Problems of follicular thyroid carcinoma diagnostics

Introduction. Follicular thyroid cancer is much less common than papillary cancer. Nevertheless, the main difficulties in preoperative diagnosis are associated with this morphological type. A fine needle aspiration biopsy is not able to distinguish a benign follicular adenoma from a follicular carcinoma, which forces surgeons to perform diagnostic resection of the thyroid gland in all patients with a cytological conclusion «follicular tumor».

Aim. To search for microRNAs specific to follicular cancer by sequencing a new generation.

Materials and methods. The data of patients with a preoperative cytological conclusion «follicular tumor» operated at the Chelyabinsk Center for Endocrine Surgery from 2021 to 2022 were analyzed. Histological preparations were reviewed twice by pathologists. Genome sequencing was performed in 8 histological samples of follicular cancer and 8 samples of follicular adenoma. The expression levels of the selected microRNAs were compared with 198 archived cytological samples of various types of thyroid tumors.

Results. The risk of malignancy at the cytological conclusion «follicular tumor» was 25.4 % (error 74.6 %). Follicular cancer was first detected in 36 patients, the incidence was 0.68 new cases per 100 thousand population per year. The diagnosis of «follicular cancer» was confirmed by 3 morphologists in 8 (36.4 %) cases. Sequencing revealed the 5 most distinct microRNAs between follicular cancer and follicular adenoma: miR-625, miR-323a, let-7a, let-7c and miR-574. The level of errors in the differentiation of follicular adenoma and follicular cancer using the microRNAs we selected was 21 % (35 % with cross-validation).

Conclusion. Molecular genetic research at the preoperative stage, aimed at differentiating follicular cancer and follicular adenoma, in comparison with cytological research has a greater, but insufficient accuracy for making a final clinical decision.

1. Bongiovanni M., Spitale A., Faquin W.C. et al. The Bethesda System for reporting thyroid cytopathology: a meta-analysis. Acta Cytol 2012;56(4):333–9. DOI: 10.1159/000339959

2. Cibas E.S., Ali S.Z. The 2017 Bethesda System for reporting thyroid cytopathology. Thyroid 2017;27(11):1341–6. DOI: 10.1089/thy.2017.0500

3. Schneider D.F., Cherney Stafford L.M., Brys N. et al. Gauging the extent of thyroidectomy for indeterminate thyroid nodules: an oncologic perspective. Endocr Pract 2017;23(4):442–50. DOI: 10.4158/EP161540.OR

4. Stewardson P., Eszlinger M., Paschke R. Diagnosis of endocrine disease: usefulness of genetic testing of fine-needle aspirations for diagnosis of thyroid cancer. Eur J Endocrinol 2022;187(3):R41–52. DOI: 10.1530/EJE-21-1293

5. Patel K.N., Yip L., Lubitz C.C. et al. The American Association of Endocrine Surgeons Guidelines for the definitive surgical management of thyroid disease in adults. Ann Surg 2020;271(3):e21–93. DOI: 10.1097/SLA.0000000000003580

6. Silaghi C.A., Lozovanu V., Georgescu C.E. et al. Thyroseq v3, Afirma GSC, and microRNA panels versus previous molecular tests in the preoperative diagnosis of indeterminate thyroid nodules: a systematic review and meta-analysis. Front Endocrinol (Lausanne) 2021;12:649522. DOI: 10.3389/fendo.2021.649522

7. Wang M.M., Beckett K., Douek M. et al. Diagnostic value of molecular testing in sonographically suspicious thyroid nodules. J Endocr Soc 2020;4(9):bvaa081. DOI: 10.1210/jendso/bvaa081

8. Azizi G., Keller J.M., Mayo M.L. et al. Shear wave elastography and Afirma™ gene expression classifier in thyroid nodules with indeterminate cytology: a comparison study. Endocrine 2018;59(3):573–84. DOI: 10.1007/s12020-017-1509-9

9. Patel K.N., Angell T.E., Babiarz J. et al. Performance of a genomic sequencing classifier for the preoperative diagnosis of cytologically indeterminate thyroid nodules. JAMA Surg 2018;153(9):817–24. DOI: 10.1001/jamasurg.2018.1153

10. Titov S.E., Lukyanov S.A., Kozorezova E.S. et al. Validation of preoperative diagnosis of malignant thyroid tumors using a molecular classifier. Voprosy onkologii = Oncology Issues 2022;68(6):741–51. (In Russ.). DOI: 10.37469/0507-3758-2022-68-6-741-751

11. Xing M., Liu R., Liu X. et al. BRAF V600E and TERT promoter mutations cooperatively identify the most aggressive papillary thyroid cancer with highest recurrence. J Clin Oncol 2014;32(25):2718–26. DOI: 10.1200/JCO.2014.55.5094

12. Xing M. Clinical utility of RAS mutations in thyroid cancer: a blurred picture now emerging clearer. BMC Med 2016;14:12. DOI: 10.1186/s12916-016-0559-9

13. Song Y.S., Park Y.J. Genomic characterization of differentiated thyroid carcinoma. Endocrinol Metab (Seoul) 2019;34(1):1–10. DOI: 10.3803/EnM.2019.34.1.1

14. De Martino M., Esposito F., Capone M. et al. Noncoding RNAs in thyroid-follicular-cell-derived carcinomas. Cancers (Basel) 2022;14(13):3079. DOI: 10.3390/cancers14133079

15. Macfarlane L.A., Murphy P.R. MicroRNA: biogenesis, function and role in cancer. Curr Genomics 2010;11(7):537–61. DOI: 10.2174/138920210793175895

