When is the right time to assess tumour markers?
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Biomarkers, Tumor↗
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Microarrays--the 21st century divining rod?
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Biomarkers, Tumor↗
Technology insight: Identification of biomarkers with tissue microarray technology.
High-throughput technologies have been developed in the hope of increasing the pace of biomedical research, and accelerating the rate of translation from bench to bedside. Using such technology in target discovery has resulted in the need for systematic validation of the targets in an equally rapid manner. For example, gene expression microarrays have highlighted many potential targets in cancer, and tissue microarrays have emerged as a powerful tool to validate these targets by measuring tumor-specific protein expression and linking it to clinical outcome. Automated quantitative analysis of the tissue microarray 'spots' is beginning to take the technology a step further, removing observer bias, and providing standards for quality control and the potential for high-throughput analysis. The validation required for translation of tissue biomarkers from the research lab to the clinical lab will probably rely heavily on the combination of tissue microarray technology with automated quantitative analysis.
Biomarkers, Tumor↗
Employment of microarray analysis to characterize biologic differences associated with tropism-modified adenoviral vectors: utilization of non-native cellular entry pathways.
In this study, we have applied high-density oligonucleotide microarray technology to characterize biologic changes associated with adenoviral vector-mediated target cell infection. We infected a human melanoma cell line, M21, with the tropism-modified vectors, Ad5lucRGD and Ad5/3luc1. In addition, we infected the M21 cell line with the Ad5luc1, a vector which primarily exploits the coxsackie and adenovirus receptor, as its primary native receptor. We found significant changes in gene expression of 5492 genes induced by Ad5luc1 infection, 2439 genes induced by Ad5/3luc1 infection, and 1251 genes induced by Ad5lucRGD infection, compared to uninfected cells. Among these changes in gene expression, 783 changes were common to Ad5/3luc1 and Ad5luc1 infections, 266 were common to Ad5lucRGD and Ad5luc1 infections, and 185 changes in gene expression were common to Ad5/3luc1 and Ad5lucRGD infections. Interestingly, 89 changes in gene expression were common to all the three groups, suggesting a commonly affected pathway. This analysis represents a unique application of microarray to study vector-related issues. Furthermore, these studies demonstrate the utility of microarray for characterizing the biologic sequelae of host-vector interaction.
Adenoviridae↗
The polycyclic aromatic hydrocarbon and geochemical biomarker composition of sediments from sea lochs on the west coast of Scotland.
Sediments from twelve sea lochs on the west coast of Scotland were analysed for parent and branched 2- to 6-ring polycyclic aromatic hydrocarbons (PAHs), n-alkanes and geochemical biomarkers (triterpanes). Where possible at least fourteen sediment samples were collected at random from each sea loch. All sea lochs were remote, most had limited industrial and urban inputs, although all had fish farms. Four lochs had moderate total PAH concentrations and eight lochs had high total PAH concentrations. Total PAH concentration was related to organic carbon content and particle size distribution, with sandier sediments having lower PAH concentrations. The highest total PAH concentrations, normalised for organic carbon, were in Loch Linnhe and Ballachulish Bay (Loch Leven), close to an aluminium smelter. PAH concentration ratios showed that pyrolysis was the main source of PAHs in most lochs. Only sediments from Loch Clash showed evidence of petrogenic input based on their geochemical biomarker (triterpane and sterane) and n-alkane profiles. PAH profiles were similar across lochs apart from Loch Linnhe and Ballachulish Bay, which had a greater proportion of heavy parent PAHs. West coast sediments had a smaller proportion of heavy PAHs than sediments collected from voes in Shetland and a smaller proportion of alkylated PAHs relative to sediments collected from coastal waters around Orkney.
Environmental Monitoring↗
Experiences from tissue microarray in soft tissue sarcomas.
The tissue microarray (TMA) technology was introduced in 1998 as a tissue preserving, high-throughput technique that allows studies of multiple markers in large sample sets. TMA slides can be analyzed using techniques such as immunohistochemistry and in situ hybridization and represents a powerful tool for the investigation of potential diagnostic and prognostic markers identified in DNA microarray studies. We review the TMA method, its reproducibility, advantages, limitations and future perspectives with specific focus on soft tissue sarcomas.
Biomarkers, Tumor↗
Application of the GA/KNN method to SELDI proteomics data.
