Evaluation of Gene Expression Profiles in Thyroid Nodule Biopsy Material to Diagnose Thyroid Cancer (original) (raw)
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Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors
Cancer Research, 2004
DNA microarrays allow quick and complete evaluation of a cell's transcriptional activity. Expression genomics is very powerful in that it can generate expression data for a large number of genes simultaneously across multiple samples. In cancer research, an intriguing application of expression arrays includes assessing the molecular components of the neoplastic process and utilizing the data for cancer classification (Miller LD, et al. Cancer Cell 2002;2:353-61). Classification of human cancers into distinct groups based on their molecular profile rather than their histological appearance may prove to be more relevant to specific cancer diagnoses and cancer treatment regimes. Several attempts to formulate a consensus about classification and treatment of thyroid carcinoma based on standard histopathological analysis have resulted in published guidelines for diagnosis and initial disease management (Sherman SI. Lancet 2003;361:501-11). In the past few decades, no improvement has been made in the differential diagnosis of thyroid tumors by fine needle aspiration biopsy, specifically suspicious or indeterminate thyroid lesions, suggesting that a new approach to this should be explored. Therefore, in this study, we developed a gene expression approach to diagnose benign versus malignant thyroid lesions in 73 patients with thyroid tumors. We successfully built a 10 and 6 gene model able to differentiate benign versus malignant thyroid tumors. Our results support the premise that a molecular classification system for thyroid tumors is possible, and this in turn may provide a more accurate diagnostic tool for the clinician managing patients with suspicious thyroid lesions.
The Journal of Molecular Diagnostics, 2006
Current preoperative diagnostic procedures for thyroid nodules rely mainly on the cytological interpretation of fine-needle aspirates (FNAs). DNA microarray analysis has been shown to reliably distinguish benign and malignant thyroid nodules in surgically resected specimens, but its diagnostic potential in thyroid FNA has not been examined. In the present study, the expression profiles of 50 benign thyroid lesions and papillary thyroid carcinoma tissue samples were compared, generating a list of 25 differentially expressed genes from this training set. A test set of 22 FNA specimens was evaluated by unsupervised hierarchical cluster analysis using this gene list, and the results were compared to FNA cytology. FNA specimens were found to fall into three clusters: malignant (n ؍ 10), benign (n ؍ 7), and indeterminate (n ؍ 5). The benign and malignant groups showed complete concordance with the final histological diagnosis except for one histologically benign lesion, which was rediagnosed as follicular variant of papillary thyroid carcinoma on histological review. Paired analysis between FNA and matched tissues samples illustrated adequate sampling with FNA. These results illustrate that microarray analysis of FNA is feasible and has the potential to improve the accuracy of FNA in categorizing benign from malignant lesions beyond routine cytological evaluation. (J Mol Diagn 2006, 8:490 -498;
Digital gene expression profiling of a series of cytologically indeterminate thyroid nodules
Cancer cytopathology, 2015
Fine-needle aspiration cytology (FNAC) has been widely accepted as the most crucial step in the preoperative assessment of thyroid nodules. Testing for the expression of specific genes should improve the accuracy of FNAC diagnosis, especially when it is performed in samples with indeterminate cytology. In total, 69 consecutive FNACs that had both cytologic and histologic diagnoses were collected, and expression levels of 34 genes were determined in RNA extracted from FNAC cells by using a custom digital mRNA counting assay. A supervised k-nearest neighbor (K-nn) learning approach was used to build a 2-class prediction model based on a subset of 27 benign and 26 malignant FNAC samples. Then, the K-nn models were used to classify the 16 indeterminate FNAC samples. Malignant and benign thyroid nodules had different gene expression profiles. The K-nn approach was able to correctly classify 10 FNAC samples as benign, whereas only 1 sample was grouped in the malignant class. Two malignant...
