A perspective on DNA microarrays in pathology research and practice

The molecular mechanisms of various long non-coding RNA (lncRNA) in human lung tumors: Shedding light on the molecular mechanisms

Although it is still mostly incomplete, unraveling the gene expression networks controlling the initiation and progression of cancer is crucial. The rapid identification and characterization of long noncoding RNAs (lncRNAs) is made possible by advancements in computational biology and RNA-seq technology. According to recent research, lncRNAs are involved in several stages in the genesis of lung cancer. These lncRNAs interact with DNA, RNA, protein molecules, and/or their combinations. They play a crucial role in transcriptional and post-transcriptional regulation, as well as chromatin architecture. Their misexpression gives cancer cells the ability to start, grow, and spread tumors. This review will focus on their abnormal expression and function in lung cancer, as well as their involvement in cancer therapy and diagnosis.

Machine learning application in personalised lung cancer recurrence and survivability prediction

Machine learning is an important artificial intelligence technique that is widely applied in cancer diagnosis and detection. More recently, with the rise of personalised and precision medicine, there is a growing trend towards machine learning applications for prognosis prediction. However, to date, building reliable prediction models of cancer outcomes in everyday clinical practice is still a hurdle. In this work, we integrate genomic, clinical and demographic data of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) patients from The Cancer Genome Atlas (TCGA) and introduce copy number variation (CNV) and mutation information of 15 selected genes to generate predictive models for recurrence and survivability. We compare the accuracy and benefits of three well-established machine learning algorithms: decision tree methods, neural networks and support vector machines. Although the accuracy of predictive models using the decision tree method has no significant advantage, the tree models reveal the most important predictors among genomic information (e.g. KRAS, EGFR, TP53), clinical status (e.g. TNM stage and radiotherapy) and demographics (e.g. age and gender) and how they influence the prediction of recurrence and survivability for both early stage LUAD and LUSC. The machine learning models have the potential to help clinicians to make personalised decisions on aspects such as follow-up timeline and to assist with personalised planning of future social care needs.

DNA-based microarray studies in visceral leishmaniasis: identification of biomarkers for diagnostic, prognostic and drug target for treatment

Visceral leishmaniasis (VL) is one of the major infectious diseases affecting the poorest regions of the world. Current therapy is not very much satisfactory. The alarming rise of drug resistance and the unavailability of an effective vaccine against VL urges research towards identifying new targets or biomarkers for its effective treatment. New technology developments offer some fresh hope in its diagnosis, treatment, and control. DNA microarray approach is now broadly used in parasitology research to facilitate the thoughtful of mechanisms of disease and identification of drug targets and biomarkers for diagnostic and therapeutic development. An electronic search on "VL" and "Microarray" was conducted in Medline and Scopus and papers published in the English mentioning use of DNA microarray on VL were selected and read to write this paper review. Functional analysis and interpretation of microarray results remain very challenging due to the inherent nature of experimental workflows, access, cost, and complexity of data obtained. We have explained and emphasized the use of curate knowledge of microarray in the case of VL for the identification of therapeutic target and biomarker and their selection/implementation in clinical use.

Transcriptomic Profiles of MV4-11 and Kasumi 1 Acute Myeloid Leukemia Cell Lines Modulated by Epigenetic Modifiers Trichostatin A and 5-Azacytidine

Background: Acute myeloid leukemia (AML) is the most common form of acute leukemias in adults which is clinically and molecularly heterogeneous. Several risk and genetic factors have been widely investigated to characterize AML. However, the concomitant epigenetic factors in controlling the gene expression lead to AML transformation was not fully understood. This study was aimed to identify epigenetically regulated genes in AML cell lines induced by epigenetic modulating agents, Trichostatin A (TSA) and 5-Azacytidine (5-Aza). Materials and Methods: MV4-11 and Kasumi 1 were treated with TSA and/or 5-Aza at IC₅₀ concentration. Gene expression profiling by microarray was utilized using SurePrint G3 Human Gene Expression v3. Gene ontology and KEGG pathway annotations were analyzed by DAVID bioinformatics software using EASE enrichment score. mRNA expression of the differentially expressed genes were verified by quantitative real time PCR. Results: Gene expression analysis revealed a significant changes in the expression of 24,822, 15,720, 15,654 genes in MV4-11 and 12,598, 8828, 18,026 genes in Kasumi 1, in response to TSA, 5-Aza and combination treatments, respectively, compared to non-treated (p<0.05). 7 genes (SOCS3, TUBA1C, CCNA1, MAP3K6, PTPRC, STAT6 and RUNX1) and 4 genes (ANGPTL4, TUBB2A, ADAM12 and PTPN6) shown to be predominantly expressed in MV4-11 and Kasumi 1, respectively (EASE<0.1). The analysis also revealed phagosome pathway commonly activated in both cell lines. Conclusion: Our data showed a distinct optimal biological characteristic and pathway in different types of leukemic cell lines. These finding may help in the identification of cell-specific epigenetic biomarker in the pathogenesis of AML.

