Embryonic signature distinguishes pediatric and adult rhabdoid tumors from other SMARCB1-deficient cancers

胚胎特征可将儿童和成人横纹肌样瘤与其他 SMARCB1 缺陷型癌症区分开来

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作者:Wilfrid Richer, Julien Masliah-Planchon, Nathalie Clement, Irene Jimenez, Laetitia Maillot, David Gentien, Benoît Albaud, Walid Chemlali, Christine Galant, Frederique Larousserie, Pascaline Boudou-Rouquette, Amaury Leruste, Celine Chauvin, Zhi Yan Han, Jean-Michel Coindre, Pascale Varlet, Paul Frene

Abstract

Extra-cranial rhabdoid tumors (RT) are highly aggressive malignancies of infancy, characterized by undifferentiated histological features and loss of SMARCB1 expression. The diagnosis is all the more challenging that other poorly differentiated cancers lose SMARCB1 expression, such as epithelioid sarcomas (ES), renal medullary carcinomas (RMC) or undifferentiated chordomas (UC). Moreover, late cases occurring in adults are now increasingly reported, raising the question of differential diagnoses and emphasizing nosological issues. To address this issue, we have analyzed the expression profiles of a training set of 32 SMARCB1-deficient tumors (SDT), with ascertained diagnosis of RT (n = 16, all < 5 years of age), ES (n = 8, all > 10 years of age), UC (n = 3) and RMC (n = 5). As compared with other SDT, RT are characterized by an embryonic signature, and up-regulation of key-actors of de novo DNA methylation processes. Using this signature, we then analysed the expression profiling of 37 SDT to infer the appropriate diagnosis. Thirteen adult onset tumors showed strong similarity with pediatric RT, in spite of older age; by exome sequencing, these tumors also showed genomic features indistinguishable from pediatric RT. In contrary, 8 tumors were reclassified within carcinoma, ES or UC categories, while the remaining could not be related to any of those entities. Our results demonstrate that embryonic signature is shared by all RT, whatever the age at diagnosis; they also illustrate that many adult-onset SDT of ambiguous histological diagnosis are clearly different from RT. Finally, our study paves the way for the routine use of expression-based signatures to give accurate diagnosis of SDT.

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