16. Santiago K., Chen Wongworawat Y., Khan S. Differential microRNA-signatures in thyroid cancer subtypes. J Oncol 2020;2020:2052396. DOI: 10.1155/2020/2052396

17. Wojtas B., Ferraz C., Stokowy T. et al. Differential miRNA expression defines migration and reduced apoptosis in follicular thyroid carcinomas. Mol Cell Endocrinol 2014;388(1–2):1–9. DOI: 10.1016/j.mce.2014.02.011

18. Stokowy T., Wojtaś B., Fujarewicz K. et al. miRNAs with the potential to distinguish follicular thyroid carcinomas from benign follicular thyroid tumors: results of a meta-analysis. Horm Metab Res 2014;46(3):171–80. DOI: 10.1055/s-0033-1363264

19. Weber F., Teresi R.E., Broelsch C.E. et al. A limited set of human microRNA is deregulated in follicular thyroid carcinoma. J Clin Endocrinol Metab 2006;91(9):3584–91. DOI: 10.1210/jc.2006-0693

20. Dom G., Frank S., Floor S. et al. Thyroid follicular adenomas and carcinomas: molecular profiling provides evidence for a continuous evolution. Oncotarget 2017;9(12):10343–59. DOI: 10.18632/oncotarget.23130

21. Titov S., Demenkov P.S., Lukyanov S.A. et al. Preoperative detection of malignancy in fine-needle aspiration cytology (FNAC) smears with indeterminate cytology (Bethesda III, IV) by a combined molecular classifier. J Clin Pathol 2020;73(11):722–7. DOI: 10.1136/jclinpath-2020-206445

22. Titov S.E., Kozorezova E.S., Demenkov P.S. et al. Preoperative typing of thyroid and parathyroid tumors with a combined molecular classifier. Cancers 2021;13(2):237. DOI: 10.3390/cancers13020237

23. Andrews S. FastQC: a quality control tool for high throughput sequence data. Available at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

24. Titov S.E., Ivanov M.K., Karpinskaya E.V. et al. miRNA profiling, detection of BRAF V600E mutation and RET-PTC1 translocation in patients from Novosibirsk oblast (Russia) with different types of thyroid tumors. BMC Cancer 2016;16:201. DOI: 10.1186/s12885-016-2240-2

25. Chen C., Ridzon D.A., Broomer A.J. et al. Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 2005;33(20):e179. DOI: 10.1093/nar/gni178

26. Livak K.J., Schmittgen T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCt method. Methods 2001;25(4):402–8. DOI: 10.1006/meth.2001.1262

27. Mercaldo N.D., Lau K.F., Zhou X.H. Confidence intervals for predictive values with an emphasis to case-control studies. Stat Med 2007;26(20):2170–83. DOI: 10.1002/sim.2677

28. Pérez-Ortiz M., Torres-Jiménez M., Gutiérrez P.A. et al. Fisher score-based feature selection for ordinal classification: a social survey on subjective well-being. In: Hybrid Artificial Intelligent Systems. Ed. by F. Martínez-Álvarez, A. Troncoso, H. Quintián, E. Corchado. HAIS 2016. Lecture Notes in Computer Science. Vol. 9648. Springer, Cham. DOI: 10.1007/978-3-319-32034-2_50

29. Kononenko I., Šimec E., Robnik-Sikonja M. Overcoming the myopia of inductive learning algorithms with RELIEFF. Applied Intelligence 1997;7(1):39–55. DOI: 10.1023/A:1008280620621

30. Li J., Cheng K., Wang S. et al. Feature selection. ACM Computing Surveys 2017;50(6):1–45. DOI: 10.1145/3136625

31. Bylesjö M., Rantalainen M., Cloarec O. et al. OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J Chemometrics 2006;20(8–10):341–51. DOI: 10.1002/cem.1006

32. Thevenot E., Roux A., Xu Y. et al. Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. J Proteome Res 2015;14(8):3322–35. DOI: 10.1021/acs.jproteome.5b00354

33. Tenenhaus M. La raegression PLS. Paris, Editions Technip, 1998.

34. Ricco R. TANAGRA: a free software for research and academic purposes. Proceedings of EGC’2005, RNTI-E-3. (In French). Available at: https://www.researchgate.net/publication/220786300_TANAGRA_un_logiciel_gratuit_pour_l'enseignement_et_la_recherche.

35. Quinlan J.R. C4.5: programs for machine learning. San Francisco: Morgan Kaufmann Publishers Inc; 1993.

36. Zinoviev A.Yu. Visualization of multidimensional data.

37. McHenry C.R., Phitayakorn R. Follicular adenoma and carcinoma of the thyroid gland. Oncologist 2011;16(5):585–93. DOI: 10.1634/theoncologist.2010-0405

38. Valderrabano P., Leon M.E., Centeno B.A. et al. Institutional prevalence of malignancy of indeterminate thyroid cytology is necessary but insufficient to accurately interpret molecular marker tests. Eur J Endocrinol 2016;174(5):621–9. DOI: 10.1530/EJE-15-1163

39. Rosai J., Kuhn E., Carcangiu M.L. Pitfalls in thyroid tumour pathology. Histopathology 2006;49:107–20. DOI: 10.1111/j.1365-2559.2006.02451.x

40. Franc B., de la Salmonière P., Lange F. et al. Interobserver and intraobserver reproducibility in the histopathology of follicular thyroid carcinoma. Hum Pathol 2003;34(11):1092–100. DOI: 10.1016/s0046-8177(03)00403-9

41. Cipriani N.A., Nagar S., Kaplan S.P. et al. Follicular thyroid carcinoma: how have histologic diagnoses changed in the last halfcentury and what are the prognostic implications? Thyroid 2015;25(11):1209–16. DOI: 10.1089/thy.2015.0297