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PubMed2004

Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia.

BACKGROUND: In patients with acute myeloid leukemia (AML), the presence or absence of recurrent cytogenetic aberrations is used to identify the appropriate therapy. However, the current classification system does not fully reflect the molecular heterogeneity of the disease, and treatment stratification is difficult, especially for patients with intermediate-risk AML with a normal karyotype. METHODS: We used complementary-DNA microarrays to determine the levels of gene expression in peripheral-blood samples or bone marrow samples from 116 adults with AML (including 45 with a normal karyotype). We used unsupervised hierarchical clustering analysis to identify molecular subgroups with distinct gene-expression signatures. Using a training set of samples from 59 patients, we applied a novel supervised learning algorithm to devise a gene-expression-based clinical-outcome predictor, which we then tested using an independent validation group comprising the 57 remaining patients. RESULTS: Unsupervised analysis identified new molecular subtypes of AML, including two prognostically relevant subgroups in AML with a normal karyotype. Using the supervised learning algorithm, we constructed an optimal 133-gene clinical-outcome predictor, which accurately predicted overall survival among patients in the independent validation group (P=0.006), including the subgroup of patients with AML with a normal karyotype (P=0.046). In multivariate analysis, the gene-expression predictor was a strong independent prognostic factor (odds ratio, 8.8; 95 percent confidence interval, 2.6 to 29.3; P<0.001). CONCLUSIONS: The use of gene-expression profiling improves the molecular classification of adult AML.

Acute Disease↗
PubMed2004

Prognostically useful gene-expression profiles in acute myeloid leukemia.

BACKGROUND: In patients with acute myeloid leukemia (AML) a combination of methods must be used to classify the disease, make therapeutic decisions, and determine the prognosis. However, this combined approach provides correct therapeutic and prognostic information in only 50 percent of cases. METHODS: We determined the gene-expression profiles in samples of peripheral blood or bone marrow from 285 patients with AML using Affymetrix U133A GeneChips containing approximately 13,000 unique genes or expression-signature tags. Data analyses were carried out with Omniviz, significance analysis of microarrays, and prediction analysis of microarrays software. Statistical analyses were performed to determine the prognostic significance of cases of AML with specific molecular signatures. RESULTS: Unsupervised cluster analyses identified 16 groups of patients with AML on the basis of molecular signatures. We identified the genes that defined these clusters and determined the minimal numbers of genes needed to identify prognostically important clusters with a high degree of accuracy. The clustering was driven by the presence of chromosomal lesions (e.g., t(8;21), t(15;17), and inv(16)), particular genetic mutations (CEBPA), and abnormal oncogene expression (EVI1). We identified several novel clusters, some consisting of specimens with normal karyotypes. A unique cluster with a distinctive gene-expression signature included cases of AML with a poor treatment outcome. CONCLUSIONS: Gene-expression profiling allows a comprehensive classification of AML that includes previously identified genetically defined subgroups and a novel cluster with an adverse prognosis.

Acute Disease↗
PubMed2004

Identification of differentially expressed genes with multivariate outlier analysis.

DNA microarray offers a powerful and effective technology to monitor the changes in the gene expression levels for thousands of genes simultaneously. It is being widely applied to explore the quantitative alternation in gene regulation in response to a variety of aspects including diseases and exposure of toxicant. A common task in analyzing microarray data is to identify the differentially expressed genes under two different experimental conditions. Because of the large number of genes and small number of arrays, and higher signal-noise ratio in microarray data, many traditional approaches seem improper. In this paper, a multivariate mixture model is applied to model the expression level of replicated arrays, considering the differentially expressed genes as the outliers of the expression data. In order to detect the outliers of the multivariate mixture model, an effective and robust statistical method is first applied to microarray analysis. This method is based on the analysis of kurtosis coefficient (KC) of the projected multivariate data arising from a mixture model so as to identify the outliers. We utilize the multivariate KC algorithm to our microarray experiment with the control and toxic treatment. After the processing of data, the differential genes are successfully identified from 1824 genes on the UCLA M07 microarray chip. We also use the RT-PCR method and two robust statistical methods, minimum covariance determinant (MCD) and minimum volume ellipsoid (MVE), to verify the expression level of outlier genes identified by KC algorithm. We conclude that the robust multivariate tool is practical and effective for the detection of differentially expressed genes.

Algorithms↗
PubMed2003

Heteroduplex-based genotyping with microchip electrophoresis and dHPLC.

