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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 cases, may help to differentiate BPDCN and MS in cases with defective/ambiguous phenotype. References 1. Weltgesundheitsorganisation. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. Revised 4th edition. (Swerdlow SH, Campo E, Harris NL, et al., eds.). Lyon: International Agency for Research on Cancer; 2017. 2. Laginestra MA, Piccaluga PP, Fuligni F, et al. Pathogenetic and diagnostic significance of microRNA deregulation in peripheral T-cell lymphoma not otherwise specified. Blood Cancer J. 2014;4:259. doi:10.1038/bcj.2014. Citation Format: Maria Rosaria Sapienza, Fabio Fuligni, Federica Melle, Valentina Tabanelli, Valentina Indio, Alessandro Pileri, Lorenzo Cerroni, Francesco Bacci, Giovanna Motta, Maria Antonella Laginestra, Saveria Mazzara, Luciano Cascione, Alessandro Laganà, Claudio Agostinelli, Manuela Ferracin, Elena Sabattini, Carlo Croce, Stefano Pileri. Development of a miRNA-based prediction tool to discriminate cutaneous blastic plasmacytoid dendritic cell neoplasm from cutaneous myeloid sarcoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1417.

Introduction and aim: Childhood cytogenetically normal acute myeloid leukemia (CN-AML) is a subgroup of pediatric AML lacking any known cytogenetic and abnormality. CN-AML accounts for 20% of pediatric AML and exhibits a very heterogeneous response to treatment and, consequently, variable outcome. With the aim of providing new insights into the molecular lesions of this subset of AML, we analyzed, through RNA-seq, 13 cases of pediatric CN-AML, corroborating our findings in an independent cohort of 152 AML patients enrolled in the AIEOP AML 2002/01 clinical trial. Methods: Paired-end RNA-seq (75×2) was performed on PoliA(+) RNA extracted from blasts at diagnosis. ChimeraScan, deFuse and FusionMap algorithms were used for chimeric transcript detection. Results: An alteration that was present in 2 out 13 cases was a chimeric transcript involving the genes nucleoporin 98kDa (NUP98) and PHD finger protein 23 (PHF23), resulting from a cryptic translocation t(11;17)(p15;p13). Both patients showed an in-frame fusion between exon 13 of NUP98 and exon 4 of PHF23 that was confirmed by RT-PCR analysis and Sanger sequencing. To date, the cryptic translocation t(11;17)(p15;p13) was never described before in pediatric AML and has been reported only once in an adult AML patient (Reader, et al. Leukemia 2007). In both our patients the same breakpoint in PHF23 gene at the beginning of exon 4 was present, which was different from that present in the adult patient. To assess the incidence of NUP98-PHF23 fusion in pediatric CN-AML, then we studied by RT-PCR and Sanger sequencing a validation cohort of 152 CN-AML children negative for the most common recurrent cytogenetic and molecular lesions, i.e. those involving MLL, CBFB, NPM1 and FLT3. Overall, 2 out of the 152 CN-AML cases were found positive for NUP98-PHF23, demonstrating that this genomic aberrancy is not rare in pediatric CN-AML (tentative frequency 2.4%). Fluorescence in situ hybridization analysis confirmed the cryptic chromosomal translocation t(7;11)(p15;p13) leading to the fusion between NUP98 and PHF23 in all cases. Conclusions: Lately, genome-wide approaches have been widely used to investigate the mutational landscape of CN-AML in adults. However, the genetic profile of childhood CN-AML still needs to be defined. Here, for the first time, we report the identification of a NUP98-PHF23 chimeric transcript in pediatric CN-AML. Together with recently published data demonstrating that NUP98-PHF23 promoted leukemogenesis and the observation that treatment with inhibitors of PHD-domain-mediated H3K4me3 binding, such as disulfiram (an FDA-approved drug), could selectively killed cells expressing NUP98-PHF23 (Gough, et al Cancer Discovery 2014), our findings enforce the role of epigenetic regulators in pediatric AML and suggest screening for this fusion gene at diagnosis in CN-AML pediatric patients. Citation Format: Marco Togni, Riccardo Masetti, Martina Pigazzi, Annalisa Astolfi, Daniele Zama, Valentina Indio, Salvatore Serravalle, Elena Manara, Valeria Bisio, Sergio Rutella, Franca Fagioli, Giuseppe Basso, Andrea Pession, Franco Locatelli. NUP98-PHF23 is a novel fusion gene in pediatric cytogenetically normal acute myeloid leukemia. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-211. doi:10.1158/1538-7445.AM2015-LB-211

Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by a 5-year survival rate of 4%. Even if some genetic hits leading to PDAC development are known, a deeper knowledge of the genetic landscape of tumor is necessary to identify druggable targets. Methods: PDAC samples from 14 localized and advanced cases of pancreatic adenocarcinomas were collected by ultrasound-guided biopsy used for DNA and RNA extraction. High resolution copy number analysis was performed on Affymetrix SNP array 6.0 and analyzed with segmentation algorithm against a reference of 270 Ceu HapMap individuals (Partek Genomic Suite). Whole transcriptome massively parallel sequencing was performed at 75x2 bp on a HiScanSQ Illumina platform. An average of 7,3x107 reads per sample were generated, with a mean read depth of 50X. Single nucleotide variants (SNVs) were detected with SNVMix2 and compared with genetic variation databases (dbSNP, 1000genomes, Cosmic). Non-synonimous SNVs were analyzed with SNPs&GO and SIFT to predict disease association. Results: Whole transcriptome sequencing showed that PDAC samples exhibited a mean of 145 (range: 61-240) non-synonimous SNVs, of which 16 on average are potentially disease-related. 9/14 patients exhibited both macroscopic and cryptic cytogenetic alterations, with a mean of 10 copy number alterations per patient, while 5 patients did not show any copy number gain or loss. Most frequent gains were observed in 18q11.2 involving GATA6 (3/14) and 19q13 targeting AKT2 (3/14) while hotspot deleted regions were found on 18q21 (7/14), 17p13 (6/14), 9p21.3 (6/14), 15q (5/14) and 1q35 (4/14). Merging copy number and RNAseq data we highlighted the major oncogenic hits of PDAC, confirming the prevalence (9/14) of KRAS mutations, in one case also NRAS (G13D), and the central role of the three oncosuppressor CDKN2A (mutated in 3 cases and deleted in 6 cases, either in hetero- or homozygosity), SMAD4 (altered by either point mutation or gene deletion in 8/14 cases), and TP53 (lost in 7/14 samples). We identified novel pathogenic single nucleotide variants and frameshift mutations, and inter- and intra-chromosomal translocation events that give rise to chimeric fusion transcripts. Even if the mutation profile was heterogeneous in different patients the signaling pathways affected were shared, and included KRAS/MAPK, TGFbeta and integrin signaling, proliferation and apoptosis, DNA damage response, and epithelial to mesenchymal transition. New oncogenic alterations emerged, as ARID1A that was inactivated by somatic mutation (G91R and P1425fs) or copy number loss in 6 patients, NOTCH2 and HMGCR, that displayed pathogenic mutations in 15-20% of the analyzed patients. Conclusions: Next generation sequencing combined with high resolution cytogenetic analysis can improve the understanding of carcinogenesis and highlight new biomarkers for early diagnosis and potential therapeutic targets in PDAC. Citation Format: Marina Macchini, Annalisa Astolfi, Riccardo Casadei, Valentina Indio, Carla Serra, Silvia Vecchiarelli, Elisa Grassi, Claudio Ricci, Marielda D'Ambra, Giovanni Taffurelli, Donatella Santini, Raffaele Pezzilli, Francesco Minni, Guido Biasco, Mariacristina Di Marco. Whole genome discovery of genetic alterations carried by pancreatic adenocarcinoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 812. doi:10.1158/1538-7445.AM2013-812