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Advancing the molecular diagnosis of thyroid nodules: defining benign lesions by molecular profiling.

BACKGROUND: Thyroid nodules are common and most are benign. Previous data from our laboratory and others has suggested that gene profiling can accurately distinguish between benign and malignant thyroid nodules and provide new leads in the study of thyroid tumorigenesis. Current preoperative techniques do not permit distinction between neoplastic and hyperplastic follicular neoplasms. These studies were undertaken to determine whether benign follicular tumors could be subcategorized by molecular profiling. METHODS: Molecular profiles of 8 follicular adenomas and 8 hyperplastic nodules were analyzed by oligonucleotide microarray analysis. A list of 402 differentially expressed genes was produced based on a comparison of these two groups. Seven additional benign follicular lesions were then added to the analysis. A hierarchical clustering analysis was performed on all 23 samples, utilizing the gene list generated from the test set, to examine the groups for potential differences and the ability of the gene list to distinguish tumor types. RESULTS: Cluster analysis of all 23 samples produced two distinct groups, one containing the adenomas and one containing the hyperplastic lesions. The analysis was able to identify follicular adenomas with a sensitivity of 84.6% and a specificity of 100%. CONCLUSIONS: These data indicate that benign thyroid lesions can be separated into distinct groups through molecular profiling. Analysis of the gene list may help further the understanding of thyroid tumorigenesis. Expression profiling may ultimately allow us to distinguish potentially malignant from benign follicular nodules.

Biomarkers↗

Schizophrenia proteomics: biomarkers on the path to laboratory medicine?

Over two million Americans are afflicted with schizophrenia, a debilitating mental health disorder with a unique symptomatic and epidemiological profile. Genomics studies have hinted towards candidate schizophrenia susceptibility chromosomal loci and genes. Modern proteomic tools, particularly mass spectrometry and expression scanning, aim to identify both pathogenic-revealing and diagnostically significant biomarkers. Only a few studies on basic proteomics have been conducted for psychiatric disorders relative to the plethora of cancer specific experiments. One such proteomic utility enables the discovery of proteins and biological marker fingerprinting profiling techniques (SELDI-TOF-MS), and then subjects them to tandem mass spectrometric fragmentation and de novo protein sequencing (MALDI-TOF/TOF-MS) for the accurate identification and characterization of the proteins. Such utilities can explain the pathogenesis of neuro-psychiatric disease, provide more objective testing methods, and further demonstrate a biological basis to mental illness. Although clinical proteomics in schizophrenia have yet to reveal a biomarker with diagnostic specificity, methods that better characterize the disorder using endophenotypes can advance findings. Schizophrenia biomarkers could potentially revolutionize its psychopharmacology, changing it into a more hypothesis and genomic/proteomic-driven science.

Journal Article↗

Role of gene expression arrays in sarcomas.

Microarray technology allows the rapid analysis of expression of thousands of genes in a sample. Gene expression profiles are likely characteristic of subtypes of sarcomas and may be useful in diagnosis and classification of this heterogeneous group of tumors. Gene expression may also be useful prognostically with respect to the natural history and response to therapy of these tumors.

Biomarkers, Tumor↗

Mouse ovary developmental RNA and protein markers from gene expression profiling.

To identify genes involved in morphogenetic events during mouse ovary development, we started with microarray analyses of whole organ RNA. Transcripts for 60% of the 15,000 gene NIA panel were detected, and about 2000 were differentially expressed in nascent newborn compared to adult ovary. Highly differentially expressed transcripts included noncoding RNAs and newly detected genes involved in transcription regulation and signal transduction. The phased pattern of newborn mouse ovary differentiation allowed us to (1) extend information on activity and stage specificity of cell type-specific genes; and (2) generate a list of candidate genes involved in primordial follicle formation, including podocalyxin (Podxl), PDGFR-beta, and a follistatin-domain-encoding gene Flst1. Oocyte-specific transcripts included many (e.g., Deltex2, Bicd2, and Zfp37) enriched in growing oocytes, as well as a novel family of untranslated RNA's (RLTR10) that is selectively expressed in early stage follicles. The results indicate that global expression profiling of whole organ RNA provides sensitive first-line information about ovarian histogenesis for which no in vitro cell models are currently available.

