Triple marker composed of p16, CD56, and TTF1 shows higher sensitivity than INSM1 for diagnosis of pulmonary small cell carcinoma: proposal for a rational immunohistochemical algorithm for diagnosis of small cell carcinoma in small biopsy and cytology specimens

摘要

Pulmonary small cell carcinoma (SCLC) can be usually diagnosed based on the morphological evaluation of routine histological or cytological preparations. However, immunohistochemistry may be also necessary in problematic cases. Insulinoma-associated 1 (INSM1) has recently been reported as a highly sensitive and specific marker that displays positivity in similar to 90%-400% of poorly differentiated pulmonary neuroendocrine tumors. We compared diagnostic performance of INSM1 and previously reported composite marker CD56 + p16 + thyroid transcription factor-1 (TTF1) in the diagnosis of SCLC in small biopsy specimens and cytoblocks. The composite marker CD56 + p16 + TTF1 correctly classified 100% of SCLC cases, and its sensitivity was significantly higher than the sensitivity of INSM1. Among 100 SCLC cases, CD56, TTF1, and p16 each individually classified more specimens correctly than INSM1 (CD56: 84%, TTF1: 89%, p16: 95%, INSM1: 81%); the difference was statistically significant only for p16. INSM1 showed the lowest classification agreement between paired biopsy and cytoblock specimens (k = 0.182), whereas CD56 and p16 displayed perfect agreement (k = 1) and TTF1 showed moderate agreement (k= 0.4). Although INSM1 is reportedly the most specific marker of SCLC, its sensitivity is not superior to p16 or composite marker CD56 + TTF1 + p16. Based on this study, we propose the following algorithm, which, in the appropriate clinical and histological context, may be useful in establishing the correct diagnosis of SCLC: First, INSM1 detection is performed, and if the result is negative, CD56 is added, followed successively by p16 and TTF1 if all previously applied markers are negative. This approach should detect most, if not all, SCLC cases, while successively trading specificity for sensitivity. (C) 2018 Elsevier Inc. All rights reserved.