Chromosomal Alterations in Gene Expression-Defined Pediatric Aggressive B-Cell Non-Hodgkin Lymphoma (B-NHL).
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 0.000 |
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".