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Microarrays, Volume 2, Issue 3 (September 2013), Pages 170-283

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Research

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Open AccessArticle Hydrogel Microwell Arrays Allow the Assessment of Protease-Associated Enhancement of Cancer Cell Aggregation and Survival
Microarrays 2013, 2(3), 208-227; doi:10.3390/microarrays2030208
Received: 2 July 2013 / Revised: 31 July 2013 / Accepted: 13 August 2013 / Published: 22 August 2013
Cited by 3 | PDF Full-text (994 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Current routine cell culture techniques are only poorly suited to capture the physiological complexity of tumor microenvironments, wherein tumor cell function is affected by intricate three-dimensional (3D), integrin-dependent cell-cell and cell-extracellular matrix (ECM) interactions. 3D cell cultures allow the investigation of cancer-associated [...] Read more.
Current routine cell culture techniques are only poorly suited to capture the physiological complexity of tumor microenvironments, wherein tumor cell function is affected by intricate three-dimensional (3D), integrin-dependent cell-cell and cell-extracellular matrix (ECM) interactions. 3D cell cultures allow the investigation of cancer-associated proteases like kallikreins as they degrade ECM proteins and alter integrin signaling, promoting malignant cell behaviors. Here, we employed a hydrogel microwell array platform to probe using a high-throughput mode how ovarian cancer cell aggregates of defined size form and survive in response to the expression of kallikreins and treatment with paclitaxel, by performing microscopic, quantitative image, gene and protein analyses dependent on the varying microwell and aggregate sizes. Paclitaxel treatment increased aggregate formation and survival of kallikrein-expressing cancer cells and levels of integrins and integrin-related factors. Cancer cell aggregate formation was improved with increasing aggregate size, thereby reducing cell death and enhancing integrin expression upon paclitaxel treatment. Therefore, hydrogel microwell arrays are a powerful tool to screen the viability of cancer cell aggregates upon modulation of protease expression, integrin engagement and anti-cancer treatment providing a micro-scaled yet high-throughput technique to assess malignant progression and drug-resistance. Full article
(This article belongs to the Special Issue Advantages of Three Dimensional (3D) Cell Cultures)
Open AccessArticle Kernel-Based Aggregation of Marker-Level Genetic Association Tests Involving Copy-Number Variation
Microarrays 2013, 2(3), 265-283; doi:10.3390/microarrays2030265
Received: 2 August 2013 / Revised: 29 August 2013 / Accepted: 30 August 2013 / Published: 4 September 2013
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Abstract
Genetic association tests involving copy-number variants (CNVs) are complicated by the fact that CNVs span multiple markers at which measurements are taken. The power of an association test at a single marker is typically low, and it is desirable to pool information [...] Read more.
Genetic association tests involving copy-number variants (CNVs) are complicated by the fact that CNVs span multiple markers at which measurements are taken. The power of an association test at a single marker is typically low, and it is desirable to pool information across the markers spanned by the CNV. However, CNV boundaries are not known in advance, and the best way to proceed with this pooling is unclear. In this article, we propose a kernel-based method for aggregation of marker-level tests and explore several aspects of its implementation. In addition, we explore some of the theoretical aspects of marker-level test aggregation, proposing a permutation-based approach that preserves the family-wise error rate of the testing procedure, while demonstrating that several simpler alternatives fail to do so. The empirical power of the approach is studied in a number of simulations constructed from real data involving a pharmacogenomic study of gemcitabine and compares favorably with several competing approaches. Full article
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Review