Prediction of Overall Survival of Patients with Completely Resected Non-Small Cell Lung Cancer: Analyses of Preoperative Spirometry, Preoperative Blood Tests, and Other Clinicopathological Data

Purpose

Risk stratification of patients with non-small cell lung cancer (NSCLC) is crucial to select the appropriate treatments, but available models for patients with complete resection are unsatisfactory. The purpose of this study was to determine a prediction model based on clinical information, routine physical and blood tests, and molecular markers.

Patients and Methods

This was a retrospective cohort study of patients who underwent surgical resection for lung cancer between 2009 to 2013. Potential prognostic factors were used to build a full prediction model based on a multivariable Cox regression analysis. A nomogram was constructed. The risk stratification cutoffs for clinical use were determined based on the model.

Results

A total of 368 NSCLC patients with R0 resection were included. The final multivariable model indicated that low diffusing capacity of the lung for carbon monoxide (HR=1.66, 95% CI: 1.18–2.34), high platelet-to-lymphocyte ratio (HR=1.42, 95% CI: 1.04–1.95), histology type of squamous cell carcinoma and others (squamous cell carcinoma vs adenocarcinoma, HR=1.40, 95% CI: 1.01–1.96; others vs adenocarcinoma, HR=2.36, 95% CI: 1.15–4.84; P trend=0.001), N>0 status (HR=1.96, 95% CI: 1.42–2.70), high serum carcinoembryonic antigen levels (HR=1.61, 95% CI: 1.13–2.27), and postoperative chemotherapy (HR=0.53, 95% CI: 0.33–0.87) were independently associated with poor OS. The patients were classified into four risk groups according to the nomogram, and the OS was different among the four groups (P<0.05).

Conclusion

A nomogram was successfully constructed based on a multivariable analysis, and the nomogram can discriminate the OS of patients with NSCLC based on risk categories, but external validation is still necessary.

Advances in diagnostic microfluidics

Microfluidics is an emerging field in diagnostics that allows for extremely precise fluid control and manipulation, enabling rapid and high-throughput sample processing in integrated micro-scale medical systems. These platforms are well-suited for both standard clinical settings and point-of-care applications. The unique features of microfluidics-based platforms make them attractive for early disease diagnosis and real-time monitoring of the disease and therapeutic efficacy. In this chapter, we will first provide a background on microfluidic fundamentals, microfluidic fabrication technologies, microfluidic reactors, and microfluidic total-analysis-systems. Next, we will move into a discussion on the clinical applications of existing and emerging microfluidic platforms for blood analysis, and for diagnosis and monitoring of cancer and infectious disease. Together, this chapter should elucidate the potential that microfluidic systems have in the development of effective diagnostic technologies through a review of existing technologies and promising directions.

Microarray-Based Gene Expression Analysis for Veterinary Pathologists: A Review

High-throughput, genome-wide transcriptome analysis is now commonly used in all fields of life science research and is on the cusp of medical and veterinary diagnostic application. Transcriptomic methods such as microarrays and next-generation sequencing generate enormous amounts of data. The pathogenetic expertise acquired from understanding of general pathology provides veterinary pathologists with a profound background, which is essential in translating transcriptomic data into meaningful biological knowledge, thereby leading to a better understanding of underlying disease mechanisms. The scientific literature concerning high-throughput data-mining techniques usually addresses mathematicians or computer scientists as the target audience. In contrast, the present review provides the reader with a clear and systematic basis from a veterinary pathologist's perspective. Therefore, the aims are (1) to introduce the reader to the necessary methodological background; (2) to introduce the sequential steps commonly performed in a microarray analysis including quality control, annotation, normalization, selection of differentially expressed genes, clustering, gene ontology and pathway analysis, analysis of manually selected genes, and biomarker discovery; and (3) to provide references to publically available and user-friendly software suites. In summary, the data analysis methods presented within this review will enable veterinary pathologists to analyze high-throughput transcriptome data obtained from their own experiments, supplemental data that accompany scientific publications, or public repositories in order to obtain a more in-depth insight into underlying disease mechanisms.

