Genetic Testing in Differentiated Thyroid Carcinoma: Indications and Clinical Implications

CT-Based Radiomics Models for Differentiation of Benign and Malignant Thyroid Nodules: A Multicenter Development-and-Validation

Background: CT is increasingly detecting thyroid nodules. Prior studies indicated a potential role of CT-based radiomics models in characterizing thyroid nodules, although lacked external validation. Objectives: To develop and validate a CT-based radiomics model for the differentiation of benign and malignant thyroid nodules. Methods: This retrospective study included 378 patients (mean age, 46.3±13.9 years; 86 men, 292 women) with 408 resected thyroid nodules (145 benign, 263 malignant) from two centers (center 1: 293 nodules, January 2018-December 2022; center 2: 115 nodules, January 2020-December 2022), who underwent preoperative multiphase neck CT (noncontrast, arterial, and venous phases). Nodules from center 1 were divided into training (n=206) and internal validation (n=87) sets; all nodules from center 2 formed an external validation set. Radiologists assessed nodules for morphologic CT features. Nodules were manually segmented on all phases, and radiomic features were extracted. Conventional (clinical and morphologic CT), noncontrast radiomics, arterial-phase radiomics, venous-phase radiomics, multiphase radiomics, and combined (clinical, morphologic, and multiphase radiomics) models were established using feature selection methods and evaluated by ROC curve analysis, calibration curves, and decision-curve analysis. Results: The combined model included patient age, three morphologic features (cystic change, edge interruption sign, abnormal cervical lymph nodes), and 28 radiomic features (from all three phases). In the external validation set, the combined model had AUC of 0.923 and, at an optimal threshold derived in the training set, sensitivity of 84.0%, specificity of 94.1%, and accuracy of 87.0%. In the external validation set, AUC was significantly higher for the combined model than for the conventional model (0.827), noncontrast radiomics model (0.847), arterial-phase radimoics model (0.826), venous-phase radiomics model (0.773), and multiphase radiomics model (0.824) (all p<.05). In the external validation set, the calibration curves indicated lowest (i.e., best) Brier score for the combined model; in decision-curve analysis, the combined model had the highest net benefit for most of the range of threshold probabilities. Conclusion: A combined model incorporating clinical, morphologic CT, and multiphasic radiomics CT features, exhibited robust performing in differentiating benign and malignant thyroid nodules. Clinical Impact: The combined radiomics model may help guide further management for thyroid nodules detected on CT.

Expression data of FOS and JUN genes and FTIR spectra provide diagnosis of thyroid carcinoma

We explore the feasibility of using FOS and JUN gene expression and ATR-FTIR for diagnosis of thyroid cancer. For the study, 38 samples (6 non-neoplastic (NN), 10 papillary thyroid carcinoma (PTC), 7 follicular thyroid carcinoma (FTC), and 15 benign tumors (BT) were subjected to RNA extraction followed by quantitative real time PCR (qRT-PCR) and 30 samples (5 NN, 9 PTC, 5 FTC, and 11 BT) were used for Attenuated Total Reflectance - Fourier Transform Infrared (ATR-FTIR) followed by multivariate analysis. Of the above, 20 samples were used for both gene expression and ATR-FTIR studies. We found FOS and JUN expression in malignant tumor samples to be significantly lower than NN and benign. ATR-FIR after multivariate analysis could identify the difficult to diagnose FTC with 93 % efficiency. Overall, results suggest the diagnostic potential of molecular biology techniques combined with ATR-FTIR spectroscopy in differentiated thyroid carcinomas (PTC and FTC) and BT.

Nitric oxide, thyroglobulin, and calcitonin: unraveling the nature of thyroid nodules

Background

Thyroid nodules (TN) are localized morphological changes in the thyroid gland and can be benign or malignant.

Objective

The present study investigates the relationships between biochemical markers in serum (s) and their homologs in washout (w) after fine-needle aspiration biopsy (FNAB) of the TN of interest and their correlation with cytology specimen findings.

