Immunostaining for Met/HGF receptor may be useful to identify malignancies in thyroid lesions classified suspicious at fine-needle aspiration biopsy

Development of metastatic poorly differentiated thyroid cancer from a sub-centimeter papillary thyroid carcinoma in a young patient with a germline MET mutation - association or random chance?

Background

Thyroid cancer dedifferentiation is an unusual observation among young patients and is poorly understood, although a recent correlation to DICER1 gene mutations has been proposed.

Case presentation

A 28-year old patient presented with a sub-centimeter cytology-verified primary papillary thyroid carcinoma (PTC) and a synchronous lateral lymph node metastasis. Following surgery, histopathology confirmed a 9 mm oxyphilic PTC and a synchronous metastasis of poorly differentiated thyroid carcinoma (PDTC). Extensive molecular examinations of both lesions revealed wildtype DICER1 sequences, but identified a somatic ETV6-NTRK3 gene fusion and a MET germline variant (c.1076G > A, p.Arg359Gln). MET is an established oncogene known to be overexpressed in thyroid cancer, and this specific alteration was not reported as a single nucleotide polymorphism (SNP), suggestive of a mutation. Both the primary PTC and the metastatic PDTC displayed strong MET immunoreactivity. A validation cohort of 50 PTCs from young patients were analyzed using quantitative real-time PCR, revealing significantly higher MET gene expression in tumors than normal thyroid controls, a finding which was particularly pronounced in BRAF V600E mutated cases. No additional tumors apart from the index case harbored the p.Arg359Gln MET mutation. Transfecting PTC cell lines MDA-T32 and MDA-T41 with a p.Arg359Gln MET plasmid construct revealed no obvious effects on cellular migratory or invasive properties, whereas overexpression of wildtype MET stimulated invasion.

Conclusions

The question of whether the observed MET mutation in any way influenced the dedifferentiation of a primary PTC into a PDTC metastasis remains to be established. Moreover, our data corroborate earlier studies, indicating that MET is aberrantly expressed in PTC and may influence the invasive behavior of these tumors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13044-021-00110-4.

Ruling in or ruling out thyroid malignancy by molecular diagnostics of thyroid nodules

Routine morphologic cytology is the basis for any kind of (integrated) molecular FNA diagnostics. The rule out (gene expression classifier) approach requires confirmation by independent studies, whereas the rule in approach (detection of BRAF, NRAS, HRAS, and KRAS and PAX8/PPARG- and RET/PTC rearrangements) has been investigated by several groups with overall reproducible results. Moreover, molecular screening for point mutations and rearrangements is feasible in routine air-dried FNA smears, offering several advantages over obtaining additional fresh FNA material. The current panel of somatic mutations (rule in approach) for indeterminate FNAs clarifies only a subgroup of indeterminate FNAs. Therefore, further markers are urgently needed that can reliably identify the malignant, but mutation negative and especially the many benign nodules, among the indeterminate FNAs. miRNA markers and the targeted next generation sequencing (NGS) technology do have the potential to identify those nodules that are mutation negative by current approaches.

Use of molecular markers in samples obtained from preoperative aspiration of thyroid

Several experiments have been carried out in order to find molecular markers that increase the diagnose accuracy of the Fine-Needle Aspiration (FNA), especially for thyroid lesions of undetermined significance. The growing number of published experiments on one or more of the different types of markers has started to justify the need to gather the pieces of information as a way to add evidence and guide the development of future research in the area. From the search arguments and criteria previously defined, 95 articles were selected from the electronic databases PUBMED, MEDLINE, SCOPUS and LILACS. From the 36 markers submitted to analysis and identified in preoperative FNA thyroid samples, only 10 (GAL3, CK-19, HBME-1, TPO, CD44, Telomerase, DAP IV, RAS, RET and BRAF) were assessed in more than two investigations, be it either in panel or individually. The minimum, medium and maximum values of sensibility, specificity, positive predictive value, negative predictive value and diagnose accuracy were obtained from the group of investigation, as well as the limitations and advantages of the use of each marker were identified. The BRAF mutation, for its unquestionable specificity, and the GAL3, for its regularity of average results obtained here, found in several locations in the cell as well as out of the cell, suggesting multiple functions of this molecule, were observed as holders of more expressive evidence in the effort of reducing the uncertainty of the diagnose in preoperative FNA of thyroid.

