Microarray-Based DNA Methylation Profiling: Validation Considerations for Clinical Testing.

Microarray-based methylation profiling has emerged as a valuable tool for refining diagnoses and revealing novel tumor subtypes, particularly in central nervous system tumors. Despite the increasing adoption of this technique in clinical genomic laboratories, no technical standards have been published in establishing minimum criteria for test validation. A working group with experience and expertise in DNA-based methylation profiling tests on central nervous system tumors collaborated to develop practical discussion points and focus on important considerations for validating this test in clinical laboratory settings. The experience in validating this methodology in a clinical setting is summarized. Specifically, the advantages and challenges associated with utilizing an in-house classifier compared with a third-party classifier are highlighted. Additionally, experiences in demonstrating the assay's sensitivity and specificity, establishing minimum sample criteria, and implementing quality control metrics are described. As methylation profiling for tumor classification expands to other tumor types and continues to evolve for various other applications, the critical considerations described here are expected to serve as a guidance for future efforts in establishing professional guidelines for this assay.

Assessing Various Control Samples for Microarray Gene Expression Profiling of Laryngeal Squamous Cell Carcinoma.

Selection of optimal control samples is crucial in expression profiling tumor samples. To address this issue, we performed microarray expression profiling of control samples routinely used in head and neck squamous cell carcinoma studies: human bronchial and tracheal epithelial cells, squamous cells obtained by laser uvulopalatoplasty and tumor surgical margins. We compared the results using multidimensional scaling and hierarchical clustering versus tumor samples and laryngeal squamous cell carcinoma cell lines. A general observation from our study is that the analyzed cohorts separated according to two dominant factors: "malignancy", which separated controls from malignant samples and "cell culture-microenvironment" which reflected the differences between cultured and non-cultured samples. In conclusion, we advocate the use of cultured epithelial cells as controls for gene expression profiling of cancer cell lines. In contrast, comparisons of gene expression profiles of cancer cell lines versus surgical margin controls should be treated with caution, whereas fresh frozen surgical margins seem to be appropriate for gene expression profiling of tumor samples.

Clinical performance of the HPV DNA Array genotyping assay in detection of CIN2+ lesions with BS GP5+/6+ MPG Luminex tested cervical samples.

Human papillomavirus (HPV) detection is used for screening of cervical cancer and genotype-specific persistence has shown to be mandatory for dysplasia development. Aim of this study was to evaluate the clinical performance of HPV DNA Array for cervical intraepithelial neoplasia 2+ (CIN2+) lesion detection. HPV DNA Array is a polymerase chain reaction-based assay that targets E1 sequences of 29 HPV types (6, 11, 16, 18, 26, 31, 33, 35, 39, 40, 42, 44, 45, 51, 52, 53, 54, 56, 58, 59, 66, 67, 68, 69, 70, 73, 82, 85, and 97). The clinical evaluation was performed against the reference assay, BS-GP5+/6+ multiplex genotyping (MPG)-Luminex, with 600 cervical smear samples of a referral population. HPV DNA Array detected CIN2+ lesions with a sensitivity of 90.2%, identical to that of MPG-Luminex. Detection of CIN3+ lesions was with a sensitivity of 90.3%, as compared with 88.7% of MPG-Luminex. It demonstrated very good agreement for HPV detection, irrespective of type, of 91.5% (κ = 0.832). HPV DNA Array is a simple and robust assay, with a short protocol of 4 hours hands-on time and automated readout by ELISpot AiDot software. It permits testing of up to 96 samples in one run and may be considered for use in organized screening programs and low resource settings.

Copolymerization of single-cell nucleic acids into balls of acrylamide gel.

We show the use of 5'-Acrydite oligonucleotides to copolymerize single-cell DNA or RNA into balls of acrylamide gel (BAGs). Combining this step with split-and-pool techniques for creating barcodes yields a method with advantages in cost and scalability, depth of coverage, ease of operation, minimal cross-contamination, and efficient use of samples. We perform DNA copy number profiling on mixtures of cell lines, nuclei from frozen prostate tumors, and biopsy washes. As applied to RNA, the method has high capture efficiency of transcripts and sufficient consistency to clearly distinguish the expression patterns of cell lines and individual nuclei from neurons dissected from the mouse brain. By using varietal tags (UMIs) to achieve sequence error correction, we show extremely low levels of cross-contamination by tracking source-specific SNVs. The method is readily modifiable, and we will discuss its adaptability and diverse applications.

