Abstract 1805: Integrative analysis of microRNAs in blastic plasmacytoid dendritic cell neoplasm
Bibliographic record
Abstract
Abstract Background: Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is an extremely rare and aggressive hematological disease, deriving from the malignant transformation of plasmacytoid, alpha-interferon producing dendritic cells (pDCs). Recent studies shed new light on BPDCN genomic and trascriptomic alterations, but the microRNA (miRNA) profile is still largely unknown. We analyzed the miRNA expression profile of BPDCN patients to enhance our understanding of the molecular mechanisms driving this disease and to identify new potential diagnostic and therapeutic biomarkers. Methods: We performed the miRNA profiling (NanoString Technologies) of 26 BPDCN patients and 4 normal pDCs samples as controls. To better investigate the functional consequences of miRNAs dysregulation, 9 BPDCN patients, were also analyzed at the transcriptomic level. We applied a Moderated t-test to find genes and miRNAs differentially expressed between BPDCNs and pDCs. The most deregulated miRNAs and mRNAs were validated by qRT-PCR and immunohistochemistry and analyzed by miRNET, a bioinformatic tool for statistical analysis and functional interpretation of miRNA and mRNA data. Results: We found that, according to the supervised clustering analysis of miRNAs, tumor samples display a molecular signature well distinct from their normal counterpart. Indeed, BPDCN patients expressed a set of 175 miRNAs significantly deregulated and potentially involved in essential biological processes and therefore in malignant transformation. Thus, to evaluate the impact of these miRNAs on the BPDCN transcriptome, miRNAs and mRNAs expression profiles were analyzed by miRNET tool. Thanks to this integrative approach, we uncovered the most relevant miRNA-mRNA networks in BPDCN setting and in particular we identified 9 up-regulated hub miRNAs, targeting multiple genes and synergistically inter-connected: hsa-mir-93-5p, hsa-mir-106b-5p, hsa-mir-19b-3p, hsa-mir-19a-3p, hsa-mir-21-5p, hsa-mir-181a-5p, hsa-mir-25-3p, hsa-mir-155-5p, hsa-mir-17-3p (in order of relevance). These 9 hub miRNAs regulate the expression of genes already described as relevant in BPDCN patients (ex. TCF4, RHOA, EP300) and, according to functional enrichment analysis, that could aberrantly interfere with TLR signaling, protein translation and DNA transcription regulation. Of interest, most of these hub miRNAs are classified as oncomir (OncoMir Cancer Database) and, if validated in an extended number of cases, promising targets for anti-miRNA based therapy. In conclusion, we identified a panel of miRNAs that regulate relevant cancer-related pathways and can be also used as new potential biomarkers and therapeutic targets in BPDCN. Citation Format: Maria Rosaria Sapienza, Manuela Ferracin, Fabio Fuligni, Federica Melle, Giovanna Motta, Maria Antonella Laginestra, Maura Rossi, Luciano Cascione, Alessandro Laganà, Claudio Agostinelli, Elena Sabattini, Alessandro Pileri, Carlo Maria Croce, Stefano Aldo Pileri. Integrative analysis of microRNAs in blastic plasmacytoid dendritic cell neoplasm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1805.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".