Validity of multiplex biomarker model of 6 genes for the differential diagnosis of thyroid nodules

Background

Currently the cytological examination of fine needle aspiration (FNA) biopsies is the standard technique for the pre-operative differential diagnosis of thyroid nodules. However, the results may be non-informative in ~20% of cases due to an inadequate sampling and the lack of highly specific, measurable cytological criteria, therefore ancillary biomarkers that could aid in these cases are clearly needed. The aim of our study was to evaluate the mRNA expression levels of 8 candidate marker genes as the diagnostic biomarkers for the discrimination of benign and malignant thyroid nodules and to find a combination of biomarkers with the highest diagnostic value.

Materials and methods

mRNA expression levels of eight candidate marker genes - BIRC5, CCND1, CDH1, CITED1, DPP4, LGALS3, MET and TFF3 was measured by real-time RT-PCR in paired nodular and surrounding normal thyroid tissue specimens of 105 consecutive patients undergoing thyroid surgery and compared between different types of thyroid lesions.

Results

Significant differences in the mRNA expression levels between the normal and malignant thyroid tissues and between benign and malignant nodules were found for BIRC5, CCND1, CITED1, DPP4, LGALS3, MET and TFF3, but not CDH1. On a single gene basis, relative quantity (RQ) of LGALS3 had the highest diagnostic value for the discrimination of malignant and benign thyroid nodules (AUC = 0.832, P < 0.0001 and 90.9% sensitivity and 65.6% specificity at the optimal cut-off on ROC curve). The only two-marker set that outperformed LGALS3 was RQ sum of LGALS3 and BIRC5 (AUC = 0.841, P < 0.0001). An application of multivariate logistic regression analysis resulted in the generation of a multiplex biomarker model based on LGALS3, BIRC5, TFF3, CCND1, MET and CITED1 that had considerably higher specificity than a single marker or two marker gene-based models (AUC = 0.895, P < 0.0001, 70.5% sensitivity and 93.4% specificity).

Conclusions

This study confirmed that mRNA expression levels of 7 out of 8 candidate genes analysed have a diagnostic value for the distinction of benign and malignant thyroid nodules. The multiplex biomarker model based on 6 genes outperformed a single marker or two marker-based models and warrants feasibility studies on FNA biopsies and the validation in a larger cohort of patients.

Identification of differentially expressed genes in papillary thyroid cancers

PURPOSE

Techniques designed to identify differentially expressed genes (DEGs) in tumors have become important in modern pathology. Genefishing technique using the annealing control primer (ACP) system has recently been developed to screen for DEG transcripts. We tried to identify DEGs involved in papillary thyroid cancer (PTC) by using Genefishing technique.

MATERIALS AND METHODS

We utilized a new differential display method, designated with Genefishing technique, to analyze DEGs in 21 cases of PTCs.

RESULTS

Comparing the gene expression profiles between PTC and normal thyroid, we detected 17 genes that were differentially expressed in PTCs and performed cloning with sequencing in 10 genes. We confirmed the expression patterns of 2 DEGs by RT-PCR assay and identified the same results in 17 out of 21 (81%) PTCs. The 2 DEGs over-expressed in PTCs were identified as DC-STAMP and type I collagen A1. They are novel genes identified first in PTCs.

CONCLUSION

We confirmed 2 DEGs in PTCs as DC-STAMP and type I collagen A1 by using Genefishing technique. Although the detailed functions of those 2 genes and their products remain to be determined, the genes will provide insights into mechanisms of carcinogenesis or tumor progression in PTCs.

Molecular classification of thyroid nodules by cytology

OBJECTIVES

Fine needle aspiration (FNA) biopsy of thyroid nodules provides cytologic specimens whose interpretation can direct patients toward either thyroidectomy or observation. Approximately 20% of FNA specimens yield an indeterminate result. Recent studies have characterized differences in gene expression between benign and malignant conditions, most often using whole tissue. Our goal was to determine the feasibility of quantitative polymerase chain reaction (qPCR)-based gene expression analysis in cytologic samples. For five genes shown to be over-expressed in thyroid carcinomas (fibronectin, galectin-3, Met/HGFR, MUC1, and GA733-precursor), we compared expression among pathologic states.