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Open AccessReview Comparative Analysis of CNV Calling Algorithms: Literature Survey and a Case Study Using Bovine High-Density SNP Data
Microarrays 2013, 2(3), 171-185; doi:10.3390/microarrays2030171
Received: 2 May 2013 / Revised: 4 June 2013 / Accepted: 5 June 2013 / Published: 25 June 2013
Cited by 9 | PDF Full-text (183 KB) | HTML Full-text | XML Full-text
Abstract
Copy number variations (CNVs) are gains and losses of genomic sequence between two individuals of a species when compared to a reference genome. The data from single nucleotide polymorphism (SNP) microarrays are now routinely used for genotyping, but they also can be [...] Read more.
Copy number variations (CNVs) are gains and losses of genomic sequence between two individuals of a species when compared to a reference genome. The data from single nucleotide polymorphism (SNP) microarrays are now routinely used for genotyping, but they also can be utilized for copy number detection. Substantial progress has been made in array design and CNV calling algorithms and at least 10 comparison studies in humans have been published to assess them. In this review, we first survey the literature on existing microarray platforms and CNV calling algorithms. We then examine a number of CNV calling tools to evaluate their impacts using bovine high-density SNP data. Large incongruities in the results from different CNV calling tools highlight the need for standardizing array data collection, quality assessment and experimental validation. Only after careful experimental design and rigorous data filtering can the impacts of CNVs on both normal phenotypic variability and disease susceptibility be fully revealed. Full article
Open AccessReview The Transcriptomics to Proteomics of Hair Cell Regeneration: Looking for a Hair Cell in a Haystack
Microarrays 2013, 2(3), 186-207; doi:10.3390/microarrays2030186
Received: 27 May 2013 / Revised: 2 July 2013 / Accepted: 4 July 2013 / Published: 25 July 2013
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Abstract
Mature mammals exhibit very limited capacity for regeneration of auditory hair cells, while all non-mammalian vertebrates examined can regenerate them. In an effort to find therapeutic targets for deafness and balance disorders, scientists have examined gene expression patterns in auditory tissues under [...] Read more.
Mature mammals exhibit very limited capacity for regeneration of auditory hair cells, while all non-mammalian vertebrates examined can regenerate them. In an effort to find therapeutic targets for deafness and balance disorders, scientists have examined gene expression patterns in auditory tissues under different developmental and experimental conditions. Microarray technology has allowed the large-scale study of gene expression profiles (transcriptomics) at whole-genome levels, but since mRNA expression does not necessarily correlate with protein expression, other methods, such as microRNA analysis and proteomics, are needed to better understand the process of hair cell regeneration. These technologies and some of the results of them are discussed in this review. Although there is a considerable amount of variability found between studies owing to different species, tissues and treatments, there is some concordance between cellular pathways important for hair cell regeneration. Since gene expression and proteomics data is now commonly submitted to centralized online databases, meta-analyses of these data may provide a better picture of pathways that are common to the process of hair cell regeneration and lead to potential therapeutics. Indeed, some of the proteins found to be regulated in the inner ear of animal models (e.g., IGF-1) have now gone through human clinical trials. Full article
(This article belongs to the Special Issue Advances in Data Analysis Methods and Tools)
Open AccessReview Improving Pathological Assessment of Breast Cancer by Employing Array-Based Transcriptome Analysis
Microarrays 2013, 2(3), 228-242; doi:10.3390/microarrays2030228
Received: 29 July 2013 / Revised: 17 August 2013 / Accepted: 22 August 2013 / Published: 29 August 2013
Cited by 3 | PDF Full-text (211 KB) | HTML Full-text | XML Full-text
Abstract
Breast cancer research has paved the way of personalized oncology with the introduction of hormonal therapy and the measurement of estrogen receptor as the first widely accepted clinical biomarker. The expression of another receptor—HER2/ERBB2/neu—was initially a sign of worse prognosis, but targeted [...] Read more.
Breast cancer research has paved the way of personalized oncology with the introduction of hormonal therapy and the measurement of estrogen receptor as the first widely accepted clinical biomarker. The expression of another receptor—HER2/ERBB2/neu—was initially a sign of worse prognosis, but targeted therapy has granted improved outcome for these patients so that today HER2 positive patients have better prognosis than HER2 negative patients. Later, the introduction of multigene assays provided the pathologists with an unbiased assessment of the tumors’ molecular fingerprint. The recent FDA approval of complete microarray pipelines has opened new possibilities for the objective classification of breast cancer samples. Here we review the applications of microarrays for determining ER and HER2 status, molecular subtypes as well as predicting prognosis and grade for breast cancer patients. An open question remains the role of single genes within such signatures. Openly available microarray datasets enable the execution of an independent cross-validation of new marker and signature candidates. In summary, we review the current state regarding clinical applications of microarrays in breast cancer molecular pathology. Full article
(This article belongs to the Special Issue Clinical Applications of Microarrays)
Open AccessReview From High-Throughput Microarray-Based Screening to Clinical Application: The Development of a Second Generation Multigene Test for Breast Cancer Prognosis
Microarrays 2013, 2(3), 243-264; doi:10.3390/microarrays2030243
Received: 17 July 2013 / Revised: 12 August 2013 / Accepted: 22 August 2013 / Published: 29 August 2013
PDF Full-text (391 KB) | HTML Full-text | XML Full-text
Abstract
Several multigene tests have been developed for breast cancer patients to predict the individual risk of recurrence. Most of the first generation tests rely on proliferation-associated genes and are commonly carried out in central reference laboratories. Here, we describe the development of [...] Read more.
Several multigene tests have been developed for breast cancer patients to predict the individual risk of recurrence. Most of the first generation tests rely on proliferation-associated genes and are commonly carried out in central reference laboratories. Here, we describe the development of a second generation multigene assay, the EndoPredict test, a prognostic multigene expression test for estrogen receptor (ER) positive, human epidermal growth factor receptor (HER2) negative (ER+/HER2−) breast cancer patients. The EndoPredict gene signature was initially established in a large high-throughput microarray-based screening study. The key steps for biomarker identification are discussed in detail, in comparison to the establishment of other multigene signatures. After biomarker selection, genes and algorithms were transferred to a diagnostic platform (reverse transcription quantitative PCR (RT-qPCR)) to allow for assaying formalin-fixed, paraffin-embedded (FFPE) samples. A comprehensive analytical validation was performed and a prospective proficiency testing study with seven pathological laboratories finally proved that EndoPredict can be reliably used in the decentralized setting. Three independent large clinical validation studies (n = 2,257) demonstrated that EndoPredict offers independent prognostic information beyond current clinicopathological parameters and clinical guidelines. The review article summarizes several important steps that should be considered for the development process of a second generation multigene test and offers a means for transferring a microarray signature from the research laboratory to clinical practice. Full article
(This article belongs to the Special Issue Clinical Applications of Microarrays)

Other

Jump to: Research, Review

Open AccessCorrection Correction: Gan, L.; Denecke, B. Profiling Pre-MicroRNA and Mature MicroRNA Expressions Using a Single Microarray and Avoiding Separate Sample Preparation. Microarrays 2013, 2, 24-33
Microarrays 2013, 2(3), 170; doi:10.3390/microarrays2030170
Received: 30 May 2013 / Accepted: 3 June 2013 / Published: 24 June 2013
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Abstract
It came to our attention that a paper has recently been published concerning one of the GEO datasets (GSE34413) we cited in our published paper [1]. The original reference (reference 27) cited for this dataset leads to a paper about a similar [...] Read more.
It came to our attention that a paper has recently been published concerning one of the GEO datasets (GSE34413) we cited in our published paper [1]. The original reference (reference 27) cited for this dataset leads to a paper about a similar study from the same research group [2]. In order to provide readers with exact citation information, we would like to update reference 27 in our previous paper to the new published paper concerning GSE34413 [3]. The authors apologize for this inconvenience. [...] Full article

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