Assessment of data processing to improve reliability of microarray experiments using genomic DNA reference

Background

Using genomic DNA as common reference in microarray experiments has recently been tested by different laboratories. Conflicting results have been reported with regard to the reliability of microarray results using this method. To explain it, we hypothesize that data processing is a critical element that impacts the data quality.

Results

Microarray experiments were performed in a γ-proteobacterium Shewanella oneidensis. Pair-wise comparison of three experimental conditions was obtained either with two labeled cDNA samples co-hybridized to the same array, or by employing Shewanella genomic DNA as a standard reference. Various data processing techniques were exploited to reduce the amount of inconsistency between both methods and the results were assessed. We discovered that data quality was significantly improved by imposing the constraint of minimal number of replicates, logarithmic transformation and random error analyses.

Conclusion

These findings demonstrate that data processing significantly influences data quality, which provides an explanation for the conflicting evaluation in the literature. This work could serve as a guideline for microarray data analysis using genomic DNA as a standard reference.

     

A stepwise framework for the normalization of array CGH data

Background  

In two-channel competitive genomic hybridization microarray experiments, the ratio of the two fluorescent signal intensities at each spot on the microarray is commonly used to infer the relative amounts of the test and reference sample DNA levels. This ratio may be influenced by systematic measurement effects from non-biological sources that can introduce biases in the estimated ratios. These biases should be removed before drawing conclusions about the relative levels of DNA. The performance of existing gene expression microarray normalization strategies has not been evaluated for removing systematic biases encountered in array-based comparative genomic hybridization (CGH), which aims to detect single copy gains and losses typically in samples with heterogeneous cell populations resulting in only slight shifts in signal ratios. The purpose of this work is to establish a framework for correcting the systematic sources of variation in high density CGH array images, while maintaining the true biological variations.     

Quantifying stability in gene list ranking across microarray derived clinical biomarkers

Background

Genomic instability in cancer leads to abnormal genome copy number alterations (CNA) as a mechanism underlying tumorigenesis. Using microarrays and other technologies, tumor CNA are detected by comparing tumor sample CN to normal reference sample CN. While advances in microarray technology have improved detection of copy number alterations, the increase in the number of measured signals, noise from array probes, variations in signal-to-noise ratio across batches and disparity across laboratories leads to significant limitations for the accurate identification of CNA regions when comparing tumor and normal samples.

Methods

To address these limitations, we designed a novel "Virtual Normal" algorithm (VN), which allowed for construction of an unbiased reference signal directly from test samples within an experiment using any publicly available normal reference set as a baseline thus eliminating the need for an in-lab normal reference set.

Results

The algorithm was tested using an optimal, paired tumor/normal data set as well as previously uncharacterized pediatric malignant gliomas for which a normal reference set was not available. Using Affymetrix 250K Sty microarrays, we demonstrated improved signal-to-noise ratio and detected significant copy number alterations using the VN algorithm that were validated by independent PCR analysis of the target CNA regions.

Conclusions

We developed and validated an algorithm to provide a virtual normal reference signal directly from tumor samples and minimize noise in the derivation of the raw CN signal. The algorithm reduces the variability of assays performed across different reagent and array batches, methods of sample preservation, multiple personnel, and among different laboratories. This approach may be valuable when matched normal samples are unavailable or the paired normal specimens have been subjected to variations in methods of preservation.     

Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types

Background  

Much of the public access cancer microarray data is asymmetric, belonging to datasets containing no samples from normal tissue. Asymmetric data cannot be used in standard meta-analysis approaches (such as the inverse variance method) to obtain large sample sizes for statistical power enrichment. Noting that plenty of normal tissue microarray samples exist in studies not involving cancer, we investigated the viability and accuracy of an integrated microarray analysis approach based on significance analysis of microarrays (merged SAM) using a collection of data from separate diseased and normal samples.     

CGHpower: exploring sample size calculations for chromosomal copy number experiments

Background  

Determining a suitable sample size is an important step in the planning of microarray experiments. Increasing the number of arrays gives more statistical power, but adds to the total cost of the experiment. Several approaches for sample size determination have been developed for expression array studies, but so far none has been proposed for array comparative genomic hybridization (aCGH).     

Detection of setup uncertainties with 3D surface registration system for conformal radiotherapy of breast cancer

Aim

To investigate the clinical application of a technique for patient set-up verification in breast cancer radiotherapy based on a 3D surface image registration system.

