New transcriptional-based insights into the pathogenesis of desmoplastic small round cell tumors (DSRCTs)

基于转录的新见解,揭示促纤维增生性小圆细胞肿瘤 (DSRCT) 的发病机制

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作者:Tiziana Negri, Silvia Brich, Fabio Bozzi, Chiara V Volpi, Ambra V Gualeni, Silvia Stacchiotti, Loris De Cecco, Silvana Canevari, Annunziata Gloghini, Silvana Pilotti

Abstract

To gain new insights into desmoplastic small round cell tumors (DSRCTs) by means of gene expression profiling (GEP). Formalin-fixed, paraffin-embedded surgical specimens obtained from seven pretreated DSRCT patients were interrogated using GEP complemented by immunohistochemistry, a cancer stem cell array, and miRNA in situ hybridisation, including the combined chimera modules miRNA-200/ZEB1 and miRNA-34/SLUG. The chimera modules divided the cases into three classes that respectively recapitulated the traits of mesenchymal epithelial reverse transition (MErT), epithelial mesenchymal transition (EMT), and hybrid/partial EMT. This indicates a close correlation between the reprogramming governed by EMT regulators and DSRCT biology, which was further confirmed by miRNA-21 and is consistent with the broad morphological spectrum of DSRCTs. Starting from the miRNA-200/ZEB1 axis, we also found that DSRCTs carry a signature of immunological ignorance that is not responsive to PD--L1 blockade. Evidence that the up-regulation of miRNA-200 and E-cadherin, and quite a high level of miRNA-21 expression segregate with the MErT supports the idea that, in addition to the hybrid/partial state, MErT is also enriched in stemness: the androgen-positive cases, whose stemness traits were confirmed by stem cell arrays, all fell into these two classes. Our findings also confirmed that tumoral cell PDGFRA expression correlates with desmoplasia, and demonstrated the co-expression of PDGFRA and ISLR/Meflin, another marker of pluripotency. Despite the limited number of cases, these findings provide unexpectedly relevant information concerning the pathogenesis of DSRCTs, and prove the validity of miRNA-based chimera circuit modelling in the clinico-pathological setting.

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