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Programmed Death - Ligand 1 Expression Distinguishes Invasive Encapsulated Follicular Variant of Papillary Thyroid Carcinoma from Noninvasive Follicular Thyroid Neoplasm with Papillary-like Nuclear Features

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Background The noninvasive Encapsulated follicular variant of papillary thyroid cancer (EFVPTC) has been reclassified as Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) without a significant risk for malignant… Click to show full abstract

Background The noninvasive Encapsulated follicular variant of papillary thyroid cancer (EFVPTC) has been reclassified as Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) without a significant risk for malignant behavior. However the evaluation remains a challenge for clinicians. We sought to determine whether programmed death-ligand 1 (PD-L1) expression may serve as a biomarker to predict invasiveness of EFVPTC and assist to distinguish these neoplasms from NIFTP. Methods Immunohistochemical staining of PD-L1 expression was performed in sections of 174 Formalin-fixed paraffin-embedded (FFPE) tissue blocks from surgery removed thyroid nodules. Results Cytoplasmic PD-L1 expression was significantly increased in invasive EFVPTC (4.76 ± 1.49) as compared to NIFTP (3.06 ± 2.16, p < 0.001). Increased cytoplasmic PD-L1 expression was associated with invasiveness in EFVPTC (p < 0.001); PD-L1 positive EFVPTC cases were at 3.16 folds higher risk in developing invasion than the PD-L1 negative cases. No significant difference in cytoplasmic PD-L1 expression was observed between NIFTP and benign nodules. Conclusion PD-L1 expression may serve as a useful biomarker in predicting invasiveness of EFVPTC and distinguishing NIFTP from invasive EFVPTC. To our knowledge this is the first report suggesting the application of a protein biomarker to confirm NIFTP as benign indolent neoplasms.

Keywords: efvptc; encapsulated follicular; follicular variant; expression; variant papillary; thyroid

Journal Title: EBioMedicine
Year Published: 2017

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