Local anesthetics elicit immune-dependent anticancer effects
Bibliographic record
Abstract
Background Retrospective clinical trials reported a reduced local relapse rate, as well as improved overall survival after injection of local anesthetics during cancer surgery. Here, we investigated the anticancer effects of six local anesthetics used in clinical practice. Results In vitro , local anesthetics induced signs of cancer cell stress including inhibition of oxidative phosphorylation, and induction of autophagy as well as endoplasmic reticulum (ER) stress characterized by the splicing of X-box binding protein 1 (XBP1s) mRNA, cleavage of activating transcription factor 6 (ATF6), phosphorylation of eIF2α and subsequent upregulation of activating transcription factor 4 (ATF4). Both eIF2α phosphorylation and autophagy required the ER stress-relevant eukaryotic translation initiation factor 2 alpha kinase 3 (EIF2AK3, best known as PERK). Local anesthetics also activated two hallmarks of immunogenic cell death, namely, the release of ATP and high-mobility group box 1 protein (HMGB1), yet failed to cause the translocation of calreticulin (CALR) from the ER to the plasma membrane. In vivo , locally injected anesthetics decreased tumor growth and improved survival in several models of tumors established in immunocompetent mice. Systemic immunotherapy with PD-1 blockade or intratumoral injection of recombinant CALR protein, increased the antitumor effects of local anesthetics. Local anesthetics failed to induce antitumor effects in immunodeficient mice or against cancers unable to activate ER stress or autophagy due to the knockout of EIF2AK3/PERK or ATG5, respectively. Uncoupling agents that inhibit oxidative phosphorylation and induce autophagy and ER stress mimicked the immune-dependent antitumor effects of local anesthetics. Conclusion Altogether, these results indicate that local anesthetics induce a therapeutically relevant pattern of immunogenic stress responses in cancer cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 teacher head, 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".