Abstract 1417: Development of a miRNA-based prediction tool to discriminate cutaneous blastic plasmacytoid dendritic cell neoplasm from cutaneous myeloid sarcoma

• Published in: Cancer research (Chicago, Ill.) Ill.), 2020-08, Vol.80 (16_Supplement), p.1417-1417
• Main Authors: Sapienza, Maria Rosaria, Fuligni, Fabio, Melle, Federica, Tabanelli, Valentina, Indio, Valentina, Pileri, Alessandro, Cerroni, Lorenzo, Bacci, Francesco, Motta, Giovanna, Laginestra, Maria Antonella, Mazzara, Saveria, Cascione, Luciano, Laganà, Alessandro, Agostinelli, Claudio, Ferracin, Manuela, Sabattini, Elena, Croce, Carlo, Pileri, Stefano
• Format: Article
• Language: English
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2020-1417

Background Blastic plasmacytoid dendritic cell neoplasm (BPDCN) and myeloid sarcoma (MS) are two extremely rare and aggressive hematological diseases. Both malignancies most commonly arise with skin lesions with or without extramedullary organ involvement before leukemic dissemination. Given its rarity and less known biology, in some cases with defective/ambiguous phenotype distinguish between these two entities may be challenging for pathologists and clinicians.1 Can the machine learning predictive model help to solve this diagnostic question? Here we performed the first study of microRNA (miRNA) profiling of BPDCN and MS in order to 1) Discover new molecular features selectively driving BPDCN respect to MS 2) Develop a machine learning based-prediction tool useful for discriminating BPDCN and MS. Methods We performed miRNA profiling (NanoString Technologies) of cutaneous biopsies of 16 BPDCN and 23 MS cases. Using Supervised Analysis, we identified 49 miRNAs differentially expressed that were randomly validated by qRT-PCR and next interrogated by functional enrichment analysis. Finally, a machine learning algorithm based on Linear Discriminant Analysis2 was applied to identify candidate miRNAs able to discriminate BPDCN from MS cases. Results In line with the overlapping clinical features of BPDCN and MS, the molecular profiling of these two diseases was proved to be extremely similar. Unsupervised Analysis well demonstrated that the miRNA profiles of the two malignancies are closely related and indeed, BPDCN and MS cases cluster together. When a Supervised Analysis was applied, we identified a set of 49 miRNAs differentially expressed in BPDCN respect to MS, 25 down- and 24 up-regulated. Of relevance, down-regulated miRNAs were predicted to be markedly involved in the apoptosis regulation of BPDCN. Machine learning predictive model identified a set of 12 miRNAs (5 up and 7 down) able to discriminate cutaneous BPDCN from MS. Conclusion This is the first miRNA profiling study in BPDCN and MS that showed how strongly these two diseases overlap at molecular level. Despite their similarity, BPDCN cases displayed a set of miRNAs significantly down-regulated when compared to MS, with possible dysregulation of cell death pathway. Of practical interest, we designed a machine learning predictive model based on the expression of 12 miRNAs, which alone may be applied to distinguish between the two hematological diseases. This tool, if validated in a larger set of c