B-cell Receptor Silencing Reveals the Origin and Dependencies of High-Grade B-cell Lymphomas with <i>MYC</i> and <i>BCL2</i> Rearrangements
Bibliographic record
Abstract
The B-cell receptor (BCR) is critical for mature B-cell lymphomas (BCL), serving as a therapeutic target. We show that high-grade BCLs with MYC and BCL2 rearrangements [HGBCL-double-hit (DH)-BCL2] predominantly exhibit immunoglobulin heavy (IGH) chain silencing, leading to BCR shutdown. IGH-silenced HGBCL-DH-BCL2 (IGHUND) tumors differ from IGH+ counterparts in germinal center (GC) zone programs, MYC expression, and immune infiltrate. Whereas IGH+ HGBCL-DH-BCL2 tumors favor IGM/IG-κ expression, IGHUND counterparts complete IGH isotype switching and IG-λ rearrangements. IGHUND lymphomas retain productive IGHV rearrangements and require IGH for optimal fitness. BCR silencing, caused by accelerated IGH turnover and reduced IGH expression, precedes HGBCL-DH-BCL2 onset, inducing RAG1/2-dependent IG light chain editing and facilitating t(8;22)/IGL::MYC translocations. IGHUND HGBCL-DH-BCL2 models exhibit reduced sensitivity to the CD79B-targeting antibody-drug conjugate polatuzumab vedotin. Collectively, HGBCL-DH-BCL2 commonly arises from isotype-switched t(14;18)+ GC B cells, which edit IG light chains, fueling intraclonal diversification, BCR extinction, and t(8;22) while maintaining IGH dependence, with clinical implications. SIGNIFICANCE: These findings link BCR silencing in IGH isotype-switched t(14;18)+ GC B cells to RAG1/2 expression, which triggers IG light chain editing and predisposes to IGL::MYC translocations, promoting HGBCL. In HGBCL with MYC and BCL2 rearrangements, BCR silencing protects from polatuzumab vedotin killing. See related commentary by Shevchenko and Hodson, p. 284.
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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".