Gene Expression Distinguishes Burkitt Lymphoma from Other Aggressive Lymphomas and Identifies Patients Who Are Highly Curable with Intensive Chemotherapeutic Regimens.
Bibliographic record
Abstract
Abstract Background Burkitt lymphoma(BL) is a potentially curable, aggressive lymphoma. The distinction between BL and diffuse large B-cell lymphoma (DLBCL) is important because they differ significantly in clinical management. The distinction can be difficult because DLBCL can resemble BL in morphology, immunophenotype and cytogenetics. We investigated whether gene expression profiling (GEP) could create a molecular definition of BL that can reliably distinguish it from DLBCL. Methods Biopsy samples were collected from 312 patients with a diagnosis of sporadic BL or Burkitt-like lymphoma, or DLBCL. All cases were reviewed by a panel of expert hematopathologists. GEP of all the samples was carried out using a specialized oligonucleotide microarray. We constructed a predictor using 197 genes to distinguish BL from each molecular subtype of DLBCL. Leave-one-out cross-validation was used to evaluate the predictor’s performance. Chemotherapy treatments were grouped into either CHOP-like(CHOP, CNOP) or intensive(BFM, CODOX-M IVAC, regimens requiring stem cell rescue). Results After pathology review, the samples were reclassified as:classic BL(25 cases), atypical BL(19), DLBCL(261), and unclassifiable lymphoma(7). All classic BL and 18/19 cases of atypical BL shared a profile that was strikingly different from that of all the molecular subtypes of DLBCL, including those DLBCL cases that have a c-myc translocation. C-myc and its target genes, and genes related to germinal center differentiation were expressed at high levels in BL. NF-kB and its target genes and MHC class-I genes were expressed at very low levels in BL. Interestingly, 10 cases that were DLBCL by pathology were classified as BL by the predictor. The diagnosis of BL was supported by FISH analysis indicating a c-myc translocation. Among adults identified as having BL by the predictor (with full clinical data in N=15), overall survival was markedly superior for those receiving intensive regimens compared to CHOP-like regimens(Fig 1). The groups were similar with regard to age, stage, performance status and sites of involvement. Conclusion This study demonstrates that the molecular characteristics of BL can be used to accurately distinguish it from DLBCL. Importantly, a subgroup of BL was identified by the predictor that could not be diagnosed as BL by conventional criteria. The ability of the predictor to identify patients who benefit from aggressive therapies suggests that it will be useful in the diagnosis and management of patients with Burkitt lymphoma. Figure Figure
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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 | 0.001 |
| 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) | 0.001 | 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".