Methods

We investigated the relationships between serum biochemical markers nitric oxide (NO), thyroglobulin (TG), and calcitonin (CT), their homologs in washout after FNAB of the TN of interest, and cytology findings of biopsy samples classified according to the Bethesda system for thyroid cytopathology in this study, which included 86 subjects.

Results

Washout TG (TGw) level positively correlates with the cytology finding of the biopsy. A higher level of TGw correlates with higher categories of the Bethesda classification and indicates a higher malignant potential. The levels of serum NO (NOs), serum TG (TGs), serum CT (CTs), and washout CT (CTw) do not correlate with the cytology finding of the biopsy, and the higher levels of washout NO (NOw) correspond to the more suspicious ultrasound findings.

Conclusion

The findings of our study suggest that TGw and NOw could be used as potential predictors of malignancy in TN.

HLA-G Gene Variability Is Associated with Papillary Thyroid Carcinoma Morbidity and the HLA-G Protein Profile

Human leukocyte antigen (HLA)-G is an immune checkpoint molecule that is highly expressed in papillary thyroid carcinoma (PTC). The HLA-G gene presents several functional polymorphisms distributed across the coding and regulatory regions (5'URR: 5' upstream regulatory region and 3'UTR: 3' untranslated region) and some of them may impact HLA-G expression and human malignancy. To understand the contribution of the HLA-G genetic background in PTC, we studied the HLA-G gene variability in PTC patients in association with tumor morbidity, HLA-G tissue expression, and plasma soluble (sHLA-G) levels. We evaluated 185 PTC patients and 154 healthy controls. Polymorphic sites defining coding, regulatory and extended haplotypes were characterized by sequencing analyses. HLA-G tissue expression and plasma soluble HLA-G levels were evaluated by immunohistochemistry and ELISA, respectively. Compared to the controls, the G0104a(5'URR)G*01:04:04(coding)UTR-03(3'UTR) extended haplotype was underrepresented in the PTC patients, while G0104a(5'URR)G*01:04:01(coding)UTR-03(3'UTR) was less frequent in patients with metastatic and multifocal tumors. Decreased HLA-G tissue expression and undetectable plasma sHLA-G were associated with the G010102a(5'URR)G*01:01:02:01(coding)UTR-02(3'UTR) extended haplotype. We concluded that the HLA-G variability was associated with PTC development and morbidity, as well as the magnitude of the encoded protein expression at local and systemic levels.

Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations

Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.

Expression based biomarkers and models to classify early and late-stage samples of Papillary Thyroid Carcinoma

INTRODUCTION

Recently, the rise in the incidences of thyroid cancer worldwide renders it to be the sixth most common cancer among women. Commonly, Fine Needle Aspiration biopsy predominantly facilitates the diagnosis of the nature of thyroid nodules. However, it is inconsiderable in determining the tumor's state, i.e., benign or malignant. This study aims to identify the key RNA transcripts that can segregate the early and late-stage samples of Thyroid Carcinoma (THCA) using RNA expression profiles.

MATERIALS AND METHODS

In this study, we used the THCA RNA-Seq dataset of The Cancer Genome Atlas, consisting of 500 cancer and 58 normal (adjacent non-tumorous) samples obtained from the Genomics Data Commons (GDC) data portal. This dataset was dissected to identify key RNA expression features using various feature selection techniques. Subsequently, samples were classified based on selected features employing different machine learning algorithms.

RESULTS

Single gene ranking based on the Area Under the Receiver Operating Characteristics (AUROC) curve identified the DCN transcript that can classify the early-stage samples from late-stage samples with 0.66 AUROC. To further improve the performance, we identified a panel of 36 RNA transcripts that achieved F1 score of 0.75 with 0.73 AUROC (95% CI: 0.62-0.84) on the validation dataset. Moreover, prediction models based on 18-features from this panel correctly predicted 75% of the samples of the external validation dataset. In addition, the multiclass model classified normal, early, and late-stage samples with AUROC of 0.95 (95% CI: 0.84-1), 0.76 (95% CI: 0.66-0.85) and 0.72 (95% CI: 0.61-0.83) on the validation dataset. Besides, a five protein-coding transcripts panel was also recognized, which segregated cancer and normal samples in the validation dataset with F1 score of 0.97 and 0.99 AUROC (95% CI: 0.91-1).