Background

Accurate and reproducible patient set-up is a prerequisite to correctly deliver fractionated radiotherapy. Various approaches are available to verify and correct patient setup for 3D image acquisition in a radiation treatment room.

Materials and methods

The study analyzed the setup reproducibility of 15 patients affected by breast cancer and candidates for conformal radiotherapy by using the AlignRT system (VisionRT, London, UK). At the initial setup, electronic portal imaging device (EPID) images were compared with Digitally Reconstructed Radiographs (DRRs) and a reference three-dimensional (3D) surface image was obtained by AlignRT. Surface images were acquired prior to every subsequent setup procedure. The systematic and random errors along longitudinal and vertical directions were measured and compared for the two systems.

Results

The procedure for surface registration, image acquisition and comparison with the reference image took less than 1 min on average. The T test for systematic error showed no significant difference between the 2 verification systems along the longitudinal (p = 0.69) and vertical (p = 0.67) axes. The T-test for random error showed a significant difference between the 2 systems along the vertical axis (p = 0.05).

Conclusion

AlignRT is fast, simple, non-invasive and seems to be reliable in detecting patient setup errors. Our results suggest that it could be used to assess the setup reproducibility for breast cancer patients.     

A unique combination of 17pter trisomy and 21qter monosomy in a boy with developmental delay,severe intellectual disability,growth retardation and dysmorphisms

Background

Microduplication at 17p13.3 and microdeletion at 21q22 are both rare chromosomal aberrations. The presence of both genomic imbalances in one patient has not been previously reported in literature. In this study, we performed a molecular diagnostic testing with a whole genome microarray on a 3-year-old boy with developmental delay, mental retardation and multiple malformations.

Methods

A routine G-banding karyotype analysis was performed using peripheral lymphocytes. Chromosome microarray analysis (CMA) was done using Affymetrix CytoScan™ HD array. Genomic imbalances were further confirmed by multiple ligation-dependent probe amplification (MLPA).

Results

The result of karyotyping was normal but CMA detected a 9.8 Mb microduplication at 17p13.3–13.1 (chr17: 1–9,875,545) and a 2.8 Mb microdeletion involving 21q22.3–qter (chr21: 45,239,077–48,097,372). The imbalances were due to a balanced translocation present in patient's mother. The patient was characterized with short stature, profound developmental delay, non-verbal, intellectual disability as well as craniofacial dysmorphism, subtle brain structural anomaly and sparse scalp hair.

Conclusions

This is the first patient reported with a combination of a microduplication at 17p13.3–13.1 and a microdeletion at 21q22.3–qter. Both genomic imbalances were undetected by conventional karyotyping but were delineated with CMA test. Synergistic effect from the two rare genomic imbalances is likely responsible for the severe clinical phenotypes observed in this patient.     

CNV-seq, a new method to detect copy number variation using high-throughput sequencing

Background  

DNA copy number variation (CNV) has been recognized as an important source of genetic variation. Array comparative genomic hybridization (aCGH) is commonly used for CNV detection, but the microarray platform has a number of inherent limitations.     

easyExon – A Java-based GUI tool for processing and visualization of Affymetrix exon array data

Background  

Alternative RNA splicing greatly increases proteome diversity and thereby contribute to species- or tissue-specific functions. The possibility to study alternative splicing (AS) events on a genomic scale using splicing-sensitive microarrays, including the Affymetrix GeneChip Exon 1.0 ST microarray (exon array), has appeared very recently. However, the application of this new technology is hindered by the lack of free and user-friendly software devoted to these novel platforms.     

Development of reverse phase protein microarrays for the validation of clusterin, a mid-abundant blood biomarker

Background  

Many putative disease blood biomarkers discovered in genomic and proteomic studies await validation in large clinically annotated cohorts of patient samples. ELISA assays require large quantities of precious blood samples and are not high-throughput. The reverse phase protein microarray platform has been developed for the high-throughput quantification of protein levels in small amounts of clinical samples.     

Multiplexed,rapid detection of H5N1 using a PCR-free nanoparticle-based genomic microarray assay

Background  

For more than a decade there has been increasing interest in the use of nanotechnology and microarray platforms for diagnostic applications. In this report, we describe a rapid and simple gold nanoparticle (NP)-based genomic microarray assay for specific identification of avian influenza virus H5N1 and its discrimination from other major influenza A virus strains (H1N1, H3N2).     

