Thyroid nodules are being identified with increasing frequency in clinical practice, representing a malignant neoplasm only in 5-15% of cases. Fine-needle aspiration biopsy (FNAB) is the most reliable preoperative test to diagnose thyroid cancer. However, non-diagnostic or indeterminate cytological results occur in up to 30% of thyroid FNA samples. A high proportion of these patients undergoes a diagnostic rather than a curative surgery, but only a minority of indeterminate thyroid nodules are diagnosed as malignant at final histology. In this clinical setting, molecular markers may be useful as diagnostic tools, complementing and integrating the information provided by cytology and improving preoperative risk stratification for indeterminate nodules.
In the last few years, a comprehensive investigation of the genomic landscape of thyroid cancer was performed, thus substantially decreasing the dark matter of thyroid cancer genome. This knowledge as well as advances in high-throughput technologies have contributed to molecular test improvement for cancer diagnosis in thyroid nodules. The current availability of targeted Next-Generation Sequencing offers a convenient and cost-effective technique for mutation detection also in those challenging samples like FNA specimens. Additionally, digital PCR provides a unique means for absolute quantification of thyroid cancer-related biomarkers (like miRNAs) in FNA samples, providing a further sensitive approach for cancer diagnosis.
The overall objective of this proposal is to improve the diagnostic and prognostic accuracy of FNA cytology by a combined molecular approach which includes mutation detection and assessment of miRNA expression levels in pre-surgical thyroid nodule specimens. Diagnostic accuracy of molecular methods will be compared with ultrasonography and cytology in order to identify a diagnostic algorithm for thyroid nodules. Potential utility of the diagnostic algorithm in clinical practice will be evaluated.
Fine needle aspiration cytology (FNAC) is considered the gold standard in the investigation of thyroid nodules. However it is unable to rule out cancer in 20-30% of all cases. In this clinical settings, molecular profiling has been suggested to improve diagnostic accuracy.
Molecular profiling of thyroid nodules (i.e., driver mutations and miRNA expression levels) and correlation with the outcome of the patients may improve diagnostic accuracy of FNAC and to facilitate optimal management of thyroid cancer patients, and their stratification into clinically meaningful categories. Indeed, identification of predictive factors will guide the extent of initial thyroid surgery, select treatment options, and determine eligibility for clinical trials.
As demonstrated in thyroid TCGA study, multi-level approaches involving mutational analysis and miR/mRNA expression profiling may help to define clinically-relevant subclasses of thyroid tumors. Combined methods will offer multiple markers for cancer diagnosis in FNA specimens, increasing positive predictive value (i.e., in RAS-mutated cases) or preoperatively identifying subset of aggressive cancers.
Indeed, the presence of multiple driver mutations in thyroid cancer may indicate a more aggressive tumor behavior. Co-occurence of BRAF or RAS mutation with TP53, PIK3CA, or AKT1 has been observed in poorly differentiated, anaplastic thyroid cancers, as well as in aggressive PTCs. In this context, targeted NGS-based approaches are particularly useful to detect multiple alterations at the same time in small samples, increasing sensitivity and negative predictive value of the test.
On the other hand, digital PCR provides a unique means for absolute quantification of thyroid cancer-related miRNAs in FNA samples, providing a further potential approach for cancer diagnosis (i.e., in those cases without a known thyroid cancer driver).
Finally, diagnostic accuracy of molecular methods will be compared with ultrasonography and cytology in order to identify a diagnostic algorithm for thyroid nodules.