Cracking the code of cancer immunotherapy resistance: emerging roles of pyroptosis and necroptosis
Bibliographic record
Abstract
Therapeutic resistance and recurrent metastasis continue to pose major obstacles in the treatment of malignant tumors worldwide. Immunogenic cell death (ICD), characterized by its ability to both eliminate cancer cells and stimulate antitumor immune responses, has emerged as a promising strategy in the field of cancer immunotherapy. As key subtypes of ICD, pyroptosis and necroptosis contribute significantly to remodeling the tumor microenvironment (TME) and modulating immune responses through their distinct death-immunity coupling mechanisms. Characterized by plasma membrane pore formation and subsequent release of cytoplasmic contents, pyroptosis and necroptosis reprogram the immune microenvironment, thereby laying the groundwork for enhanced antitumor immune responses. Paradoxically, the chronic activation of pyroptosis and necroptosis pathways may contribute to cancer progression. Sustained inflammation within the TME promotes the release of pro-angiogenic and immunosuppressive factors, driving myeloid-derived suppressor cells (MDSCs) recruitment, extracellular matrix remodeling, and metastatic niche formation, thereby facilitating tumorigenesis and metastasis. The context-dependent dual roles of pyroptosis and necroptosis—shaped by tumor histotype, chronic inflammation, and stromal context—highlight the need for a nuanced understanding of their tumor-specific functions across cancer types. This review outlines the underlying mechanisms of pyroptosis and necroptosis, and summarizes recent advances, aiming to inform and inspire novel strategies in overcoming cancer immunotherapy resistance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".