Targeting immune checkpoint LAIR1 with antibody blockade or 3-in-1 CAR T cells enhances antitumor response
Bibliographic record
Abstract
Tumor-associated macrophages (TAMs) are abundant in the tumor microenvironment (TME) and dampen the immune response, negatively affecting patient survival. Therefore, targeting TAMs could address the limitations of current cancer treatments. However, drug development in this area remains limited. The leukocyte-associated Ig-like receptor 1 (LAIR1), also called CD305, is prominently expressed on the surface of TAMs. We have uncovered what we believe to be a previously unrecognized immunosuppressive LAIR1/factor XIII A/collagen IV pathway across various cancer types. Inhibition of LAIR1, either through knockout (Lair1-/-), antibody blockade (anti-Lair1 antibody), or a chimeric antigen receptor (CAR) design (3-in-1 CAR by combining tumor targeting, T cell trafficking, and remodeling of the immunosuppressive TME in 1 CAR construct) provided an enhanced antitumor response. LAIR1 inhibition enhanced peripheral and intratumoral CD8 memory T cell populations, induced a phenotypic shift of M2-like macrophages toward M1 macrophages, and normalized tumor collagen IV and structural components in the TME, facilitating effective tumor-T cell interactions and tumor suppression. Enhanced antitumor responses were observed when Lair1-/- or anti-Lair1 antibody was used alone or in combination with CAR T cells or when the 3-in-1 CAR T cells were used solely in tumor models resistant to chemotherapy-radiation-programmed cell death protein 1 (PD-1) blockade. These findings position LAIR1 inhibition as a promising strategy for cancer immunotherapies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".