Spatial and functional targeting of intratumoral Tregs reverses CD8+ T cell exhaustion and promotes cancer immunotherapy
Bibliographic record
Abstract
Intratumoral Tregs are key mediators of cancer immunotherapy resistance, including anti-programmed cell death (ligand) 1 [anti-PD-(L)1] immune checkpoint blockade (ICB). The mechanisms driving Treg infiltration into the tumor microenvironment (TME) and the consequence on CD8+ T cell exhaustion remain elusive. Here, we report that heat shock protein gp96 (also known as GRP94) was indispensable for Treg tumor infiltration, primarily through the roles of gp96 in chaperoning integrins. Among various gp96-dependent integrins, we found that only LFA-1 (αL integrin), and not αV, CD103 (αE), or β7 integrin, was required for Treg tumor homing. Loss of Treg infiltration into the TME by genetic deletion of gp96/LFA-1 potently induced rejection of tumors in multiple ICB-resistant murine cancer models in a CD8+ T cell-dependent manner, without loss of self-tolerance. Moreover, gp96 deletion impeded Treg activation primarily by suppressing IL-2/STAT5 signaling, which also contributed to tumor regression. By competing for intratumoral IL-2, Tregs prevented the activation of CD8+ tumor-infiltrating lymphocytes, drove thymocyte selection-associated high mobility group box protein (TOX) induction, and induced bona fide CD8+ T cell exhaustion. By contrast, Treg ablation led to striking CD8+ T cell activation without TOX induction, demonstrating clear uncoupling of the 2 processes. Our study reveals that the gp96/LFA-1 axis plays a fundamental role in Treg biology and suggests that Treg-specific gp96/LFA-1 targeting represents a valuable strategy for cancer immunotherapy without inflicting autoinflammatory conditions.
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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) | 0.000 | 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) | 0.001 | 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".