This work compares the methods of mutation detection via denaturing high-performance liquid chromatography (dHPLC) and a microchip-based heteroduplex analysis (HA) method. The mutations analyzed were 185delAG and 5382insC in BRCA1 and 6174delT in BRCA2 with, as additional examples, 188del11 and 5396 + 1G --> A in BRCA1. Our HA method is based upon the use of a replaceable, highly denaturing sieving matrix that has dynamic coating capabilities, rendering our method relatively insensitive to contamination. We have found significant advantages in the microchip analysis in terms of reagent consumption, ease of use, versatility, simplicity of the protocol, the lack of constraints upon sample preparation or content, and the lack of parameters that need be adjusted. Although HA methods have a lower sensitivity than that of dHPLC, the electropherograms of the present HA method appear to provide more information and may allow mutations within the same amplicon to be distinguished. Although the dHPLC method has a remarkably high sensitivity, with this sensitivity there come constraints that may prevent it, in its present form, from being used in some applications, particularly those involving higher levels of integration. The advantages of the present HA method, along with recent developments in microchip-based single-nucleotide polymorphism (SNP) detection and high-throughput arrays, suggest that microchip-based systems could provide compact and integrated platforms capable of large-scale genotyping or mutational screening.

BRCA1 Protein↗
PubMed2006

CASPAR: a hierarchical bayesian approach to predict survival times in cancer from gene expression data.

MOTIVATION: DNA microarrays allow the simultaneous measurement of thousands of gene expression levels in any given patient sample. Gene expression data have been shown to correlate with survival in several cancers, however, analysis of the data is difficult, since typically at most a few hundred patients are available, resulting in severely underdetermined regression or classification models. Several approaches exist to classify patients in different risk classes, however, relatively little has been done with respect to the prediction of actual survival times. We introduce CASPAR, a novel method to predict true survival times for the individual patient based on microarray measurements. CASPAR is based on a multivariate Cox regression model that is embedded in a Bayesian framework. A hierarchical prior distribution on the regression parameters is specifically designed to deal with high dimensionality (large number of genes) and low sample size settings, that are typical for microarray measurements. This enables CASPAR to automatically select small, most informative subsets of genes for prediction. RESULTS: Validity of the method is demonstrated on two publicly available datasets on diffuse large B-cell lymphoma (DLBCL) and on adenocarcinoma of the lung. The method successfully identifies long and short survivors, with high sensitivity and specificity. We compare our method with two alternative methods from the literature, demonstrating superior results of our approach. In addition, we show that CASPAR can further refine predictions made using clinical scoring systems such as the International Prognostic Index (IPI) for DLBCL and clinical staging for lung cancer, thus providing an additional tool for the clinician. An analysis of the genes identified confirms previously published results, and furthermore, new candidate genes correlated with survival are identified.

Bayes Theorem↗
PubMed2005

Genomewide cDNA microarray screening of genes related to benefits and toxicities of platinum-based chemotherapy in patients with advanced lung cancer.

The authors conducted a study using cDNA microarray analysis to determine whether expression levels of genes in tumors were correlated with the outcome of chemotherapy. Forty-seven patients were studied, and all except 3 received platinum-based chemotherapy. The expression levels of 1176 genes in transbronchial biopsy specimens of tumors that were obtained before chemotherapy were analyzed using the Atlas Human Cancer 1.2 Array. Multivariate regression analysis revealed that 3 genes were each independent factors related to tumor resistance to chemotherapy and patient survival (P < 0.01). Among various chemotherapy-related toxicities, 1, 3, 3, 1, and 1 genes were also revealed to be independent factors that were correlated with neutropenia, anemia, diarrhea, infection, and increased serum creatinine respectively (P < 0.01). It is concluded that not only the benefits but also the toxicities of chemotherapy can be predicted by cDNA microarray using tumor specimens obtained before chemotherapy.

Adult↗
PubMed2005

Genome-wide transcriptional analysis of the phosphate starvation stimulon of Bacillus subtilis.

Bacillus subtilis responds to phosphate starvation stress by inducing the PhoP and SigB regulons. While the PhoP regulon provides a specific response to phosphate starvation stress, maximizing the acquisition of phosphate (P(i)) from the environment and reducing the cellular requirement for this essential nutrient, the SigB regulon provides nonspecific resistance to stress by protecting essential cellular components, such as DNA and membranes. We have characterized the phosphate starvation stress response of B. subtilis at a genome-wide level using DNA macroarrays. A combination of outlier and cluster analyses identified putative new members of the PhoP regulon, namely, yfkN (2',3' cyclic nucleotide 2'-phosphodiesterase), yurI (RNase), yjdB (unknown), and vpr (extracellular serine protease). YurI is thought to be responsible for the nonspecific degradation of RNA, while the activity of YfkN on various nucleotide phosphates suggests that it could act on substrates liberated by YurI, which produces 3' or 5' phosphoribonucleotides. The putative new PhoP regulon members are either known or predicted to be secreted and are likely to be important for the recovery of inorganic phosphate from a variety of organic sources of phosphate in the environment.