CONCLUSION

We identified 36 important RNA transcripts whose expression segregated early and late-stage samples with reasonable accuracy. The models and dataset used in this study are available from the webserver CancerTSP (http://webs.iiitd.edu.in/raghava/cancertsp/).

Impact of Age-Related Genetic Differences on the Therapeutic Outcome of Papillary Thyroid Cancer

The incidence of papillary thyroid carcinoma (PTC) has been increasing worldwide. PTC is the most common type of differentiated thyroid cancer and usually shows good prognosis. However, some PTC is driven to advanced stage by epithelial-mesenchymal transition (EMT)-mediated drug resistance, which is particularly noticeable in pediatric patients. There are limited options for systemic treatment, necessitating development of new clinical approaches. Here, we aimed to clarify genetic differences due to age of patients with PTC, and thereby aid in developing novel therapeutics. Patients with biochemically and histologically confirmed PTC were included in this study. PTC cells were acquired from young and older patients showing drug resistance, and were compared via microarray analysis. Cellular proliferation and other properties were determined after treatments with lenvatinib and sorafenib. In vivo, tumor volume and other properties were examined using a mouse xenograft model. Lenvatinib-treated group showed obvious suppression of markers of anti-apoptosis, EMT, and the FGFR signaling pathway, compared with control and Sorafenib-treated group. In the xenograft models, lenvatinib treatment induced significant tumor shrinkage and blocked the proto-oncogene Bcl-2 (B cell lymphoma/leukemia gene-2) and FGFR signaling pathway, along with reduced levels of EMT markers, compared with control and Sorafenib-treated group. Our findings clarify the age-dependent characteristics of pediatric PTC, giving insights into the relationship between young age and poor prognosis. Furthermore, it provides a basis for developing novel therapeutics tailored to the age at diagnosis.

Histopathological Verification of the Diagnostic Performance of the EU-TIRADS Classification of Thyroid Nodules-Results of a Multicenter Study Performed in a Previously Iodine-Deficient Region

Background: To validate the European Thyroid Imaging and Reporting Data System EU-TIRADS classification in a multi-institutional database of thyroid nodules by analyzing the obtained scores and histopathology results. Methods: A total of 842 thyroid lesions (613 benign, 229 malignant) were identified in 428 patients (mean age 62.7 years) and scored according to EU-TIRADS, using ultrasound examination. In all tumors, histopathological verification was performed. Results: In EU-TIRADS 2 (154 nodules) all nodules were benign; in EU-TIRADS 3, only 3/93 malignancies were identified. In EU-TIRADS 4, 12/103 were malignant, and in EU-TIRADS 5 (278 benign vs. 214 malignant). The malignant nodules that would not have qualified for biopsy were: EU-TIRADS 3, 2/3 (67%) malignancies were <20 mm, in EU-TIRADS 4, 7/12 (58%) were <15 mm. In EU-TIRADS 5, 72/214 (34%) were <10 mm; in total, 81/229 (36%) malignant lesions would have been missed. The cutoff between EU-TIRADS 3/4 had sensitivity of 100%, specificity of 25.1%. Using cutoff for EU-TIRADS 5, 93.4%, 54.6%, respectively. Conclusion: The application of EU-TIRADS guidelines allowed us to achieve moderate specificity. The vast majority of malignancies in EU-TIRADS 3, 4, and 5 would not have been recommended for biopsy because having a smaller size than that proposed classification.

