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Three-gene molecular diagnostic model for thyroid cancer.

ABSTRACT: The preoperative diagnosis of thyroid nodules primarily depends upon fine needle aspiration (FNA) cytology. However, up to 25% of FNA samples have associated "suspicious or indeterminate", but not diagnostic cytologic reports, resulting in difficulty deciding appropriate clinical management for these patients. We hypothesize that the use of molecular markers as an adjunct to FNA cytology can improve the distinction of benign from malignant nodules that have associated suspicious or indeterminate cytology.Using microarray analysis, we previously identified and reported on 75 genes useful in the distinction of benign versus malignant thyroid nodules. In the present study, we have further validated the expression of 14 of these markers in a large number of thyroid samples by immunohistochemistry (IHC) analysis of 154 thyroid tumors and quantitative real-time RT-PCR (QRT-PCR) analysis of 95 FNA samples. Of the 154 tumors analyzed by IHC, 44 samples (29%) had associated suspicious or indeterminate FNA cytology.Receiver operating characteristic using three-gene model, (HMGA2, MRC2, and SFN) analysis for the detection of malignant nodules resulted in areas under the curve (AUCs) of?0.95 (80% sensitivity; 100% specificity) and?0.84 (71% sensitivity; 84% specificity) for the IHC data in tumors, and QRT-PCR data in FNA samples, respectively.Our results suggest that a three-gene model for the cytological diagnosis of indeterminate thyroid nodules is both feasible and promising. Implementation of this as an adjunct to thyroid cytology may significantly impact the clinical management of patients with suspicious or indeterminate thyroid FNA nodules.

SUBMITTER: Prasad NB

PROVIDER: S-EPMC3286810 | biostudies-other | 2012 Mar

REPOSITORIES: biostudies-other

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Three-gene molecular diagnostic model for thyroid cancer.

Prasad Nijaguna B NB Kowalski Jeanne J Tsai Hua-Ling HL Talbot Kristin K Somervell Helina H Kouniavsky Guennadi G Wang Yongchun Y Dackiw Alan P B AP Westra William H WH Clark Douglas P DP Libutti Steven K SK Umbricht Christopher B CB Zeiger Martha A MA

Thyroid : official journal of the American Thyroid Association 20120126 3

<h4>Background</h4>The preoperative diagnosis of thyroid nodules primarily depends upon fine needle aspiration (FNA) cytology. However, up to 25% of FNA samples have associated "suspicious or indeterminate", but not diagnostic cytologic reports, resulting in difficulty deciding appropriate clinical management for these patients. We hypothesize that the use of molecular markers as an adjunct to FNA cytology can improve the distinction of benign from malignant nodules that have associated suspicio ...[more]

PMID: 22280184

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Project description:BACKGROUND: Single sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression-based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, luminal A, and luminal B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets of tumor-intrinsic genes. SCMs use a mixture of Gaussian distributions based on sets of genes with expression specifically correlated with three key breast cancer genes (estrogen receptor [ER], HER2, and aurora kinase A [AURKA]). The aim of this study was to compare the robustness, classification concordance, and prognostic value of these classifiers with those of a simplified three-gene SCM in a large compendium of microarray datasets. METHODS: Thirty-six publicly available breast cancer datasets (n = 5715) were subjected to molecular subtyping using five published classifiers (three SSPs and two SCMs) and SCMGENE, the new three-gene (ER, HER2, and AURKA) SCM. We used the prediction strength statistic to estimate robustness of the classification models, defined as the capacity of a classifier to assign the same tumors to the same subtypes independently of the dataset used to fit it. We used Cohen ? and Cramer V coefficients to assess concordance between the subtype classifiers and association with clinical variables, respectively. We used Kaplan-Meier survival curves and cross-validated partial likelihood to compare prognostic value of the resulting classifications. All statistical tests were two-sided. RESULTS: SCMs were statistically significantly more robust than SSPs, with SCMGENE being the most robust because of its simplicity. SCMGENE was statistically significantly concordant with published SCMs (? = 0.65-0.70) and SSPs (? = 0.34-0.59), statistically significantly associated with ER (V = 0.64), HER2 (V = 0.52) status, and histological grade (V = 0.55), and yielded similar strong prognostic value. CONCLUSION: Our results suggest that adequate classification of the major and clinically relevant molecular subtypes of breast cancer can be robustly achieved with quantitative measurements of three key genes.

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Dataset Information

Three-gene molecular diagnostic model for thyroid cancer.

Publications

Three-gene molecular diagnostic model for thyroid cancer.

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