Animals↗

Paralytic shellfish toxin profiles and toxin variability of the genus Alexandrium (Dinophyceae) isolated from the Southeast China Sea.

Paralytic shellfish toxin (PST) profiles of 16 Alexandrium isolates from the Southeast China Sea were analyzed by high-pressure liquid chromatography. Toxin content and composition of three A. tamarense isolates, ATDH01, ATGX02 and ATMJ02, were also investigated at different growth phases and under various culture conditions. Our results showed that six strains of A. affine were non-toxic, while 10 strains of A. tamarense and A. catenella were toxic. These toxic isolates grown in the same culture conditions consistently produced an unusually high proportion of the N-sulfocarbamoyl toxin C1/2 (around 60-80% of total toxins) and medium amounts of gonyautoxin GTX5 (around 15-30% of total) with only trace quantities (<5% of total) of other saxitoxin derivatives (i.e. GTX1, GTX3, GTX4 and neoSTX). The toxin composition of three A. tamarense isolates did not vary with the growth phases, although higher toxin contents (Qt, fmolcell(-1)) were found in the exponential phase. Variations in temperature, salinity and nutrient levels affected toxin content of three A. tamarense isolates but they did not have pronounced effects on the toxin composition (mole %). These results indicate that toxin composition remained relatively constant under various culture conditions, suggesting that toxin composition could be used as a stable biomarker for the Alexandrium species in this region. However, comparison of toxin profiles between isolates from different localities require special caution since isolates even from the same region can have distinct toxin profiles.

Animals↗

A novel approach for clustering proteomics data using Bayesian fast Fourier transform.

MOTIVATION: Bioinformatics clustering tools are useful at all levels of proteomic data analysis. Proteomics studies can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens. The high dimensionality of data generated from these studies requires the development of improved bioinformatics tools for efficient and accurate data analyses. For proteome profiling of a particular system or organism, a number of specialized software tools are needed. Indeed, significant advances in the informatics and software tools necessary to support the analysis and management of these massive amounts of data are needed. Clustering algorithms based on probabilistic and Bayesian models provide an alternative to heuristic algorithms. The number of clusters (diseased and non-diseased groups) is reduced to the choice of the number of components of a mixture of underlying probability. The Bayesian approach is a tool for including information from the data to the analysis. It offers an estimation of the uncertainties of the data and the parameters involved. RESULTS: We present novel algorithms that can organize, cluster and derive meaningful patterns of expression from large-scaled proteomics experiments. We processed raw data using a graphical-based algorithm by transforming it from a real space data-expression to a complex space data-expression using discrete Fourier transformation; then we used a thresholding approach to denoise and reduce the length of each spectrum. Bayesian clustering was applied to the reconstructed data. In comparison with several other algorithms used in this study including K-means, (Kohonen self-organizing map (SOM), and linear discriminant analysis, the Bayesian-Fourier model-based approach displayed superior performances consistently, in selecting the correct model and the number of clusters, thus providing a novel approach for accurate diagnosis of the disease. Using this approach, we were able to successfully denoise proteomic spectra and reach up to a 99% total reduction of the number of peaks compared to the original data. In addition, the Bayesian-based approach generated a better classification rate in comparison with other classification algorithms. This new finding will allow us to apply the Fourier transformation for the selection of the protein profile for each sample, and to develop a novel bioinformatic strategy based on Bayesian clustering for biomarker discovery and optimal diagnosis.

Algorithms↗

Selection of patient samples and genes for outcome prediction.