2',3'-Cyclic-Nucleotide Phosphodiesterases↗
PubMed2006

A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets.

BACKGROUND: Gene selection is an important step when building predictors of disease state based on gene expression data. Gene selection generally improves performance and identifies a relevant subset of genes. Many univariate and multivariate gene selection approaches have been proposed. Frequently the claim is made that genes are co-regulated (due to pathway dependencies) and that multivariate approaches are therefore per definition more desirable than univariate selection approaches. Based on the published performances of all these approaches a fair comparison of the available results can not be made. This mainly stems from two factors. First, the results are often biased, since the validation set is in one way or another involved in training the predictor, resulting in optimistically biased performance estimates. Second, the published results are often based on a small number of relatively simple datasets. Consequently no generally applicable conclusions can be drawn. RESULTS: In this study we adopted an unbiased protocol to perform a fair comparison of frequently used multivariate and univariate gene selection techniques, in combination with a ränge of classifiers. Our conclusions are based on seven gene expression datasets, across several cancer types. CONCLUSION: Our experiments illustrate that, contrary to several previous studies, in five of the seven datasets univariate selection approaches yield consistently better results than multivariate approaches. The simplest multivariate selection approach, the Top Scoring method, achieves the best results on the remaining two datasets. We conclude that the correlation structures, if present, are difficult to extract due to the small number of samples, and that consequently, overly-complex gene selection algorithms that attempt to extract these structures are prone to overtraining.

Algorithms↗
PubMed2006

Gene expression profiling in the striatum of inbred mouse strains with distinct opioid-related phenotypes.

BACKGROUND: Mouse strains with a contrasting response to morphine provide a unique model for studying the genetically determined diversity of sensitivity to opioid reward, tolerance and dependence. Four inbred strains selected for this study exhibit the most distinct opioid-related phenotypes. C57BL/6J and DBA/2J mice show remarkable differences in morphine-induced antinociception, self-administration and locomotor activity. 129P3/J mice display low morphine tolerance and dependence in contrast to high sensitivity to precipitated withdrawal observed in SWR/J and C57BL/6J strains. In this study, we attempted to investigate the relationships between genetic background and basal gene expression profile in the striatum, a brain region involved in the mechanism of opioid action. RESULTS: Gene expression was studied by Affymetrix Mouse Genome 430v2.0 arrays with probes for over 39.000 transcripts. Analysis of variance with the control for false discovery rate (q < 0.01) revealed inter-strain variation in the expression of ~3% of the analyzed transcripts. A combination of three methods of array pre-processing was used to compile a list of ranked transcripts covered by 1528 probe-sets significantly different between the mouse strains under comparison. Using Gene Ontology analysis, over-represented patterns of genes associated with cytoskeleton and involved in synaptic transmission were identified. Differential expression of several genes with relevant neurobiological function (e.g. GABA-A receptor alpha subunits) was validated by quantitative RT-PCR. Analysis of correlations between gene expression and behavioural data revealed connection between the level of mRNA for K homology domain containing, RNA binding, signal transduction associated 1 (Khdrbs1) and ATPase Na+/K+ alpha2 subunit (Atp1a2) with morphine self-administration and analgesic effects, respectively. Finally, the examination of transcript structure demonstrated a possible inter-strain variability of expressed mRNA forms as for example the catechol-O-methyltransferase (Comt) gene. CONCLUSION: The presented study led to the recognition of differences in the gene expression that may account for distinct phenotypes. Moreover, results indicate strong contribution of genetic background to differences in gene transcription in the mouse striatum. The genes identified in this work constitute promising candidates for further animal studies and for translational genetic studies in the field of addictive and analgesic properties of opioids.

Animals↗
PubMed2006

Systematic interpretation of microarray data using experiment annotations.

