Molecular Characterization Using Oncoscan Chromosome Microarray in an International Cohort of 51 Patients with Blastic Plasmacytoid Dendritic Cell Neoplasm (BPDCN)
Notice bibliographique
Résumé
Abstract Introduction Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare and aggressive hematological malignancy with multi-organ and frequent skin involvement, and poor clinical outcomes. Based on the limited available data, the estimated incidence is 0.44% of all hematologic malignancies, representing less than 1% of acute leukemias, and 0.7% of cutaneous lymphomas. Due to the rarity of this entity, there have been relatively few studies characterizing the molecular profile of BPDCN. We examined a cohort of 51 patients with BPDCN using OncoScan chromosome microarray, which provides genome-wide copy number abnormality (CNA) analysis. Methods An international cohort of BPDCN cases were collected from centers in Brazil (Laboratorio de Patologia, Botucatu), Swtizerland (University of Zurich), France (Hospital St. Louis, Paris), Peru (Instituto Nacional de Enfermedades Neoplasicas, Lima), Canada (Department of Pathology, University of Montreal), Italy (Derpartment of Pathology, University of Bologna), and US (Department of Pathology - The Ohio State University, Department of Hematopathology - MD Anderson Cancer Center; and Department of Pathology - University of Virginia). A total of 58 tissue blocks from 51 patient samples were retrieved. The diagnosis of BPDCN was done and confirmed by at least three independent hematopathologists or dermatopathologists in accordance with the WHO classification (Lyon 2017). For the purpose of the molecular analysis substratification, cases were classified as 'BPDCN' if they were positive for TCF4, and 'BPDCN-like' if they were negative for TCF4. Immunohistochemistry for CD123, CD4, and CD56 was performed in all cases. Exclusion criteria included expression of MPO, lysozyme, CD3, CD19, CD20, CD22, and/or EBV. DNA was extracted from FFPE samples via standard techniques and processed on OncoScan CNV Plus microarray (ThermoFisher Scientific) according to manufacturer's recommended protocol. Copy number abnormalities and select single nucleotide variants and insertions/deletions (74 mutations in 9 genes) were analyzed on Chromosome Analysis Suite software (ChAS v4.1; ThermoFisher Scientific). Additional analysis was performed using Nexus Copy Number (BioDiscovery, version 10.0). Results To date, we have successfully analyzed 45 cases of BPDCN with Oncoscan, revealing widespread CNA in the vast majority of cases (44/45; 98%). Alterations of chromosome 9 were common in this cohort, particularly CNAs involving CDKN2A/B at 9p21.3. Twenty-five cases (56%) demonstrated CNA including CDKN2A/B, with ten of these cases demonstrating a homozygous loss of CDKN2A/B (22%). Alterations of chromosome 13 were also frequently detected with loss of RB1 (located at 13q14.2) detected in 24 cases (53%). The RUNX1 gene (21q22.12) was a common target of CNAs in this cohort, seen in nine cases (20%). Eight of these cases showed a copy number gain of RUNX1, which is a recurrent finding in a variety of hematological malignancies, particularly myeloid neoplasms. The remaining case with RUNX1 CNA showed a focal, homozygous loss of the gene, demonstrating that dysregulation of RUNX1 through CNA is a common event in BPDCN. We observed frequent deletions of ETV6 (53%), IKZF1 (33%), and TP53(16%) in our cohort. The ARHGAP26 gene (5q31.3), which is associated primarily with juvenile myelomonocytic leukemia, was included in CNA in 13 cases (29%), with both gains and losses observed in this cohort. Oncoscan can detect a limited number of single nucleotide variants in nine genes that are frequently mutated in cancers (BRAF, EGFR, IDH1, IDH2, KRAS, NRAS, PIK3CA, PTEN, and TP53). Mutations were detected in ten cases (22%), with NRAS and TP53 variants detected in three cases each and KRAS and IDH2 variants detected in two cases each. Conclusions Our preliminary data demonstrates complex genomic alterations in BPDCN, with the RB1 locus on chromosome 13, the CDKN2A/B locus on chromosome 9, and the ETV6 locus on chromosome 12 most commonly detected. However, widespread genomic alterations were detected involving a variety of cancer-associated genes further characterizing CNA in BPDCN. Analysis of additional BPDCN cases is progress. Disclosures Khoury: Kiromic: Research Funding; Angle: Research Funding; Stemline Therapeutics: Research Funding. Porcu: Viracta: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Innate Pharma: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BeiGene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Research Funding; Daiichi: Honoraria, Research Funding; Kiowa: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Spectrum: Consultancy; DrenBio: Consultancy. Gru: StemLine: Honoraria, Research Funding, Speakers Bureau; CRISPT Therapeutics: Research Funding; Innate Pharma: Research Funding.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,001 | 0,000 |
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 ».