SUMMARY: Proteomics technology has shown promise in identifying biomarkers for disease, toxicant exposure and stress. We show by example that the genetic algorithm/k-nearest neighbors method, developed for mining high-dimensional microarray gene expression data, is also capable of mining surface enhanced laser desorption/ionization-time-of-flight proteomics data. AVAILABILITY: The source code of the program and documentation on how to use it are freely available to non-commercial users at http://dir.niehs.nih.gov/dirbb/lifiles/softlic.htm
Algorithms↗
Gene selection using a two-level hierarchical Bayesian model.
SUMMARY: The fundamental problem of gene selection via cDNA data is to identify which genes are differentially expressed across different kinds of tissue samples (e.g. normal and cancer). cDNA data contain large number of variables (genes) and usually the sample size is relatively small so the selection process can be unstable. Therefore, models which incorporate sparsity in terms of variables (genes) are desirable for this kind of problem. This paper proposes a two-level hierarchical Bayesian model for variable selection which assumes a prior that favors sparseness. We adopt a Markov chain Monte Carlo (MCMC) based computation technique to simulate the parameters from the posteriors. The method is applied to leukemia data from a previous study and a published dataset on breast cancer. SUPPLEMENTARY INFORMATION: http://stat.tamu.edu/people/faculty/bmallick.html.
Algorithms↗
Boosting proportional hazards models using smoothing splines, with applications to high-dimensional microarray data.
MOTIVATION: An important area of research in the postgenomics era is to relate high-dimensional genetic or genomic data to various clinical phenotypes of patients. Due to large variability in time to certain clinical events among patients, studying possibly censored survival phenotypes can be more informative than treating the phenotypes as categorical variables. Due to high dimensionality and censoring, building a predictive model for time to event is more difficult than the classification/linear regression problem. We propose to develop a boosting procedure using smoothing splines for estimating the general proportional hazards models. Such a procedure can potentially be used for identifying non-linear effects of genes on the risk of developing an event. RESULTS: Our empirical simulation studies showed that the procedure can indeed recover the true functional forms of the covariates and can identify important variables that are related to the risk of an event. Results from predicting survival after chemotherapy for patients with diffuse large B-cell lymphoma demonstrate that the proposed method can be used for identifying important genes that are related to time to death due to cancer and for building a parsimonious model for predicting the survival of future patients. In addition, there is clear evidence of non-linear effects of some genes on survival time.
Algorithms↗
Serial analysis of gene expression and cancer.
Serial analysis of gene expression has been widely used to characterize gene expression patterns associated with tumor formation. These studies resulted in the identification of tumor-specific markers, transcriptional pathways, or therapeutic targets. In this review, recent applications and developments of serial analysis of gene expression and their impact on the diagnosis and treatment of cancer are discussed. A combination of serial analysis of gene expression and small-scale microarray analysis represents a strategy that should facilitate the identification and exploitation of tumor-specific gene expression for diagnostic or therapeutic purposes. In addition, cancer diagnosis and treatment may benefit from a complementation between serial analysis of gene expression and quantitative proteomics in the future.
Biomarkers, Tumor↗
Emerging prognostic factors in diffuse large B cell lymphoma.
PURPOSE OF REVIEW: Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma subtype, characterized by marked clinical and biologic heterogeneity. Gene expression studies together with new monoclonal antibody production are playing an increasing role in determining important prognostic factors/biomarkers predictive of outcome. Despite these technical advances, much confusion exists in the literature as to what constitutes the important biomarkers for determining patient outcome. The purpose of this review is to highlight recent advances in our understanding of novel biomarkers in DLBCL and how these might be incorporated into current risk-adjustment models for prognosis. RECENT FINDINGS: Microarray gene expression analyses have revolutionized our approach to biomarkers in non-Hodgkin lymphomas. Thousands of genes can now be simultaneously analyzed for individual patients, creating a wealth of new data. This has resulted in an improved understanding of the basic biology, as well as the development of new outcome predictors. Monoclonal antibody reagents for some of these biomarkers already exist, allowing for their rapid validation at the level of protein expression and potential clinical translation. SUMMARY: A molecular classification of DLBCL is a current reality, and together with routine morphology, immunophenotype, and molecular cytogenetics, has allowed us to more accurately subclassify DLBCL and determine clinically relevant subgroups. The time is right to begin to consider how these novel biomarkers should be incorporated into current prognostic models to move beyond the clinically based International Prognostic Index
Biomarkers, Tumor↗
Quantitative relationships between pigment-related mRNA and biochemical melanoma markers in melanoma cell lines.
