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PubMed2004

Gene expression profiling identifies clinically relevant subtypes of prostate cancer.

Prostate cancer, a leading cause of cancer death, displays a broad range of clinical behavior from relatively indolent to aggressive metastatic disease. To explore potential molecular variation underlying this clinical heterogeneity, we profiled gene expression in 62 primary prostate tumors, as well as 41 normal prostate specimens and nine lymph node metastases, using cDNA microarrays containing approximately 26,000 genes. Unsupervised hierarchical clustering readily distinguished tumors from normal samples, and further identified three subclasses of prostate tumors based on distinct patterns of gene expression. High-grade and advanced stage tumors, as well as tumors associated with recurrence, were disproportionately represented among two of the three subtypes, one of which also included most lymph node metastases. To further characterize the clinical relevance of tumor subtypes, we evaluated as surrogate markers two genes differentially expressed among tumor subgroups by using immunohistochemistry on tissue microarrays representing an independent set of 225 prostate tumors. Positive staining for MUC1, a gene highly expressed in the subgroups with "aggressive" clinicopathological features, was associated with an elevated risk of recurrence (P = 0.003), whereas strong staining for AZGP1, a gene highly expressed in the other subgroup, was associated with a decreased risk of recurrence (P = 0.0008). In multivariate analysis, MUC1 and AZGP1 staining were strong predictors of tumor recurrence independent of tumor grade, stage, and preoperative prostate-specific antigen levels. Our results suggest that prostate tumors can be usefully classified according to their gene expression patterns, and these tumor subtypes may provide a basis for improved prognostication and treatment stratification.

Biomarkers, Tumor↗
PubMed2004

Quantitative analysis of amoA mRNA expression as a new biomarker of ammonia oxidation activities in a complex microbial community.

AIMS: To quantitatively analyse the changes to amoA mRNA (ammonia mono-oxygenase encoding mRNA) profiles in response to a change in ammonia oxidation activity in a complex microbial community. METHODS AND RESULTS: The amoA mRNA levels in both a batch-mode incubation and a continuously fed nitrification reactor were determined by real-time reverse transcription-PCR analysis. The amoA mRNA level changed rapidly in response to the change in environmental conditions which affect ammonia oxidation activity. CONCLUSION: An increase in amoA mRNA level can be detected within 1-2 h in response to an initiation of cell activity whereas a decrease in amoA mRNA level is detected within 24 h in response to a cessation of activity. SIGNIFICANCE AND IMPACT OF THE STUDY: amoA mRNA, which shows sensitive response to ammonia oxidation activity, can be used as a biomarker of ammonia oxidation activity in wastewater treatment processes where many bacterial species exist.

Ammonia↗
PubMed2005

Diagnosis of early relapse in ovarian cancer using serum proteomic profiling.

Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry data has been increasingly analyzed for identifying biomarkers to help early detection of the disease. Ovarian cancer commonly recurs at the rate of 75% within a few months or several years later after standard treatment. Since recurrent ovarian cancer is relatively difficult to be diagnosed and small tumors generally respond better to treatment, new methods for the detection of early relapse in ovarian cancer are urgently needed. Here, we propose a new algorithm SVM-MB/RFE (SVM-Markov Blanket/Recursive Feature Elimination) based on SVM-RFE, which identifies biomarkers for predicting the early recurrence of ovarian cancer. In this approach, we first apply t-test for feature pruning and then binning using 5-fold cross validation. Finally, 58 peaks are obtained from 27,000 of the raw data. Such dramatically reduced features relax the computational burden in the next step of our algorithm. We compare the performance of three feature selection algorithms and demonstrate that SVM-MB/RFE outperforms other methods.

Algorithms↗
PubMed2026

Spatial Proteomic Profiling, a Novel Method for Detecting Diagnostic and Prognostic Proteins in Pediatric Sarcoma.