Classification of Benign and Malignant Thyroid Nodules Using a Combined Clinical Information and Gene Expression Signatures

BACKGROUND

A key challenge in thyroid carcinoma is preoperatively diagnosing malignant thyroid nodules. A novel diagnostic test that measures the expression of a 3-gene signature (DPP4, SCG5 and CA12) has demonstrated promise in thyroid carcinoma assessment. However, more reliable prediction methods combining clinical features with genomic signatures with high accuracy, good stability and low cost are needed.

METHODOLOGY/PRINCIPAL FINDINGS

25 clinical information were recorded in 771 patients. Feature selection and validation were conducted using random forest. Thyroid samples and clinical data were obtained from 142 patients at two different hospitals, and expression of the 3-gene signature was measured using quantitative PCR. The predictive abilities of three models (based on the selected clinical variables, the gene expression profile, and integrated gene expression and clinical information) were compared. Seven clinical characteristics were selected based on a training set (539 patients) and tested in three test sets, yielding predictive accuracies of 82.3% (n = 232), 81.4% (n = 70), and 81.9% (n = 72). The predictive sensitivity, specificity, and accuracy were 72.3%, 80.5% and 76.8% for the model based on the gene expression signature, 66.2%, 81.8% and 74.6% for the model based on the clinical data, and 83.1%, 84.4% and 83.8% for the combined model in a 10-fold cross-validation (n = 142).

CONCLUSIONS

These findings reveal that the integrated model, which combines clinical data with the 3-gene signature, is superior to models based on gene expression or clinical data alone. The integrated model appears to be a reliable tool for the preoperative diagnosis of thyroid tumors.

The Role of PET/CT Molecular Imaging in the Diagnosis of Recurrence and Surveillance of Patients Treated for Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is the leading cause of cancer mortality worldwide and its prognosis remains poor. Molecular imaging with ¹⁸F-FDG PET/CT can metabolically characterize the nature of lesions as benign or malignant, allowing a better staging at the diagnosis of this kind of patient. This advantage can also be applied in the re-staging due to the suspicion of recurrent disease. Many patients have a recurrence of the disease, including surgically treated patients. In the current context, with new personalized oncological treatments, the surveillance for recurrence and its accurate diagnosis are crucial to improve their survival. In this paper, we revise the current knowledge about the clinical and molecular factors related to the recurrent disease. In the context of new, promising, available personalized treatments, the role of molecular imaging with PET/CT and ¹⁸F-FDG and non-¹⁸F-FDG radiotracers in the follow-up of NSCLC-treated patients is especially attractive and interesting.

Biologic Evaluation of Diabetes and Local Recurrence in Non-Small Cell Lung Cancer

A recent multicenter study led by our institution demonstrated that local recurrence of non-small cell lung cancer (NSCLC) was significantly more frequent in patients with diabetes, raising the possibility of different tumor biology in diabetics. Epithelial-to-mesenchymal transition (EMT) plays a key role in local tumor recurrence and metastasis. In the present study, we investigated differences of tumor microenvironment between patients with and without diabetes by examining expression of EMT markers. Seventy-nine NSCLC patients were selected from the cohort of our early multicenter study. These patients were classified into 4 groups: 39 with adenocarcinoma with (n = 19) and without (n = 20) diabetes, and 40 with squamous cell carcinoma with (n = 20) and without (n = 20) diabetes. Immunohistochemical expression of eight EMT markers was analyzed, including transforming growth factor-beta (TGF-β), epidermal growth factor receptor (EGFR), insulin-like growth factor 1 receptor (IGF-1R), vimentin, E-cadherin, N-cadherin, HtrA1, and beta-catenin. Five markers (E-cadherin, HtrA1, TGF-β, IGF-1R and vimentin) demonstrated significantly higher expression in diabetics than in non-diabetics in both histology types. N-cadherin had higher expression in diabetics, though the difference did not reach statistical significance. EGFR showed a higher expression in diabetics in squamous cell carcinoma only. Beta-catenin was the only marker with no difference in expression between diabetics versus non-diabetics. Our findings suggest that diabetes is associated with enhanced EMT in NSCLC, which may contribute to growth and invasiveness of NSCLC.

