World Congress on Thyroid Cancer 3.5
June 20 – 22, 2019
World Congress on Thyroid Cancer 4.0
July 29 – August 1, 2021
WCTC3.5 Steering Committee:
Rocco Bellatone, Co-Chair
Celestino Lombardi, Co-Chair
Gregory W. Randolph, MD
Bryan McIver, MD
Jeremy Freeman, MD
Ian J. Witterick, MD
Ashok R. Shaha, MD
Jatin P. Shah, MD
EP101 – A new highly sensitive and specific miRNA-profiling molecular diagnostic test for classification of thyroid nodules
Gama, Ricardo Ribeiro²; Santos, Marcos Tadeu dos1; Buzolin, Ana Lígia¹; Barreto, Miguel³; Hall, Mathew³; Faustino, Pedro³; Dufloth, Rozany Mucha²; Silva, Eduardo Caetano Albino da²; Ventura, Luis Marcelo² and Carvalho, André Lopes²
1 ONKOS Diagnósticos Moleculares Inc, Ribeirão Preto, SP, Brazil
2 Hospital de Câncer de Barretos, Barretos, SP, Brazil
3 SimplicityBio Inc, Monthey, Switzerland
Background/purpose: Thyroid nodules can be identified, by ultrasound, in up to 64% of the population . Currently, fine-needle aspiration (FNA) represents the gold standard for the initial assessment of malignancy diagnosing. However, FNA cannot classify 15%-30% of nodules, known as indeterminate (Bethesda III, IV or V) , which are often referred for thyroidectomy due to the risk of malignancy. For this particular category the surgical pathology report reclassifies 70%-80% of cases as benign, highlighting the considerable rate of unnecessary surgeries [2-7]. Although clinically useful, current available molecular classifiers cannot offer both, high sensitivity and specificity, limiting its use and clinical application [8-11].
Objectives: We sought to develop a miRNA-profiling-based molecular diagnostic test able to classify indeterminate thyroid nodules into benign or malignant, with high sensitivity and specificity.
Methods: The expression of 96 miRNA was analyzed by qPCR from 80 FNA samples with indeterminate citology (final surgical pathology – 40 benign and 40 malignant). The cytology slides and pathology tissues were double-blinded revised by independent pathologists. Expression data was used for biomarkers selection and signatures generation for the molecular classifier development.
Results: A molecular signature based on the expression of 18 miRNAs achieved 93% sensibility and 86% specificity to predict the final pathology report. The observed area under the curve (AUC) was 0.944.
Discussion & Conclusion: We successfully developed a new miRNA-profiling-based molecular classifier with higher sensitive and specificity compared to commercially available tests. A multicenter clinical validation study using FNA smears slides as primary material for testing, is ongoing.
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