Expression Concordance of 325 Novel RNA Biomarkers between Data Generated by NanoString nCounter and Affymetrix GeneChip.

BACKGROUND: With the development of new drug combinations and targeted treatments for multiple types of cancer, the ability to stratify categories of patient populations and to develop companion diagnostics has become increasingly important. A panel of 325 RNA biomarkers was selected based on cancer-related biological processes of healthy cells and gene expression changes over time during nonmalignant epithelial cell organization. This "cancer in reverse" approach resulted in a panel of biomarkers relevant for at least 7 cancer types, providing gene expression profiles representing key cellular signaling pathways beyond mutations in "driver genes." Objective. To further investigate this biomarker panel, the objective of the current study is to (1) validate the assay reproducibility for the 325 RNA biomarkers and (2) compare gene expression profiles side by side using two technology platforms. METHODS AND RESULTS: We have mapped the 325 RNA transcripts and in a custom NanoString nCounter expression panel to be compared to all potential probe sets in the Affymetrix Human Genome U133 Plus 2.0. The experiments were conducted with 10 unique biological formalin-fixed paraffin-embedded (FFPE) breast tumor samples. Each site extracted RNA from four sections of 10-micron thick FFPE tissue over three different days by two different operators using an optimized standard operating procedure and quality control criteria. Samples were analyzed using mas5 in BioConductor and NanoStringNorm in R. Pearson correlation showed reproducibility between sites for all 60 samples with r = 0.995 for Affymetrix and r = 0.999 for NanoString. Correlation in multiple days and multiple users was for Affymetrix r = (0.962 - 0.999) and for NanoString r = (0.982 - 0.991). CONCLUSION: The 325 RNA biomarkers showed reproducibility in two technology platforms with moderate to high concordance. Future directions include performing clinical validation studies and generating rationale for patient selection in clinical trials using the technically validated assay.

A Hybrid Gene Selection Method Based on ReliefF and Ant Colony Optimization Algorithm for Tumor Classification.

For the DNA microarray datasets, tumor classification based on gene expression profiles has drawn great attention, and gene selection plays a significant role in improving the classification performance of microarray data. In this study, an effective hybrid gene selection method based on ReliefF and Ant colony optimization (ACO) algorithm for tumor classification is proposed. First, for the ReliefF algorithm, the average distance among k nearest or k non-nearest neighbor samples are introduced to estimate the difference among samples, based on which the distances between the samples in the same class or the different classes are defined, and then it can more effectively evaluate the weight values of genes for samples. To obtain the stable results in emergencies, a distance coefficient is developed to construct a new formula of updating weight coefficient of genes to further reduce the instability during calculations. When decreasing the distance between the same samples and increasing the distance between the different samples, the weight division is more obvious. Thus, the ReliefF algorithm can be improved to reduce the initial dimensionality of gene expression datasets and obtain a candidate gene subset. Second, a new pruning rule is designed to reduce dimensionality and obtain a new candidate subset with the smaller number of genes. The probability formula of the next point in the path selected by the ants is presented to highlight the closeness of the correlation relationship between the reaction variables. To increase the pheromone concentration of important genes, a new phenotype updating formula of the ACO algorithm is adopted to prevent the pheromone left by the ants that are overwhelmed with time, and then the weight coefficients of the genes are applied here to eliminate the interference of difference data as much as possible. It follows that the improved ACO algorithm has the ability of the strong positive feedback, which quickly converges to an optimal solution through the accumulation and the updating of pheromone. Finally, by combining the improved ReliefF algorithm and the improved ACO method, a hybrid filter-wrapper-based gene selection algorithm called as RFACO-GS is proposed. The experimental results under several public gene expression datasets demonstrate that the proposed method is very effective, which can significantly reduce the dimensionality of gene expression datasets, and select the most relevant genes with high classification accuracy.

