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Novel method for in vitro diagnosis of a thyroid tumour and related kit

BiomarcatoriBiomarkersIndeterminate CytologyMutationsThyroid cancerThyroid Nodules


The present invention relates to a new method and relative kit for the diagnosis of a thyroid tumor able to overcome the intrinsic diagnostic limits of cytology and personalize treatment of thyroid cancer patients.

Technical features

The test is based on a combined molecular approach that involves next-generation sequencing (NGS)-based detection of thyroid cancer drivers (point mutations and indels in 19 genes and 204 gene fusions involving 7 known thyroid cancer driver genes) and digital polymerase chain reaction (PCR) evaluation of the expression levels of an microRNA strongly associated with thyroid cancer. Samples with single-assay positivity (mutation detection or miRNA expression above the calculated cut-off value) or combined-assay positivity (mutation detection and miRNA expression) in the molecular assay were considered positive for thyroid malignancy; those with negative findings in both assays were classified as negative for thyroid malignancy. Thyroid nodules are being identified with increasing frequency in clinical practice, but very few of the lesions are malignant (7–15%). Analytical validations were performed on various cytological samples and clinical validations. Ready for placement on the reference market – TRL4.

Possible Applications

  • The preoperative molecular diagnosis of cytologically indeterminate thyroid nodules with the aim of identifying benign nodules and limiting the number of unnecessary surgeries;
  • Early identification of predictive factors, useful in planning the surgical strategy;
  • Simultaneous testing of multiple tumor markers.


  • Fast, reliable and less expensive molecular diagnosis of samples;
  • Relatively low number of molecular markers tested;
  • Execution of the test also on the same material used for the cytological diagnosis, avoiding to subject the patient to a second dedicated sample;
  • High performance on sensitivity and negative predictive value.