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Pitfalls in Pediatric Supratentorial ZFTA Fusion-Positive Ependymoma: A Clinicopathological Study of 17 Cases
Lian Yang1,2,3, Lusheng Li4, Bin Zou4
1Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing, China.
Aims:
Supratentorial ependymoma with ZFTA fusion represents a molecularly defined entity with characteristic but heterogeneous histopathological features. In routine diagnostic practice, morphologic diversity and limitations of individual molecular assays may lead to diagnostic uncertainty. This study aimed to systematically characterize the clinicopathological spectrum of pediatric supratentorial ZFTA fusion-positive ependymomas and to highlight potential diagnostic pitfalls.
Methods:
Seventeen pediatric cases with confirmed ZFTA fusion were retrospectively analyzed. Clinical, radiological, histopathological, immunohistochemical (including L1CAM, p65/RELA, etc.), and molecular findings (FISH, NGS, DNA methylation profiling), along with follow-up data, were evaluated.
Results:
The median patient age was 7 years (range, 1-14 years). Non-classical morphologic patterns (embryonal-like, sieve-like, ependymoblastic-like) occurred in 8/17 cases (47.1%), mostly in WHO grade 3 tumors. Crucially, all 8 cases retained focal conventional ependymal features upon thorough sampling and maintained diffuse L1CAM positivity, nuclear p65/RELA, and dot-like EMA positivity in deceptive areas. Molecularly, FISH yielded a 28.6% (4/14) false-negative rate in high-grade cases, and NGS showed 1 false-negative result (7.1%), rescued by DNA methylation profiling. Overall median follow-up was 48 months (range, 6-68). WHO grade 2 tumors had zero recurrence (median follow-up, 60 months), whereas WHO grade 3 tumors showed median time to recurrence of 6 months (range, 3-12).
Conclusions:
Pediatric ZFTA fusion-positive ependymomas exhibit morphologic divergence and false-negative molecular risks. Extensive tissue sampling, integrated IHC surrogate panels (p65/L1CAM/GFAP/EMA), and complementary molecular platforms are essential for accurate diagnosis.