Gene expression profiles with clinical outcome data enable monitoring of disease progression and prediction of patient survival at the molecular level. We present a new computational method for outcome prediction. Our idea is to use an informative subset of original training samples. This subset consists of only short-term survivors who died within a short period and long-term survivors who were still alive after a long follow-up time. These extreme training samples yield a clear platform to identify genes whose expression is related to survival. To find relevant genes, we combine two feature selection methods -- entropy measure and Wilcoxon rank sum test -- so that a set of sharp discriminating features are identified. The selected training samples and genes are then integrated by a support vector machine to build a prediction model, by which each validation sample is assigned a survival/relapse risk score for drawing Kaplan-Meier survival curves. We apply this method to two data sets: diffuse large-B-cell lymphoma (DLBCL) and primary lung adenocarcinoma. In both cases, patients in high and low risk groups stratified by our risk scores are clearly distinguishable. We also compare our risk scores to some clinical factors, such as International Prognostic Index score for DLBCL analysis and tumor stage information for lung adenocarcinoma. Our results indicate that gene expression profiles combined with carefully chosen learning algorithms can predict patient survival for certain diseases.

Biomarkers, Tumor↗

mRNA level of alpha-2-macroglobulin as an aging biomarker of human fibroblasts in culture.

Cellular senescence is a well-established model system for studying the molecular basis of aging. To identify a reliable biomarker for cellular age and further study the gene expression of aging, we profiled the gene expression difference between aged and young cultured human embryonic lung fibroblasts by high-density complementary deoxyribonucleic acid (cDNA) arrays. Among the differentially expressed genes, alpha-2-macroglobulin (alpha(2)M) was selected for further study. Its gene expression level as a function of population doubling level (PDL) in cultured fibroblasts was determined by RT-PCR and northern hybridization. mRNA level of alpha(2)M showed a positive linear-correlation with cumulative PDL. Additional assays revealed that the levels of alpha(2)M increased in irreversible growth arrest induced by sublethal H(2)O(2), but not in quiescent state of cultured fibroblasts induced by serum-deprivation, and remained stable in Hela cells. These results suggest that mRNA level of alpha(2)M can be used as a biomarker of aging in cultured fibroblasts. mRNA level of alpha(2)M showed significant difference between newborn and old human leucocytes, which suggest that the mRNA level of alpha(2)M may be used as a biomarker of aging in vivo.

Biomarkers↗

Characterization of the human urinary proteome: a method for high-resolution display of urinary proteins on two-dimensional electrophoresis gels with a yield of nearly 1400 distinct protein spots.

The abundance profile of the human urinary proteome is known to change as a result of diseases or drug toxicities, particularly of those affecting the kidney and the urogenital tract. A consequence of such insults is the ability to identify proteins in urine, which may be useful as quantitative biomarkers. To succeed in discovering them, reproducible urine sample preparation methods and good protein resolution in two-dimensional electrophoresis (2-DE) gels for parallel semiquantitative protein measurements are desirable. Here, we describe a protein fractionation strategy enriching proteins of molecular masses (M(r)) lower than 30 kDa in a fraction separate from larger proteins. The fraction containing proteins with M(r)s higher than 30 kDa was subsequently subjected to immunoaffinity subtraction chromatography removing most of the highly abundant albumin and immunoglobulin G. Following 2-DE display, superior protein spot resolution was observed. Subsequent high-throughput mass spectrometry analysis of ca. 1400 distinct spots using matrix-assisted laser desorption/ionization-time of flight peptide mass fingerprinting and liquid chromatography-electrospray ionization tandem mass spectrometry lead to the successful identification of 30% of the proteins. As expected from high levels of post-translational modifications in most urinary proteins and the presence of proteolytic products, ca. 420 identified spots collapsed into 150 unique protein annotations. Only a third of the proteins identified in this study are described as classical plasma proteins in circulation, which are known to be relatively abundant in urine despite their retention to a large extent in the glomerular blood filtration process. As a proof of principle that our urinary proteome display effort holds promise for biomarker discovery, proteins isolated from the urine of a renal cell carcinoma patient were profiled prior to and after nephrectomy. Particularly, the decrease in abundance of the kininogen 2-DE gel spot train in urine after surgery was striking.