Diagnostic Molecular Pathology, 2010
Microarray technology provides a new opportunity to improve the diagnostic accuracy of fine needle aspiration (FNA) in evaluating thyroid nodules. Here, we evaluate whether ex vivo FNA and tissue samples can be used interchangeably in microarray and whether the method of acquisition affects the precision of the gene list that is generated. To assess whether FNA samples provide adequate material for reliable gene expression analysis, paired tissue and FNA samples were collected from 13 thyroid nodules; 7 malignant, 6 benign. RNA was extracted from each specimen, converted to complimentary DNA and hybridized to AffymetrixU-133 GeneChips. Cluster analysis was then performed using 61 genes predetermined to differentiate benign from malignant nodules. Clustering patterns were evaluated using 2-group K-means and hierarchical analysis. Twelve concordant pairs were used to generate differentially expressed genes between the sampling methods. Twenty-five of 26 samples clustered concordantly with the pathologic diagnosis. The sensitivity, specificity, and accuracy were 100%, 100%, and 100% for FNA and 85.7%, 100%, and 92.3% for tissue, respectively. Two-group K-means revealed an adjacent grouping for 12 of 13 pairs. Hierarchical analysis clustered 8 of 13 pairs together. Sixtyseven genes were differentially expressed between FNA and the tissue sampling methods. These genes predominantly represented stromal components and were upregulated in the tissue compared with FNA samples. We conclude that FNA is a reliable alternative to tissue samples in predicting malignancy with microarray.
The Journal of Clinical Endocrinology & Metabolism, 2006
Context: There are an increasing number of studies analyzing gene expression profiles in various benign and malignant thyroid tumors. This creates the opportunity to validate results obtained from one microarray study with those from other data sets. This process requires rigorous methods for accurate comparison. Objective: The ability to compare data sets derived from different Affymetrix GeneChip generations and the influence of intra-and interindividual comparisons of gene expression data were evaluated to build multigene classifiers of benign thyroid nodules to verify a previously proposed papillary thyroid carcinoma (PTC) classifier and to look for molecular pathways essential for PTC oncogenesis. Methods: Gene expression profile data sets from autonomously functioning and cold thyroid nodules and from PTC were analyzed by support vector machines. GenMAPP analysis was used for PTC data analysis to examine the expression patterns of biologically relevant gene sets. Results: Only intraindividual reference samples allowed the identification of subtle changes in the expression patterns of relevant signaling cascades, such as the MAPK pathway in PTC. Using an artificial intelligence approach, the autonomously functioning and cold thyroid nodule multigene classifiers were derived and evaluated by cross-comparisons. Conclusion: We recommend defining classifiers within one generation of gene chips and subsequently checking them across different array generations. Using this approach, we have demonstrated the specificity of a previously reported PTC classifier on an independent collection of benign tumors. Moreover, we propose multigene classifiers for different types of benign thyroid nodules.
PLoS ONE, 2009
Background: Genetic markers for thyroid cancers identified by microarray analysis have offered limited predictive accuracy so far because of the few classes of thyroid lesions usually taken into account. To improve diagnostic relevance, we have simultaneously analyzed microarray data from six public datasets covering a total of 347 thyroid tissue samples representing 12 histological classes of follicular lesions and normal thyroid tissue. Our own dataset, containing about half the thyroid tissue samples, included all categories of thyroid lesions.
Cancer Research, 2005
The study looked for an optimal set of genes differentiating between papillary thyroid cancer (PTC) and normal thyroid tissue and assessed the sources of variability in gene expression profiles. The analysis was done by oligonucleotide microarrays (GeneChip HG-U133A) in 50 tissue samples taken intraoperatively from 33 patients (23 PTC patients and 10 patients with other thyroid disease). In the initial group of 16 PTC and 16 normal samples, we assessed the sources of variability in the gene expression profile by singular value decomposition which specified three major patterns of variability. The first and the most distinct mode grouped transcripts differentiating between tumor and normal tissues. Two consecutive modes contained a large proportion of immunity-related genes. To generate a multigene classifier for tumor-normal difference, we used support vector machines-based technique (recursive feature replacement). It included the following 19 genes: DPP4,