PI3Kα/δ inhibition promotes anti-tumor immunity through direct enhancement of effector CD8+ T-cell activity
Bibliographic record
Abstract
PI3K inhibitors with differential selectivity to distinct PI3K isoforms have been tested extensively in clinical trials, largely to target tumor epithelial cells. PI3K signaling also regulates the immune system and inhibition of PI3Kδ modulate the tumor immune microenvironment of pre-clinical mouse tumor models by relieving T-regs-mediated immunosuppression. PI3K inhibitors as a class and PI3Kδ specifically are associated with immune-related side effects. However, the impact of mixed PI3K inhibitors in tumor immunology is under-explored. Here we examine the differential effects of AZD8835, a dual PI3Kα/δ inhibitor, specifically on the tumor immune microenvironment using syngeneic models. Continuous suppression of PI3Kα/δ was not required for anti-tumor activity, as tumor growth inhibition was potentiated by an intermittent dosing/schedule in vivo. Moreover, PI3Kα/δ inhibition delivered strong single agent anti-tumor activity, which was associated with dynamic suppression of T-regs, improved CD8<sup>+</sup> T-cell activation and memory in mouse syngeneic tumor models. Strikingly, AZD8835 promoted robust CD8<sup>+</sup> T-cell activation dissociated from its effect on T-regs. This was associated with enhancing effector cell viability/function. Together these data reveal novel mechanisms by which PI3Kα/δ inhibitors interact with the immune system and validate the clinical compound AZD8835 as a novel immunoncology drug, independent of effects on tumor cells. These data support further clinical investigation of PI3K pathway inhibitors as immuno-oncology agents.
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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".