Albumins↗

Proteome profile of the MCF7 cancer cell line: a mass spectrometric evaluation.

The development of novel proteomic technologies that will enable the discovery of disease specific biomarkers is essential in the clinical setting to facilitate early diagnosis and increase survivability rates. We are reporting a shotgun two-dimensional (2D) strong cationic exchange/reversed-phase liquid chromatography/electrospray ionization tandem mass spectrometry (SCX/RPLC/ESI-MS/MS) protocol for the analysis of proteomic constituents in cancerous cells. The MCF7 breast cancer cell line was chosen as a model system. A series of optimization steps were performed to improve the LC/MS experimental setup, sample preparation, data acquisition and database search protocols, and a data filtering strategy was developed to enable confident identification of a large number of proteins and potential biomarkers. This research has resulted in the identification of >2000 proteins using multiple filtering and p-value sorting. Approximately 1600-1900 proteins had p < 0.001, and, of these, approximately 60% were matched by >or=2 unique peptides. Alternatively, >99% of the proteins identified by >or=2 unique peptides had p < 0.001. When searching the data against a reversed database of proteins, the rate of false positive identifications was 0.1% at the peptide level and 0.4% at the protein level. The typical reproducibility in detecting overlapping proteins across replicate runs exceeded 90% for proteins matched by >or=2 unique peptides. According to their biological function, approximately 200 proteins were involved in cancer-relevant cellular processes, and over 25 proteins were previously described in the literature as putative cancer biomarkers, as they were found to be differentially expressed between normal and cancerous cell states. Among these, biomarkers such PCNA, cathepsin D, E-cadherin, 14-3-3-sigma, antigen Ki-67, TP53RK, and calreticulin were identified. These data were generated by subjecting to MS analysis approximately 42 microg of sample, analyzing 16 SCX peptide fractions, and interpreting approximately 55,000 MS2 spectra. Total MS time required for analysis was 40 h.

Biomarkers, Tumor↗

MALDI-TOFMS compared with other polyphasic taxonomy approaches for the identification and classification of Bacillus pumilus spores.

To verify the efficacy of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) protein profiling for identifying and differentiating bacterial species, several strains of Bacillus pumilus were examined in a thorough taxonomic study incorporating a polyphasic approach. Sixteen isolates of putative B. pumilus isolated from spacecraft assembly facilities, the Mars Odyssey spacecraft, and the International Space Station, were characterized for their biochemical and molecular profiles using the Biolog system, DNA techniques, and MALDI-TOFMS protein profiling. MALDI-TOFMS protein profiling was more accurate than Biolog metabolic profiling, more discriminating than 16S rDNA sequence analysis, and complemented the results of gyrB sequence analysis and DNA-DNA hybridization for the identification of the B. pumilus spores. This is the first report whereby MALDI-TOFMS generated protein profiles from a set of microbes is compared directly with DNA-DNA hybridization yielding a positive correlation. Unique, cluster-specific biomarker peaks have been identified in the spores of the B. pumilus examined in this study. MALDI-TOFMS protein profiling is a rapid and simple analysis and has been demonstrated as a useful taxonomic tool for differentiating spores of the genus Bacillus. For practical purposes, it would be ideal (and necessary) to have a publicly available, standardized MALDI profile database, to facilitate the use of the technique as a diagnostic method to differentiate bacterial species.

Bacillus↗

Preeclampsia: increased expression of soluble ADAM 12.