Pediatric sarcomas comprise approximately 10% of all childhood solid malignancies and are characterized by distinct genetic and proteomic alterations that have potential diagnostic, prognostic, and therapeutic significance. We have utilized digital spatial profiling (DSP) to identify protein expression in pediatric Ewing sarcoma (ES), Osteosarcoma (OS), Alveolar rhabdomyosarcoma (ARMS), and Embryonal rhabdomyosarcoma (ERMS), in association with clinical outcomes. Formalin-fixed, paraffin-embedded sections from a tissue microarray block containing eight ES, eight OS, five ARMS, and three ERMS cases were subjected to proteomic DSP on a GeoMx NanoString platform yielding information on expression of 580 proteins. Proteins related to epigenetic regulation, signaling pathways, and mesenchymal differentiation were broadly expressed across all tumor types. Tumor-specific protein profiles were defined based on highly expressed proteins. Differentially expressed proteins include Cyclin D1 in ES, S100A4 in OS and IKKi/IKKe in ARMS and ERMS. Immunohistochemical validation confirmed variable expression of H3K27me3 across the tumors, and elevated expression of Cyclin D1 in ES and S100 in OS. These findings underscore the utility of DSP as a high-resolution proteomic tool for the identification of clinically relevant biomarkers in pediatric sarcomas. The results provide a foundation for further investigation of candidate proteins with potential diagnostic, prognostic, and therapeutic applications.

Humans↗
PubMed2005

Identifying simple discriminatory gene vectors with an information theory approach.

In the feature selection of cancer classification problems, many existing methods consider genes individually by choosing the top genes which have the most significant signal-to-noise statistic or correlation coefficient. However the information of the class distinction provided by such genes may overlap intensively, since their gene expression patterns are similar. The redundancy of including many genes with similar gene expression patterns results in highly complex classifiers. According to the principle of Occam's razor, simple models are preferable to complex ones, if they can produce comparable prediction performances to the complex ones. In this paper, we introduce a new method to learn accurate and low-complexity classifiers from gene expression profiles. In our method, we use mutual information to measure the relation between a set of genes, called gene vectors, and the class attribute of the samples. The gene vectors are in higher-dimensional spaces than individual genes, therefore, they are more diverse, or contain more information than individual genes. Hence, gene vectors are more preferable to individual genes in describing the class distinctions between samples since they contain more information about the class attribute. We validate our method on 3 gene expression profiles. By comparing our results with those from literature and other well-known classification methods, our method demonstrated better or comparable prediction performances to the existing methods, however, with lower-complexity models than existing methods.

Algorithms↗
PubMed2005

Optimal molecular profiling of tissue and tissue components: defining the best processing and microdissection methods for biomedical applications.

Isolation of well-preserved pure cell populations is a prerequisite for sound studies of the molecular basis of any tissue-based biological phenomenon. This article reviews current methods for obtaining anatomically specific signals from molecules isolated from tissues, a basic requirement for productive linking of phenotype and genotype. The quality of samples isolated from tissue and used for molecular analysis is often glossed over or omitted from publications, making interpretation and replication of data difficult or impossible. Fortunately, recently developed techniques allow life scientists to better document and control the quality of samples used for a given assay, creating a foundation for improvement in this area. Tissue processing for molecular studies usually involves some or all of the following steps: tissue collection, gross dissection/identification, fixation, processing/embedding, storage/archiving, sectioning, staining, microdissection/annotation, and pure analyte labeling/identification and quantification. We provide a detailed comparison of some current tissue microdissection technologies, and provide detailed example protocols for tissue component handling upstream and downstream from microdissection. We also discuss some of the physical and chemical issues related to optimal tissue processing, and include methods specific to cytology specimens. We encourage each laboratory to use these as a starting point for optimization of their overall process of moving from collected tissue to high quality, appropriately anatomically tagged scientific results. In optimized protocols is a source of inefficiency in current life science research. Improvement in this area will significantly increase life science quality and productivity. The article is divided into introduction, materials, protocols, and notes sections. Because many protocols are covered in each of these sections, information relating to a single protocol is not contiguous. To get the greatest benefit from this article, readers are advised to read through the entire article first, identify protocols appropriate to their laboratory for each step in their workflow, and then reread entries in each section pertaining to each of these single protocols.

Biomarkers↗
PubMedDate not supplied

[Biological markers: utilization in drug development and approval].

