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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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; both teacher heads agree on what is shown here.
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".