ECE2014 Oral Communications Adrenal & Thyroid (5 abstracts)
Validation of follicular thyroid cancer molecular classifier in fine-needle aspiration biopsy samples
Aleksandra Pfeifer 1, , Wojtas Bartosz 1, , Oczko-Wojciechowska Malgorzata 1 , Kukulska Aleksandra 1 , Czarniecka Agnieszka 2 , Eszlinger Markus 6 , Musholt Thomas 7 , Stokowy Tomasz 1, , Stobiecka Ewa 4 , Krajewska Jolanta 1 , Rusinek Dagmara 1 , Tyszkiewicz Tomasz 1 , Kowal Monika 1 , Jarzab Michal 3 , Hauptmann Steffen 8 , Lange Dariusz 4 , Paschke Ralf 6 & Jarzab Barbara 1
1Department of Nuclear Medicine and Endocrine Oncology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland; 2Clinic of Oncological and Reconstructive Surgery, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland; 3Radiotherapy and Chemotherapy Clinic, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland; 4Tumor Pathology Department, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland; 5Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland; 6Division of Endocrinology and Nephrology, University of Leipzig, Leipzig, Germany; 7Department of General, Visceral, and Transplantation Surgery, University Medical Center of the Johannes Gutenberg University, Mainz, Germany; 8Department of Pathology, Martin Luther University Halle-Wittenberg, Halle (Salle), Germany; 9Pracownia Neurobiologii Molekularnej, Centrum Neurobiologii, Instytut Biologii Doswiadczalnej im. Nenckiego PAN, Warszawa, Poland.
Introduction: Follicular adenomas (FTA) and carcinomas (FTC) are thyroid tumours that are indistinguishable in the fine needle aspiration biopsy (FNAB). In our previous research we concentrated on post-operative material and developed the classifier, which discriminates the FTC and FTA, based on formalin-fixed paraffin-embedded (FFPE) material. The classifier was based on expression of five genes and gave the promising sensitivity of 71% and specificity of 72%. The aim of the current study was to validate that classifier on pre-operative fine-needle aspiration biopsy (FNAB) samples.
Material and methods: Tumour fine needle aspiration biopsy samples were derived from 9 FTC patients and 8 FTA patients. RNA was isolated using QIAGEN RNeasy Micro Kit. PCR amplification was performed with Universal Probe Library fluorescent probes (Roche) and 5′-nuclease assay, starting from 50 ng of total RNA. Normalization was carried out in the GeNorm application. The Diagonal Linear discriminant analysis (DLDA) method was used as a classification algorithm. The leave-one-out cross-validation (LOOCV) was used to assess the classifier performance.
Results: We measured the expression of five genes (ELMO1, EMCN, ITIH5, KCNAB1, SLCO2A1) in FNAB thyroid samples. We normalised the data and performed the classification in LOOCV loop. We calculated the performance of the FTC classification and obtained the accuracy of 71%, sensitivity of 67%, specificity of 75%, PPV of 75% and NPV of 67%.
Conclusions: We validated a simple FTC classifier that is based on the expression of five genes (ELMO1, EMCN, ITIH5, KCNAB1, SLCO2A1). We confirmed that it is useful in the discrimination of FTC and FTA when applied to FNAB samples.
Volume 35
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Endocrine Abstracts
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