Enhanced antitumor immunity through sequential targeting of PI3Kδ and LAG3
Bibliographic record
Abstract
Background Despite striking successes, immunotherapies aimed at increasing cancer-specific T cell responses are unsuccessful in most patients with cancer. Inactivating regulatory T cells (Treg) by inhibiting the PI3Kδ signaling enzyme has shown promise in preclinical models of tumor immunity and is currently being tested in early phase clinical trials in solid tumors. Methods Mice bearing 4T1 mammary tumors were orally administered a PI3Kδ inhibitor (PI-3065) daily and tumor growth, survival and T cell infiltrate were analyzed in the tumor microenvironment. A second treatment schedule comprised PI3Kδ inhibitor with anti-LAG3 antibodies administered sequentially 10 days later. Results As observed in human immunotherapy trials with other agents, immunomodulation by PI3Kδ-blockade led to 4T1 tumor regressor and non-regressor mice. Tumor infiltrating T cells in regressors were metabolically fitter than those in non-regressors, with significant enrichments of antigen-specific CD8 + T cells, T cell factor 1 (TCF1) + T cells and CD69 − T cells, compatible with induction of a sustained tumor-specific T cell response. Treg numbers were significantly reduced in both regressor and non-regressor tumors compared with untreated tumors. The remaining Treg in non-regressor tumors were however significantly enriched with cells expressing the coinhibitory receptor LAG3, compared with Treg in regressor and untreated tumors. This striking difference prompted us to sequentially block PI3Kδ and LAG3. This combination enabled successful therapy of all mice, demonstrating the functional importance of LAG3 in non-regression of tumors on PI3Kδ inhibition therapy. Follow-up studies, performed using additional cancer cell lines, namely MC38 and CT26, indicated that a partial initial response to PI3Kδ inhibition is an essential prerequisite to a sequential therapeutic benefit of anti-LAG3 antibodies. Conclusions These data indicate that LAG3 is a key bottleneck to successful PI3Kδ-targeted immunotherapy and provide a rationale for combining PI3Kδ/LAG3 blockade in future clinical studies.
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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".