Abstract 1417: Development of a miRNA-based prediction tool to discriminate cutaneous blastic plasmacytoid dendritic cell neoplasm from cutaneous myeloid sarcoma
Notice bibliographique
Résumé
Abstract 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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».