LILRB1 Blockade Enhances Bispecific T Cell Engager Antibody–Induced Tumor Cell Killing by Effector CD8+ T Cells
Bibliographic record
Abstract
Abstract Elicitation of tumor cell killing by CD8+ T cells is an effective therapeutic approach for cancer. In addition to using immune checkpoint blockade to reinvigorate existing but unresponsive tumor-specific T cells, alternative therapeutic approaches have been developed, including stimulation of polyclonal T cell cytolytic activity against tumors using bispecific T cell engager (BiTE) molecules that simultaneously engage the TCR complex and a tumor-associated Ag. BiTE molecules are efficacious against hematologic tumors and are currently being explored as an immunotherapy for solid tumors. To understand mechanisms regulating BiTE molecule–mediated CD8+ T cell activity against solid tumors, we sought to define human CD8+ T cell populations that efficiently respond to BiTE molecule stimulation and identify factors regulating their cytolytic activity. We find that human CD45RA+CCR7− CD8+ T cells are highly responsive to BiTE molecule stimulation, are enriched in genes associated with cytolytic effector function, and express multiple unique inhibitory receptors, including leukocyte Ig-like receptor B1 (LILRB1). LILRB1 and programmed cell death protein 1 (PD1) were found to be expressed by distinct CD8+ T cell populations, suggesting different roles in regulating the antitumor response. Engaging LILRB1 with its ligand HLA-G on tumor cells significantly inhibited BiTE molecule–induced CD8+ T cell activation. Blockades of LILRB1 and PD1 induced greater CD8+ T cell activation than either treatment alone. Together, our data suggest that LILRB1 functions as a negative regulator of human CD8+ effector T cells and that blocking LILRB1 represents a unique strategy to enhance BiTE molecule therapeutic activity against solid tumors.
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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 | 0.000 | 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 | 0.001 |
| 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".