Analytical Validation of Multiplex Biomarker Assay to Stratify Colorectal Cancer into Molecular Subtypes.

Previously, we classified colorectal cancers (CRCs) into five CRCAssigner (CRCA) subtypes with different prognoses and potential treatment responses, later consolidated into four consensus molecular subtypes (CMS). Here we demonstrate the analytical development and validation of a custom NanoString nCounter platform-based biomarker assay (NanoCRCA) to stratify CRCs into subtypes. To reduce costs, we switched from the standard nCounter protocol to a custom modified protocol. The assay included a reduced 38-gene panel that was selected using an in-house machine-learning pipeline. We applied NanoCRCA to 413 samples from 355 CRC patients. From the fresh frozen samples (n = 237), a subset had matched microarray/RNAseq profiles (n = 47) or formalin-fixed paraffin-embedded (FFPE) samples (n = 58). We also analyzed a further 118 FFPE samples. We compared the assay results with the CMS classifier, different platforms (microarrays/RNAseq) and gene-set classifiers (38 and the original 786 genes). The standard and modified protocols showed high correlation (> 0.88) for gene expression. Technical replicates were highly correlated (> 0.96). NanoCRCA classified fresh frozen and FFPE samples into all five CRCA subtypes with consistent classification of selected matched fresh frozen/FFPE samples. We demonstrate high and significant subtype concordance across protocols (100%), gene sets (95%), platforms (87%) and with CMS subtypes (75%) when evaluated across multiple datasets. Overall, our NanoCRCA assay with further validation may facilitate prospective validation of CRC subtypes in clinical trials and beyond.

Analytical validation of a prognostic prostate cancer gene expression assay using formalin fixed paraffin embedded tissue.

BACKGROUND: There is a clear need for assays that can predict the risk of metastatic prostate cancer following curative procedures. Importantly these assays must be analytically robust in order to provide quality data for important clinical decisions. DNA microarray based gene expression assays measure several analytes simultaneously and can present specific challenges to analytical validation. This study describes the analytical validation of one such assay designed to predict metastatic recurrence in prostate cancer using primary formalin fixed paraffin embedded tumour material. METHODS: Accuracy was evaluated with a method comparison study between the assay development platform (Prostate Disease Specific Array) and an alternative platform (Xcel™ microarray) using 50 formalin-fixed, paraffin-embedded prostate cancer patient samples. An additional 70 samples were used to establish the assay reportable range. Determination of assay precision and sensitivity was performed on multiple technical replicates of three prostate cancer samples across multiple variables (operators, days, runs, reagent lots, and equipment) and RNA/cDNA inputs respectively using the appropriate linear mixed model. RESULTS: The overall agreement between the development and alternative platform was 94.7% (95% confidence interval, 86.9-98.5%). The reportable range was determined to be 0.150 to 1.107 for core needle biopsy samples and - 0.214 to 0.844 for radical prostatectomy samples. From the precision study, the standard deviations for assay repeatability and reproducibility were 0.032 and 0.040 respectively. The sensitivity study demonstrated that a total RNA input and cDNA input of 50 ng and 3.5 µg respectively was conservative. CONCLUSION: The Metastatic Assay was found to be highly reproducible and precise. In conclusion the studies demonstrated an acceptable analytical performance for the assay and support its potential use in the clinic.

Validation of EUROArray HPV test using the VALGENT framework.