Inhibitory roles of miR‑9 on papillary thyroid cancer through targeting BRAF

MicroRNA‑9 (miR‑9) is reported to be underexpressed in papillary thyroid carcinoma (PTC) tissues; however, the molecular mechanisms underlying the implication of miR‑9 in PTC have yet to be elucidated. The present study aimed to explore the potential roles of miR‑9 in PTC. PTC tissue samples and paired non‑cancerous adjacent tissues were collected from 60 patients with PTC. The human TPC‑1 thyroid gland papillary carcinoma cell line was used to investigate the molecular mechanisms underlying the roles of miR‑9 in PTC. The levels of miR‑9 and its downstream target gene BRAF were detected through reverse transcription‑quantitative polymerase chain reaction. MTT assay and flow cytometry were performed to evaluate cell viability and apoptosis, respectively. A mouse xenograft tumor model was established to observe the effects of miR‑9 on thyroid gland tumorigenesis in vivo. The present study revealed that the expression of miR‑9 was significantly reduced in PTC tissues compared with paired normal tissues. In addition, miR‑9 upregulation suppressed the expression of BRAF in TPC‑1 cells in vitro. Luciferase reporter assay demonstrated that BRAF may be a direct target gene of miR‑9 in TPC‑1 cells. In addition, following transfection with miR‑9 mimics, the viability of TPC‑1 cells was suppressed and their apoptosis was enhanced; conversely, transfection with miR‑9 inhibitor exerted the opposite effects in vitro. miR‑9 overexpression or downregulation also affected in vivo PTC tumorigenesis in athymic mice. The present findings suggested that miR‑9 may suppress the viability of PTC cells and inhibit tumor growth through directly targeting the expression of BRAF in PTC.

Bethesda thyroid categories and family history of thyroid disease

OBJECTIVE

Thyroid cancer is the most common type of endocrine-related cancer worldwide. The aim of this article was to assess the relationship between thyroid diseases diagnosed by fine needle aspiration (FNA) and family history of thyroid disease.

DESIGN

The study was conducted in a tertiary high-volume thyroid cancer centre. Fine needle aspiration (FNA) of a thyroid nodule detected on neck ultrasound for any reason was performed in all included patients.

PATIENTS

A total of 10 709 patients were included in the study.

MEASUREMENTS

Correlation of cytological findings classified according to the Bethesda system and family history was calculated using Fisher's exact test.

RESULTS

There were 2580 (24.09%) patients with non-malignant thyroid diseases in the family and 198 (1.85%) patients who had a history of thyroid cancer in the family. A total of 2778 (25.94%) patients had positive family history of thyroid diseases, and 7931 (74.06%) patients had negative family history. In patients with papillary thyroid carcinoma in family history, the difference between those with benign (Bethesda 2) and malignant thyroid FNA diagnosis (Bethesda 6) was found to be statistically significant (P = .0432).

CONCLUSIONS

Family history plays a significant role in the development of thyroid cancer, and having first-degree relatives with not only medullary, but also papillary thyroid cancer strongly predicts the risk of developing the malignant thyroid disease. In contrast, benign thyroid disorders in family history do not lead to the development of thyroid cancer.

Gene Fusions in Thyroid Cancer

BACKGROUND

Gene fusions are known in many cancers as driver or passenger mutations. They play an important role in both the etiology and pathogenesis of cancer and are considered as potential diagnostic and prognostic markers and possible therapeutic targets. The spectrum and prevalence of gene fusions in thyroid cancer ranges from single cases up to 80%, depending on the specific type of cancer. During last three years, massive parallel sequencing technologies have revealed new fusions and allowed detailed characteristics of fusions in different types of thyroid cancer.

SUMMARY

This article reviews all known fusions and their prevalence in papillary, poorly differentiated and anaplastic, follicular, and medullary carcinomas. The mechanisms of fusion formation are described. In addition, the mechanisms of oncogenic transformation, such as altered gene expression, forced oligomerization, and subcellular localization, are given.

CONCLUSION

The prognostic value and perspectives of the utilization of gene fusions as therapeutic targets are discussed.