An immunohistochemical germinal center B-cell dark zone signature identifies Burkitt lymphoma and molecular high-grade B-cell lymphomas
Bibliographic record
Abstract
OBJECTIVE: We hypothesized that a set of immunohistochemistry (IHC) stains could be used to distinguish Burkitt lymphoma (BL), the quintessential B-cell lymphoma with a germinal center B-cell (GCB) dark zone (DZ) expression signature, from diffuse large B-cell lymphoma, not otherwise specified (DLBCL, NOS). This might also be applicable to high-grade B-cell lymphomas (HGBCLs) with MYC and BCL2 rearrangements (double-hit lymphomas [DHLs]) and triple-hit lymphomas (THLs). METHODS: A 5-marker IHC algorithm was designed from gene lists that distinguish physiologic DZ from light zone GCBs. RESULTS: In training and validation cohorts, we distinguished BL from DLBCL, NOS with high sensitivity and specificity. Because DHLs/THLs are enriched for the gene expression DZ signature (DZsig), we evaluated 19 DHLs/THLs and 4 HGBCLs, NOS. Most (83%) cases were IHC DZ. The NanoString DLBCL90 assay was performed on 34 cases to correlate IHC DZ results with the molecular DZsig. The IHC DZ call was significantly associated with the DZsig (P = .0011). The sensitivity and specificity of IHC to recognize DZsig+ cases among DLBCL, NOS and DHLs with BCL2 rearrangements/THLs were 91% and 100%, respectively. CONCLUSIONS: The IHC DZ algorithm can support a diagnosis of BL and identifies MYC-BCL2 DHLs/THLs with a molecular DZsig.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".