Abstract IA01: B cell checkpoints as regulators of tumor immunity and self-tolerance
Bibliographic record
Abstract
Abstract B cells are increasingly recognized as key modulators of anti-tumor immunity, yet their dual roles in promoting cancer progression and maintaining immune tolerance remain incompletely understood. We identified a distinct subset of B cells expressing T cell immunoglobulin and mucin domain 1 (TIM-1) that functionally shapes both tumor immunity and autoimmunity. In murine melanoma models, TIM-1+ B cells progressively accumulate in tumor-draining lymph nodes and display a unique immunoregulatory phenotype, marked by co-expression of PD-1, TIGIT, LAG-3, and TIM-3. Conditional deletion of TIM-1 in B cells significantly reduced tumor burden and enhanced tumor-specific CD8+ T cell responses, mediated by increased type I interferon signaling and improved B cell-driven antigen presentation. Conversely, TIM-1 signaling is essential for immune tolerance. Aged mice with B cell-specific deletion of Tim-1 or Tigit spontaneously developed multi-organ autoimmunity, including central nervous system inflammation and paralysis. These findings position B cell checkpoint molecules such as TIM-1 at the intersection of immune activation and tolerance. Modulating this pathway, by either disrupting or activating TIM-1 signaling, could yield novel therapeutic opportunities in both cancer immunotherapy and autoimmune disease. Citation Format: Lloyd Bod. B cell checkpoints as regulators of tumor immunity and self-tolerance [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr IA01.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".