TRAIL pathway is associated with inhibition of colon cancer by protopanaxadiol

Among important components of American ginseng, protopanaxadiol (PPD) showed more active anticancer potential than other triterpenoid saponins. In this study, we determined the in vivo effects of PPD in a mouse cancer model first. Then, using human colorectal cancer cell lines, we observed significant cancer cell growth inhibition by promoting G1 cell cycle redistribution and apoptosis. Subsequently, we characterized the downstream genes targeted by PPD in HCT-116 cancer cells. Using Affymetrix high density GeneChips, we obtained the gene expression profile of the cells. Microarray data indicated that the expression levels of 76 genes were changed over two-fold after PPD, of which 52 were upregulated while the remaining 24 were downregulated. Ingenuity pathway analysis of top functions affected was carried out. Data suggested that by regulating the interactions between p53 and DR4/DR5, the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) pathway played a key role in the action of PPD, a promising colon cancer inhibitory compound.

Recurrence after surgery in patients with NSCLC

Surgery remains the only potentially curative modality for early-stage non-small cell lung cancer (NSCLC) patients and tissue availability is made possible. However, a proportion of lung cancer patients develop recurrence, even after curative resection. This review discusses the superiority of surgery, the reasons for recurrence, the timing and pattern of recurrence, the identification of factors related to recurrence, current provisions for treatment and perspectives about surgery for patients with NSCLC.

Epidermal growth factor receptor-GEP100-Arf6 axis affects the prognosis of lung adenocarcinoma

The overexpression of Arf6 and GEP100 is responsible for the invasive activity that is crucial for the activation of the epidermal growth factor receptor (EGFR) signaling pathways in human cancer. However, whether or not the expression of the EGFR-GEP100-Arf6 axis can be used as a biomarker for the prognosis of lung cancer has yet to be fully determined. Tumor specimens were collected from 182 patients who underwent a complete resection for lung adenocarcinoma. We analyzed phospho-EGFR (p-EGFR), GEP100, and Arf6 expression levels in the primary tumor by immunohistochemical analysis. The expression of p-EGFR, GEP100, and Arf6 was observed in 65 (35.7%), 95 (52.2%), and 20 (11.0%) patients, respectively. Significant associations between p-EGFR and GEP100 expression and vessel invasion were identified. The expression of these individual molecules was not associated with any statistically significant differences in survival. However, triple positive expression of p-EGFR, GEP100, and Arf6 was significantly associated with an increased risk of death based on the multivariate analysis. The EGFR-GEP100-Arf6 axis affected the prognosis of patients with primary lung adenocarcinoma. The combination of p-EGFR, GEP100, and Arf6 staining can predict the prognosis of patients after surgery.

Prognosis of recurrence after complete resection in early-stage non-small cell lung cancer

Background

Tumor recurrence is the most common cause of treatment failure, even after complete resection of early-stage non-small cell lung cancer (NSCLC). In this study, we investigated the prognosis of patients with early recurrence in order to identify independent risk factors related to early recurrence.

Methods

Between February 1995 and December 2012, 242 patients who underwent surgical resection for stage I NSCLC at Dong-A University Hospital were reviewed. The factors predicting overall survival (OS) and early recurrence were investigated. We also investigated the relationship between the patterns and period of recurrence and clinicopathological factors.

Results

For patients with stage IA and IB NSCLC, the 5-year OS rate was 75.7% and 57.3% (p=0.006), respectively. A multivariate Cox proportional hazards model demonstrated that gender (p=0.004), comorbidity number (p=0.038), resection type (p=0.002), and tumor size (p=0.022) were the statistically significant predictors of OS. Moreover, the multivariate analysis revealed that smoking history (p=0.023) and histologic grade (p=0.012) were the independent predictors of early recurrence. Additionally, only histologic grade (poor differentiation) was found to be significantly associated with a higher frequency of distant metastasis; there was no relationship between the patterns and period of recurrence and clinicopathological factors.

Conclusion

The present study demonstrated that smoking history and histologic grade were independent prognostic factors for early recurrence within two years in patients with early-stage NSCLC. Patients with these predictive factors may be good candidates for adjuvant therapy.