Preeclampsia is a multisystemic pregnancy-associated disease affecting about 3-7% of pregnancies worldwide and is still a principal cause of fetal and maternal morbidity and mortality. To identify potential markers, we have compared gene expression profiles from control and preeclamptic placental tissues taken at various age-matched gestational stages using complementary DNA microarray analysis. Besides previously identified preeclampsia-associated genes, novel differentially expressed transcripts were found. The soluble form of the disintegrin metalloprotease ADAM 12 (a disintegrin and metalloproteinase 12; meltrin-alpha) represented the most upregulated transcript. This was confirmed by in situ hybridization of sections of preeclamptic placentas and by serum protein analysis of preeclamptic pregnant women. Thus, ADAM 12 could serve as an early biomarker for preeclampsia that may be of predictive and/or functional significance.

ADAM Proteins↗

Recent innovations in colposcopy practice.

Colposcopic practice continues to evolve. As its need has expanded, so has the role of training, audit and continuing medical education. The recently published National Health Service Cervical Screening Programme clinical guidelines document covers almost every aspect of clinical practice in an evidence-based directory. Excision of the transformation zone (TZ) may be a very minor or major entity. The recent TZ classification system of the International Federation of Cervical Pathology and Colposcopy attempts to clarify and standardize nomenclature so that therapy can be realistically compared. The role of human papillomavirus (HPV) in clinical practice continues to be controversial and has not yet found a place in the UK. For the evaluation of borderline nuclear abnormal smear and for post-treatment surveillance, HPV is clinically useful and efficient. Other biological tumour markers are likely to become clinically useful as their predictive profiles emerge.

Ambulatory Surgical Procedures↗

Comprehensive lipid analysis: a powerful metanomic tool for predictive and diagnostic medicine.

The power and accuracy of predictive diagnostics stand to improve dramatically as a result of lipid metanomics. The high definition of data obtained with this approach allows multiple rather than single metabolites to be used in markers for a group. Since as many as 40 fatty acids are quantified from each lipid class, and up to 15 lipid classes can be quantified easily, more than 600 individual lipid metabolites can be measured routinely for each sample. Because these analyses are comprehensive, only the most appropriate and unique metabolites are selected for their predictive value. Thus, comprehensive lipid analysis promises to greatly improve predictive diagnostics for phenotypes that directly or peripherally involve lipids. A broader and possibly more exciting aspect of this technology is the generation of metabolic profiles that are not simply markers for disease, but metabolic maps that can be used to identify specific genes or activities that cause or influence the disease state. Metanomics is, in essence, functional genomics from metabolite analysis. By defining the metabolic basis for phenotype, researchers and clinicians will have an extraordinary opportunity to understand and treat disease. Much in the same way that gene chips allow researchers to observe the complex expression response to a stimulus, metanomics will enable researchers to observe the complex metabolic interplay responsible for defining phenotype. By extending this approach beyond the observation of individual dysregulations, medicine will begin to profile not single diseases, but health. As health is the proper balance of all vital metabolic pathways, comprehensive or metanomic analysis lends itself very well to identifying the metabolite distributions necessary for optimum health. Comprehensive and quantitative analysis of lipids would provide this degree of diagnostic power to researchers and clinicians interested in mining metabolic profiles for biological meaning.

Biomarkers↗

Surface enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry to identify patients with chronic obstructive pulmonary disease.

There are currently no blood tests to identify the majority of smokers at risk for chronic obstructive pulmonary disease (COPD). We used plasma protein profiles from surface enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry to identify a panel of protein biomarkers that can distinguish patients with COPD from closely matched controls. Plasma was obtained from 30 COPD subjects and 30 controls matched for age, sex, and smoking history. Plasma protein profiles were generated using Cu2+-immobilized metal affinity capture (IMAC) and strong anion exchanger (Q10) protein chips. Classification and regression tree (CART) analysis identified a panel of 5 biomarkers using the IMAC protein chip that could distinguish COPD patients from controls with sensitivity and specificity of 91.67% and 88.33%, respectively. The 10-fold cross-validation yielded 81.67% sensitivity and 81.67% specificity. This demonstrates the feasibility of using SELDI as a diagnostic test for COPD; however, larger cohorts will be needed to validate these biomarkers and determine their predictive value longitudinally.

