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BACKGROUND: Thyroid fine-needle aspiration (FNA) is a reliable and cost-effective diagnostic tool for establishing the nature of thyroid nodules, although up to 30% of FNAs are still classified as "indeterminate." Molecular testing of FNAs could improve preoperative diagnosis, thereby reducing unnecessary surgery. In this multicenter prospective study the authors investigated, using a 7-gene assay, the distribution and diagnostic impact of BRAF, RAS, RET/PTC, and PAX8/ PPARg, the most frequent genomic alterations occurring during thyroid oncogenesis. METHODS: In total, of 1172 routine FNAs from 7 centers in southern Italy were classified according to the Bethesda System for Reporting ThyroidCytopathology. Each specimen was tested, and molecular data were compared with available histology or cytologic follow-up. RESULTS: In particular, for atypia of undetermined significance/follicular lesion of undetermined significance cases, the 7-gene test confirmed the high positive predictive value of BRAFV600E and BRAF-like mutations (80%) and the moderate positive predictive value of RAS-like alterations (32.4%), suggesting different surgical management, depending on the type of mutation. The rate of mutation-positive FNAs was strictly related to the risk of malignancy of each diagnostic class, supporting the identification of prognostically relevant diagnostic categories. CONCLUSIONS:The 7-gene panel test improves the preoperative risk stratification of indeterminate thyroid FNAs, especially when considering the biologic significance of the different types of mutations. Moreover, the rate of mutation-positive FNAs is related to the risk of malignancy of each diagnostic class.

The application of next generation sequencing (NGS) technology to cytological samples has significantly modified molecular cytopathology practice. Cytological samples represent a valid source of high‐quality DNA for NGS analysis, especially for predicting patients' response to targeted treatments and for refining the risk of malignancy in indeterminate cytological diagnoses. However, several pre‐analytical factors may influence the reliability of NGS clinical analysis. Here, we briefly review the challenges of NGS in cytology practice, focusing on those pre‐analytical factors that may negatively affect NGS success rates and routine diagnostic applications. Finally, we address the future directions of the field.

IntroductionLung cancer is the most frequent cause of cancer death, worldwide (1). In the last years, several clinical trials have defined the pivotal role of the molecular assessment of different biomarkers, such as epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), ROS proto-oncogene 1 receptor tyrosine kinase (ROS1), v-Raf murine sarcoma viral oncogene homolog B (BRAF), and programmed death-ligand 1 (PD-L1), in order to administrate either tyrosine kinase inhibitors (TKIs) or immune-checkpoint inhibitors to improve survival and quality of life of advanced stage non-small cell lung cancer (NSCLC) patients (2-14).

AimsIn the time of COVID-19, predictive molecular pathology laboratories must still timely select oncological patients for targeted treatments. However, the need to respect social distancing measures may delay results generated by laboratory-developed tests based on sequential steps a long hands-on time. Laboratory workflows should now be simplified.MethodsThe organisation of the University of Naples Federico II predictive pathology laboratory was assessed before (March–April 2019) and during (March–April 2020) the Italian lockdown.ResultsThe number of patients undergoing single or multiple biomarker testing was similar in 2019 (n=43) and in 2020 (n=45). Considering adequate samples for molecular testing, before the outbreak, next-generation sequencing was mostly used (35/42, 83.3%). Testing six genes had a reagent cost of €98/patient. Conversely, in 2020, almost all cases (38/41, 92.7%) were analysed by automated testing. This latter had for any single assay/gene a significant reagent cost (€95–€136) and a faster mean turnaround time (5.3 vs 7.9 working days).ConclusionIn the times of coronavirus, laboratory fully automated platforms simplify predictive molecular testing. Laboratory staff may be more safely and cost-effectively managed.