Mutations in the IgG B cell receptor associated with class-switched B cell lymphomas
Bibliographic record
Abstract
Summary Immunoglobulin class-switching from IgM to IgG enhances B cell receptor (BCR) signalling 1,2 and promotes germinal centre (GC) B cell responses to antigens 3,4 . In contrast, non-Hodgkin lymphomas derived from GC B cells typically avoid IgG BCR expression and retain the unswitched IgM BCR, suggesting that the IgG BCR may protect B cells from malignant transformation 5,6 . However, the mechanism of this phenomenon and its significance for the pathogenicity of IgG-expressing lymphomas remains unclear. Here, we report that IgG-positive follicular lymphoma (FL) and the related EZB subset of diffuse large B cell lymphoma (DLBCL) acquire mutations in the IgG heavy chain, disrupting its unique intracellular tail. Enforced class switching of IgM-expressing EZB DLBCL cell lines to IgG reduces BCR surface levels, signalling via phosphoinositide-3 kinase (PI3K), levels of MYC, cell proliferation and in vivo growth. Inhibiting GSK3, a target of BCR-PI3K signalling, or stimulating the BCR rescues IgG + cell proliferation. In contrast, IgG tail-truncating mutations enhance BCR surface expression, intracellular signalling and competitive growth. These findings suggest that the expansion of IgG-switched GC-like B lymphoma cells is limited by low tonic PI3K activity of the wild-type IgG BCR, but a subset of these cancers acquires mutations of the IgG intracellular tail that reverse this effect, promoting the oncogenicity of their BCRs. The presence of IgG tail mutations underscores the importance of isotype-specific BCR signalling in the pathogenesis of FL and EZB DLBCL and can potentially inform therapeutic targeting with BCR signalling inhibitors or antibody-drug conjugates.
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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) | 0.002 | 0.001 |
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".