The objectives of the reflection on biomarkers are far ahead of the issues of surrogacy, and constitute a major stake for pharmacology: enhancement of the liability of positive or negative screening for candidate drugs, speed-up of development processing, accurate identification of responders to a specific drug, and optimization of drug monitoring. Examples illustrate the situations where biomarkers are useful: diagnosis, prognosis, prediction of therapeutic response, either favourable or unwanted. The advance of pharmacogenetics leaves hope for optimization of drug use through the adaptation of drug choice to an individual profile. Some deficiencies have been identified concerning policies of transfer, evaluation or interdisciplinary validation. The optimization of the use of highest level evidence data, which is possible when appropriate biomarkers and clinical data are systematically collected during all the steps in drug development, the formal modelling of physiological, pathological and pharmacological processes and their assessment through computer simulation, and the simultaneous conduct of pragmatic and cognitive approaches, appear to be the necessary conditions for fulfillment of the objectives defined above.

Biomarkers↗
PubMed2005

Gene expression profiling studies of aging in cardiac and skeletal muscles.

To examine transcriptional alterations associated with aging in skeletal muscle and the heart, we and others have used DNA microarrays to compare the gene expression profile of young and old animals. Aging results in a differential gene expression pattern specific to each tissue, and most alterations can be completely or partially prevented by caloric restriction (CR) in both heart and skeletal muscle. Transcriptional patterns of tissues from calorie-restricted animals suggests that CR retards the aging process by reducing endogenous damage and by inducing metabolic shifts associated with specific transcriptional profiles. These studies demonstrate that DNA microarrays can be used in cardiovascular aging research to generate panels of hundreds of transcriptional biomarkers, providing a new tool to measure biological age of cardiac and skeletal muscles and to test interventions designed to retard aging in these tissues.

Aging↗
PubMed2004

Expression profiling identifies chemokine (C-C motif) ligand 18 as an independent prognostic indicator in gastric cancer.

BACKGROUND & AIMS: Gastric cancer is one of the major cancers worldwide. Expression profiling has proven useful in delineating novel prognostic markers in various cancer types. We previously analyzed gene-expression patterns in 90 gastric adenocarcinomas by using complementary DNA microarrays and prioritized a list of genes whose expression levels predict patient outcome. METHODS: We identified a specific gene of interest, chemokine (C-C motif) ligand 18 (CCL18), on the basis of a high absolute standardized log Cox hazard ratio, a high variance in expression among all tumor samples, and putative biologic function. Detailed analysis of CCL18 expression with clinicopathologic and survival data was performed (n = 89). Quantitative reverse-transcription polymerase chain reaction was used to verify the microarray expression data and was further applied to analyze an independent cohort of tumor samples (n = 59). The cellular source of CCL18 was determined with immunohistochemistry and in situ hybridization. RESULTS: High CCL18 expression levels were associated with prolonged overall (P = 0.001; hazard ratio, 0.586) and disease-free (P = 0.002; hazard ratio, 0.416) patient survival in the array-based data set by univariate analysis. The observations were confirmed in an independent set of 59 patients by using quantitative reverse-transcription polymerase chain reaction. In multivariate analysis, tumor stage and CCL18 levels were independent prognostic factors for predicting both overall and disease-free survival. We found that CCL18 was expressed by a subpopulation of tumor-associated macrophages that were preferentially located at the tumor invasion front. CONCLUSIONS: Macrophage-derived CCL18 may function as a local antitumor immunomodulator that affects patient outcome. Our study suggests CCL18 as a novel candidate for antitumor therapeutics and risk stratification in gastric cancer patients.

Adenocarcinoma↗
PubMed2006

Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: gene expression analyses across three different platforms.

