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Results for “Biomarker Profiling”

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At least 1,261 records · Page 70Linked to original sources
PubMed2004

Differential gene expression in ovarian carcinoma: identification of potential biomarkers.

Ovarian cancer remains the fifth leading cause of cancer death for women in the United States. In this study, the gene expression of 20 ovarian carcinomas, 17 ovarian carcinomas metastatic to the omentum, and 50 normal ovaries was determined by Gene Logic Inc. using Affymetrix GeneChip HU_95 arrays containing approximately 12,000 known genes. Differences in gene expression were quantified as fold changes in gene expression in ovarian carcinomas compared to normal ovaries and ovarian carcinoma metastases. Genes up-regulated in ovarian carcinoma tissue samples compared to more than 300 other normal and diseased tissue samples were identified. Seven genes were selected for further screening by immunohistochemistry to determine the presence and localization of the proteins. These seven genes were: the beta8 integrin subunit, bone morphogenetic protein-7, claudin-4, collagen type IX alpha2, cellular retinoic acid binding protein-1, forkhead box J1, and S100 calcium-binding protein A1. Statistical analyses showed that the beta8 integrin subunit, claudin-4, and S100A1 provided the best distinction between ovarian carcinoma and normal ovary tissues, and may serve as the best candidate tumor markers among the seven genes studied. These results suggest that further exploration into other up-regulated genes may identify novel diagnostic, therapeutic, and/or prognostic biomarkers in ovarian carcinoma.

Adenocarcinoma, Papillary↗
PubMed2005

Breast cancer biomarkers.

Substantial progress has been made over the past three decades in our understanding of the epidemiology, clinical course and basic biology of breast cancer. This chapter considers the existing ancillary tests and emerging molecular markers in breast cancer prognosis assessment and the prediction of response of breast cancer to treatment of the disease.

Biomarkers, Tumor↗
PubMed2004

Gene expression screening of salivary gland neoplasms: molecular markers of potential histogenetic and clinical significance.

Salivary gland neoplasms comprise phenotypically and biologically diverse lesions of uncertain histogenesis. The molecular events associated with their development and clinicopathological heterogeneity remain unknown. To reveal these events, we performed microarray expression analysis using a nylon-filter membrane platform on 18 primary lesions representing the most common benign and malignant types. Our study identified a small set of genes that are differentially altered between normal salivary gland tissues and benign and malignant tumors. Of the 5000 genes arrayed, 136 genes were differentially expressed by normal tissue, benign tumors, and various malignant neoplasms. Hierarchical clustering analysis differentiated between adenoid cystic carcinomas (ACCs) and other malignant subtypes. Non-ACC specimens manifested overlapping patterns of gene expression within and between tumors. Most of the differentially expressed genes share functional similarities with members of the adhesion, proliferation, and signal transduction pathways. Our study identified: 1) a set of genes that differentiate normal tissue from tumor specimens, 2) genes that differentiate pleomorphic adenoma and ACCs from other malignant salivary gland neoplasms, and 3) different patterns of expression between ACCs arising from major and minor salivary gland sites. The differentially expressed genes provide new information on potential genetic events of biological significance in future studies of salivary gland tumorigenesis.

Biomarkers, Tumor↗
PubMed2005

Identification and characterization of optimal gene expression markers for detection of breast cancer metastasis.

Sentinel lymph node (SLN) status is highly predictive of overall axillary lymph node involvement in breast cancer. Historically, SLN-positive patients have undergone axillary lymph node dissection in a second surgery. Intraoperative SLN analysis could reduce the cost and complications of a second surgery; however, existing histopathological methods lack standardization and exhibit poor sensitivity. Rapid molecular methods may lead to improved intraoperative diagnosis of SLN metastasis. In this study, we used a genome-wide gene expression analysis of breast and other tissues to identify seven putative markers for detecting breast cancer metastasis. We assessed the utility of these markers for identifying clinically actionable metastases in lymph nodes through reverse transcriptase-polymerase chain reaction analysis of SLNs from 254 breast cancer patients. Polymerase chain reaction signals were compared to pathology on a per-patient basis. The optimal two-gene combination, mammaglobin and cytokeratin 19, detected clinically actionable metastasis in breast SLNs with 90% sensitivity and 94% specificity. Application of stringent criteria for identifying presumptive hematoxylin- and eosin-positive samples increased sensitivity and specificity to 91 and 97%, respectively. This study represents the first comprehensive demonstration of the utility of gene expression markers for detecting clinically actionable breast metastases. An intraoperative molecular assay using these markers has the potential to significantly reduce second surgeries for patients undergoing SLN dissection.

