzed in different laboratories could be clearly differentiated only by LEVs, leading to the identification of ‘laboratory signatures’. In the examined dataset, GSE10797, filtering out high-LEV genes did not compromise the discovery of biological processes that are constructed by differentially expressed genes. In conclusion, LEVs provide a new filtering parameter for microarray analysis of gene expression and it may improve the inter- and intralaboratory comparability of Affymetrix GeneChips data.

Coincidence between transcriptome analyses on different microarray platforms using a parametric framework

A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Microarrays are widely employed to acquire transcriptome information, and several platforms of chips are currently in use. However, discrepancies among studies are frequently reported, particularly among those performed using different platforms, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks are based on different philosophies and yield different results, but all involve normalization against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using several slide-glass-type chips and GeneChip. The model is based on a common statistical characteristic of microarray data, and each set of chip data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other frameworks.

Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells

Background

In this study we assessed the respective ability of Affymetrix and Illumina microarray methodologies to answer a relevant biological question, namely the change in gene expression between resting monocytes and macrophages derived from these monocytes. Five RNA samples for each type of cell were hybridized to the two platforms in parallel. In addition, a reference list of differentially expressed genes (DEG) was generated from a larger number of hybridizations (mRNA from 86 individuals) using the RNG/MRC two-color platform.

Results

Our results show an important overlap of the Illumina and Affymetrix DEG lists. In addition, more than 70% of the genes in these lists were also present in the reference list. Overall the two platforms had very similar performance in terms of biological significance, evaluated by the presence in the DEG lists of an excess of genes belonging to Gene Ontology (GO) categories relevant for the biology of monocytes and macrophages. Our results support the conclusion of the MicroArray Quality Control (MAQC) project that the criteria used to constitute the DEG lists strongly influence the degree of concordance among platforms. However the importance of prioritizing genes by magnitude of effect (fold change) rather than statistical significance (p-value) to enhance cross-platform reproducibility recommended by the MAQC authors was not supported by our data.

Conclusion

Functional analysis based on GO enrichment demonstrates that the 2 compared technologies delivered very similar results and identified most of the relevant GO categories enriched in the reference list.

Improved microarray gene expression profiling of virus-infected cells after removal of viral RNA

Background

Sensitivity and accuracy are key points when using microarrays to detect alterations in gene expression under different conditions. Critical to the acquisition of reliable results is the preparation of the RNA. In the field of virology, when analyzing the host cell's reaction to infection, the often high representation of viral RNA (vRNA) within total RNA preparations from infected cells is likely to interfere with microarray analysis. Yet, this effect has not been investigated despite the many reports that describe gene expression profiling of virus-infected cells using microarrays.

Results

In this study we used coronaviruses as a model to show that vRNA indeed interferes with microarray analysis, decreasing both sensitivity and accuracy. We also demonstrate that the removal of vRNA from total RNA samples, by means of virus-specific oligonucleotide capturing, significantly reduced the number of false-positive hits and increased the sensitivity of the method as tested on different array platforms.

Conclusion

We therefore recommend the specific removal of vRNA, or of any other abundant 'contaminating' RNAs, from total RNA samples to improve the quality and reliability of microarray analyses.

Discovery of ADP-ribosylation and other plant defense pathway elements through expression profiling of four different Arabidopsis-Pseudomonas R-avr interactions

