Chromosomal Alterations in Gene Expression-Defined Pediatric Aggressive B-Cell Non-Hodgkin Lymphoma (B-NHL).
Notice bibliographique
Résumé
Abstract Abstract 2922 Poster Board II-898 Introduction: Lymphomas derived from mature lymphocytes in children and adolescents are predominantly aggressive B-cell non-Hodgkin lymphomas (B-NHL), including Burkitt lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL). Differences in the clinical course between pediatric and adult aggressive B-NHL suggest distinct pathogenetic mechanisms. This study sought to identify both shared and unique genetic alterations between gene expression-defined pediatric and adult cases of BL and DLBCL. Patients and Methods: Gene expression profiling (GEP) was done on 45 BL and 18 DLBCL specimens from patients 18 years of age or younger, and 38 BL and 106 DLBCL from adult patients. Pediatric specimens were collected from the Cooperative Human Tissue Network (CHTN) pediatric NHL repository through the Children's Oncology Group (COG) and adult specimens were collected from the Nebraska Lymphoma Study Group Registry and Tissue Bank through the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). Previously-published gene signatures were used to classify lymphomas molecularly into mBL and mDLBCL groups (Dave et al., NEJM, 2006). The mDLBCL tumors were further classified into activated B-cell-like (ABC), germinal center B-cell-like (GCB), and primary mediastinal B-cell lymphoma (PMBL) subtypes (Rosenwald et al., JEM, 2003). High resolution array comparative genomic hybridization (aCGH) was done on a subset of the pediatric cases using the 250K NspI Human Mapping Array (Affymetrix) to detect DNA copy number alterations (CNA). Results: Molecular classification of the pediatric cases resulted in a 20% reclassification rate for cases with a morphologic diagnosis of BL or DLBCL. Among the 63 pediatric cases, there were 38 mBL (3 of which were DLBCL by morphology), 23 mDLBCL (9 of which were BL by morphology) and two cases which were unclassifiable by the molecular gene signatures. Comparison of the GEP profiles for adult and pediatric mBL failed to identify pathways that differed significantly; however high resolution aCGH analysis revealed a number of abnormalities in the pediatric cases not previously reported in BL, including gains of 3q21, 11q13 and 16p11. A predominance of the GCB to ABC subtype (3:1) was found among pediatric mDLBCL patients. Two cases with mediastinal tumors were classified as PMBL molecularly. Both PMBL cases were female and carried copy number gains of the Rel/BCL11A locus. Comparison of adult and pediatric GCB mDLBCL gene expression revealed enrichment in B-cell surface molecules and markers of antigen-dependent B-cell activation in the adult cases. aCGH analysis identified abnormalities that were both shared (+12q15, +19q13, -6q) between adult and pediatric mDLBCL and unique (-4p14, -19q13.32, +16p11.2) to the pediatric cases. Correlation of DNA copy number and gene expression revealed potential candidate genes for these loci. Conclusions: Pediatric BL and DLBCL classified by morphology were reclassified molecularly in a significant fraction of cases. Although pediatric BL and DLBCL are treated similarly, defining homogeneous molecular entities will be relevant for developing new therapies and future clinical trials. In general, pediatric cases have a more favorable outcome relative to adult patients. However, it is unclear whether this is due to the ability of children to tolerate very intensive therapies or whether distinct pathogenetic mechanisms modulate the disease course. Higher B-cell receptor signaling in adult relative to pediatric GCB DLBCL may be relevant to the outcome. The identification of previously undetected chromosomal alterations unique to the pediatric cases also suggests distinct pathogenetic mechanisms. Elucidation of the underlying genes may provide insight into factors which modulate outcome and could provide novel therapeutic targets with reduced toxicity. Disclosures: Gascoyne: Roche Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees, 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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».