Biomarkers, Tumor↗
PubMed2003

The chloride conductance channel ClC-K is a specific marker for the Xenopus pronephric distal tubule and duct.

Almost nothing is known about the physiology of the pronephros other than that it is efficient at excreting large volumes of dilute urine. The distal segment of pronephric nephrons is the most poorly characterized region of this organ, in part due to the absence of early markers of this structure. In this report the embryonic expression of a Cl(-) conductance channel, x ClC-K, is described with high level transcription of this gene in the distal segment beginning at Xenopus stage 31. x ClC-K is also expressed at lower levels in the pronephric duct. This gene serves as a convenient marker for investigating the development of this physiologically critical nephric segment and also serves to identify the potential site of action of a component of the chloride handling system of the pronephros. As ClC-K channels are involved in human kidney disease the frog pronephros may be a useful system in which to study the in vivo activity of this channel.

Animals↗
PubMed2005

In vivo protein biotinylation for identification of organ-specific antigens accessible from the vasculature.

We describe a new methodology, based on terminal perfusion of rodents with a reactive ester derivative of biotin that enables the covalent modification of proteins readily accessible from the bloodstream. Biotinylated proteins from total organ extracts can be purified on streptavidin resin in the presence of strong detergents, digested on the resin and subjected to liquid chromatography-tandem mass spectrometry for identification. In the present study, in vivo biotinylation procedure led to the identification of hundreds of proteins in different mouse organs, including some showing a restricted pattern of expression in certain body tissues. Furthermore, biotinylation of mice with F9 subcutaneous tumors or orthotopic kidney tumors revealed both quantitative and qualitative differences in the recovery of biotinylated proteins, as compared to normal tissues. This technology is applicable to proteomic investigations of the differential expression of accessible proteins in physiological and pathological processes in animal models, and to human surgical specimens using ex vivo perfusion procedures.

Animals↗
PubMed2006

[Proteomics of breast cancer: From differential to functional analysis].

From differential analysis to identify biomarkers, to functional analysis for finding new therapeutic targets, proteomics bring new comprehensive information for a better understanding of the molecular basis of oncology and new perspectives for the clinic. However the major limitation of proteomic investigations, more generally of post-genomic approaches, remains the molecular and cellular complexity of the mammary gland that is still a major challenge.

Biomarkers, Tumor↗
PubMed2004

Natural killer cells distinguish innocuous and destructive forms of pancreatic islet autoimmunity.

In both human patients and murine models, the progression from insulitis to diabetes is neither immediate nor inevitable, as illustrated by the innocuous versus destructive infiltrates of BDC2.5 transgenic mice on the nonobese diabetic (NOD) versus C57BL/6.H-2g7 genetic backgrounds. Natural killer (NK)-cell-specific transcripts and the proportion of NK cells were increased in leukocytes from the aggressive BDC2.5/B6.H-2g7 lesions. NK cell participation was also enhanced in the aggressive lesions provoked by CTLA-4 blockade in BDC2.5/NOD mice. In this context, depletion of NK cells significantly inhibited diabetes development. NOD and B6.H-2g7 mice exhibit extensive variation in NK receptor expression, reminiscent of analogous human molecules. NK cells can be important players in type 1 diabetes, a role that was previously underappreciated.

Animals↗
PubMed2005

Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry.