STUDY DESIGN

Prospective laboratory analysis of 20 thyroidectomy specimens.

METHODS

Routine microscopy was performed. Cytologic samples were obtained from the dominant nodules, and RNA was extracted. Preliminary analysis using fluorometry and reverse-transcriptase (RT)-PCR was performed. Expression levels of the test genes in nodules and from control samples were measured by real-time qPCR. Fold changes in gene expression were compared.

RESULTS

Only one specimen did not yield sufficient intact RNA for gene expression analysis. RT-PCR revealed satisfactory RNA recovery in all other specimens. qPCR showed significant over-expression of fibronectin in the papillary carcinomas compared with the goiters (P = .0013), follicular adenomas (P = .0014), and follicular carcinomas (P = .0001). Differences in both fibronectin and MUC1 expression between the follicular carcinomas and the follicular adenomas were also significant (P = .025 and .045, respectively).

CONCLUSIONS

Cytologic specimens were a satisfactory source of tissue for qPCR-based gene expression analysis. Both fibronectin and MUC1 were differentially expressed in follicular adenomas and follicular carcinomas, and fibronectin expression differed in papillary carcinomas compared with the other lesions. These results may form the basis of a clinical predictor for lesions with indeterminate or suspicious cytology.

A multi-gene approach to differentiate papillary thyroid carcinoma from benign lesions: gene selection using support vector machines with bootstrapping

Selection of novel molecular markers is an important goal of cancer genomics studies. The aim of our analysis was to apply the multivariate bioinformatical tools to rank the genes - potential markers of papillary thyroid cancer (PTC) according to their diagnostic usefulness. We also assessed the accuracy of benign/malignant classification, based on gene expression profiling, for PTC. We analyzed a 180-array dataset (90 HG-U95A and 90 HG-U133A oligonucleotide arrays), which included a collection of 57 PTCs, 61 benign thyroid tumors, and 62 apparently normal tissues. Gene selection was carried out by the support vector machines method with bootstrapping, which allowed us 1) ranking the genes that were most important for classification quality and appeared most frequently in the classifiers (bootstrap-based feature ranking, BBFR); 2) ranking the samples, and thus detecting cases that were most difficult to classify (bootstrap-based outlier detection). The accuracy of PTC diagnosis was 98.5% for a 20-gene classifier, its 95% confidence interval (CI) was 95.9-100%, with the lower limit of CI exceeding 95% already for five genes. Only 5 of 180 samples (2.8%) were misclassified in more than 10% of bootstrap iterations. We specified 43 genes which are most suitable as molecular markers of PTC, among them some well-known PTC markers (MET, fibronectin 1, dipeptidylpeptidase 4, or adenosine A1 receptor) and potential new ones (UDP-galactose-4-epimerase, cadherin 16, gap junction protein 3, sushi, nidogen, and EGF-like domains 1, inhibitor of DNA binding 3, RUNX1, leiomodin 1, F-box protein 9, and tripartite motif-containing 58). The highest ranking gene, metallophosphoesterase domain-containing protein 2, achieved 96.7% of the maximum BBFR score.

Perspectives and limitations of microarray-based gene expression profiling of thyroid tumors

Microarray technology has become a powerful tool to analyze the gene expression of tens of thousands of genes simultaneously. Microarray-based gene expression profiles are available for malignant thyroid tumors (i.e., follicular thyroid carcinoma, and papillary thyroid carcinoma), and for benign thyroid tumors (such as autonomously functioning thyroid nodules and cold thyroid nodules). In general, the two main foci of microarray investigations are improved understanding of the pathophysiology/molecular etiology of thyroid neoplasia and the detection of genetic markers that could improve the differential diagnosis of thyroid tumors. Their results revealed new features, not known from one-gene studies. Simultaneously, the increasing number of microarray analyses of different thyroid pathologies raises the demand to efficiently compare the data. However, the use of different microarray platforms complicates cross-analysis. In addition, there are other important differences between these studies: 1) some studies use intraindividual comparisons, whereas other studies perform interindividual comparisons; 2) the reference tissue is defined as strictly nonnodular healthy tissue or also contains benign lesions such as goiter, follicular adenoma, and hyperplastic nodules in some studies; and 3) the widely used Affymetrix GeneChip platform comprises several GeneChip generations that are only partially compatible. Moreover, the different studies are characterized by strong differences in data analysis methods, which vary from simple empiric filters to sophisticated statistic algorithms. Therefore, this review summarizes and compares the different published reports in the context of their study design. It also illustrates perspectives and solutions for data set integration and meta-analysis, as well as the possibilities to combine array analysis with other genetic approaches.