BACKGROUND: Robust clinical and analytical validation of human papillomavirus (HPV) tests is a pre-requisite for their use in cervical cancer screening given the transience of most high-risk HPV infections. OBJECTIVES: To evaluate the EUROArray HPV test (PCR-based full HPV genotyping test) using the international validation of the VALGENT framework, which offers an opportunity to determine analytical and clinical performance according to internationally accepted performance metrics. STUDY DESIGN: A total of 1300 consecutive and 300 abnormal cervical samples derived from the Slovenian screening programme were tested with the EUROArray HPV test. Clinical performance for the detection of cervical intraepithelial neoplasia grade 2 and above (CIN2+) was performed and compared to a standard comparator test (Hybrid Capture 2). Intra- and inter-laboratory reproducibility of the assay was performed in a subset of 500 samples. RESULTS: The relative clinical sensitivity and specificity of EUROArray HPV vs HC2 was 0.93 (95% Confidence Interval (CI), 0.88-0.99; P non-inferiority(ni) = 0.1413) and 1.01 (95% CI, 0.99-1.02; Pni = 0.0001), respectively. Application of an a-posteriori cut-off for HPV16 led to relative values of 0.98 (95% CI, 0.92-1.03; Pni = 0.0076) and 1.00 (95% CI, 0.97-1.03; Pni = 0.007), respectively. The assay showed excellent intra- and inter-laboratory reproducibility (concordance ≥ 94%, Kappas ≥0.85). CONCLUSION: At the predefined cut-off, EUROArray HPV was less sensitive than HC2 for the detection of CIN2+. However, when an optimised cut-off was applied, EUROArray HPV fulfilled international criteria for its use in cervical cancer screening.

Correlation between RNA-Seq and microarrays results using TCGA data.

RNA sequencing (RNA-Seq) and microarray are two of the most commonly used high-throughput technologies for transcriptome profiling; however, they both have their own inherent strengths and limitations. This research aims to analyze the correlation between microarrays and RNA-Seq detection of transcripts in the same tissue sample to explore the reproducibility between the techniques. Using data of RNA-Seq v2 and three different microarrays provided by The Cancer Genome Atlas, 11,120 genes of 111 lung squamous cell carcinoma samples were simultaneously detected by the four methods. Then we analyzed the Pearson correlation between microarrays and RNA-Seq. Finally, in the six comparison results, 9984 (89.8%) genes, irrespective of which two methods were used, simultaneously showed the existence of correlation, whereas only 83 (0.1%) genes proved to have no significant correlation in either comparison. In addition, the comparisons between 3266 (29.3%) genes showed high correlation (R≥0.8) in all six comparisons, only for 1643 (14.8%) genes correlation were not as high in either comparison. Meanwhile, transcripts with extreme high or low expression levels were more highly discrepant across the methods. In conclusion, we found that, for most transcripts, the results obtained by RNA-Seq and microarrays were highly reproducible.

[A possibility to use the DNA-based probes as internal standards for Agilent Technologies microarray transcriptomic analysis]. / Ispol'zovanie DNK-matritsy v kachestve kontrolia pri provedenii transkriptomnogo analiza na standartnykh mikrochipakh Agilent Technologies.

Accuracy of the microarray technology results is raised by using the multi-stage normalization of results. One of the principal requirements of such normalization is usage of internal standards. The routine Agilent microarray-based gene expression analysis protocol utilizes a Spike-In Kit during preparation of the samples representing a mixture of RNA fragments in different ratios. RNA probes which were synthesized in vitro conditions could be also used to establish how the magnitude of the fluorescent signal reflects the presence of RNA in the sample. A significant disadvantage of this type of standards is a difficulty of their production and the low RNA stability. In accordance with the Agilent protocol, the presence of the T7 promoter is necessary for the synthesis of labeled cRNA during sample preparation procedure. We hypothesized that we can successfully synthesize any RNA sequence having such type of promoter in its start position. Moreover, DNA sequence would serve as a matrix in this case. Using a set of different genes attached downstream of the T7-promoter in the plasmid DNA we have demonstrated in this study that such system can serve as a reliable template for the fluorescent labeled RNA sequence synthesis. In comparison with the routinely used internal RNA based controls, this template is stable, easy to manufacture and can be easily obtained in large quantities.

Cautionary Note on Using Cross-Validation for Molecular Classification.