The combined method of LC-MS/MS is increasingly being used to explore differences in the proteomic composition of complex biological systems. The reliability and utility of such comparative protein expression profiling studies is critically dependent on an accurate and rigorous assessment of quantitative changes in the relative abundance of the myriad of proteins typically present in a biological sample such as blood or tissue. In this review, we provide an overview of key statistical and computational issues relevant to bottom-up shotgun global proteomic analysis, with an emphasis on methods that can be applied to improve the dependability of biological inferences drawn from large proteomic datasets. Focusing on a start-to-finish approach, we address the following topics: 1) low-level data processing steps, such as formation of a data matrix, filtering, and baseline subtraction to minimize noise, 2) mid-level processing steps, such as data normalization, alignment in time, peak detection, peak quantification, peak matching, and error models, to facilitate profile comparisons; and, 3) high-level processing steps such as sample classification and biomarker discovery, and related topics such as significance testing, multiple testing, and choice of feature space. We report on approaches that have recently been developed for these steps, discussing their merits and limitations, and propose areas deserving of further research.

Animals↗
PubMed2004

A novel approach for increasing sensitivity and correcting saturation artifacts of radioactively labeled cDNA arrays.

MOTIVATION: The radioactivity labeled DNA array platform is a robust and accurate way for a high-throughput measurement of gene expression levels in biological samples. Despite its high degree of sensitivity and reproducibility, this platform has several sources of variation. These are related to the presence of saturation effects in the array images and impede the degree of accuracy at which gene expression levels are determined. RESULTS: Here we describe a simple, but effective, approach for combining expression data from a series of autoradiographic exposures of variable length. This technique increases the sensitivity of this array platform by detecting low-expressed genes at longer exposures. It also improves the measurement accuracy of highly abundant genes by considering only values from the linear portion of dependency between the exposure times and gene intensities. As a result, the described approach improves the outcome of the subsequent steps of array data normalization and mining.

Algorithms↗
PubMed2005

A two-stage normalization method for partially degraded mRNA microarray data.

MOTIVATION: The goal of the study is to obtain genetic information from exfoliated colonocytes in the fecal stream rather than directly from mucosa cells within the colon. The latter is obtained through invasive procedures. The difficulties encountered by this procedure are that certain probe information may be compromised due to partially degraded mRNA. Proper normalization is essential to obtaining useful information from these fecal array data. RESULTS: We propose a new two-stage semiparametric normalization method motivated by the features observed in fecal microarray data. A location-scale transformation and a robust inclusion step were used to roughly align arrays within the same treatment. A non-parametric estimated non-linear transformation was then used to remove the potential intensity-based biases. We compared the performance of the new method in analyzing a fecal microarray dataset with those achieved by two existing normalization approaches: global median transformation and quantile normalization. The new method favorably compared with the global median and quantile normalization methods. AVAILABILITY: The R codes implementing the two-stage method may be obtained from the corresponding author.

Algorithms↗
PubMed2006

Survival analysis of longitudinal microarrays.

MOTIVATION: The development of methods for linking gene expressions to various clinical and phenotypic characteristics is an active area of genomic research. Scientists hope that such analysis may, for example, describe relationships between gene function and clinical events such as death or recovery. Methods are available for relating gene expression to measurements that are categorized or continuous, but there is less work in relating expressions to an observed event time such as time to death, response or relapse. When gene expressions are measured over time, there are methods for differentiating temporal patterns. However, methods have not yet been proposed for the survival analysis of longitudinally collected microarrays. RESULTS: We describe an approach for the survival analysis of longitudinal gene expression data. We construct a measure of association between the time to an event and gene expressions collected over time. Statistical significance is addressed using permutations and control of the false discovery rate. Our proposed method is illustrated on a dataset from a multi-center research study of inflammation and response to injury that aims to uncover the biological reasons why patients can have dramatically different outcomes after suffering a traumatic injury (www.gluegrant.org).

Algorithms↗
PubMed2005

Limits of predictive models using microarray data for breast cancer clinical treatment outcome.

