Progression of AITL-like tumors in mice is driven by Tfh signature proteins and T-B cross talk
Bibliographic record
Abstract
Angioimmunoblastic T-cell lymphoma (AITL) is an aggressive peripheral T-cell lymphoma driven by a pool of neoplastic cells originating from T follicular helper (Tfh) cells and concomitant expansion of B cells. Conventional chemotherapies for AITL have shown limited efficacy, and as such, there is a need for improved therapeutic options. Because AITL originates from Tfh cells, we hypothesized that AITL tumors continue to rely on essential Tfh components and intimate T-cell-B-cell (T-B) interactions. Using a spontaneous AITL mouse model (Roquinsan/+ mice), we found that acute loss of Bcl6 activity in growing tumors drastically reduced tumor size, demonstrating that AITL-like tumors critically depend on the Tfh lineage-defining transcription factor Bcl6. Because Bcl6 can upregulate expression of signaling lymphocytic activation molecule-associated protein (SAP), which is known to promote T-B conjugation, we next targeted the SAP-encoding Sh2d1a gene. We observed that Sh2d1a deletion from CD4+ T cells in fully developed tumors also led to tumor regression. Further, we provide evidence that tumor progression depends on T-B cross talk facilitated by SAP and high-affinity LFA-1. In our study, AITL-like tumors relied heavily on molecular pathways that support Tfh cell identity and T-B collaboration, revealing potential therapeutic targets for AITL.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".