Contemporary diagnostic approach to the thyroid nodule

Thyroid nodules are common, with an estimated incidence of 5%-10% in the United States. The current gold standard for diagnosis is fine needle aspiration biopsy (FNAB). The incidence of indeterminate diagnoses varies from 10% to 25%. Surgical resection is usually indicated to exclude the diagnosis of cancer in these patients. However, only a minority (about 20%) of indeterminate thyroid nodules actually harbor a malignancy, resulting in surgery for diagnostic purposes alone in many patients. The increased detection of benign nodules and microcarcinomas reinforces the need for improved non-operative methods to differentiate benign from malignant disease and discriminate low-risk from high-risk cancers. In this article we present a current, rational diagnostic approach to the patient with a thyroid nodule, evaluate new advances including thyroid genomic and predictor models, and propose the development of prospective trials to incorporate these new additions into clinical decision making. Given how many questions still exist for patients with thyroid nodules, partnership and collaboration, or the "bench to bedside" concept should find its way into most every thyroid surgeon and endocrinologist's lexicon.

Meta-analysis and meta-review of thyroid cancer gene expression profiling studies identifies important diagnostic biomarkers

PURPOSE

An estimated 4% to 7% of the population will develop a clinically significant thyroid nodule during their lifetime. In many cases, preoperative diagnoses by needle biopsy are inconclusive. Thus, there is a clear need for improved diagnostic tests to distinguish malignant from benign thyroid tumors. The recent development of high-throughput molecular analytic techniques should allow the rapid evaluation of new diagnostic markers. However, researchers are faced with an overwhelming number of potential markers from numerous thyroid cancer expression profiling studies.

MATERIALS AND METHODS

To address this challenge, we have carried out a comprehensive meta-review of thyroid cancer biomarkers from 21 published studies. A gene ranking system that considers the number of comparisons in agreement, total number of samples, average fold-change and direction of change was devised.

RESULTS

We have observed that genes are consistently reported by multiple studies at a highly significant rate (P < .05). Comparison with a meta-analysis of studies reprocessed from raw data showed strong concordance with our method.

CONCLUSION

Our approach represents a useful method for identifying consistent gene expression markers when raw data are unavailable. A review of the top 12 candidates revealed well known thyroid cancer markers such as MET, TFF3, SERPINA1, TIMP1, FN1, and TPO as well as relatively novel or uncharacterized genes such as TGFA, QPCT, CRABP1, FCGBP, EPS8 and PROS1. These candidates should help to develop a panel of markers with sufficient sensitivity and specificity for the diagnosis of thyroid tumors in a clinical setting.

Vascular endothelial growth factor in thyroid cancers

Vascular endothelial growth factor (VEGF) is an essential peptide in new vessel growth in physiology (endometrial growth, embryonic development); pathological conditions (diabetic retinopathy, rheumatoid arthritis); as well as in tumor cell growth, particularly distant metastases. This study focused on VEGF structure, receptors, and angiogensis in tumors, especially their roles in thyroid cancer. The VEGF mRNA undergoes alternative splicing events that generate four homodimeric isoforms, including VEGF121, VEGF165, VEGF189, or VEGF206. Using VEGF purified from a culture medium conditioned by A-431 human epidermoid carcinoma cells, VEGF-binding site complexes of 230, 170, and 125 kDa were detected on human umbilical vein endothelial cells. The VEGF specifically induced the tyrosine phosphorylation of a 190-kDa polypeptide, which had similar mass to the largest binding site detected through affinity cross-linking. A transmembrane receptor belongs to the tyrosine kinase family, fms-like tyrosine kinase (FLT). These receptor tyrosine kinases encoded by the FLT gene family have distinct functions in regulating blood vessel growth and differentiation. Regulation of VEGF is a complex, multistep mechanism in various kinds of cells and tissues. Hypoxia-dependent and -independent mechanisms are illustrated in different cancer tissues. Hypoxic tumor cells may switch to a proangiogenic phenotype, which increases VEGF transcription. Clinical applications of VEGF in cancer have included diagnosis, prediction of prognosis, and treatment in different solid tumors, including thyroid tumors. Studies involving thyroid cancer cell lines, serum level determination, immunohistocytochemical staining, molecular biological studies, and gene therapy to the in vivo clinical trials, have shown that antiangiogensis therapy can provide another treatment modality for thyroid cancer. Future studies focused on recombinant human anti-VEGF research involving patients with advanced thyroid cancer, and investigation of the protection of high-risk patients by using novel antiangiogenic vaccines, are warranted.

