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

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.

Dangerous liaisons: interplay between SWI/SNF, NuRD, and Polycomb in chromatin regulation and cancer

“…An embryonic gene expression signature distinguishes RT from other cancers that are SMARCB1-deficient (Richer et al 2017). Consistent with SWI/SNF's role as a member of the TrxG, dysregulation of Hox genes is frequently observed in RTs (Chun et al 2016;Torchia et al 2016;Richer et al 2017;Pinto et al 2018). Although more research is required to determine the significance of specific transcriptional perturbations in RT, the retention of an embryonic signature is likely a key aspect in the development of these tumors.…”

“…While several studies have addressed the alterations in gene expression in RT; interpretations have been complicated by the substantial heterogeneity among tumors (Chun et al 2016;Torchia et al 2016;Richer et al 2017;Pinto et al 2018). An embryonic gene expression signature distinguishes RT from other cancers that are SMARCB1-deficient (Richer et al 2017).…”

“…While several studies have addressed the alterations in gene expression in RT; interpretations have been complicated by the substantial heterogeneity among tumors (Chun et al 2016;Torchia et al 2016;Richer et al 2017;Pinto et al 2018). An embryonic gene expression signature distinguishes RT from other cancers that are SMARCB1-deficient (Richer et al 2017). Consistent with SWI/SNF's role as a member of the TrxG, dysregulation of Hox genes is frequently observed in RTs (Chun et al 2016;Torchia et al 2016;Richer et al 2017;Pinto et al 2018).…”

Renal medullary carcinomas depend uponSMARCB1loss and are sensitive to proteasome inhibition

“…To determine if the RMC cell lines clustered separately among other SMARCB1-deficient cancers, we performed gene expression analysis of our RMC models and compared them to publicly available datasets of MRT cell lines, patients with RMC, MRT or ATRT or synovial sarcoma (a cancer driven by the fusion oncoprotein SSX-S18 which displaces SMARCB1; Methods ) (Barretina et al, 2012; Calderaro et al, 2016; Han et al, 2016; Johann et al, 2016; Richer et al, 2017). Using tSNE, we found that the RMC cell lines closely mapped to a French cohort of RMC, MRT and ATRT patients ( Fig.…”

“…We performed Affymetrix Human Genome U133 Plus 2.0 on our RMC cell lines. We then combined the following GEO datasets using a GenePattern module with robust multi-array (RMA) normalization GSE64019, GSE70421, GSE70678, GSE36133, GSE94321 (Barretina et al, 2012; Calderaro et al, 2016; Johann et al, 2016; Richer et al, 2017; Wang et al, 2016). We utilized COMBAT and then tSNE to account for batch effects and to identify clusters of similarity (Chen et al, 2011; Johnson et al, 2007; Maaten, 2014).…”