An Aged/Autoimmune B-cell Program Defines the Early Transformation of Extranodal Lymphomas
Bibliographic record
Abstract
A third of patients with diffuse large B-cell lymphoma (DLBCL) present with extranodal dissemination, which is associated with inferior clinical outcomes. MYD88L265P is a hallmark extranodal DLBCL mutation that supports lymphoma proliferation. Yet extranodal lymphomagenesis and the role of MYD88L265P in transformation remain mostly unknown. Here, we show that B cells expressing Myd88L252P (MYD88L265P murine equivalent) activate, proliferate, and differentiate with minimal T-cell costimulation. Additionally, Myd88L252P skewed B cells toward memory fate. Unexpectedly, the transcriptional and phenotypic profiles of B cells expressing Myd88L252P, or other extranodal lymphoma founder mutations, resembled those of CD11c+T-BET+ aged/autoimmune memory B cells (AiBC). AiBC-like cells progressively accumulated in animals prone to develop lymphomas, and ablation of T-BET, the AiBC master regulator, stripped mouse and human mutant B cells of their competitive fitness. By identifying a phenotypically defined prospective lymphoma precursor population and its dependencies, our findings pave the way for the early detection of premalignant states and targeted prophylactic interventions in high-risk patients. SIGNIFICANCE: Extranodal lymphomas feature a very poor prognosis. The identification of phenotypically distinguishable prospective precursor cells represents a milestone in the pursuit of earlier diagnosis, patient stratification, and prophylactic interventions. Conceptually, we found that extranodal lymphomas and autoimmune disorders harness overlapping pathogenic trajectories, suggesting these B-cell disorders develop and evolve within a spectrum. See related commentary by Leveille et al. (Blood Cancer Discov 2023;4:8-11). This article is highlighted in the In This Issue feature, p. 1.
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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".