Depleting IL1R2+ Tumor-Infiltrating Regulatory T Cells with an ADCC-Prone Nanobody Construct Boosts the Efficacy of Anti–PD-1 Immunotherapy
Bibliographic record
Abstract
Eliminating immunosuppressive cells, such as regulatory T cells (Treg), is a promising approach to boost immunotherapy success. However, this approach may suffer from systemic autoimmune adverse events, highlighting the need to specifically target tumor-infiltrating Tregs (tiTreg). Based on cellular indexing of transcriptomes and epitopes by sequencing and single-cell RNA sequencing data from mouse models of triple-negative breast cancer (TNBC) and colorectal carcinoma, as well as a meta-analysis of human TNBC and colorectal carcinoma datasets, we obtained a comprehensive overview of the tiTreg heterogeneity and IL1R2 expression. Several IL1R2-expressing tiTreg clusters were identified in mouse and human TNBC and colorectal carcinoma tumors, with some level of conservation. IL1R2 was identified as a surface marker that was most highly expressed by activated and strongly T-cell-suppressive tiTregs in the tumor microenvironment but not by peripheral Tregs. IL1R2 upregulation resulted from T-cell receptor-mediated Treg triggering in a Rel-dependent fashion, but the receptor itself was dispensable for tiTreg abundance and activation and did not influence tumor growth. Accordingly, the blockade of IL1R2, by using an Ab-dependent cell-mediated cytotoxicity (ADCC)-dead anti-IL1R2 nanobody-Fc construct, had no impact on tumor growth. Conversely, anti-IL1R2 nanobody-Fc constructs with an optimized ADCC functionality, mediated by the SDALIE mutation, resulted in the specific depletion of IL1R2+ tiTregs, elicited antitumor immunity, and reduced tumor growth in synergy with anti-PD-1 therapy. Collectively, these findings identify IL1R2 as a marker for highly activated and suppressive tiTregs that is suitable as a target for ADCC-dependent tiTreg depletion, which can synergize with immune checkpoint blockade. SIGNIFICANCE: IL1R2+ Treg depletion using IL1R2-targeting ADCC-prone constructs is a potential cancer therapy to selectively target tumor-infiltrating Tregs and circumvent autoimmune complications caused by systemic Treg depletion.
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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".