Virchows Archiv : an international journal of pathology · 2025
Immunohistochemical analysis of 147 cases of low-grade endometrial stromal sarcoma: refining the immunohistochemical profile of LG-ESS on a large, molecularly confirmed series.
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Low-grade endometrial stromal sarcoma (LG-ESS) can present diagnostic challenges, due to its overlapping morphological features with other uterine mesenchymal tumors. Misdiagnosis rates remain significant, and immunohistochemical data for LG-ESS are limited to small series and inconsistent antibody panels. This study aimed to refine the IHC profile of LG-ESS by analyzing a large, molecularly confirmed series of 147 cases using a panel of 24 antibodies, including newer markers like transgelin and smoothelin. CD10 and IFITM1, key endometrial stromal markers, were expressed in 86% (92% of those extensively) and 69% (60% of those extensively) of cases, with fusion-positive tumors showing significantly higher expression. Smooth muscle markers (α-SMA, desmin, h-caldesmon, calponin, transgelin) were variably expressed, predominantly in focal or low-intensity patterns, with α-SMA reaching the highest frequency of expression (44%). However, the intensity of smooth muscle marker expression was usually very low. Smoothelin was rarely expressed. Hormone receptors were frequently positive, with PR showing a higher frequency (92% vs. 83%) and intensity than ER. Markers like S-100, HMB45, and CD117 were largely negative; all tumors were p53 wild-type, with preserved SMARCB1/SMARCA4 expression and ALK and ROS1 negativity. This work represents the largest molecularly validated IHC study on LG-ESS, providing a robust diagnostic profile for routine pathology. By addressing key diagnostic limitations and examining newer markers, our study supports a more standardized approach to diagnosing LG-ESS and underscores the value of immunohistochemical panels, particularly in fusion-negative tumors where diagnosis relies on morphological and immunohistochemical interpretation. These findings contribute critical data for improving diagnostic accuracy.