The use of reverse transcription polymerase chain reaction (RT-PCR) analysis of melanoma-specific transcripts for the identification of circulating melanoma cells has shown very variable results in different studies on melanoma patients. We have therefore developed quantitative methods to study both analytical and biological variations as possible causes of this phenomenon. Pigment-related and S-100 beta transcripts were quantified in 12 different melanoma cell lines and related to the amounts of 5-S-cysteinyldopa, pigment and S-100B protein. A real-time PCR method was used and the results were expressed as absolute number of transcripts per cell. Tyrosinase, tyrosinase-related protein (TRP)-1, TRP-2 and MART-1/Melan-A mRNA varied from undetectable (< 10(-4) transcripts/cell) to 10(3) transcripts/cell, i.e. by a factor > 10(7) in the different cell lines. S-100 beta mRNA varied from 2.8 to 165 transcripts/cell, i.e. by a factor of 60. Tyrosinase, TRP-1 and TRP-2 mRNA correlated significantly with the amount of 5-S-cysteinyldopa, an intermediate pigment metabolite (P < 0.001, P < 0.001 and P < 0.01, respectively). The amount of S-100 beta mRNA correlated significantly with the amount of S-100B protein (P < 0.001). No cross-correlations were seen between the pigment-related and S-100-related analytes. We conclude that one reason behind the negative results of RT-PCR measurement of pigment-related mRNA may be that these transcripts are not always expressed in the particular cells present in the patient's blood. Furthermore, variation in the expression of the order of 10(7) must have great impact on the diagnostic sensitivity. Measurement of S-100 beta mRNA would be more sensitive, but the use of this transcript is hampered by its presence in the blood cells.
Antigens, Neoplasm↗
Evolving concepts in pheochromocytoma and paraganglioma.
PURPOSE OF REVIEW: The pheochromocytoma field has recently undergone a paradigm shift. This review will highlight some of these novel findings, including their impact on our understanding of the disease biology and influence on clinical management. RECENT FINDINGS: Identification of novel susceptibility loci and recognition of a high rate of germline mutations in pheochromocytomas indicate that their genetic diversity is broader and more complex than previously estimated. Further, increased risk of tumor malignancy and aggressiveness in certain patients with succinate dehydrogenase subunit B(SDHB) mutations suggest that they may have prognostic value as predictors of pheochromocytoma behavior. Finally, discovery of a shared activation of the hypoxic response in pheochromocytomas with mutations in VHL and SDH genes and uncovering of a common JunB-mediated apoptosis defect in the major hereditary groups of pheochromocytoma have provided a mechanistic basis for the clinical similarities between these distinct syndromes. SUMMARY: The notion that 'sporadic'-appearing tumors may in fact be components of one of multiple hereditary syndromes has a major impact on surveillance and follow-up of patients and their at-risk family members. Likewise, the ability to predict tumor malignancy has the potential to improve the prognosis of these patients. Importantly, insights into the biology of pheochromocytomas have provided clues on pathway interactions in cancers and have laid the ground for generation of new hypotheses on the cell-of-origin of these tumors. Pheochromocytomas have therefore emerged as key models for understanding cancer biology and for paving the way for future designer treatment in this and other cancers.
Biomarkers, Tumor↗
Loss of Stk11/Lkb1 expression in pancreatic and biliary neoplasms.