BACKGROUND: Gene expression profiling has been used to define molecular phenotypes of complex diseases such as breast cancer. The luminal A and basal-like subtypes have been repeatedly identified and validated as the two main subtypes out of a total of five molecular subtypes of breast cancer. These two are associated with distinctly different gene expression patterns and more importantly, a significant difference in clinical outcome. To further validate and more thoroughly characterize these two subtypes at the molecular level in tumors at an early stage, we report a gene expression profiling study using three different DNA microarray platforms. RESULTS: Expression data from 20 tumor biopsies of early stage breast carcinomas were generated on three different DNA microarray platforms; Applied Biosystems Human Genome Survey Microarrays, Stanford cDNA Microarrays and Agilent's Whole Human Genome Oligo Microarrays, and the resulting gene expression patterns were analyzed. Both unsupervised and supervised analyses identified the different clinically relevant subtypes of breast tumours, and the results were consistent across all three platforms. Gene classification and biological pathway analyses of the genes differentially expressed between the two main subtypes revealed different molecular mechanisms descriptive of the two expression-based subtypes: Signature genes of the luminal A subtype were over-represented by genes involved in fatty acid metabolism and steroid hormone-mediated signaling pathways, in particular estrogen receptor signaling, while signature genes of the basal-like subtype were over-represented by genes involved in cell proliferation and differentiation, p21-mediated pathway, and G1-S checkpoint of cell cycle-signaling pathways. A minimal set of 54 genes that best discriminated the two subtypes was identified using the combined data sets generated from the three different array platforms. These predictor genes were further verified by TaqMan Gene Expression assays. CONCLUSION: We have identified and validated the two main previously defined clinically relevant subtypes, luminal A and basal-like, in a small set of early stage breast carcinomas. Signature genes characterizing these two subtypes revealed that distinct molecular mechanisms might have been pre-programmed at an early stage in different subtypes of the disease. Our results provide further evidence that these breast tumor subtypes represent biologically distinct disease entities and may require different therapeutic strategies. Finally, validated by multiple gene expression platforms, including quantitative PCR, the set of 54 predictor genes identified in this study may define potential prognostic molecular markers for breast cancer.

Biomarkers, Tumor↗
PubMed2006

Differential expression of a gene signature for scavenger/lectin receptors by endothelial cells and macrophages in human lymph node sinuses, the primary sites of regional metastasis.

Sentinel lymph node biopsy for several cancers has shown that metastatic tumour cells are preferentially arrested in the lymph node sinuses. To study the molecular components of this sinusoidal trap, gene profiling of lymph node (sinuses) versus tonsil (no sinuses) was performed. Among other groups of molecules, an intriguing gene signature of scavenger and lectin-like receptors was identified. Nine of the 13 genes were preferentially expressed in sinusoidal cells by immunohistochemistry. Using stabilin-2 and monoclonal antibody 3A5 as exclusive endothelial cell (EC) and macrophage (Mvarphi) markers, respectively, lymph node sinusoidal ECs (stabilin-2+, LYVE-1+, DC-SIGNR+, MARCO+, stabilin-1+, MMR+) and sinusoidal Mvarphi (MMR+, DC-SIGN+, sialoadhesin+, CD163+, stabilin-1+ ) showed distinct, but overlapping expression patterns of the signature molecules by double labelling immunofluorescence. The number of stabilin-1+ sinusoidal Mvarphi, however, varied considerably between samples, indicating turnover/differentiation dynamics in this sinusoidal cell population. In the hepatic sinuses, LYVE-1 and CD36 were strongly up-regulated on both sinusoidal ECs and Mvarphi, while DC-SIGNR and DC-SIGN were strongly down-regulated; in contrast to lymph node sinusoidal ECs, MARCO was confined to Mvarphi (Kupffer cells) in the liver sinuses. As Mvarphi are not present in the wall and lumen of splenic sinuses, splenic sinuses expressed a considerably reduced repertoire of scavenger/lectin receptors lacking sialoadhesin, CD36, CD163, and MARCO; in addition, DC-SIGNR was absent from splenic sinusoidal ECs, while DC-SIGN and thrombomodulin were strongly expressed. Interestingly, most of the signature molecules are known to mediate tumour cell adhesion in addition to their functions as scavenger or pattern recognition receptors. This study establishes a gene and tissue database platform to test the hypothesis that additive expression of the lymph node sinus signature genes in sinusoidal ECs and Mvarphi may contribute to selective tumour cell metastasis in lymph nodes and liver including organ-specific mechanisms, such as intraluminal retention or transmigration, while sparing the spleen.

Biomarkers↗
PubMed1996

Biotransformation of allyl chloride in the rat. Influence of inducers on the urinary metabolic profile.

