The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management

BMC Cancer

Research Article

Federica Panebianco¹  Chiara Mazzanti²  Ivo Marchetti³  Generoso Bevilacqua³  Francesca Lessi⁴  Paolo Aretini⁴  Sara Franceschi⁴  Giancarlo Di Coscio⁵  Sara Tomei⁶ 

[1] Division of Surgical, Molecular, and Ultrastructural Pathology, University of Pisa and Pisa University Hospital, Via Roma 57, 56100, Pisa, Italy;Department of Pathology, University of Pittsburgh School of Medicine, 200 Lothrop St, 15261, Pittsburgh, PA, USA;Division of Surgical, Molecular, and Ultrastructural Pathology, University of Pisa and Pisa University Hospital, Via Roma 57, 56100, Pisa, Italy;Pisa Science Foundation, Via Panfilo Castaldi 2, 5612, Pisa, Italy;Division of Surgical, Molecular, and Ultrastructural Pathology, University of Pisa and Pisa University Hospital, Via Roma 57, 56100, Pisa, Italy;Section of Cytopathology, University of Pisa and Pisa University Hospital, Via Roma 57, 56100, Pisa, Italy;Pisa Science Foundation, Via Panfilo Castaldi 2, 5612, Pisa, Italy;Section of Cytopathology, University of Pisa and Pisa University Hospital, Via Roma 57, 56100, Pisa, Italy;Sidra Medical and Research Center, Research Branch, Division of Translational Medicine, Al Corniche Street, PO 26999, Doha, Qatar;

关键词: Thyroid cancer;    Preoperative diagnosis;    Indeterminate lesions;    Molecular marker;    Computational model;   

DOI  :  10.1186/s12885-015-1917-2

 received in 2015-04-19, accepted in 2015-11-06,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundPapillary thyroid cancer is the most common endocrine malignancy. The most sensitive and specific diagnostic tool for thyroid nodule diagnosis is fine-needle aspiration (FNA) biopsy with cytological evaluation. Nevertheless, FNA biopsy is not always decisive leading to “indeterminate” or “suspicious” diagnoses in 10 %–30 % of cases. BRAF V600E detection is currently used as molecular test to improve the diagnosis of thyroid nodules, yet it lacks sensitivity. The aim of the present study was to identify novel molecular markers/computational models to improve the discrimination between benign and malignant thyroid lesions.MethodsWe collected 118 pre-operative thyroid FNA samples. All 118 FNA samples were characterized for the presence of the BRAF V600E mutation (exon15) by pyrosequencing and further assessed for mRNA expression of four genes (KIT, TC1, miR-222, miR-146b) by quantitative polymerase chain reaction. Computational models (Bayesian Neural Network Classifier, discriminant analysis) were built, and their ability to discriminate benign and malignant tumors were tested. Receiver operating characteristic (ROC) analysis was performed and principal component analysis was used for visualization purposes.ResultsIn total, 36/70 malignant samples carried the V600E mutation, while all 48 benign samples were wild type for BRAF exon15. The Bayesian neural network (BNN) and discriminant analysis, including the mRNA expression of the four genes (KIT, TC1, miR-222, miR-146b) showed a very strong predictive value (94.12 % and 92.16 %, respectively) in discriminating malignant from benign patients. The discriminant analysis showed a correct classification of 100 % of the samples in the malignant group, and 95 % by BNN. KIT and miR-146b showed the highest diagnostic accuracy of the ROC curve, with area under the curve values of 0.973 for KIT and 0.931 for miR-146b.ConclusionsThe four genes model proposed in this study proved to be highly discriminative of the malignant status compared with BRAF assessment alone. Its implementation in clinical practice can help in identifying malignant/benign nodules that would otherwise remain suspicious.

【 授权许可】

CC BY   
© Panebianco et al. 2015