Data from microarray studies have been used to develop predictive models for treatment outcome in breast cancer, such as a recently proposed predictive model for antiestrogen response after tamoxifen treatment that was based on the expression ratio of two genes. We attempted to validate this model on an independent cohort of 58 patients with resectable estrogen receptor-positive breast cancer. We measured expression of the genes HOXB13 and IL17BR with real time-quantitative polymerase chain reaction and assessed the association between their expression and outcome by use of univariate logistic regression, area under the receiver-operating-characteristic curve (AUC), a two-sample t test, and a Mann-Whitney test. We also applied standard supervised methods to the original microarray dataset and to another independent dataset from similar patients to estimate the classification accuracy obtainable by using more than two genes in a microarray-based predictive model. We could not validate the performance of the two-gene predictor on our cohort of samples (relation between outcome and the following genes estimated by logistic regression: for HOXB13, odds ratio [OR] = 1.04, 95% confidence interval [CI] = 0.92 to 1.16, P = .54; for IL17BR, OR = 0.69, 95% CI = 0.40 to 1.20, P = .18; and for HOXB13/IL17BR, OR = 1.30, 95% CI = 0.88 to 1.93, P = .18). Similar results were obtained with the AUC, a two-sample two-sided t test, and a Mann-Whitney test. In addition, estimates of classification accuracies applied to two independent microarray datasets highlighted the poor performance of treatment-response predictive models that can be achieved with the sample sizes of patients and informative genes to date.

Adult↗
PubMed2005

Automated evaluation of Her-2/neu status in breast tissue from fluorescent in situ hybridization images.

The evaluation of fluorescent in situ hybridization (FISH) images is one of the most widely used methods to determine Her-2/neu status of breast samples, a valuable prognostic indicator. Conventional evaluation is a difficult task since it involves manual counting of dots in multiple images. In this paper, we present a multistage algorithm for the automated classification of FISH images from breast carcinomas. The algorithm focuses not only on the detection of FISH dots per image, but also on combining results from multiple images taken from a slice for overall case classification. The algorithm includes mainly two stages for nuclei and dot detection respectively. The dot segmentation consists of a top-hat filtering stage followed by template matching to separate real signals from noise. Nuclei segmentation includes a nonlinearity correction step, global thresholding to identify candidate regions, and a geometric rule to distinguish between holes within a nucleus and holes between nuclei. Finally, the marked watershed transform is used to segment cell nuclei with markers detected as regional maxima of the distance transform. Combining the two stages allows the measurement of FISH signals ratio per cell nucleus and the collective classification of cases as positive or negative. The system was evaluated with receiver operating characteristic analysis and the results were encouraging for the further development of this method.

Algorithms↗
PubMed2003

Selecting differentially expressed genes from microarray experiments.

High throughput technologies, such as gene expression arrays and protein mass spectrometry, allow one to simultaneously evaluate thousands of potential biomarkers that could distinguish different tissue types. Of particular interest here is distinguishing between cancerous and normal organ tissues. We consider statistical methods to rank genes (or proteins) in regards to differential expression between tissues. Various statistical measures are considered, and we argue that two measures related to the Receiver Operating Characteristic Curve are particularly suitable for this purpose. We also propose that sampling variability in the gene rankings be quantified, and suggest using the "selection probability function," the probability distribution of rankings for each gene. This is estimated via the bootstrap. A real dataset, derived from gene expression arrays of 23 normal and 30 ovarian cancer tissues, is analyzed. Simulation studies are also used to assess the relative performance of different statistical gene ranking measures and our quantification of sampling variability. Our approach leads naturally to a procedure for sample-size calculations, appropriate for exploratory studies that seek to identify differentially expressed genes.

Biomarkers, Tumor↗
PubMed2005

Molecular predictors of clinical outcome in patients with head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) involves the upper aerodigestive tract and can destroy the structure and function of organs involved in voice, speech, taste, smell and hearing, as well as vital structures necessary for survival. HNSCC has long been a treatment challenge because of the high rate of recurrences and of advanced disease at the time of diagnosis. Molecular identification of tissue biomarkers in diagnostic biopsy specimens may not only identify patients at risk for developing HNSCC but may also select patients that may benefit from more aggressive treatment modalities. Several biomarkers studied to date such as the proteins p53, cyclin D1, p16, Cox-2 enzyme, epidermal growth factor and vascular endothelial growth factor receptors, matrix metalloproteinases and the Fhit marker for genomic instability could be manipulated for the therapeutic benefit of these patients. This review presents the most updated information on molecular biomarkers with the greatest prognostic potential in HNSCC and discusses some factors that contribute to the controversy concerning their prognostic importance.

Biomarkers, Tumor↗