BACKGROUND: Up to now, microarray data are mostly assessed in context with only one or few parameters characterizing the experimental conditions under study. More explicit experiment annotations, however, are highly useful for interpreting microarray data, when available in a statistically accessible format. RESULTS: We provide means to preprocess these additional data, and to extract relevant traits corresponding to the transcription patterns under study. We found correspondence analysis particularly well-suited for mapping such extracted traits. It visualizes associations both among and between the traits, the hereby annotated experiments, and the genes, revealing how they are all interrelated. Here, we apply our methods to the systematic interpretation of radioactive (single channel) and two-channel data, stemming from model organisms such as yeast and drosophila up to complex human cancer samples. Inclusion of technical parameters allows for identification of artifacts and flaws in experimental design. CONCLUSION: Biological and clinical traits can act as landmarks in transcription space, systematically mapping the variance of large datasets from the predominant changes down toward intricate details.

Cluster Analysis↗
PubMed2005

Common-path phase-shift interferometry surface plasmon resonance imaging system.

Surface plasmon resonance (SPR) and common-path phase-shift interferometry (PSI) techniques are integrated in a biosensing imaging system for measuring the two-dimensional spatial phase variation caused by biomolecular interactions on a sensing chip without the need for additional labeling. The common-path PSI technique has the advantage of long-term stability, even when it is subjected to external disturbances. Hence the system meets the requirements of the real-time kinetic studies involved in biomolecular interaction analysis. The proposed SPR-PSI imaging system demonstrates a detection limit of a 2 x 10(-7) refractive-index change, a long-term phase stability of 2.5 x 10(-4) pi rms for 4 h, and a spatial phase resolution of 10(-3) pi with a lateral resolution of 100 microm.

Biosensing Techniques↗
PubMed2003

Analyzing microarray data using cluster analysis.

As pharmacogenetics researchers gather more detailed and complex data on gene polymorphisms that effect drug metabolizing enzymes, drug target receptors and drug transporters, they will need access to advanced statistical tools to mine that data. These tools include approaches from classical biostatistics, such as logistic regression or linear discriminant analysis, and supervised learning methods from computer science, such as support vector machines and artificial neural networks. In this review, we present an overview of another class of models, cluster analysis, which will likely be less familiar to pharmacogenetics researchers. Cluster analysis is used to analyze data that is not a priori known to contain any specific subgroups. The goal is to use the data itself to identify meaningful or informative subgroups. Specifically, we will focus on demonstrating the use of distance-based methods of hierarchical clustering to analyze gene expression data.

Cluster Analysis↗
PubMed2006

[Microarray data analysis].

Microarrays might be used for future diagnostic and prognostic purposes. High-density oligonucleotide arrays are promising in this respect. The microarray data consist of intensity files, which are transformed into expression matrices by the application of several mathematical modifications. However, pitfalls regarding data analysis seem to be a critical factor for the impact of this new technology. This article focuses on the data analysis, from raw data file to marker gene lists used to retrieve knowledge about underlying biological processes.

Gene Expression Profiling↗
PubMed2003

Identification of human remains by immobilized sequence-specific oligonucleotide probe analysis of mtDNA hypervariable regions I and II.

AIM: A rapid analysis of mitochondrial DNA (mtDNA) sequences with an array of immobilized sequence-specific oligonucleotide (SSO) probes was tested on 18 skeletal elements recovered from mass graves in Croatia, which could not be genotyped with common forensic nuclear DNA systems (PM+DQA1 and short tandem repeat analysis). METHODS: We used duplex polymerase chain reaction (PCR) amplification of the mtDNA hypervariable regions I and II (HVI and HVII) (444 bp and 415 bp amplicons, respectively) and subsequent linear array typing, which targets six polymorphic regions and two additional sites within the human mtDNA HVI and HVII. The remaining amplified products were subjected to direct sequence analysis to obtain complete sequence information for the targeted HV regions. RESULT: Duplex PCR amplification of the mtDNA HVI and HVII was successful in providing sufficient product for typing with the array of SSO probes in 14 out of the 18 sample extracts. We report here the sequence match of one set of remains with a panel of immobilized SSO probes, followed by direct sequence analysis. The corresponding mtDNA haplotype obtained for the bone sample and the putative maternal reference was unique in a database of 105 randomly selected Croatian individuals. CONCLUSION: Mitochondrial DNA typing with an array of immobilized SSO probes can be a benefit to forensic DNA analysis of mass disaster remains and identity testing of single and mass graves.

Blood Stains↗
PubMed2004

The Human MitoChip: a high-throughput sequencing microarray for mitochondrial mutation detection.