The challenge of gene expression profiling in heterogeneous clinical samples

Almost all samples used in tumor biology, such as tissues and bodily fluids, are heterogeneous, i.e., consist of different cell types. Evaluating the degree of heterogeneity in samples can increase our knowledge on processes such as clonal selection and metastasis. In addition, generating expression profiles from specific sub populations of cells can reveal their distinct functions. Tissue heterogeneity also poses a challenge, as it can confound the interpretation of gene expression data. This chapter will (1) give a brief overview on how heterogeneity may influence gene expression profiling data and (2) describe the methods that are currently available to assess transcriptional biomarkers in a heterogeneous cell population.

R/EBcoexpress: an empirical Bayesian framework for discovering differential co-expression

R/EBcoexpress implements the approach of Dawson and Kendziorski using R, a freely available, open source statistical programming language. The approach identifies differential co-expression (DC) by examining the correlations among gene pairs using an empirical Bayesian approach, producing a false discovery rate controlled list of DC pairs. This interrogation of DC gene pairs complements but is distinct from differential expression analyses, under the general goal of understanding differential regulation across biological conditions.

AVAILABILITY AND IMPLEMENTATION

R/EBcoexpress is freely available and hosted on Bioconductor; a source file and vignette may be found at http://www.bioconductor.org/packages/release/bioc/html/EBcoexpress.html

An empirical Bayesian approach for identifying differential coexpression in high-throughput experiments

A common goal of microarray and related high-throughput genomic experiments is to identify genes that vary across biological condition. Most often this is accomplished by identifying genes with changes in mean expression level, so called differentially expressed (DE) genes, and a number of effective methods for identifying DE genes have been developed. Although useful, these approaches do not accommodate other types of differential regulation. An important example concerns differential coexpression (DC). Investigations of this class of genes are hampered by the large cardinality of the space to be interrogated as well as by influential outliers. As a result, existing DC approaches are often underpowered, exceedingly prone to false discoveries, and/or computationally intractable for even a moderately large number of pairs. To address this, an empirical Bayesian approach for identifying DC gene pairs is developed. The approach provides a false discovery rate controlled list of significant DC gene pairs without sacrificing power. It is applicable within a single study as well as across multiple studies. Computations are greatly facilitated by a modification to the expectation-maximization algorithm and a procedural heuristic. Simulations suggest that the proposed approach outperforms existing methods in far less computational time; and case study results suggest that the approach will likely prove to be a useful complement to current DE methods in high-throughput genomic studies.

Quantitative analysis of p53 expression in human normal and cancer tissue microarray with global normalization method

Tissue microarray based immunohistochemical staining and proteomics are important tools to create and validate clinically relevant cancer biomarkers. Immunohistochemical stains using formalin-fixed tissue microarray sections for protein expression are scored manually and semi-quantitatively. Digital image analysis methods remove some of the drawbacks of manual scoring but may need other methods such as normalization to provide across the board utility. In the present study, quantitative proteomics-based global normalization method was used to evaluate its utility in the analysis of p53 protein expression in mixed human normal and cancer tissue microarray. Global normalization used the mean or median of β-actin to calculate ratios of individual core stain intensities, then log transformed the ratios, calculate a mean or median and subtracted the value from the log of ratios. In the absence of global normalization of p53 protein expression, 44% (42 of 95) of tissue cores were positive using the median of intensity values and 40% (38 of 95) using the mean of intensities as cut-off points. With global normalization, p53 positive cores changed to 20% (19 of 95) when using median of intensities and 15.8%(15 of 95) when the mean of intensities were used. In conclusion, the global normalization method helped to define positive p53 staining in the tissue microarray set used. The method used helped to define clear cut-off points and confirmed all negatively stained tissue cores. Such normalization methods should help to better define clinically useful biomarkers.

Comparative profiling of primary colorectal carcinomas and liver metastases identifies LEF1 as a prognostic biomarker

Purpose

We sought to identify genes of clinical significance to predict survival and the risk for colorectal liver metastasis (CLM), the most common site of metastasis from colorectal cancer (CRC).

Patients and Methods

We profiled gene expression in 31 specimens from primary CRC and 32 unmatched specimens of CLM, and performed Significance Analysis of Microarrays (SAM) to identify genes differentially expressed between these two groups. To characterize the clinical relevance of two highly-ranked differentially-expressed genes, we analyzed the expression of secreted phosphoprotein 1 (SPP1 or osteopontin) and lymphoid enhancer factor-1 (LEF1) by immunohistochemistry using a tissue microarray (TMA) representing an independent set of 154 patients with primary CRC.