Purpose Reproducibility of scientific experimentation has become a major concern because of the perception that many published biomedical studies cannot be replicated. In this article, we draw attention to the connection between inflated overoptimistic findings and the use of cross-validation for error estimation in molecular classification studies. We show that, in the absence of careful design to prevent artifacts caused by systematic differences in the processing of specimens, established tools such as cross-validation can lead to a spurious estimate of the error rate in the overoptimistic direction, regardless of the use of data normalization as an effort to remove these artifacts. Methods We demonstrated this important yet overlooked complication of cross-validation using a unique pair of data sets on the same set of tumor samples. One data set was collected with uniform handling to prevent handling effects; the other was collected without uniform handling and exhibited handling effects. The paired data sets were used to estimate the biologic effects of the samples and the handling effects of the arrays in the latter data set, which were then used to simulate data using virtual rehybridization following various array-to-sample assignment schemes. Results Our study showed that (1) cross-validation tended to underestimate the error rate when the data possessed confounding handling effects; (2) depending on the relative amount of handling effects, normalization may further worsen the underestimation of the error rate; and (3) balanced assignment of arrays to comparison groups allowed cross-validation to provide an unbiased error estimate. Conclusion Our study demonstrates the benefits of balanced array assignment for reproducible molecular classification and calls for caution on the routine use of data normalization and cross-validation in such analysis.

Quality Control Usage in High-Density Microarrays Reveals Differential Gene Expression Profiles in Ovarian Cancer.

There are several existing reports of microarray chip use for assessment of altered gene expression in different diseases. In fact, there have been over 1.5 million assays of this kind performed over the last twenty years, which have influenced clinical and translational research studies. The most commonly used DNA microarray platforms are Affymetrix GeneChip and Quality Control Software along with their GeneChip Probe Arrays. These chips are created using several quality controls to confirm the success of each assay, but their actual impact on gene expression profiles had not been previously analyzed until the appearance of several bioinformatics tools for this purpose. We here performed a data mining analysis, in this case specifically focused on ovarian cancer, as well as healthy ovarian tissue and ovarian cell lines, in order to confirm quality control results and associated variation in gene expression profiles. The microarray data used in our research were downloaded from ArrayExpress and Gene Expression Omnibus (GEO) and analyzed with Expression Console Software using RMA, MAS5 and Plier algorithms. The gene expression profiles were obtained using Partek Genomics Suite v6.6 and data were visualized using principal component analysis, heat map, and Venn diagrams. Microarray quality control analysis showed that roughly 40% of the microarray files were false negative, demonstrating over- and under-estimation of expressed genes. Additionally, we confirmed the results performing second analysis using independent samples. About 70% of the significant expressed genes were correlated in both analyses. These results demonstrate the importance of appropriate microarray processing to obtain a reliable gene expression profile.

Analysis of tissue from products of conception and perinatal losses using QF-PCR and microarray: A three-year retrospective study resulting in an efficient protocol.

OBJECTIVE: To evaluate the performance of a laboratory protocol for direct genetic analysis performed on tissues obtained from miscarriages, stillbirth and postnatal death. METHODS: Samples were collected between July 1st, 2011 and June 30th, 2014. QF-PCR analysis was the initial test followed by aCGH analysis performed on the normal QF-PCR specimens. RESULTS: Of the 1195 submitted specimens, a total of 1071 samples were confirmed as true fetal. The failure rate was 1.4%. Of those, 30.8% yielded abnormal results. Of the latter, 57.6% had abnormal QF-PCR and 42.4% had abnormal microarray result. Autosomal trisomies were detected in 61.2%, triploidy in 7.6%, monosomy X in 9.1%, sex-chromosome aneuploidy (apart from monosomy X) in 1.5%, molar pregnancies in 5.8% and copy number variants in 14.2% including microdeletions/microduplications and cryptic unbalanced rearrangements. The highest diagnostic yield was observed in the 1st trimester specimens at 67.6%. We confirmed that maternal age correlates with the likelihood of autosomal trisomies but not with triploidy, sex chromosome aneuploidies, molar pregnancy, or CNVs. CONCLUSION: An efficient laboratory protocol, based on QF-PCR and aCGH of uncultured cells has replaced standard cytogenetic analysis in testing of tissue from all pregnancy losses in our center and resulted in reduced test failure rate and increased diagnostic yield.

EUROarray human papillomavirus (HPV) assay is highly concordant with other commercial assays for detection of high-risk HPV genotypes in women with high grade cervical abnormalities.