Allyl chloride (AC) is used as intermediate in the synthesis of epichlorohydrin (ECH). We investigated the biotransformation of AC in rats to select potential urinary biomarkers of exposure. For this purpose, we developed analytical methods to measure different selected urinary metabolites of AC. The earlier described urinary metabolites of AC [allyl mercapturic acid (ALMA) and 3-hydroxypropyl mercapturic acid (HPMA)], as well as two urinary metabolites of ECH [alpha-chlorohydrin (alpha-CH) and 3-chloro-2-hydroxypropyl mercapturic acid (CHPMA)], were determined in this study. After intraperitoneal administration of AC, in doses ranging from 66 to 590 mumol/kg, control rats excreted 30 +/- 6.5% of the AC dose as ALMA. HPMA was a minor urinary metabolite of AC (< 3% of the AC dose), and, for this metabolite, no clear dose-excretion relationship was found. Two other minor urinary metabolites were also found as well, namely CHPMA and alpha-CH, suggesting the formation of ECH. CHPMA excretion was linear from 66 to 330 mumol/kg AC and amounted to 0.21 +/- 0.08% of the AC dose. alpha-CH excretion was linear in the dose range used and was excreted for 0.13 +/- 0.02% of the AC dose. In addition, we investigated the influence of three different enzyme inducers on the urinary metabolite profile of AC, namely pyrazole, beta-naphthoflavone, and phenobarbital. Pyrazole only increased the urinary excretion of alpha-CH. beta-Naphthoflavone induction only enhanced the ALMA excretion significantly. Phenobarbital inducted both the excretion of CHPMA and alpha-CH. From these studies, we conclude that urinary excretion of ALMA, CHPMA, and alpha-CH can be used as biomarkers in humans potentially exposed to AC. However, ALMA seems to be the more appropriate biomarker, because enzyme induction may play a confounding role if CHPMA or alpha-CH is used.

Acetylcysteine↗
PubMed2005

Urinary porphyrins as biomarkers for arsenic exposure among susceptible populations in Guizhou province, China.

Coal is widely used in PR China. Unfortunately, coal from some areas in Guizhou Province contains elevated levels of arsenic. This has caused arsenicosis in individuals who use arsenic-contaminated coal for the purposes of heating, cooking and drying of food in poorly ventilated dwellings. The population at risk has been estimated to be approximately 200,000 people. Clinical symptoms of arsenicosis may include changes of skin pigmentation, hyperkeratosis of hand and feet, skin cancers, liver damage, persistent cough and chronic bronchitis. We analyzed the porphyrin excretion profile using a HPLC method in urine samples collected from 113 villagers who lived in Xing Ren district, a coal-borne arsenicosis endemic area and from 30 villagers from Xing Yi where arsenicosis is not prevalent. Urinary porphyrins were higher in the arsenic exposed group than those in the control group. The correlation between urinary arsenic and porphyrin concentrations demonstrated the effect of arsenic on heme biosynthesis resulting in increased porphyrin excretion. Both uroporphyrin and coproporphyrin III showed significant increases in the excretion profile of the younger age (<20 years) arsenic-exposed group, suggesting that porphyrins could be used as early warning biomarkers of chronic arsenic exposure in humans. Greater increases of urinary arsenic and porphyrins in women, children and older age groups who spend much of their time indoors suggest that they might be at a higher risk. Whether elevated porphyrins could predict adverse health effects associated with both cancer and non-cancer end-points in chronically arsenic-exposed populations need further investigation.

Adult↗
PubMed2005

Comparative proteomic analysis of intra- and interindividual variation in human cerebrospinal fluid.

Cerebrospinal fluid (CSF) is a potential source of biomarkers for many disorders of the central nervous system, including Alzheimer disease (AD). Prior to comparing CSF samples between individuals to identify patterns of disease-associated proteins, it is important to examine variation within individuals over a short period of time so that one can better interpret potential changes in CSF between individuals as well as changes within a given individual over a longer time span. In this study, we analyzed 12 CSF samples, composed of pairs of samples from six individuals, obtained 2 weeks apart. Multiaffinity depletion, two-dimensional DIGE, and tandem mass spectrometry were used. A number of proteins whose abundance varied between the two time points was identified for each individual. Some of these proteins were commonly identified in multiple individuals. More importantly, despite the intraindividual variations, hierarchical clustering and multidimensional scaling analysis of the proteomic profiles revealed that two CSF samples from the same individual cluster the closest together and that the between-subject variability is much larger than the within-subject variability. Among the six subjects, comparison between the four cognitively normal and the two very mildly demented subjects also yielded some proteins that have been identified in previous AD biomarker studies. These results validate our method of identifying differences in proteomic profiles of CSF samples and have important implications for the design of CSF biomarker studies for AD and other central nervous system disorders.