Results

Supervised analysis using SAM identified 963 genes with significantly higher expression in CLM compared to primary CRC, with a false discovery rate of <0.5%. TMA analysis showed SPP1 and LEF1 protein overexpression in 60% and 44% of CRC cases, respectively. Subsequent occurrence of CLM was significantly correlated with the overexpression of LEF1 (chi-square p = 0.042), but not SPP1 (p = 0.14). Kaplan Meier analysis revealed significantly worse survival in patients with overexpression of LEF1 (p<0.01), but not SPP1 (p = 0.11). Both univariate and multivariate analyses identified stage (p<0.0001) and LEF1 overexpression (p<0.05) as important prognostic markers, but not tumor grade or SPP1.

Conclusion

Among genes differentially expressed between CLM and primary CRC, we demonstrate overexpression of LEF1 in primary CRC to be a prognostic factor for poor survival and increased risk for liver metastasis.

Cross-platform comparison of microarray-based multiple-class prediction

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

Expression profiling of cytogenetically normal acute myeloid leukemia identifies microRNAs that target genes involved in monocytic differentiation

MicroRNAs are short ribonucleic acids (RNAs) that play an important role in many aspects of cellular biology such as differentiation and apoptosis, due to their role in the regulation of gene expression. Using microRNA microarrays, we characterized the microRNA gene expression of 27 patients with acute myeloid leukemia (AML) with normal cytogenetics, focusing on the microRNAs differentially expressed between the M1 and M5 French-American-British (FAB) subtypes. An accurate delineation of these two AML entities was observed based on the expression of 12 microRNAs. We hypothesized that these microRNAs may potentially be involved in the differentiation block of M1 blasts and consequently monocytic differentiation. Using publically available mRNA data and microRNA target prediction software, we identified several key myeloid factors that may be targeted by our candidate microRNAs. The expression changes of the candidate microRNAs during monocytic differentiation of AML cell lines treated with Vitamin D and phorbol 12-myristate 13-acetate were examined. All six candidate microRNAs were significantly down-regulated over the time course by quantitative reverse transcriptase polymerase chain reaction suggesting a link between these microRNAs and monocytic differentiation. To further characterize these microRNAs, we confirmed by luciferase assays that these microRNA target several key myeloid factors such as MAFB, IRF8, and KLF4 identifying a possible mechanism for the control of differentiation by these microRNAs.

Separate production of single-stranded DNA is not necessary: circuit denaturation of double-stranded DNA followed by hybridization of single strands on oligonucleotide microchips

An approach to circuit renaturation-hybridization of dsDNA on oligonucleotide microchips is described. A close circuit cycling device has been developed, and the feasibility of the proposed technique was demonstrated on two platforms. First, a commercial microchip for detection of rifampicin resistance in Mycobacterium tuberculosis was used. Hybridization of a 126 nt long single-stranded DNA (ssDNA) fragment of the rpoB gene according to manufacturer's protocol has been compared to hybridization of the same double-stranded DNA (dsDNA) fragment using the developed approach. Hybridization signals obtained by both methods were comparable in intensity and correlated closely. Second, a 22 nt long hairpin-forming oligonucleotide was designed and hybridized with a custom microchip containing probes complementary to both strands of the oligonucleotide. Conventional hybridization of this oligonucleotide did not yield any significant signals. Cleavage of the hairpin loop resulted in the formation of a 9 bp long intermolecular duplex. Hybridization of the duplex using the suggested technique yielded strong signals. The proposed approach allows analyzing target DNA in double-stranded form bypassing the preparation of single-stranded targets. Moreover, both complementary chains could be analyzed simultaneously, providing a reliable internal control. Being combined with fragmentation this method opens new possibilities in analyzing ssDNA with complex secondary structure.