The purpose of this study was to evaluate the performance of the EUROIMMUN EUROArray HPV genotyping assay against the Roche Cobas 4800, Roche HPV Amplicor, Roche Linear Array and Qiagen Hybrid Capture 2 assays in the detection of high-risk HPV (HR-HPV) from liquid based cervical cytology samples collected from women undergoing follow-up for abnormal cervical cytology results. Cervical specimens from 404 women undergoing management of high-grade cytological abnormality were evaluated by EUROarray HPV for detection of HR-HPV genotypes and prediction of histologically-confirmed cervical intraepithelial neoplasia grade 2 or higher (≥CIN2). The results were compared to Hybrid Capture 2, Cobas 4800 HPV, Amplicor and Linear Array HPV. Positivity for 14 HR-HPV types was 80.0 % for EUROarray (95 % CI; 75.7-83.8 %). Agreement (κ, 95 % CI) between the EUROarray and other HPV tests for detection of HR-HPV was good to very good [Hybrid Capture κ = 0.62 (0.54-0.71); Cobas κ = 0.81 (0.74-0.88); Amplicor κ = 0.68 (0.60-0.77); Linear Array κ = 0.77 (0.70-0.85)]. For detection of HR-HPV, agreement with EUROarray was 87.90 % (Hybrid Capture), 93.58 % (Cobas), 92.84 % (Amplicor) and 92.59 % (Linear Array). Detection of HR-HPV was not significantly different between EUROarray and any other test (p < 0.001). EUROarray was concordant with other assays evaluated for detection of high-risk HPV and showed sensitivity and specificity for detection of ≥ CIN2 of 86 % and 71 %, respectively.

Validation of a next-generation sequencing-based protocol for 24-chromosome aneuploidy screening of blastocysts.

OBJECTIVE: To validate a 24-chromosome aneuploidy preimplantation genetic screening protocol based on multiple annealing and looping-based amplification cycle (MALBAC) and next-generation sequencing (NGS). DESIGN: Single-nucleotide polymorphism (SNP) array and MALBAC-NGS analysis. SETTING: University-affiliated in vitro fertilization (IVF) center. PATIENT(S): Fifteen women from whom 30 blastocysts were obtained for genotyping. INTERVENTION(S): Not applicable. MAIN OUTCOME MEASURE(S): Chromosomal status comparison of results of array comparative genomic hybridization (aCGH), SNP array, and MALBAC-NGS for 24-chromosome aneuploidy screening. RESULT(S): Trophectoderm biopsy samples from blastocysts were first analyzed using array comparative genomic hybridization (aCGH); the embryos with detected with chromosomal abnormalities were rebiopsied, and dissociated into two portions, and subjected to SNP array and MALBAC-NGS for 24-chromosome aneuploidy screening. All 30 samples were successfully genotyped by array CGH, SNP array, and MALBAC-NGS. All blastocysts were correctly identified as aneuploid, and there was a 100% concordance in terms of diagnosis provided between the three methods. In the 720 detected chromosomes, the concordance rate between MALBAC-NGS and array CGH was 99.31% (715 of 720), and the concordance rate between MALBAC-NGS and SNP array was 99.58% (717 of 720). When compared with aCGH, MALBAC-NGS specificity for aneuploidy call was 99.85% (674 of 675; 95% CI, 99.17-99.97) with a sensitivity of 91.11% (41 of 45; 95% CI, 79.27-96.49). When compared with SNP array, MALBAC-NGS specificity for aneuploidy call was 99.85% (676 of 677; 95% CI, 99.17-99.97) with a sensitivity of 95.35% (41 of 43; 95% CI, 85.54-98.72). CONCLUSION(S): MALBAC-NGS provides concordant chromosomal results when compared with aCGH and SNP array in blastocysts with chromosomal abnormalities.

miR-16-5p Is a Stably-Expressed Housekeeping MicroRNA in Breast Cancer Tissues from Primary Tumors and from Metastatic Sites.

