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← Childhood Cancer Genomics (PDQ®)

HEALTH PROFESSIONAL · SOURCE READING

Nonmedulloblastoma Embryonal Tumors

Source: Childhood Cancer Genomics (PDQ®)–Health Professional Version, National Cancer Institute.

Source updated: April 30, 2025 · Captured 2026-09-09.

Selected source text with whitespace normalised. This Triangle page is not an NCI PDQ summary. Independent clinical review is pending.

Context: Central Nervous System Tumors

This section describes the genomic characteristics of embryonal tumors other than medulloblastoma and atypical teratoid/rhabdoid tumor. The 2016 WHO classification removed the term primitive neuroectodermal tumors (PNET) from the diagnostic lexicon.[24] This change resulted from the recognition that many tumors previously classified as CNS PNETs have the common finding of amplification of the C19MC region on chromosome 19. These entities included ependymoblastoma, embryonal tumors with abundant neuropil and true rosettes (ETANTR), and some cases of medulloepithelioma. The 2016 WHO classification categorized tumors with C19MC amplification as embryonal tumor with multilayered rosettes (ETMR), C19MC-altered. Tumors previously classified as CNS PNETs are now termed CNS embryonal tumor, not otherwise specified (NOS), with the recognition that tumors in this category will likely be classified by their defining genomic lesions in future editions of the WHO classification.

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The 2021 WHO classification categorizes nonmedulloblastoma embryonal tumors primarily by histological and immunohistological features and, in some cases, by molecular findings. The classification includes the following:[1,78]

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AT/RT.

Cribriform neuroepithelial tumor.

ETMR.

CNS neuroblastoma, FOXR2-activated.

CNS tumor with BCOR internal tandem duplication.

CNS embryonal tumor, not elsewhere classified (NEC)/NOS.

NEC is defined as a tumor not elsewhere classified. The NOS nomenclature is used for tumors that cannot be further classified. For many lesions, there are overlapping histological features, and methylation-based clustering is critical for specific diagnosis.[1,78]

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