A dissection of plant defense pathways was initiated through gene expression profiling of the responses of a single Arabidopsis thaliana genotype to isogenic Pseudomonas syringae strains expressing one of four different cloned avirulence (avr) genes. Differences in the expression profiles elicited by different resistance (R)-avr interactions were observed. A role for poly(ADP-ribosyl)ation in plant defense responses was suggested initially by the upregulated expression of genes encoding NUDT7 and poly(ADP-ribose) glycohydrolase in multiple R-avr interactions. Gene knockout plant lines were tested for 20 candidate genes identified by the expression profiling, and Arabidopsis NUDT7 mutants allowed less growth of virulent P. syringae (as previously reported) but also exhibited a reduced hypersensitive-response phenotype. Inhibitors of poly(ADP-ribose) polymerase (PARP) disrupted FLS2-mediated basal defense responses such as callose deposition. EIN2 (ethylene response) and IXR1 and IXR2 (cellulose synthase) mutants impacted the FLS2-mediated responses that occur during PARP inhibition, whereas no impacts were observed for NPR1, PAD4, or NDR1 mutants. In the expression profiling work, false-positive selection and grouping of genes was reduced by requiring simultaneous satisfaction of statistical significance criteria for each of three separate analysis methods, and by clustering genes based on statistical confidence values for each gene rather than on average fold-change of transcript abundance.

Development of genomic markers that predict response to molecularly targeted antileukemic therapy

BACKGROUND

The cancer genome is characterized by the accumulation of multiple mutations and alterations that ultimately result in the deregulation of various cell-signaling pathways. Knowledge of these genetic alterations has provided a unique opportunity to develop therapies targeted against these pathways and to identify which patients are likely to benefit from them.

OBJECTIVE

The progress that has been made in identifying genomic biomarkers that can predict response to antileukemic therapies is highlighted.

METHODS

Global gene expression profiling approaches utilizing tipifarnib in acute myeloid leukemia as an in-depth example are focused on. The challenges in developing associated theranostic molecular assays are discussed.

CONCLUSION

The integration of validated genomic-based assays with common morphological tests may allow for improved prediction of antileukemic drug response.

Specificity of DNA microarray hybridization: characterization, effectors and approaches for data correction

Microarray-hybridization specificity is one of the main effectors of microarray result quality. In the present review, we suggest a definition for specificity that spans four hybridization levels, from the single probe to the microarray platform. For increased hybridization specificity, it is important to quantify the extent of the specificity at each of these levels, and correct the data accordingly. We outline possible effects of low hybridization specificity on the obtained results and list possible effectors of hybridization specificity. In addition, we discuss several studies in which theoretical approaches, empirical means or data filtration were used to identify specificity effectors, and increase the specificity of the hybridization results. However, these various approaches may not yet provide an ultimate solution; rather, further tool development is needed to enhance microarray-hybridization specificity.

Evolution of gene expression in the Drosophila olfactory system

Host plant shifts by phytophagous insects play a key role in insect evolution and plant ecology. Such shifts often involve major behavioral changes as the insects must acquire an attraction and/or lose the repulsion to the new host plant's odor and taste. The evolution of chemotactic behavior may be due, in part, to gene expression changes in the peripheral sensory system. To test this hypothesis, we compared gene expression in the olfactory organs of Drosophila sechellia, a narrow ecological specialist that feeds on the fruit of Morinda citrifolia, with its close relatives Drosophila simulans and Drosophila melanogaster, which feed on a wide variety of decaying plant matter. Using whole-genome microarrays and quantitative polymerase chain reaction, we surveyed the entire repertoire of Drosophila odorant receptors (ORs) and odorant-binding proteins (OBPs) expressed in the antennae. We found that the evolution of OR and OBP expression was accelerated in D. sechellia compared both with the genome average in that species and with the rate of OR and OBP evolution in the other species. However, some of the gene expression changes that correlate with D. sechellia's increased sensitivity to Morinda odorants may predate its divergence from D. simulans. Interspecific divergence of olfactory gene expression cannot be fully explained by changes in the relative abundance of different sensilla as some ORs and OBPs have evolved independently of other genes expressed in the same sensilla. A number of OR and OBP genes are upregulated in D. sechellia compared with its generalist relatives. These genes include Or22a, which likely responds to a key odorant of M. citrifolia, and several genes that are yet to be characterized in detail. Increased expression of these genes in D. sechellia may have contributed to the evolution of its unique chemotactic behavior.