For quantitative microRNA analyses in formalin-fixed paraffin-embedded (FFPE) tissue, expression levels have to be normalized to endogenous controls. To investigate the most stably-expressed microRNAs in breast cancer and its surrounding tissue, we used tumor samples from primary tumors and from metastatic sites. MiRNA profiling using TaqMan(®) Array Human MicroRNA Cards, enabling quantification of 754 unique human miRNAs, was performed in FFPE specimens from 58 patients with metastatic breast cancer. Forty-two (72%) samples were collected from primary tumors and 16 (28%) from metastases. In a cross-platform analysis of a validation cohort of 32 FFPE samples from patients with early breast cancer genome-wide microRNA expression analysis using SurePrintG3 miRNA (8 × 60 K)(®) microarrays from Agilent(®) was performed. Eleven microRNAs could be detected in all samples analyzed. Based on NormFinder and geNorm stability values and the high correlation (rho ≥ 0.8) with the median of all measured microRNAs, miR-16-5p, miR-29a-3p, miR-126-3p, and miR-222-3p are suitable single gene housekeeper candidates. In the cross-platform validation, 29 human microRNAs were strongly expressed (mean log2-intensity > 10) and 21 of these microRNAs including miR-16-5p and miR-29a-3p were also stably expressed (CV < 5%). Thus, miR-16-5p and miR-29a-3p are both strong housekeeper candidates. Their Normfinder stability values calculated across the primary tumor and metastases subgroup indicate that miR-29a-3p can be considered as the strongest housekeeper in a cohort with mainly samples from primary tumors, whereas miR-16-5p might perform better in a metastatic sample enriched cohort.

MERAV: a tool for comparing gene expression across human tissues and cell types.

The oncogenic transformation of normal cells into malignant, rapidly proliferating cells requires major alterations in cell physiology. For example, the transformed cells remodel their metabolic processes to supply the additional demand for cellular building blocks. We have recently demonstrated essential metabolic processes in tumor progression through the development of a methodological analysis of gene expression. Here, we present the Metabolic gEne RApid Visualizer (MERAV, http://merav.wi.mit.edu), a web-based tool that can query a database comprising ∼4300 microarrays, representing human gene expression in normal tissues, cancer cell lines and primary tumors. MERAV has been designed as a powerful tool for whole genome analysis which offers multiple advantages: one can search many genes in parallel; compare gene expression among different tissue types as well as between normal and cancer cells; download raw data; and generate heatmaps; and finally, use its internal statistical tool. Most importantly, MERAV has been designed as a unique tool for analyzing metabolic processes as it includes matrixes specifically focused on metabolic genes and is linked to the Kyoto Encyclopedia of Genes and Genomes pathway search.

Normalization of Affymetrix miRNA Microarrays for the Analysis of Cancer Samples.

microRNA (miRNA) microarray normalization is a critical step for the identification of truly differentially expressed miRNAs. This is particularly important when dealing with cancer samples that have a global miRNA decrease. In this chapter, we provide a simple step-by-step procedure that can be used to normalize Affymetrix miRNA microarrays, relying on robust normal-exponential background correction with cyclic loess normalization.

Circulating Messenger RNA Profiling with Microarray and Next-generation Sequencing: Cross-platform Comparison.

BACKGROUND: Circulating mRNA is a less invasive and more easily accessed source of samples for biomedical research and clinical applications. However, it is of poor quality. We explored and compared the ability of two high-throughput platforms for the profiling of circulating mRNA regarding their ability to retrieve useful information out of this type of samples. MATERIALS AND METHODS: Circulating mRNAs from three non-small cell lung cancer patients and three healthy controls were analyzed by the cDNA-mediated annealing, selection, extension, and ligation (DASL) assay and high-throughput RNA sequencing (RSEQ). Twelve genes were selected for further confirmation by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). RESULTS: The overall expression profiles derived from the two platforms showed modest-to-moderate correlation. Genes with higher expression levels had higher cross-platform concordance than those of medium- and low-expression levels. In addition, the pathway signatures identified by gene set enrichment analysis from both platforms were in agreement. The RT-q PCR results for the selected genes correlated well with that of RSEQ. CONCLUSION: Genes with higher expression levels have cross-platform concordance and can be potential biomarkers. Furthermore, RSEQ is a better tool for profiling circulating mRNAs.