Algorithms↗

Distinct transcriptional profiles of adrenocortical tumors uncovered by DNA microarray analysis.

Comprehensive expression profiling of tumors using DNA microarrays has been used recently for molecular classification and biomarker discovery, as well as a tool to identify and investigate genes involved in tumorigenesis. Application of this approach to a cohort of benign and malignant adrenocortical tissues would be potentially informative in all of these aspects. In this study, we generated transcriptional profiles of 11 adrenocortical carcinomas (ACCs), 4 adrenocortical adenomas (ACAs), 3 normal adrenal cortices (NCs), and 1 macronodular hyperplasia (MNH) using Affymetrix HG_U95Av2 oligonucleotide arrays representing approximately 10,500 unique genes. The expression data set was used for unsupervised hierarchical cluster analysis as well as principal component analysis to visually represent the expression data. An analysis of variance on the three classes (NC, ACA plus MNH, and ACC) revealed 91 genes that displayed at least threefold differential expression between the ACC cohort and both the NC and ACA cohorts at a significance level of P < 0.01. Included in these 91 genes were those known to be up-regulated in adrenocortical tumors, such as insulin-like growth factor (IGF2), as well as novel differentially expressed genes such as osteopontin (SPP) and serine threonine kinase 15 (STK15). Increased expression of IGF2 was identified in 10 of 11 ACCs (90.9%) and was verified by quantitative reverse transcriptase-polymerase chain reaction. Select proliferation-related genes (TOP2A and Ki-67) were validated at the protein level using immunohistochemistry and adrenocortical tissue microarrays. Our results demonstrated significant and consistent gene expression changes in ACCs compared to benign adrenocortical lesions. Moreover, we identified several genes that represent potential diagnostic markers and may play a role in the pathogenesis of ACC.

Adenoma↗

Material-enhanced laser desorption/ionization (MELDI)--a new protein profiling tool utilizing specific carrier materials for time of flight mass spectrometric analysis.

Over the past couple of years, proteomics pattern analysis has emerged as an effective method for the early diagnosis of diseases such as ovarian, breast, or prostate cancer, without identification of single biomarkers. MALDI-TOF MS, for example, offers a simple approach for fast and reliable protein profiling, especially by using carrier materials with various physical and chemical properties, in combination with a MALDI matrix. This approach is referred to as material-enhanced laser desorption/ionization (MELDI). In this paper, we report the development and application of derivatized carrier materials [cellulose, silica, poly(glycidyl methacrylate/divinylbenzene) (GMA/DVB) particles, and diamond powder] for fast and direct MALDI-TOF MS protein profiling. The applicability of MELDI for rapid protein profiling was evaluated with human serum samples. These carriers, having various hydrophobicities, resulted in characteristic mass fingerprints, even if all materials were derivatized with iminodiacetic acid (IDA) to yield an immobilized metal affinity chromatography (IMAC) functionality. Our study demonstrates that analyzing complex biological samples, such as human serum, by employing different MELDI carrier materials yielded type- and size-dependent performance variation.

Indicators and Reagents↗

Complementary gene and protein expression studies and integrative approaches in toxicogenomics.

Parallel transcript and protein profiling is a strategy to gain further insight into the mechanisms of toxicity and disease. The technologies used to measure expression at the transcript and protein levels each convey different information and have different technical capabilities that can complement each other when combined. In this review, over twenty studies are considered for the use of -Omics platform, the chemical or disease being profiled, tissues, the number of genes and proteins found by each platform and common expression products. A strategy is suggested for toxicant expression profiling that combines the transcriptomics and proteomics of both the target tissue and blood/serum that could provide a more complete characterization of toxicity as well as synergize expression technologies toward biomarker discovery.

Animals↗