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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".