Kinetic effects on signal normalization in oligonucleotide microchips with labeled immobilized probes

Among various factors affecting operation of oligonucleotide microchips, the variations in concentration and in homogeneous distribution of immobilized probes over the cells are one of the most important. The labeling of immobilized probes ensures the complete current monitoring on the probe distribution and is reliable and convenient. Using hydrogel-based oligonucleotide microchips, the applicability of Cy3-labeled immobilized probes for quality control and signal normalization after hybridization with Cy5-labeled target DNA was investigated. This study showed that proper signal normalization should be different in thermodynamic conditions and in transient regime with hybridization far from saturation. This kinetic effect holds for both hydrogel-based and surface oligonucleotide microchips. Besides proving basic features, the technique was assessed on a sampling batch of 50 microchips developed for identifying mutations responsible for rifampicin and isoniazid resistance of Mycobacterium tuberculosis.

Diagnostic microarrays in hematologic oncology: applications of high- and low-density arrays

Microarrays have become important tools for high-throughput analysis of gene expression, chromosome aberrations, and gene mutations in cancer cells. In addition to high-density experimental microarrays, low-density, gel-based biochip technology represents a versatile platform for translation of research into clinical practice. Gel-based microarrays (biochips) consist of nanoliter gel drops on a hydrophobic surface with different immobilized biopolymers (primarily nucleic acids and proteins). Because of the high immobilization capacity of the gel, such biochips have a high probe concentration and high levels of fluorescence signals after hybridization, which allow the use of simple, portable detection systems. The notable accuracy of the analysis is reached as a result of the high level of discrimination between positive and negative gel-bound probes. Different applications of biochips in the field of hematologic oncology include analysis of chromosomal translocations in leukemias, diagnostics of T-cell lymphomas, and pharmacogenetics.

Hydrogel-based protein and oligonucleotide microchips on metal-coated surfaces: enhancement of fluorescence and optimization of immunoassay

Manufacturing of hydrogel-based microchips on metal-coated substrates significantly enhances fluorescent signals upon binding of labeled target molecules. This observation holds true for both oligonucleotide and protein microchips. When Cy5 is used as fluorophore, this enhancement is 8-10-fold in hemispherical gel elements and 4-5-fold in flattened gel pads, as compared with similar microchips manufactured on uncoated glass slides. The effect also depends on the hydrophobicity of metal-coated substrate and on the presence of a layer of liquid over the gel pads. The extent of enhancement is insensitive to the nature of formed complexes and immobilized probes and remains linear within a wide range of fluorescence intensities. Manufacturing of gel-based protein microarrays on metal-coated substrates improves their sensitivity using the same incubation time for immunoassay. Sandwich immunoassay using these microchips allows shortening the incubation time without loss of sensitivity. Unlike microchips with probes immobilized directly on a surface, for which the plasmon mechanism is considered responsible for metal-enhanced fluorescence, the enhancement effect observed using hydrogel-based microchips on metal-coated substrates might be explained within the framework of geometric optics.

Development of a malignancy-associated proteomic signature for diffuse large B-cell lymphoma

The extreme pathological diversity of non-Hodgkin's lymphomas has made their accurate histological assessment difficult. New diagnostics and treatment modalities are urgently needed for these lymphomas, particularly in drug development for cancer-specific targets. Previously, we showed that a subset of B cell lymphoma, diffuse large B cell lymphoma, may be characterized by two major, orthogonal axes of gene expression: one set of transcripts that is differentially expressed between resting and proliferating, nonmalignant cells (ie, a "proliferative signature") and another set that is expressed only in proliferating malignant cells (ie, a "cancer signature"). A differential proteomic analysis of B cell proliferative states, similar to previous transcriptional profiling analyses, holds great promise either to reveal novel factors that participate in lymphomagenesis or to define biomarkers of onset or progression. Here, we use a murine model of diffuse large B cell lymphoma to conduct unbiased two-dimensional gel electrophoresis and mass spectrometry-based comparative proteomic analyses of malignant proliferating B cells and tissue-matched, normal resting, or normal proliferating cells. We show that the expression patterns of particular proteins or isoforms across these states fall into eight specific trends that provide a framework to identify malignancy-associated biomarkers and potential drug targets, a signature proteome. Our results support the central hypothesis that clusters of proteins of known function represent a panel of expression markers uniquely associated with malignancy and not normal proliferation.

Gene expression profiling reveals a new classification of adrenocortical tumors and identifies molecular predictors of malignancy and survival

PURPOSE

Adrenocortical tumors, especially cancers, remain challenging both for their diagnosis and prognosis assessment. The aim of this article is to identify molecular predictors of malignancy and of survival.