We have documented previously somatic mutations of STK11/LKB11, the gene responsible for Peutz-Jeghers syndrome (PJS), in a small proportion of sporadic pancreatic adenocarcinomas, intraductal papillary mucinous neoplasms (IPMNs), and biliary adenocarcinomas. In this report, we characterize the expression of Stk11, the protein product of the STK11 gene, in a larger series of pancreatic and biliary neoplasms. First, the specificity of the Stk11 antibody was established in 23 neoplasms (22 IPMNs and 1 biliary adenocarcinoma) with known STK11 gene status. Complete absence of labeling was seen in the neoplastic cells of 3 of the 3 (100%) cases with previously documented biallelic inactivation of the STK11 gene, whereas 16 of the 20 (80%) IPMNs, presumably with at least one wild-type STK11 gene, retained Stk11 expression in the neoplastic cells. The marked decrease or absence of Stk11 expression in four neoplasms with wild-type STK11 suggests that additional mechanisms may account for the lack of Stk11 expression. Subsequently, to further evaluate Stk11 expression in pancreatic and biliary neoplasms, tissue microarrays (TMAs) were constructed from a series of nearly 100 ductal adenocarcinomas and biliary neoplasms. Stk11 expression was lost in 4 of the 56 (7%) pancreatic adenocarcinomas and 1 of the 38 (2.6%) biliary cancers by immunohistochemistry; the absence of labeling was confirmed by repeated immunohistochemical labeling of complete tissue sections for the same cases. Thus, Stk11 expression is abrogated in a small proportion of pancreatic and biliary neoplasms. The inactivation of Stk11 in 27% (6/22) of IPMNs versus 7% (4/56) of pancreatic adenocarcinomas suggests genetic disparities in the pathogenesis of these closely related neoplasms. Immunohistochemical analysis for Stk11 expression may be a valid surrogate for genetic analysis of STK11 gene mutations in cancers.
Adenocarcinoma↗
Exploring the human genome in cancer with genomic approaches.
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Antineoplastic Agents↗
Gene array studies in renal neoplasia.
Renal cell carcinoma (RCC) is comprised of several distinct histologic subtypes many of which have characteristic cytogenetic abnormalities. The molecular pathogenesis of some of these neoplasms is beginning to be elucidated. Yet renal cell carcinoma is often discovered at an advanced clinical stage and effective pharmacologic therapies for this disease remain to be discovered. For these reasons, renal cell carcinoma is ideally suited to the genome scale investigation made possible by DNA microarrays. A number of DNA array studies of renal cell carcinoma have been published. Renal cell carcinomas have also been studied by array based comparative genomic hybridization. The purpose of this review will be to summarize these studies, to compare the results of the different studies, and to suggest future areas of investigation with a particular emphasis on clinically relevant advances.
Biomarkers, Tumor↗
Improving biomolecular pattern discovery and visualization with hybrid self-adaptive networks.
There is an increasing need to develop powerful techniques to improve biomedical pattern discovery and visualization. This paper presents an automated approach, based on hybrid self-adaptive neural networks, to pattern identification and visualization for biomolecular data. The methods are tested on two datasets: leukemia expression data and DNA splice-junction sequences. Several supervised and unsupervised models are implemented and compared. A comprehensive evaluation study of some of their intrinsic mechanisms is presented. The results suggest that these tools may be useful to support biological knowledge discovery based on advanced classification and visualization tasks.
Algorithms↗
Identification of a necroptosis-related lncRNA prognostic signature and the hub RBP HNRNPK in esophageal squamous cell carcinoma.
ObjectiveEsophageal squamous cell carcinoma (ESCC) is a malignant tumor with poor prognosis. Necroptosis is important for tumor immunity, but its role in ESCC remains unclear. This retrospective bioinformatics study aimed to investigate the prognostic value of necroptosis-related long non-coding RNAs (lncRNAs) and to identify key lncRNA-binding proteins (RBPs) in ESCC patients.MethodsRNA transcriptome and clinical data of ESCC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Necroptosis-related lncRNAs were identified through correlation analysis with necroptosis-related genes, subjected to consensus cluster analysis, and used to construct a prognostic risk model via least absolute shrinkage and selection operator (LASSO) regression. The hub RBP was experimentally validated by quantitative polymerase chain reaction (qPCR) using 30 pairs of ESCC and adjacent normal tissues from patients who underwent surgical resection.ResultsA total of 30 necroptosis-related lncRNAs were significantly correlated with overall survival (OS). The upregulated lncRNAs in the risk model were associated with high immune scores, innate immune cell infiltration, cluster 2 classification, and advanced T-stage disease (p < 0.05). Three hub RBPs (HNRNPA1, HNRNPC, and HNRNPK) were identified through protein-protein interaction network analysis. qPCR confirmed that HNRNPK was significantly overexpressed in ESCC tissues compared to adjacent normal tissues (p < 0.05).ConclusionsThe necroptosis-related lncRNA risk model is an independent prognostic factor for ESCC patients. HNRNPK was identified as a hub RBP significantly overexpressed in ESCC tissues. We hypothesize that HNRNPK may promote tumor progression through regulating proto-oncogene expression or modulating the immune microenvironment, though this requires further mechanistic validation.
Humans↗