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

Federica Panebianco, Chiara Mazzanti, Sara Tomei, Paolo Aretini, Sara Franceschi, Francesca Lessi, Giancarlo Di Coscio, Generoso Bevilacqua, Ivo Marchetti

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Background: Papillary 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. Methods: We 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. Results: In 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. Conclusions: The 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.

Original languageEnglish
Article number918
JournalBMC Cancer
Volume15
Issue number1
DOIs
Publication statusPublished - 19 Nov 2015

Fingerprint

Fine Needle Biopsy
Thyroid Neoplasms
Discriminant Analysis
Thyroid Nodule
ROC Curve
Thyroid Gland
Gene Expression
Messenger RNA
Mutation
Neural Networks (Computer)
Principal Component Analysis
Area Under Curve
Neoplasms
Polymerase Chain Reaction
Genes

Keywords

  • Computational model
  • Indeterminate lesions
  • Molecular marker
  • Preoperative diagnosis
  • Thyroid cancer

ASJC Scopus subject areas

  • Oncology
  • Genetics
  • Cancer Research

Cite this

Panebianco, F., Mazzanti, C., Tomei, S., Aretini, P., Franceschi, S., Lessi, F., ... Marchetti, I. (2015). The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management. BMC Cancer, 15(1), [918]. https://doi.org/10.1186/s12885-015-1917-2

The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management. / Panebianco, Federica; Mazzanti, Chiara; Tomei, Sara; Aretini, Paolo; Franceschi, Sara; Lessi, Francesca; Di Coscio, Giancarlo; Bevilacqua, Generoso; Marchetti, Ivo.

In: BMC Cancer, Vol. 15, No. 1, 918, 19.11.2015.

Research output: Contribution to journalArticle

Panebianco, F, Mazzanti, C, Tomei, S, Aretini, P, Franceschi, S, Lessi, F, Di Coscio, G, Bevilacqua, G & Marchetti, I 2015, 'The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management', BMC Cancer, vol. 15, no. 1, 918. https://doi.org/10.1186/s12885-015-1917-2
Panebianco, Federica ; Mazzanti, Chiara ; Tomei, Sara ; Aretini, Paolo ; Franceschi, Sara ; Lessi, Francesca ; Di Coscio, Giancarlo ; Bevilacqua, Generoso ; Marchetti, Ivo. / The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management. In: BMC Cancer. 2015 ; Vol. 15, No. 1.
@article{e59724df151e489897c8a35b2c36ae39,
title = "The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management",
abstract = "Background: Papillary 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. Methods: We 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. Results: In 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. Conclusions: The 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.",
keywords = "Computational model, Indeterminate lesions, Molecular marker, Preoperative diagnosis, Thyroid cancer",
author = "Federica Panebianco and Chiara Mazzanti and Sara Tomei and Paolo Aretini and Sara Franceschi and Francesca Lessi and {Di Coscio}, Giancarlo and Generoso Bevilacqua and Ivo Marchetti",
year = "2015",
month = "11",
day = "19",
doi = "10.1186/s12885-015-1917-2",
language = "English",
volume = "15",
journal = "BMC Cancer",
issn = "1471-2407",
publisher = "BioMed Central",
number = "1",

}

TY - JOUR

T1 - The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management

AU - Panebianco, Federica

AU - Mazzanti, Chiara

AU - Tomei, Sara

AU - Aretini, Paolo

AU - Franceschi, Sara

AU - Lessi, Francesca

AU - Di Coscio, Giancarlo

AU - Bevilacqua, Generoso

AU - Marchetti, Ivo

PY - 2015/11/19

Y1 - 2015/11/19

N2 - Background: Papillary 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. Methods: We 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. Results: In 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. Conclusions: The 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.

AB - Background: Papillary 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. Methods: We 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. Results: In 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. Conclusions: The 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.

KW - Computational model

KW - Indeterminate lesions

KW - Molecular marker

KW - Preoperative diagnosis

KW - Thyroid cancer

UR - http://www.scopus.com/inward/record.url?scp=84947784206&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84947784206&partnerID=8YFLogxK

U2 - 10.1186/s12885-015-1917-2

DO - 10.1186/s12885-015-1917-2

M3 - Article

VL - 15

JO - BMC Cancer

JF - BMC Cancer

SN - 1471-2407

IS - 1

M1 - 918

ER -