Pyroptosis activates conventional type I dendritic cells to mediate the priming of highly functional anticancer T cells
Bibliographic record
Abstract
Background Initiation of antitumor immunity is reliant on the stimulation of dendritic cells (DCs) to present tumor antigens to naïve T cells and generate effector T cells that can kill cancer cells. Induction of immunogenic cell death after certain types of cytotoxic anticancer therapies can stimulate T cell-mediated immunity. However, cytotoxic therapies simultaneously activate multiple types of cellular stress and programmed cell death; hence, it remains unknown what types of cancer cell death confer superior antitumor immunity. Methods Murine cancer cells were engineered to activate apoptotic or pyroptotic cell death after Dox-induced expression of procell death proteins. Cell-free supernatants were collected to measure secreted danger signals, cytokines, and chemokines. Tumors were formed by transplanting engineered tumor cells to specifically activate apoptosis or pyroptosis in established tumors and the magnitude of immune response measured by flow cytometry. Tumor growth was measured using calipers to estimate end point tumor volumes for Kaplan-Meier survival analysis. Results We demonstrated that, unlike apoptosis, pyroptosis induces an immunostimulatory secretome signature. In established tumors pyroptosis preferentially activated CD103 + and XCR1 + type I conventional DCs (cDC1) along with a higher magnitude and functionality of tumor-specific CD8 + T cells and reduced number of regulatory T cells within the tumor. Depletion of cDC1 or CD4 + and CD8 + T cells ablated the antitumor response leaving mice susceptible to a tumor rechallenge. Conclusion Our study highlights that distinct types of cell death yield varying immunotherapeutic effect and selective activation of pyroptosis can be used to potentiate multiple aspects of the anticancer immunity cycle.
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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".