GeneRank: Using search engine technology for the analysis of microarray experiments

Background  

Interpretation of simple microarray experiments is usually based on the fold-change of gene expression between a reference and a "treated" sample where the treatment can be of many types from drug exposure to genetic variation. Interpretation of the results usually combines lists of differentially expressed genes with previous knowledge about their biological function. Here we evaluate a method – based on the PageRank algorithm employed by the popular search engine Google – that tries to automate some of this procedure to generate prioritized gene lists by exploiting biological background information.     

A nitty-gritty aspect of correlation and network inference from gene expression data

Background  

All currently available methods of network/association inference from microarray gene expression measurements implicitly assume that such measurements represent the actual expression levels of different genes within each cell included in the biological sample under study. Contrary to this common belief, modern microarray technology produces signals aggregated over a random number of individual cells, a "nitty-gritty" aspect of such arrays, thereby causing a random effect that distorts the correlation structure of intra-cellular gene expression levels.     

Performance of high-throughput DNA quantification methods

Background

The accuracy and precision of estimates of DNA concentration are critical factors for efficient use of DNA samples in high-throughput genotype and sequence analyses. We evaluated the performance of spectrophotometric (OD) DNA quantification, and compared it to two fluorometric quantification methods, the PicoGreen^® assay (PG), and a novel real-time quantitative genomic PCR assay (QG) specific to a region at the human BRCA1 locus. Twenty-Two lymphoblastoid cell line DNA samples with an initial concentration of ~350 ng/uL were diluted to 20 ng/uL. DNA concentration was estimated by OD and further diluted to 5 ng/uL. The concentrations of multiple aliquots of the final dilution were measured by the OD, QG and PG methods. The effects of manual and robotic laboratory sample handling procedures on the estimates of DNA concentration were assessed using variance components analyses.

Results

The OD method was the DNA quantification method most concordant with the reference sample among the three methods evaluated. A large fraction of the total variance for all three methods (36.0–95.7%) was explained by sample-to-sample variation, whereas the amount of variance attributable to sample handling was small (0.8–17.5%). Residual error (3.2–59.4%), corresponding to un-modelled factors, contributed a greater extent to the total variation than the sample handling procedures.

Conclusion

The application of a specific DNA quantification method to a particular molecular genetic laboratory protocol must take into account the accuracy and precision of the specific method, as well as the requirements of the experimental workflow with respect to sample volumes and throughput. While OD was the most concordant and precise DNA quantification method in this study, the information provided by the quantitative PCR assay regarding the suitability of DNA samples for PCR may be an essential factor for some protocols, despite the decreased concordance and precision of this method.

     

Allele-specific disparity in breast cancer

Background

Molecular alterations critical to development of cancer include mutations, copy number alterations (amplifications and deletions) as well as genomic rearrangements resulting in gene fusions. Massively parallel next generation sequencing, which enables the discovery of such changes, uses considerable quantities of genomic DNA (> 5 ug), a serious limitation in ever smaller clinical samples. However, a commonly available microarray platforms such as array comparative genomic hybridization (array CGH) allows the characterization of gene copy number at a single gene resolution using much smaller amounts of genomic DNA. In this study we evaluate the sensitivity of ultra-dense array CGH platforms developed by Agilent, especially that of the 1 million probe array (1 M array), and their application when whole genome amplification is required because of limited sample quantities.

Methods

We performed array CGH on whole genome amplified and not amplified genomic DNA from MCF-7 breast cancer cells, using 244 K and 1 M Agilent arrays. The ADM-2 algorithm was used to identify micro-copy number alterations that measured less than 1 Mb in genomic length.

Results

DNA from MCF-7 breast cancer cells was analyzed for micro-copy number alterations, defined as measuring less than 1 Mb in genomic length. The 4-fold extra resolution of the 1 M array platform relative to the less dense 244 K array platform, led to the improved detection of copy number variations (CNVs) and micro-CNAs. The identification of intra-genic breakpoints in areas of DNA copy number gain signaled the possible presence of gene fusion events. However, the ultra-dense platforms, especially the densest 1 M array, detect artifacts inherent to whole genome amplification and should be used only with non-amplified DNA samples.

Conclusions

This is a first report using 1 M array CGH for the discovery of cancer genes and biomarkers. We show the remarkable capacity of this technology to discover CNVs, micro-copy number alterations and even gene fusions. However, these platforms require excellent genomic DNA quality and do not tolerate relatively small imperfections related to the whole genome amplification.