Abstract
When classifying cellular uterine mesenchymal neoplasms, histological distinction of endometrial stromal from smooth muscle neoplasms can be difficult. The only widely established marker of endometrial stromal differentiation, CD10, has marginal specificity. We took a bioinformatics approach to identify more specific markers of endometrial stromal differentiation by searching the Human Protein Atlas, a public database of protein expression profiles. After screening the database using different methods, interferon-induced transmembrane protein 1 (IFITM1) was selected for further analysis. Immunohistochemistry for IFITM1 was performed using tissue sections from the selected cases of proliferative endometrium (22), secretory endometrium (6), inactive endometrium (19), adenomyosis (10), conventional leiomyoma (11), cellular leiomyoma (16), endometrial stromal nodule (2), low-grade endometrial stromal sarcoma (16), high-grade endometrial stromal sarcoma (2) and undifferentiated uterine sarcoma (2). Stained slides were scored in terms of intensity and distribution. Normal endometrial samples uniformly showed diffuse and strong IFITM1 staining. Endometrial stromal neoplasms, particularly low-grade endometrial stromal sarcoma, showed higher IFITM1 expression compared with smooth muscle neoplasms (P<0.0001). IFITM1 immunohistochemistry has high sensitivity and specificity, particularly in the distinction between low-grade endometrial stromal sarcoma and leiomyoma (81.2 and 86.7%, respectively). Our results indicate that IFITM1 is a sensitive and specific marker of endometrial stromal differentiation across the spectrum from proliferative endometrium to metastatic stromal sarcoma. IFITM1 is a potential valuable addition to immunohistochemical panels used in the diagnosis of cellular mesenchymal uterine tumors. Further studies with larger number of cases are necessary to corroborate this impression and determine the utility of IFITM1 in routine practice. This study is a clear example of how bioinformatics, particularly tools for mining genomic and proteomic databases, can enhance and accelerate biomarker development in diagnostic pathology.
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