Abstract PR04: Temporal single cell profiling identifies B-cell specific checkpoint molecules that regulate anti-tumor immunity
Bibliographic record
Abstract
Abstract B cells play key roles in both innate and adaptive immunity. Distinct specialized B cell subsets engage a range of responses from antigen presentation to antibody production and B cells are one of the most abundant cell types of tumor infiltrating leukocytes (TILs), especially in melanoma. However, their role in anti-tumor immunity remains unclear. Here, we examined the B cell repertoire at single cell resolution from tumor-infiltrating B cells and tumor-draining lymph nodes (dLN) and identify and characterize a subset of B cells expressing the checkpoint molecule TIM-1 and a unique transcriptional signature, including multiple co-inhibitory molecules such as PD-1, TIM-3, TIGIT and LAG-3. While conditional deletion of these co-inhibitory molecules on B cells had little or no effect on tumor burden, selective deletion of Havcr1 (the gene encoding TIM-1) in B cells both dramatically inhibited tumor growth and enhanced effector T cell responses. Mechanistically, loss of TIM-1 enhanced the type 1 interferon response in B cells, which augmented B cell activation and increased antigen presentation and co-stimulation, resulting in increased expansion of tumor-specific effector T cells. Our results demonstrate that manipulation of TIM-1-expressing B cells enables engagement of the second arm of adaptive immunity to promote anti-tumor immunity and inhibit tumor growth. Citation Format: Lloyd Bod. Temporal single cell profiling identifies B-cell specific checkpoint molecules that regulate anti-tumor immunity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr PR04.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".