ECM1 and TMPRSS4 Are Diagnostic Markers of Malignant Thyroid Neoplasms and Improve the Accuracy of Fine Needle Aspiration Biopsy

OBJECTIVE

The objective of this study was to determine whether genes that regulate cellular invasion and metastasis are differentially expressed and could serve as diagnostic markers of malignant thyroid nodules.

SUMMARY AND BACKGROUND DATA

Patients whose thyroid nodules have indeterminate or suspicious cytologic features on fine needle aspiration (FNA) biopsy require thyroidectomy because of a 20% to 30% risk of thyroid cancer. Cell invasion and metastasis is a hallmark of malignant phenotype; therefore, genes that regulate these processes might be differentially expressed and could serve as diagnostic markers of malignancy.

METHODS

Differentially expressed genes (2-fold higher or lower) in malignant versus benign thyroid neoplasms were identified by extracellular matrix and adhesion molecule cDNA array analysis and confirmed by real-time quantitative polymerase chain reaction (PCR). The area under the receiver operating characteristic (AUC) curve was calculated to determine diagnostic accuracy of gene expression level cutoffs established by logistic regression analysis.

RESULTS

By cDNA array analysis, ADAMTS8, ECM1, MMP8, PLAU, SELP, and TMPRSS4 were upregulated, and by quantitative PCR, ECM1, SELP, and TMPRSS4 mRNA expression was higher in malignant (n = 57) than in benign (n = 38) thyroid neoplasms (P< 0.002). ECM1 and TMPRSS4 mRNA expression levels were independent predictors of a malignant thyroid neoplasm (P < 0.003). The AUC was 0.956 for ECM1 and 0.926 for TMPRSS4. Combining both markers improved their diagnostic use (AUC 0.985; sensitivity, 91.7%; specificity, 89.8%; positive predictive value, 85.7%; negative predictive value, 82.8%). ECM1 and TMPRSS4 expression analysis improved the diagnostic accuracy of FNA biopsy in 35 of 38 indeterminate or suspicious results. The level of ECM1 mRNA expression was higher in TNM stage I differentiated thyroid cancers than in stage II and III tumors (P < or = 0.031).

CONCLUSIONS

ECM1 and TMPRSS4 are excellent diagnostic markers of malignant thyroid nodules and may be used to improve the diagnostic accuracy of FNA biopsy. ECM1 is also a marker of the extent of disease in differentiated thyroid cancers.

Neural network analysis for evaluating cancer risk in thyroid nodules with an indeterminate diagnosis at aspiration cytology: identification of a low-risk subgroup

Thyroid nodules with a predominant follicular structure are often diagnosed as indeterminate at fine-needle aspiration biopsy (FNAB). We studied 453 patients with a thyroid nodule diagnosed as indeterminate at FNAB by using a feed-forward artificial neural network (ANN) analysis to integrate cytologic and clinical data, with the goal of subgrouping patients into a high-risk and in a low-risk category. Three hundred seventy-one patients were used to train the network and 82 patients were used to validate the model. The cytologic smears were blindly reviewed and classified in a high-risk and a low-risk subgroup on the basis of standard criteria. Neural network analysis subdivided the 371 lesions of the first series into a high-risk group (cancer rate of approximately 33% at histology) and a low-risk group (cancer rate of 3%). Only cytologic parameters contributed to this classification. Analysis of the receiver operating characteristic (ROC) curves demonstrated that the ANN model discriminated with higher sensitivity and specificity between benign and malignant nodules compared to standard cytologic criteria (p < 0.001). This value did not show degradation when ANN predictions were applied to the validation series of 82 nodules. In conclusion, neural network analysis of cytologic data may be a useful tool to refine the risk of cancer in patients with lesions diagnosed as indeterminate by FNAB.