PATIENTS AND METHODS

One hundred fifty-three unilateral adrenocortical tumors were studied by microarray (n = 92) or reverse transcription quantitative polymerase chain reaction (n = 148). A two-gene predictor of malignancy was built using the disease-free survival as the end point in a training cohort (n = 47), then validated in an independent validation cohort (n = 104). The best candidate genes were selected using Cox models, and the best gene combination was validated using the log-rank test. Similarly, for malignant tumors, a two-gene predictor of survival was built using the overall survival as the end point in a training cohort (n = 23), then tested in an independent validation cohort (n = 35).

RESULTS

Unsupervised clustering analysis discriminated robustly the malignant and benign tumors, and identified two groups of malignant tumors with very different outcome. The combined expression of DLG7 and PINK1 was the best predictor of disease-free survival (log-rank P approximately 10(-12)), could overcome the uncertainties of intermediate pathological Weiss scores, and remained significant after adjustment to the Weiss score (P < 1.3 x 10(-2)). Among the malignant tumors, the combined expression of BUB1B and PINK1 was the best predictor of overall survival (P < 2 x 10(-6)), and remained significant after adjusting for MacFarlane staging (P < .005).

CONCLUSION

Gene expression analysis unravels two distinct groups of adrenocortical carcinomas. The molecular predictors of malignancy and of survival are reliable and provide valuable independent information in addition to pathology and tumor staging. These original tools should provide important improvements for adrenal tumors management.

Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression

PURPOSE

One of the main challenges of lung cancer research is identifying patients at high risk for recurrence after surgical resection. Simple, accurate, and reproducible methods of evaluating individual risks of recurrence are needed.

EXPERIMENTAL DESIGN

Based on a combined analysis of time-to-recurrence data, censoring information, and microarray data from a set of 138 patients, we selected statistically significant genes thought to be predictive of disease recurrence. The number of genes was further reduced by eliminating those whose expression levels were not reproducible by real-time quantitative PCR. Within these variables, a recurrence prediction model was constructed using Cox proportional hazard regression and validated via two independent cohorts (n = 56 and n = 59).

RESULTS

After performing a log-rank test of the microarray data and successively selecting genes based on real-time quantitative PCR analysis, the most significant 18 genes had P values of <0.05. After subsequent stepwise variable selection based on gene expression information and clinical variables, the recurrence prediction model consisted of six genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, and IFI44). Two pathologic variables, pStage and cellular differentiation, were developed. Validation by two independent cohorts confirmed that the proposed model is significantly accurate (P = 0.0314 and 0.0305, respectively). The predicted median recurrence-free survival times for each patient correlated well with the actual data.

CONCLUSIONS

We have developed an accurate, technically simple, and reproducible method for predicting individual recurrence risks. This model would potentially be useful in developing customized strategies for managing lung cancer.

Transcriptome analysis of endocrine tumors: clinical perspectives

There is considerable interest in the application of DNA microarrays to the pathologic evaluation of endocrine neoplasms. Improvements in tumor classification and prognostication, prediction of response to therapy, and comprehensive assessment of tumoral hormone production represent the major anticipated benefits. Here, some of the microarray studies that support the clinical use of transcriptome profiling for endocrine tumors are reviewed. In addition, some of the barriers to clinical implementation are discussed.

A decade of genome-wide gene expression profiling in acute myeloid leukemia: flashback and prospects

The past decade has shown a marked increase in the use of high-throughput assays in clinical research into human cancer, including acute myeloid leukemia (AML). In particular, genome-wide gene expression profiling (GEP) using DNA microarrays has been extensively used for improved understanding of the diagnosis, prognosis, and pathobiology of this heterogeneous disease. This review discusses the progress that has been made, places the technologic limitations in perspective, and highlights promising future avenues.

Genome-wide studies in thyroid neoplasia

There is much interest in the application of genome biology to the field of thyroid neoplasia, despite the relatively low mortality rate associated with thyroid cancer in general. The principal reason for this interest is that the field of thyroid neoplasia stands to benefit from the application of genomic information to address a variety of pathologic and clinical issues. In addition to practical patient care issues, there is an excellent opportunity of expand the basic understanding of thyroid carcinogenesis. In this article, the most relevant genomic work on thyroid tumors performed to date is reviewed along with some general comments about the potential impact of genomic biology on thyroid pathology and the management of patients with thyroid nodules and cancer.