Cerebrospinal Fluid Proteins↗
PubMed2001

Directly coupled high-performance liquid chromatography and nuclear magnetic resonance spectroscopic with chemometric studies on metabolic variation in Sprague--Dawley rats.

We report here the first combined use of NMR-PR (pattern recognition) analysis and directly coupled HPLC--NMR analysis to identify metabolic subpopulations in normal laboratory animals and their discriminating endogenous urinary biomarkers. Urine samples obtained from control Sprague-Dawley rats (n = 68) were analyzed using (1)H NMR spectroscopy and principal components (PC) analysis to investigate physiological variability. Two distinct subpopulations of animals were classified based on metabolite excretion profiles. Analysis of the PC loadings established the spectral regions that were responsible for classification of the subpopulations and was used to direct the identification of biomarkers using a directly coupled HPLC--NMR analysis. One population had low urinary hippurate levels together with an increased concentration of 3-(3-hydroxyphenyl)propionic acid (3-HPPA)and 3-hydroxycinnamic acid (3-HCA). The other subpopulation excreted high levels of hippurate. Thus, we report the bimodal occurrence of hippuric acid and chlorogenic acid metabolites in a genetically homogeneous population of rats maintained under identical conditions, which may have significance in relation to the understanding of the consequences of biochemical variation in animals used for drug toxicity testing.

Animals↗
PubMed2004

Prediction of PPAR-alpha ligand-mediated physiological changes using gene expression profiles.

Peroxisome proliferator-activated receptor (PPAR)-alpha controls the transcription of a variety of genes involved in lipid metabolism and is the target receptor for the hypolipidemic drug class of fibrates. In the present study, the molecular and physiological effects of seven different PPAR-activating drugs have been examined in a rodent model of dyslipidemia. The drugs examined were selected to display varying potencies and efficacies toward PPAR-alpha. To help elucidate the link between the gene regulation elicited by PPAR-alpha ligands and the concomitant physiological changes, we have used cDNA microarray analysis to identify smaller gene sets that are predictive of the function of these ligands. A number of genes showed strong correlations to the relative PPAR-alpha efficacy of the drugs. Furthermore, using multivariate analysis, a strong relationship between the drug-induced triglyceride lowering and the transcriptional profiles of the different drugs could be found.

Animals↗
PubMed2003

High-throughput optimization by statistical designs: example with rat liver slices cryopreservation.

The purpose of this study was to optimize cryopreservation conditions of rat liver slices in a high-throughput format, with focus on reproducibility. A statistical design of 32 experiments was performed and intracellular lactate dehydrogenase (LDHi) activity and antipyrine (AP) metabolism were evaluated as biomarkers. At freezing, modified University of Wisconsin solution was better than Williams'E medium, and pure dimethyl sulfoxide was better than a cryoprotectant mixture. The best cryoprotectant concentrations were 10% for LDHi and 20% for AP metabolism. Fetal calf serum could be used at 50 or 80%, and incubation of slices with the cryoprotectant could last 10 or 20 min. At thawing, 42 degrees C was better than 22 degrees C. After thawing, 1h was better than 3h of preculture. Cryopreservation increased the interslice variability of the biomarkers. After cryopreservation, LDHi and AP metabolism levels were up to 84 and 80% of fresh values. However, these high levels were not reproducibly achieved. Two factors involved in the day-to-day variability of LDHi were identified: the incubation time with the cryoprotectant and the preculture time. In conclusion, the statistical design was very efficient to quickly determine optimized conditions by simultaneously measuring the role of numerous factors. The cryopreservation procedure developed appears suitable for qualitative metabolic profiling studies.

Animals↗