Clinicopathological correlations and immunohistochemical expression of ATRX and Rb in soft tissue leiomyosarcoma:...

Soft tissue leiomyosarcoma (STLMS) remains a challenging malignancy with variable clinical outcomes, highlighting the need for reliable prognostic biomarkers. Recent genomic studies have proposed risk stratification models based on RB1 and ATRX alterations; however, the utility of corresponding immunohistochemical (IHC) surrogates has not been fully explored. In this study, we analyzed clinicopathological features and ATRX/Rb IHC expression in a cohort of 70 STLMS cases to identify correlates of aggressive behavior and assess their prognostic value. Metastasis was significantly associated with female sex (p = 0.004), age ≤ 60 years (p = 0.022), internal cavity location (p = 0.014), necrosis (p = 0.032), elevated mitotic rate (p = 0.01), high FNCLCC grade (p < 0.001), larger tumor size (p = 0.03), and deep/internal origin. Loss of ATRX staining (44% of cases) correlated significantly with larger tumor size (p = 0.003), deep location (p = 0.015), and internal cavity site (p = 0.007), linking ATRX deficiency to more extensive local disease. Rb loss was frequent (74%) but lacked strong associations with most clinicopathological variables. Although ATRX, Rb, and combined IHC patterns did not significantly stratify overall survival (OS), disease-free survival (DFS), or disease-specific survival (DSS), FNCLCC grade robustly differentiated outcomes, particularly for DFS (p = 0.000741) and DSS (p = 0.0206), with a clear trend in OS (p = 0.0711). Low-grade tumors exhibited no metastases and favorable survival. These findings validate FNCLCC grading as a cornerstone prognostic tool and demonstrate that ATRX IHC identifies clinically relevant subsets with aggressive local features. The observed discordance between IHC and genomic classifiers underscores the complexity of translating molecular alterations to protein-level surrogates, likely due to regulatory mechanisms beyond mutation. This work provides practical insights for risk assessment in STLMS and supports the integration of traditional grading with targeted IHC and molecular profiling for refined patient management.