Somatic mitochondrial mutations are common in human cancers, and can be used as a tool for early detection of cancer. We have developed a mitochondrial Custom Reseq microarray as an array-based sequencing platform for rapid and high-throughput analysis of mitochondrial DNA. The MitoChip contains oligonucleotide probes synthesized using standard photolithography and solid-phase synthesis, and is able to sequence >29 kb of double-stranded DNA in a single assay. Both strands of the entire human mitochondrial coding sequence (15,451 bp) are arrayed on the MitoChip; both strands of an additional 12,935 bp (84% of coding DNA) are arrayed in duplicate. We used 300 ng of genomic DNA to amplify the mitochondrial coding sequence in three overlapping long PCR fragments. We then sequenced >2 million base pairs of mitochondrial DNA, and successfully assigned base calls at 96.0% of nucleotide positions. Replicate experiments demonstrated >99.99% reproducibility. In matched fluid samples (urine and pancreatic juice, respectively) obtained from five patients with bladder cancer and four with pancreatic cancer, the MitoChip detected at least one cancer-associated mitochondrial mutation in six (66%) of nine samples. The MitoChip is a high-throughput sequencing tool for the reliable identification of mitochondrial DNA mutations from primary tumors in clinical samples.

Body Fluids↗
PubMed2003

Heterogeneity in control of mRNA stability by AU-rich elements.

AU-rich elements (AREs), located in the 3'-untranslated region of unstable cytokine and chemokine mRNAs, promote rapid decay of otherwise stable mRNAs and may mediate selective mRNA stabilization in response to stimulation with interleukin-1 (IL-1). AREs vary considerably, however, in both size and sequence context. To assess the heterogeneity involved in control of mRNA stability by ARE motifs, human mRNA sequences from IL-1alpha-stimulated HEK293 cells and T98G cells were screened for either instability or stability using both cDNA (950 ARE containing sequences) and Affymetrix oligonucleotide (U95Av2 GeneChip) array analysis. Although ARE-containing mRNAs exhibited a broad range of stability, IL-1alpha promoted stability in a subset of mRNAs that were unstable when transcriptionally induced by tumor necrosis factor alpha. Stabilization of granulocyte/macrophage-colony stimulating factor and IL-8 mRNAs by IL-1alpha was achieved only after 2 h of stimulation, required ongoing protein synthesis, and depended on the activation of p38 MAPK. In contrast, stabilization of Gro3 mRNA in response to IL-1alpha was achieved immediately and was insensitive to inhibitors of protein synthesis and p38 MAPK activation. In concert, these findings demonstrate that ARE sequences are functionally heterogeneous; only a subset of unstable mRNAs is sensitive to stabilization by IL-1alpha. Moreover, IL-1alpha promotes stabilization of unstable mRNAs through distinct mechanistic pathways that distinguish between specific mRNA sequences.

3' Untranslated Regions↗
PubMed2002

High-density genechip oligonucleotide probe arrays.

High-density DNA probe arrays provide a highly parallel approach to nucleic acid sequence analysis that is transforming gene-based biomedical research. Photolithographic DNA synthesis has enabled the large-scale production of GeneChip probe arrays containing hundreds of thousands of oligonucleotide sequences on a glass "chip" about 1.5 cm2 in size. The manufacturing process integrates solid-phase photochemical oligonucleotide synthesis with lithographic techniques similar to those used in the microelectronics industry. Due to their very high information content, GeneChip probe arrays are finding widespread use in the hybridization-based detection and analysis of mutations and polymorphisms ("genotyping"), and in a wide range of gene expression studies.

Base Sequence↗
PubMed2004

Microarray-based AMASE as a novel approach for mutation detection.

Alterations in the p53 tumor suppressor gene are important events in many cases of human cancers. We have developed a novel microarray based approach for re-sequencing and mutation detection of the p53 gene. The method facilitates rapid and simple scanning of the target gene sequence and could be expanded to include other candidate cancer genes. The methodology employs the previously described apyrase-mediated allele-specific extension reaction (AMASE). In order to re-sequence the selected region, four extension oligonucleotides with different 3'-termini were used for each base position and they were covalently attached to the glass slide's surface. The amplified single-stranded DNA templates were then hybridized to the array followed by in situ extension with fluorescently labeled dNTPs in the presence of apyrase. The model system used was based on analysis of a 15 bp stretch in exon 5 of the p53 gene. Mutations were scored as allelic fractions calculated as (wt)/(wt + mut) signals. When apyrase was included in the extension reactions of wild type templates, the mean allelic fraction was 0.96. When apyrase was excluded with the same wild type templates, significantly lower allelic fractions were obtained. Two 60-mer synthetic oligonucleotides were used to establish the detectable amount of mutations with AMASE and a clear distinction between all the points could be made. Several samples from different stages of skin malignancies were also analyzed. The results from this study imply the possibility to efficiently and accurately re-sequence the entire p53 gene with AMASE technology.

Alleles↗